Methods of classifying cell differentiation status and related compositions for differentiating cells

Abstract

Provided herein are methods for classifying the differentiation status of an in vitro cell population, such as an in vitro neuronal cell population, and methods for selecting and / or implanting an in vitro cell population having a desired differentiation status. Also provided herein are computing devices and related compositions, articles of manufacture, and kits for performing the provided methods, including methods for treating subjects having a disease or condition, such as a neurodegenerative disease, such as Parkinson's disease.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims priority to U.S. Provisional Application No. 63 / 331,783, filed on April 15, 2022, entitled “METHODS OF CLASSIFYING THE DIFFERENTIATION STATE OF CELLS AND RELATED COMPOSITIONS OF DIFFERENTIATED CELLS” and U.S. Provisional Application No. 63 / 353,525, filed on June 17, 2022, entitled “METHODS OF CLASSIFYING THE DIFFERENTIATION STATE OF CELLS AND RELATED COMPOSITIONS OF DIFFERENTIATED CELLS”, the contents of each of these provisional applications are incorporated herein by reference in their entirety for all purposes. Technical Field

[0003] The present disclosure relates to methods for classifying the differentiation state of an in vitro cell population, such as an in vitro neuronal cell population, and methods for selecting and / or implanting an in vitro cell population having a desired differentiation state. Also provided herein are computing devices and related compositions, articles, and kits for performing the provided methods, including methods for treating subjects with a disease or condition, such as a neurodegenerative disease, such as Parkinson's disease. Background Art

[0004] It is contemplated that various methods for differentiating pluripotent stem cells into lineage-specific cell colonies and the resulting cell compositions can be used for cell replacement therapy for patients with diseases that result in limited cell colony loss of function. In some aspects, it is desirable to administer cells with a specific differentiation state. Improved methods for classifying and identifying the cells are needed. Summary of the invention

[0005] In some embodiments, provided herein is a computing device for classifying the differentiation state of an in vitro cell population, the device comprising a memory comprising: a first reference data set comprising representations of gene expression levels of one or more genes differentially expressed between cells in a first differentiation state and cells in a second differentiation state; and a second reference data set comprising representations of gene expression levels of one or more genes differentially expressed between cells in a second differentiation state and cells in a third differentiation state.

[0006] In some embodiments of any of the provided embodiments, the computing device also includes a processor that implements instructions stored in the memory to perform a method comprising: (a) receiving a test data set as input, the test data set including expression levels of genes expressed in one or more test cells contained in an in vitro cell population, wherein the expression levels in the test data set include expression levels of: (i) one or more genes among the genes whose expression levels are represented by a first reference data set, and (ii) one or more genes among the genes whose expression levels are represented by a second reference data set; (b) using the test data set and the first reference data set to calculate a first similarity score indicating whether the differentiation state of the test cell is more similar to the first differentiation state or more similar to the second differentiation state; (c) using the test data set and the second reference data set to calculate a second similarity score indicating whether the differentiation state of the test cell is more similar to the second differentiation state or more similar to the third differentiation state; and (d) classifying the differentiation state of the one or more test cells based on one or both of the first similarity score and the second similarity score.

[0007] In some embodiments, the classification is based on one of the first similarity score and the second similarity score. In some embodiments, the classification is based on the lower of the first similarity score and the second similarity score. In some embodiments, the classification is based on the higher of the first similarity score and the second similarity score. In some embodiments, the classification is based on the first similarity score. In some embodiments, the classification is based on the second similarity score.

[0008] In some embodiments, the classification is based on both the first similarity score and the second similarity score.

[0009] In some embodiments of any of the provided embodiments, the computing device also includes a processor that implements instructions stored in the memory to perform a method comprising: (a) receiving a test data set as input, the test data set including expression levels of genes expressed in one or more test cells contained in an in vitro cell population, wherein the expression levels in the test data set include expression levels of: (i) one or more genes among the genes whose expression levels are represented by a first reference data set, and (ii) one or more genes among the genes whose expression levels are represented by a second reference data set; (b) using the test data set and the first reference data set to calculate a first similarity score indicating whether the differentiation state of the test cell is more similar to the first differentiation state or more similar to the second differentiation state; (c) using the test data set and the second reference data set to calculate a second similarity score indicating whether the differentiation state of the test cell is more similar to the second differentiation state or more similar to the third differentiation state; and (d) classifying the differentiation state of the one or more test cells based on the first similarity score and the second similarity score.

[0010] In some embodiments of any of the provided embodiments, the memory further comprises a control data set comprising a representation of gene expression levels of one or more genes expressed in cells in one or more control differentiation states, the control differentiation state being the same or different from one of the first differentiation state, the second differentiation state, or the third differentiation state. In some embodiments of any of the provided embodiments, the test data set comprises gene expression levels of one or more genes among the genes whose representations of expression levels are included in the control data set; the instructions comprise calculating a degree of correlation between the representations of gene expression levels of one or more genes in the control data set and the gene expression levels of one or more genes in the test data set to calculate a correlation score; and the classification of the differentiation state of the one or more test cells is based on the correlation score and one or both of the first similarity score and the second similarity score.

[0011] In some embodiments, the classification is based on the relevance score and one of the first similarity score and the second similarity score. In some embodiments, the classification is based on the relevance score and the lower of the first similarity score and the second similarity score. In some embodiments, the classification is based on the relevance score and the higher of the first similarity score and the second similarity score. In some embodiments, the classification is based on the relevance score and the first similarity score. In some embodiments, the classification is based on the relevance score and the second similarity score.

[0012] In some embodiments, the classification is based on the relevance score and both the first similarity score and the second similarity score.

[0013] In some embodiments of any of the provided embodiments, the memory further comprises a control data set comprising a representation of gene expression levels of one or more genes expressed in cells in one or more control differentiation states, the control differentiation state being the same or different from one of the first differentiation state, the second differentiation state, or the third differentiation state. In some embodiments of any of the provided embodiments, the test data set comprises gene expression levels of one or more genes among the genes whose representations of expression levels are included in the control data set; the instructions comprise calculating a degree of correlation between the representations of gene expression levels of one or more genes in the control data set and the gene expression levels of one or more genes in the test data set to calculate a correlation score; and the classification of the differentiation state of the one or more test cells is based on the first similarity score, the second similarity score, and the correlation score.

[0014] In some embodiments of any of the provided embodiments, the relevance score is calculated before calculating the first similarity score and the second similarity score, and if the relevance score of the test cell does not meet a predefined cutoff value, the method is terminated.

[0015] In some embodiments of any of the provided embodiments, the control data set includes gene expression levels that are normalized per million mapped read counts (CPM) and filtered to include only gene expression levels that exceed a threshold CPM value. In some embodiments of any of the provided embodiments, the control data set includes the centroid of the gene expression levels of one or more genes in the control data set. In some embodiments of any of the provided embodiments, the correlation score is calculated in the following manner: the gene expression levels of one or more genes in the test data set are normalized and the correlation of the gene expression levels of one or more genes in the test data set with the centroid is calculated. In some embodiments of any of the provided embodiments, the control data set includes the coefficient of variation (CV) value of the gene expression levels of one or more genes in the control data set, and the correlation with the centroid is weighted by the inverse of the CV value.

[0016] In some embodiments of any of the provided embodiments, the in vitro cell population is from a cell culture differentiated from a pluripotent cell subjected to suitable differentiation conditions. In some embodiments of any of the provided embodiments, the first differentiation state is earlier in a stem cell differentiation pathway than the second differentiation state. In some embodiments of any of the provided embodiments, the second differentiation state is earlier in a stem cell differentiation pathway than the third differentiation state. In some embodiments of any of the provided embodiments, the first differentiation state is in a cell differentiation pathway parallel to the cell differentiation pathway of the second differentiation state.

[0017] In some embodiments of any provided embodiment, the cell colony is selected from the group consisting of the following items: stem cell derived cardiomyocytes, stem cell derived skeletal muscle cells, stem cell derived renal tubular cells, stem cell derived erythrocytes, stem cell derived smooth muscle cells, stem cell derived pneumocytes, stem cell derived thyroid cells, stem cell derived pancreatic cells, stem cell derived epidermal cells, stem cell derived pigment cells and stem cell derived neuronal cells. In some embodiments of any provided embodiment, the cell colony is a stem cell derived neuronal cell. In some embodiments of any provided embodiment, the second differentiation state is a differentiation state of directional dopaminergic neuronal cells. In some embodiments of any provided embodiment, the second differentiation state is a differentiation state of cells suitable for engraftment.

[0018] In some embodiments of any of the embodiments provided, the second differentiation state is a differentiation state of a hematopoietic progenitor cell.

[0019] In some embodiments of any of the provided embodiments, the first reference data set includes representations of gene expression levels of one or more genes selected from Table E1. In some embodiments of any of the provided embodiments, the second reference data set includes representations of gene expression levels of one or more genes selected from Table E2. In some embodiments of any of the provided embodiments, the first reference data set includes representations of gene expression levels of at least 20 genes selected from Table E1. In some embodiments of any of the provided embodiments, the second reference data set includes representations of gene expression levels of at least 20 genes selected from Table E2. In some embodiments of any of the provided embodiments, the first reference data set includes representations of gene expression levels of at least 50 genes selected from Table E1. In some embodiments of any of the provided embodiments, the second reference data set includes representations of gene expression levels of at least 50 genes selected from Table E2.

[0020] In some embodiments of any of the provided embodiments, an in vitro assay is used to characterize at least one of the first differentiation state, the second differentiation state, and the third differentiation state. In some embodiments of any of the provided embodiments, an in vivo assay is used to characterize at least one of the first differentiation state, the second differentiation state, and the third differentiation state. In some embodiments of any of the provided embodiments, the in vivo assay includes determining whether the reference cell is able to survive, colonize, and / or innervate a tissue when administered to an animal or human subject. In some embodiments of any of the provided embodiments, the in vivo assay includes determining whether the reference cell improves or reverses symptoms of a neurodegenerative disease when implanted in an animal or human subject.

[0021] In some embodiments of any of the provided embodiments, the animal subject comprises an animal model of Parkinson's disease. In some embodiments of any of the provided embodiments, the memory further comprises one or more additional reference data sets, wherein each additional reference data set comprises a representation of gene expression levels of one or more genes differentially expressed between cells in the second differentiation state and cells in the additional differentiation state, wherein: the processor implements instructions to calculate one or more additional similarity scores indicating whether the differentiation state of the test cell is more similar to the second differentiation state or more similar to one of the one or more additional differentiation states using the additional reference data sets, and the classification of the differentiation state of the one or more test cells is based on the first similarity score, the second similarity score, and the one or more additional similarity scores.

[0022] In some embodiments in any of the provided embodiments, machine learning is used to obtain the representation of the gene expression level in the first reference data set and / or the second reference data set. In some embodiments in any of the provided embodiments, the machine learning includes principal component analysis. In some embodiments in any of the provided embodiments, the representation of the gene expression level in the first reference data set and / or the second reference data set includes normalized gene expression level. In some embodiments in any of the provided embodiments, if the first similarity score and the second similarity score indicate that the differentiation state of one or more test cells is more similar to the second differentiation state, the differentiation state of one or more test cells is classified as being in the second differentiation state. In some embodiments in any of the provided embodiments, if the first similarity score indicates that the differentiation state of one or more test cells is more similar to the second differentiation state, the differentiation state of one or more test cells is classified as being in the second differentiation state. In some embodiments in any of the provided embodiments, if the second similarity score indicates that the differentiation state of one or more test cells is more similar to the second differentiation state, the differentiation state of one or more test cells is classified as being in the second differentiation state.

[0023] In some embodiments, the present invention also provides a method for selecting a cell population having a desired differentiation state, the method comprising: (a) calculating a first similarity score using a test data set and a first reference data set, wherein: the first reference data set includes a representation of gene expression levels of one or more genes differentially expressed between cells in a first differentiation state and cells in a second differentiation state, the test data set includes expression levels of genes expressed in one or more test cells contained in an in vitro cell population, wherein the expression levels in the test data set include expression levels of one or more genes among the genes whose representations of expression levels are included in the first reference data set, and the first similarity score indicates that the differentiation state of the test cell is more similar to the first differentiation state; (a) determining whether the differentiation state of the test cell is more similar to the first differentiation state or to a second differentiation state; (b) calculating a second similarity score using the test data set and the second reference data set, wherein: the second reference data set includes representations of gene expression levels of one or more genes differentially expressed between cells in the second differentiation state and cells in the third differentiation state, the expression levels in the test data set include expression levels of one or more genes among the genes whose representations of expression levels are included in the second reference data set, and the second similarity score indicates whether the differentiation state of the test cell is more similar to the second differentiation state or to the third differentiation state; and (c) classifying the differentiation state of the one or more test cells based on one or both of the first similarity score and the second similarity score.

[0024] In some embodiments, the classification is based on one of the first similarity score and the second similarity score. In some embodiments, the classification is based on the lower of the first similarity score and the second similarity score. In some embodiments, the classification is based on the higher of the first similarity score and the second similarity score. In some embodiments, the classification is based on the first similarity score. In some embodiments, the classification is based on the second similarity score.

[0025] In some embodiments, the classification is based on both the first similarity score and the second similarity score.

[0026] In some embodiments, the present invention also provides a method for selecting a cell population having a desired differentiation state, the method comprising: (a) calculating a first similarity score using a test data set and a first reference data set, wherein: the first reference data set includes a representation of gene expression levels of one or more genes differentially expressed between cells in a first differentiation state and cells in a second differentiation state, the test data set includes expression levels of genes expressed in one or more test cells contained in an in vitro cell population, wherein the expression levels in the test data set include expression levels of one or more genes among the genes whose expression levels are represented in the first reference data set, and the first similarity score indicates that the differentiation state of the test cell is more similar to the first differentiation state or more similar to the second differentiation state; (b) calculating a second similarity score using the test data set and the second reference data set, wherein: the second reference data set includes representations of gene expression levels of one or more genes differentially expressed between cells in the second differentiation state and cells in the third differentiation state, the expression levels in the test data set include expression levels of one or more genes among the genes whose representations of expression levels are included in the second reference data set, and the second similarity score indicates whether the differentiation state of the test cell is more similar to the second differentiation state or more similar to the third differentiation state; and (c) classifying the differentiation state of one or more test cells based on the first similarity score and the second similarity score.

[0027] In some embodiments of any of the provided embodiments, the test data set includes gene expression levels of one or more genes whose expression levels are included in a control data set, the control data set including representations of gene expression levels of one or more genes expressed in cells in a control differentiation state, which control differentiation state may be the same as or different from one of the first differentiation state, the second differentiation state, or the third differentiation state; the method also includes calculating a degree of correlation between the representations of the gene expression levels of the one or more genes in the control data set and the gene expression levels of the one or more genes in the test data set to calculate a correlation score; and classification of the differentiation state of the one or more test cells is based on the correlation score and one or both of the first similarity score and the second similarity score.

[0028] In some embodiments, the classification is based on the relevance score and one of the first similarity score and the second similarity score. In some embodiments, the classification is based on the relevance score and the lower of the first similarity score and the second similarity score. In some embodiments, the classification is based on the relevance score and the higher of the first similarity score and the second similarity score. In some embodiments, the classification is based on the relevance score and the first similarity score. In some embodiments, the classification is based on the relevance score and the second similarity score.

[0029] In some embodiments, the classification is based on the relevance score and both the first similarity score and the second similarity score.

[0030] In some embodiments of any of the provided embodiments, the test dataset includes gene expression levels of one or more genes whose representations of expression levels are included in a control dataset, the control dataset including representations of gene expression levels of one or more genes expressed in cells in a control differentiation state, which control differentiation state can be the same as or different from one of the first differentiation state, the second differentiation state, or the third differentiation state; the method also includes calculating a degree of correlation between the representations of the gene expression levels of the one or more genes in the control dataset and the gene expression levels of the one or more genes in the test dataset to calculate a correlation score; and the classification of the differentiation state of the one or more test cells is based on the first similarity score, the second similarity score, and the correlation score.

[0031] In some embodiments of any of the provided embodiments, the relevance score is calculated before calculating the first similarity score and the second similarity score, and if the relevance score of the test cell does not meet a predefined cutoff value, the method is terminated.

[0032] In some embodiments of any of the provided embodiments, the control data set includes gene expression levels that are normalized per million mapped read counts (CPM) and filtered to include only gene expression levels that exceed a threshold CPM value. In some embodiments of any of the provided embodiments, the control data set includes the centroid of the gene expression levels of one or more genes in the control data set. In some embodiments of any of the provided embodiments, the correlation score is calculated in the following manner: the gene expression levels of one or more genes in the test data set are normalized and the correlation of the gene expression levels of one or more genes in the test data set with the centroid is calculated. In some embodiments of any of the provided embodiments, the control data set includes the coefficient of variation (CV) value of the gene expression levels of one or more genes in the control data set, and the correlation with the centroid is weighted by the inverse of the CV value.

[0033] In some embodiments of any of the provided embodiments, the first differentiation state is earlier in the stem cell differentiation pathway than the second differentiation state. In some embodiments of any of the provided embodiments, the second differentiation state is earlier in the stem cell differentiation pathway than the third differentiation state. In some embodiments of any of the provided embodiments, the first differentiation state is in a cell differentiation pathway that is parallel to that of the second differentiation state.

[0034] In some embodiments of any of the provided embodiments, the cell colony is selected from the group consisting of stem cell-derived cardiomyocytes, stem cell-derived skeletal muscle cells, stem cell-derived renal tubular cells, stem cell-derived erythrocytes, stem cell-derived smooth muscle cells, stem cell-derived lung cells, stem cell-derived thyroid cells, stem cell-derived pancreatic cells, stem cell-derived epidermal cells, stem cell-derived pigment cells, and stem cell-derived neuronal cells. In some embodiments of any of the provided embodiments, the cell colony is a stem cell-derived neuronal cell.

[0035] In some embodiments of any of the provided embodiments, the second differentiation state is a differentiation state of cells committed to dopaminergic neurons. In some embodiments of any of the provided embodiments, the second differentiation state is a differentiation state of cells suitable for colonization.

[0036] In some embodiments of any of the embodiments provided, the second differentiation state is a differentiation state of a hematopoietic progenitor cell.

[0037] In some embodiments of any of the provided embodiments, the first reference data set includes representations of gene expression levels of one or more genes selected from Table E1. In some embodiments of any of the provided embodiments, the second reference data set includes representations of gene expression levels of one or more genes selected from Table E2. In some embodiments of any of the provided embodiments, the first reference data set includes representations of gene expression levels of at least 20 genes selected from Table E1. In some embodiments of any of the provided embodiments, the second reference data set includes representations of gene expression levels of at least 20 genes selected from Table E2. In some embodiments of any of the provided embodiments, the first reference data set includes representations of gene expression levels of at least 50 genes selected from Table E1. In some embodiments of any of the provided embodiments, the second reference data set includes representations of gene expression levels of at least 50 genes selected from Table E2.

[0038] In some embodiments of any of the provided embodiments, at least one of the first differentiation state, the second differentiation state, and the third differentiation state is characterized using an in vivo assay. In some embodiments of any of the provided embodiments, the in vivo assay comprises determining whether the reference cell is able to survive, colonize, and / or innervate a tissue when administered to an animal or human subject.

[0039] In some embodiments of any of the provided embodiments, the in vivo assay includes determining whether the reference cell improves or reverses the symptoms of a neurodegenerative disease when implanted in an animal or human subject. In some embodiments of any of the provided embodiments, the animal subject includes an animal model of Parkinson's disease. In some embodiments of any of the provided embodiments, the method also includes calculating one or more additional similarity scores using one or more additional reference data sets, wherein: each additional reference data set includes a representation of gene expression levels of one or more genes differentially expressed between cells in a second differentiation state and cells in an additional differentiation state; one or more additional similarity scores indicate whether the differentiation state of the test cell is more similar to the second differentiation state or more similar to one of the one or more additional differentiation states, and the classification of the differentiation state of the one or more test cells is based on the first similarity score, the second similarity score, and the one or more additional similarity scores.

[0040] In some embodiments of any of the provided embodiments, the representation of gene expression levels in the first reference data set and / or the second reference data set is obtained using machine learning. In some embodiments of any of the provided embodiments, the machine learning comprises principal component analysis. In some embodiments of any of the provided embodiments, the representation of gene expression levels in the first reference data set and / or the second reference data set comprises normalized gene expression levels.

[0041] In some embodiments of any of the provided embodiments, the method further includes classifying the differentiation state of the one or more test cells as being in the second differentiation state if the first similarity score indicates that the differentiation state of the one or more test cells is more similar to the second differentiation state. In some embodiments of any of the provided embodiments, the method further includes classifying the differentiation state of the one or more test cells as being in the second differentiation state if the second similarity score indicates that the differentiation state of the one or more test cells is more similar to the second differentiation state. In some embodiments of any of the provided embodiments, the method further includes selecting an in vitro cell population comprising one or more test cells classified as having the second differentiation state as having a desired differentiation state.

[0042] In some embodiments of any of the provided embodiments, the method further comprises classifying the differentiation state of the one or more test cells as being in the second differentiation state if the first similarity score and the second similarity score indicate that the differentiation state of the one or more test cells is more similar to the second differentiation state. In some embodiments of any of the provided embodiments, the method further comprises selecting an in vitro cell population comprising the one or more test cells classified as having the second differentiation state as having a desired differentiation state.

[0043] In some embodiments, the present invention also provides a method for selecting a cell population with a desired differentiation state, the method comprising (a) obtaining a test data set, the test data set comprising the gene expression level of one or more genes of one or more test cells contained in the in vitro cell population, the one or more genes selected from AC010247.2, ANKRD33B, APC2, AQP4, ASCL1, AURKB, BARHL2, CACNA1G, CAPN6, CBLN1, CCNB2, CDH1, CDH2 0. CHGA, COL1A1, COL1A2, COL22A1, COL4A1, CRABP1, DBX1, DCN, DCX, DDC, DOCK10, E2F4, EDNRB, ESRP1, EZH2, FABP7, F BLN1, FLRT3, FOXA2, FOXM1, GAP43, GFAP, GFRA1, GJA1, GLRA2, HES1, HES2, HES5, ITGA5, JPH4, LDHA, LIN28A, LIX1, LMX 1A, LUM, NCAM1, NES, NEUROG2, NGFR, NKX2-2, NMNAT2, NPTX1, NR4A2, NR4A2(NURR1), NSG2, NFYA, OLFM3, OLIG1, OLIG2 , OTX2, P4HA1, PBX1, PDGFRA, PIEZO2, PITX3, PLP1, PMEL, PMP2, POSTN, POU2F2, PPP2R2B, PRTG, PTTG1, REST, RET, RFX 4, RFX4, SALL4, SIN3A, SLC16A3, SLC18A2, SLC1A, SLC1A2, SLC1A3, SLC4A4, SMAD4, SNAP25, SOX10, SOX2, SOX9, STMN2, SUZ12, SV2B, SYN1, SYT1, SYT13, TH, TOP2A, TPH1, TPM2 and TXNIP; and (b) applying the gene expression levels as input to a process configured to predict whether a cell population has a desired differentiation state.

[0044] In some embodiments of any provided embodiments, the in vitro cell colony comprises stem cell-derived neuronal cells. In some embodiments of any provided embodiments, the desired differentiation state is a differentiation state of directed dopaminergic neuronal cells. In some embodiments of any provided embodiments, the desired differentiation state is a differentiation state of cells suitable for implantation.

[0045] In some embodiments of any of the provided embodiments, the desired differentiation state is a differentiation state of a hematopoietic progenitor cell.

[0046] In some embodiments, the present invention also provides a method for selecting a cell population predicted to exhibit neurite outgrowth after implantation into a brain region, the method comprising (a) obtaining a test data set, the test data set comprising gene expression levels of one or more genes of one or more test cells contained in an in vitro cell population, the one or more genes selected from AC010247.2, ANKRD33B, APC2, AQP4, ASCL1, AURKB, BARHL2, CACNA1G, CAPN6, CBLN1, CCNB2, CDH1 , CDH20, CHGA, COL1A1, COL1A2, COL22A1, COL4A1, CRABP1, DBX1, DCN, DCX, DDC, DOCK10, E2F4, EDNRB, ESRP1, EZH2, FABP 7. FBLN1, FLRT3, FOXA2, FOXM1, GAP43, GFAP, GFRA1, GJA1, GLRA2, HES1, HES2, HES5, ITGA5, JPH4, LDHA, LIN28A, LIX1, L MX1A, LUM, NCAM1, NES, NEUROG2, NGFR, NKX2-2, NMNAT2, NPTX1, NR4A2, NR4A2(NURR1), NSG2, NFYA, OLFM3, OLIG1, OLIG2 , OTX2, P4HA1, PBX1, PDGFRA, PIEZO2, PITX3, PLP1, PMEL, PMP2, POSTN, POU2F2, PPP2R2B, PRTG, PTTG1, REST, RET, RFX4, RFX4, SALL4, SIN3A, SLC16A3, SLC18A2, SLC1A, SLC1A2, SLC1A3, SLC4A4, SMAD4, SNAP25, SOX10, SOX2, SOX9, STMN2, SUZ12, SV2B, SYN1, SYT1, SYT13, TH, TOP2A, TPH1, TPM2, and TXNIP; and (b) applying the gene expression levels as input to a process configured to predict whether a cell population will exhibit neurite outgrowth after implantation into a brain region.

[0047] In some embodiments of any of the provided embodiments, the one or more genes include at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95 or more of AC010247.2, ANKRD33B, APC2, AQP4, ASCL1, AUR KB, BARHL2, CACNA1G, CAPN6, CBLN1, CCNB2, CDH1, CDH20, CHGA, COL1A1, COL1A2, COL22A1, COL4A1, CRABP1, DBX1, DCN , DCX, DDC, DOCK10, E2F4, EDNRB, ESRP1, EZH2, FABP7, FBLN1, FLRT3, FOXA2, FOXM1, GAP43, GFAP, GFRA1, GJA1, GLRA2, H ES1, HES2, HES5, ITGA5, JPH4, LDHA, LIN28A, LIX1, LMX1A, LUM, NCAM1, NES, NEUROG2, NGFR, NKX2-2, NMNAT2, NPTX1, N R4A2, NR4A2(NURR1), NSG2, NFYA, OLFM3, OLIG1, OLIG2, OTX2, P4HA1, PBX1, PDGFRA, PIEZO2, PITX3, PLP1, PMEL, PMP2, POSTN, POU2F2, PPP2R2B, PRTG, PTTG1, REST, RET, RFX4, RFX4, SALL4, SIN3A, SLC16A3, SLC18A2, SLC1A, SLC1A2, SLC1 A3, SLC4A4, SMAD4, SNAP25, SOX10, SOX2, SOX9, STMN2, SUZ12, SV2B, SYN1, SYT1, SYT13, TH, TOP2A, TPH1, TPM2 and TXNIP.

[0048] In some embodiments of any provided embodiment, the process includes a machine learning model. In some embodiments of any provided embodiment, the gene expression level of one or more genes has been used to train the machine learning model. In some embodiments of any provided embodiment, one or more outputs of the machine learning model are used to predict whether the cell colony has a desired differentiation state. In some embodiments of any provided embodiment, one or more outputs of the machine learning model are used to predict whether the cell colony will show neurite outgrowth after being implanted in a brain region. In some embodiments of any provided embodiment, the method also includes classifying the differentiation state of one or more test cells based on one or more outputs of the machine learning model. In some embodiments of any provided embodiment, the method also includes predicting whether the test cell will show neurite outgrowth after being implanted in a brain region based on one or more outputs of the machine learning model. In some embodiments of any provided embodiment, the method also includes selecting an in vitro cell colony comprising one or more test cells classified as having a desired differentiation state. In some embodiments of any provided embodiment, the method also includes selecting an in vitro cell colony comprising one or more test cells predicted to show neurite outgrowth after being implanted in a brain region.

[0049] In some embodiments, a method for implanting a cell colony with a desired differentiation state into a subject is also provided herein, the method comprising: (a) selecting a cell colony with a desired differentiation state using any of the methods provided; and (b) implanting the cell colony into a subject. In some embodiments of any of the embodiments provided, the cells with a desired differentiation state are directed dopaminergic cells, and the cell colony is implanted into a brain region of the subject. In some embodiments of any of the embodiments provided, the cells with a desired differentiation state are from cell cultures differentiated from pluripotent cells under conditions that cause neural differentiation of the cells.

[0050] In some embodiments of any of the provided embodiments, the cells having a desired differentiation state are hematopoietic progenitor cells, and the cell population is implanted into a brain region of a subject. In some embodiments of any of the provided embodiments, the cells having a desired differentiation state are from a cell culture differentiated from pluripotent cells under conditions that cause neural differentiation of the cells.

[0051] In some embodiments, provided herein is a pharmaceutical composition comprising a pharmaceutical carrier and a population of cells having a desired differentiation state, wherein the cells are selected using any of the provided methods.

[0052] In some embodiments of any of the provided embodiments, the cells having a desired differentiation state are neuronal cells suitable for treating neurodegenerative diseases when implanted in the brain of a subject in need of such treatment. In some embodiments of any of the provided embodiments, the neuronal cells include committed dopaminergic cells. In some embodiments of any of the provided embodiments, the neuronal cells include neuronal cells capable of engraftment.

[0053] In some embodiments of any of the embodiments provided, the neuronal cells include hematopoietic progenitor cells.

[0054] In some embodiments, the present invention also provides a method for training a machine learning model for classifying the differentiation state of an in vitro cell population, the method comprising: (a) for multiple reference cell populations, obtaining gene expression levels of one or more genes differentially expressed between cells in a first differentiation state and cells in a second differentiation state, and applying the gene expression levels as input to training a first machine learning model to predict whether the in vitro cell population contains one or more test cells having a differentiation state that is more similar to the first differentiation state or the second differentiation state; and (b) for multiple reference cell populations, obtaining gene expression levels of one or more genes differentially expressed between cells in a second differentiation state and cells in a third differentiation state, and applying the gene expression levels as input to training a second machine learning model to predict whether the in vitro cell population contains one or more test cells having a differentiation state that is more similar to the second differentiation state or the third differentiation state.

[0055] In some embodiments, the present invention also provides a method for training a machine learning model for classifying the differentiation state of an in vitro cell population, the method comprising: (a) selecting one or more genes that are differentially expressed between cells in a first differentiation state and cells in a second differentiation state, and applying the expression levels of the selected genes of multiple reference cell populations as input to training a first machine learning model to predict whether the in vitro cell population contains one or more test cells having a differentiation state that is more similar to the first differentiation state or the second differentiation state; and (c) selecting one or more genes that are differentially expressed between cells in the second differentiation state and cells in a third differentiation state, and applying the expression levels of the selected genes of multiple reference cell populations as input to training a second machine learning model to predict whether the in vitro cell population contains one or more test cells having a differentiation state that is more similar to the second differentiation state or the third differentiation state.

[0056] In some embodiments of any of the provided embodiments, the method also includes obtaining gene expression levels of one or more genes expressed in cells in a control differentiation state, which control differentiation state can be the same as or different from one of the first differentiation state, the second differentiation state, or the third differentiation state, and applying the gene expression levels as input to training a control machine learning model to predict whether the in vitro cell population contains one or more test cells that are similar to the cells in the control differentiation state.

[0057] Also provided herein is a pharmaceutical composition comprising a pharmaceutical carrier and a population of neuronal cells, wherein the cells are selected using any of the provided methods.

[0058] Also provided herein is an in vitro stem cell-derived neuronal cell population, the in vitro stem cell-derived neuronal cell population comprising cells expressing one or more genes selected from the group consisting of: CCNB2, AURKB, PTTG1, TOP2A, NEUROG2, HES1, REST, E2F4, FOXM1, SIN3A, NFYA, LIN28A, FLRT3, ITGA5, NES, SOX2, SOX9 and RFX4. In some embodiments, the in vitro stem cell-derived neuronal cell population is a cell population wherein: (1) at least one gene from the one or more genes is selected from the group consisting of: CCNB2, AURKB, PTTG1, TOP2A, NEUROG2, HES1, REST, E2F4, FOXM1, SIN3A, NFYA, LIN28A, FLRT3 and ITGA5; and (2) at least one gene from the one or more genes is selected from the group consisting of: NES, SOX2, SOX9 and RFX4. In some embodiments, at least one of the one or more genes is REST.

[0059] In some embodiments of any of the embodiments of the in vitro stem cell-derived neuronal cell population, at least 50% of the cells within the population express the one or more genes.

[0060] In some embodiments of any of the embodiments of the in vitro stem cell-derived neuronal cell population, at least 60% of the cells within the population express the one or more genes.

[0061] In some embodiments of any of the embodiments of the in vitro stem cell-derived neuronal cell population, at least 70% of the cells within the population express the one or more genes.

[0062] In some embodiments of any of the embodiments of the in vitro stem cell-derived neuronal cell population, at least 80% of the cells within the population express the one or more genes.

[0063] In some embodiments of any of the embodiments of the in vitro stem cell-derived neuronal cell population, at least 90% of the cells within the population express the one or more genes.

[0064] In some embodiments of any of the embodiments of the in vitro stem cell-derived neuronal cell population, cells in the population express EN1 and CORIN. In some embodiments, less than 20% of the total cells in the composition express TH. In some embodiments, less than 10% of the total cells in the composition express TH.

[0065] In some embodiments of any of the embodiments of the in vitro stem cell-derived neuronal cell population, the expression is RNA expression. In some embodiments, RNA expression is measured by RNA sequencing.

[0066] In some embodiments of any of the embodiments of the in vitro stem cell-derived neuronal cell population, the population has been differentiated in vitro from pluripotent stem cells (PSCs).

[0067] In some embodiments of any embodiment of the in vitro stem cell-derived neuronal cell colony, the one or more genes are genes overexpressed in the cells of the colony compared with iPSC. In some embodiments, one or more genes are genes overexpressed in the cells of the colony compared with the cells of the precursor colony differentiated from iPSC. In some embodiments, one or more genes are genes overexpressed in the cells of the colony compared with the cells of the mature dopaminergic neuronal cell colony differentiated from iPSC. In some embodiments, mature dopaminergic neuronal cells express LMX1A and / or NR4A2 (NURR1). In some embodiments, in the cells in the stereotyped dopaminergic neuronal cell colony, at least 40%, at least 50%, at least 60%, at least 70% or at least 80% of the cells express LMX1A and / or NR4A2. In some embodiments, overexpression is greater than or greater than about 1.5 times, 2.0 times, 3.0 times, 4.0 times or 5 times the positive log2 fold change.

[0068] In some embodiments of any embodiment of the in vitro stem cell-derived neuronal cell population, the one or more genes are genes whose expression is reduced in the cells of the population compared to iPSC. In some embodiments, one or more genes are genes whose expression is reduced in the cells of the population compared to the cells of the precursor population differentiated from iPSC. In some embodiments, the one or more genes are genes whose expression is reduced in the cells of the population compared to the cells of the mature dopaminergic neuronal cell population differentiated from iPSC. In some embodiments, mature dopaminergic neuronal cells express LMX1A and / or NR4A2 (NURR1). In some embodiments, in the cells in the dopaminergic neuronal cell population, at least 40%, at least 50%, at least 60%, at least 70% or at least 80% of the cells express LMX1A and / or NR4A2. In some embodiments, the reduced expression is greater than or greater than about 1.5 times, 2.0 times, 3.0 times, 4.0 times or 5 times the negative log2 fold change.

[0069] In some embodiments of any of the embodiments of the in vitro stem cell-derived neuronal cell population, less than 30%, less than 20%, or less than 10% of the cells in the population express LMX1A and / or NR4A2.

[0070] In some embodiments of any embodiment of the in vitro stem cell derived neuron cell colony, the cells in the colony can be colonized and dominate other cells in vivo. In some embodiments, the cells in the colony can show neurite outgrowth when administered to the brain of a subject. In some embodiments, the cells in the colony can produce dopamine and optionally do not produce or substantially do not produce norepinephrine.

[0071] In some embodiments of any of the embodiments of the in vitro stem cell-derived neuronal cell population, the population comprises at least 5 million total cells, at least 10 million total cells, at least 15 million total cells, at least 20 million total cells, at least 30 million total cells, at least 40 million total cells, at least 50 million total cells, at least 100 million total cells, at least 150 million total cells, or at least 200 million total cells. In some embodiments, the population comprises just or about 5 million total cells to just or about 200 million total cells, just or about 5 million total cells to just or about 150 million total cells, just or about 5 million total cells to just or about 100 million total cells, just or about 5 million total cells to just or about 50 million total cells, just or about 5 million total cells to just or about 25 million total cells, just or about 5 million total cells to just or about 10 million total cells, just or about 10 million total cells to just or about 200 million total cells, just or about 10 million total cells to just or about 150 million total cells, just or about 10 million total cells to just or about 100 million total cells, just or about 10 million total cells to just or about 50 million total cells, just or about 25 million total cells, or about 10 million total cells to just or about 25 million total cells, just or about 25 million total cells to just or about 200 million total cells, just or about 25 million total cells to just or about 150 million total cells, just or about 25 million total cells to just or about 100 million total cells, just or about 25 million total cells to just or about 50 million total cells, just or about 50 million total cells to just or about 200 million total cells, just or about 50 million total cells to just or about 150 million total cells, just or about 50 million total cells to just or about 100 million total cells, just or about 100 million total cells to just or about 200 million total cells, just or about 100 million total cells to just or about 150 million total cells, or just or about 150 million total cells to just or about 200 million total cells.

[0072] In some embodiments of any of the embodiments of the in vitro stem cell-derived neuronal cell population, at least about 70%, 75%, 80%, 85%, 90%, or 95% of the total cells in the composition are viable.

[0073] Also provided herein is a pharmaceutical composition comprising a pharmaceutical carrier and an in vitro stem cell-derived neuronal cell population as provided herein.

[0074] In any of the provided pharmaceutical composition embodiments, the composition comprises a cryoprotectant. In some embodiments, the cryoprotectant is selected from the group consisting of glycerol, propylene glycol, and dimethyl sulfoxide (DMSO).

[0075] In any of the embodiments of the provided pharmaceutical compositions, the composition is used to treat a neurodegenerative disease or condition in a subject, optionally wherein the neurodegenerative disease or condition comprises a loss of dopaminergic neurons. In some embodiments, the neurodegenerative disease or condition comprises a loss of dopaminergic neurons in the substantia nigra, optionally in the SNc. In some embodiments, the neurodegenerative disease or condition is Parkinson's disease. In some embodiments, the neurodegenerative disease or condition is Parkinson's syndrome.

[0076] In any embodiment of the pharmaceutical composition provided, the composition is used to treat a neurodegenerative disease or condition in a subject, wherein the neurodegenerative disease or condition comprises a loss of microglia. In some embodiments, the neurodegenerative disease or condition is Parkinson's disease. In some embodiments, the neurodegenerative disease or condition is Parkinson's syndrome. In some embodiments, the neurodegenerative disease or condition is an age-related neurodegenerative disease. In some embodiments, the neurodegenerative disease or condition is Alzheimer's disease. In some embodiments, the neurodegenerative disease or condition is frontotemporal dementia.

[0077] Also provided herein is a method of treatment comprising implanting a therapeutically effective amount of any of the provided pharmaceutical compositions into a brain region of a subject in need thereof. In some embodiments, the number of cells implanted into the subject is about 0.25×10 6 cells to about 20×10 6 cells, about 0.25×10 6 cells to about 15×10 6 cells, about 0.25×10 6 cells to about 10×10 6 cells, about 0.25×10 6 cells to about 5×10 6 cells, about 0.25×10 6 cells to about 1×10 6 cells, about 0.25×10 6 cells to about 0.75×10 6 cells, about 0.25×10 6 cells to about 0.5×10 6 cells, about 0.5×10 6 cells to about 20×10 6 cells, about 0.5×10 6 cells to about 15×10 6 cells, about 0.5×10 6 cells to about 10×10 6 cells, about 0.5×106 cells to about 5×10 6 cells, about 0.5×10 6 cells to about 1×10 6 cells, about 0.5×10 6 cells to about 0.75×10 6 cells, about 0.75×10 6 cells to about 20×10 6 cells, about 0.75×10 6 cells to about 15×10 6 cells, about 0.75×10 6 cells to about 10×10 6 cells, about 0.75×10 6 cells to about 5×10 6 cells, about 0.75×10 6 cells to about 1×10 6 cells, about 1×10 6 cells to about 20×10 6 cells, about 1×10 6 cells to about 15×10 6 cells, about 1×10 6 cells to about 10×10 6 cells, about 1×10 6 cells to about 5×10 6 cells, about 5×10 6 cells to about 20×10 6 cells, about 5×10 6 cells to about 15×10 6 cells, about 5×10 6 cells to about 10×10 6 cells, about 10×10 6 cells to about 20×10 6 cells, about 10×10 6 cells to about 15×10 6 cells or about 15×10 6 cells to about 20×10 6 cells.

[0078] In any embodiment of the method of treatment provided, the subject suffers from a neurodegenerative disease or condition. In some embodiments, the neurodegenerative disease or condition includes the loss of dopaminergic neurons. In some embodiments, the subject has lost at least 50%, at least 60%, at least 70% or at least 80% of dopaminergic neurons. In some embodiments, the subject has lost at least 50%, at least 60%, at least 70% or at least 80% of dopaminergic neurons in the substantia nigra (SN), optionally in the SN compact part (SNc). In some embodiments, the neurodegenerative disease or condition is Parkinson's syndrome. In some embodiments, the neurodegenerative disease or condition is Parkinson's disease.

[0079] In any of the embodiments of the methods of treatment provided, the subject suffers from a neurodegenerative disease or condition. In some embodiments, the neurodegenerative disease or condition comprises a loss of microglia. In some embodiments, the neurodegenerative disease or condition is Parkinson's syndrome. In some embodiments, the neurodegenerative disease or condition is Parkinson's disease. In some embodiments, the neurodegenerative disease or condition is an age-related neurodegenerative disease. In some embodiments, the neurodegenerative disease or condition is Alzheimer's disease. In some embodiments, the neurodegenerative disease or condition is frontotemporal dementia.

[0080] In embodiments of any of the provided methods, the implantation in the brain region is a brain region that is the substantia nigra. In some embodiments, the implantation is performed by stereotactic injection. In some embodiments, the cells of the pharmaceutical composition are autologous to the subject. BRIEF DESCRIPTION OF THE DRAWINGS

[0081] Figure 1A Shown is a decision tree of an exemplary method for identifying a cell colony in a desired differentiation state (e.g., intermediate differentiation state, such as oriented state) using gene expression levels. In this exemplary method, the gene expression level of a test cell colony is first evaluated to determine whether the expression level is similar to the expression level of the reference cell colony used during method development. If the expression level does not have too large a difference or singularity, then the expression level is evaluated next to determine whether they are more consistent with the expression level of an early state cell (e.g., precursor cell) colony or with the expression level of an intermediate state cell (e.g., oriented cell) colony. If the expression level is more consistent with the expression level of an intermediate state cell colony, then the expression level is evaluated at last to determine whether they are more consistent with the expression level of an late state cell (e.g., committed cell) colony or with the expression level of an intermediate state cell colony. If the gene expression level is more consistent with the expression level of an intermediate state cell colony, then the test colony is identified as an intermediate state cell colony.

[0082] Figure 1B It is shown how the provided methods can be used to identify cells in intermediate differentiation states, such as committed states. Figure 1B As shown, the provided method can be used for multiple target cell types and multiple in vitro differentiation schemes. Different differentiation schemes within the same target cell type can give different optimal intermediate opportunities. The intermediate differentiation stage can be when the cell colony is most suitable for, for example, transplantation (such as treatment of a disease or condition). As shown herein, the method trained with the gene expression level of cells from the first differentiation scheme can also be used to identify cells in an intermediate differentiation state in a second differentiation scheme. Figure 1B The times shown in days (d) are for example only.

[0083] Figure 2A and Figure 2B A flow chart is shown for training and using an exemplary machine learning method for identifying intermediate state cell (e.g., committed cells) populations using gene expression levels. Figure 2A A flow chart is shown for determining a cutoff value for a singularity score that indicates whether the gene expression levels of the test cell population are similar to the gene expression levels of a reference cell population used to train the method, and how the cutoff value can be applied to a test cell population. Figure 2B Shown is a flow chart of how the first model for training to distinguish early state cells (e.g., precursor cells) from intermediate state cells (e.g., committed cells; Model A); a separate second model for training to distinguish late state cells (e.g., committed cells) from intermediate state cells (e.g., committed cells; Model B); and these two models can be applied to test cell populations. These procedures applied to reference cell populations harvested at different time points during a neural differentiation protocol are described in Example 1.

[0084] FIG. 3A to FIG. 3H Shown are the results of a machine learning approach trained using reference cell populations harvested at different time points during the neural differentiation protocol. FIG. 3A to FIG. 3F Results for a neural cell population are shown. Figure 3G Results for the glial test cell population are shown. Figure 3H Results for test cell populations of various cell types are shown.

[0085] 4A to 4D and FIG. 5A to FIG. 5D Shown are the results of a machine learning approach trained using reference cell populations harvested at different time points during the microglial differentiation protocol. 4A to 4D Results for a reference cell population are shown. FIG. 5A to FIG. 5D Validation results for a test cell population not used for model training are shown. DETAILED DESCRIPTION

[0086] In some embodiments, provided herein is a method for classifying the differentiation state of a cell colony. In some embodiments, provided herein is a method for selecting a cell colony with a desired differentiation state (e.g., a cell colony classified as having a desired differentiation state by any of the methods provided). In some embodiments, provided herein is a method for implanting a cell colony with a desired differentiation state (e.g., a cell colony classified or selected according to any of the methods provided).

[0087] In some embodiments, the methods provided relate to classifying the differentiation state of a cell colony. In some embodiments, the classification is based on the characteristics of one or more test cells of the cell colony. In some embodiments, the classification is based on the gene expression level of one or more test cells of the cell colony.

[0088] In some embodiments, there is also provided herein a method for identifying a cell colony predicted to show neurite outgrowth after implantation in a brain region. In some embodiments, there is also provided herein a method for selecting a cell colony predicted to show neurite outgrowth after implantation in a brain region (e.g., a cell colony so identified by any method in the method provided). In some embodiments, there is also provided herein a method for implanting a cell colony predicted to show neurite outgrowth after implantation in a brain region (e.g., a cell colony so identified or selected according to any method in the method provided).

[0089] In some embodiments, the population is an in vitro cell population.

[0090] In some embodiments, the method includes a step for calculating a first similarity score and a second similarity score using gene expression levels. In some embodiments, the classification is based on one or both of the first similarity score and the second similarity score. In some embodiments, the classification is based on one of the first similarity score and the second similarity score. In some embodiments, the classification is based on the lower of the first similarity score and the second similarity score. In some embodiments, the classification is based on the higher of the first similarity score and the second similarity score. In some embodiments, the classification is based on the first similarity score. In some embodiments, the classification is based on the second similarity score. In some embodiments, the first similarity score indicates whether the differentiation state of the cell colony is more similar to the first differentiation state or the second differentiation state. In some embodiments, the second similarity score indicates whether the differentiation state of the cell colony is more similar to the second differentiation state or the third differentiation state.

[0091] In some embodiments, the method includes a step for calculating a first similarity score and a second similarity score using the gene expression levels. In some embodiments, the classification is based on the first similarity score and the second similarity score. In some embodiments, the first similarity score indicates whether the differentiation state of the cell population is more similar to the first differentiation state or the second differentiation state. In some embodiments, the second similarity score indicates whether the differentiation state of the cell population is more similar to the second differentiation state or the third differentiation state.

[0092] The first differentiation state, the second differentiation state, and the third differentiation state may be in the same or different stem cell differentiation pathways. In some embodiments, the first differentiation state, the second differentiation state, and the third differentiation state are all in the same stem cell differentiation pathway. In some embodiments, the second differentiation state is an intermediate differentiation state relative to the first differentiation pathway and the third differentiation pathway. For example, in some embodiments, the first differentiation state is earlier than the second differentiation state in the stem cell differentiation pathway, and the second differentiation state is earlier than the third differentiation state in the stem cell differentiation pathway. In other embodiments, the second differentiation state and the third differentiation state are in different stem cell differentiation pathways, and the first differentiation state is a differentiation state of cells that can differentiate into the second differentiation state or the third differentiation state.

[0093] When a single or a small amount of features or characteristics (such as gene expression markers or functional characteristics) are unavailable (e.g., unknown) or cannot be actually used to determine cell identity (e.g., cell differentiation state), the provided method allows for determining cell identity (e.g., cell differentiation state). In some aspects, certain cell populations (including directed dopaminergic cells) differentiated from pluripotent stem cells can be cells in a differentiation stage, in which these cells cannot be identified by one or a small amount of features or characteristics. In some aspects, the differentiated cells can enter a differentiation state, in which there is no clear biomarker that can be used to determine the identity of the cell, such as a differentiation state. Although pluripotent stem cells can be positively identified with clear biomarkers (e.g., the expression level of a specific gene), and differentiated cells can be positively identified based on functional markers, the individual markers used to identify cells at different transient stages throughout the differentiation are unknown. In the absence of such markers, it has been difficult to characterize, define and / or identify pre-differentiated cells with a specific cell phenotype in the past. In some aspects, the method provided herein overcomes the lack of a single or a small amount of features or characteristics (e.g., biomarkers) by examining genomes and their expression levels. This approach does not rely on the knowledge of individual marker genes, but instead uses a whole transcriptome approach to characterize and identify the differentiation state of cells.

[0094] Induced pluripotent stem cells (iPSC) are considered to be useful as cell therapy because at least they can differentiate into specialized cell types. For example, iPSC (such as pluripotent stem cells) can differentiate into specific cell types that can be used to replace diseased or damaged tissues. In some cases, therapeutic treatment may include administering (e.g., injecting) cells that are being differentiated that have not yet entered the final differentiation state to the subject. The inability to determine the identity of the differentiated cells throughout the differentiation process can lead to uncertainty about the success of the process. For example, the differentiation process may need to be run to completion in order to determine whether the differentiation process is successful. Therefore, if it is not possible to determine whether the cells being differentiated are gradually undergoing a transient phase as required, the differentiation process will become time-consuming and inefficient, and may hinder the treatment of the subject, such as when the differentiation process fails. In some embodiments, the method provided improves the differentiation process, such as by allowing the cell identity to be determined throughout the differentiation state, which can be used to determine whether the cells undergoing the differentiation process are undergoing proper differentiation and / or differentiation according to defined standards. For example, if it is determined that the cell is not properly differentiated, the process can be terminated and optionally the process can be restarted with different iPSC clones from the subject.

[0095] For certain cell therapies using cells differentiated from pluripotent stem cells, it is advantageous to use cells in the intermediate stage of the differentiation process. In some embodiments, the methods and devices of the present invention can be used to identify cells in the intermediate stage that are most effective when used for cell therapy. For example, when neural cells are in the intermediate stage between the early stage (e.g., the stage of precursor cells) and the later stage (e.g., the stage of committed cells), the neural cells obtained by differentiation from pluripotent stem cells may be more likely to be implanted in the brain of the subject being treated.

[0096] In some embodiments, computing devices are also provided herein, including computing devices for performing any of the methods provided. In some embodiments, compositions, products, and kits comprising cell colonies are also provided herein, and the cell colonies include cell colonies classified as having a desired differentiation state by any of the methods provided. In some embodiments, methods for implanting a cell colony with a desired differentiation state (e.g., classified according to any of the methods provided) into a subject are also provided herein.

[0097] All publications, including patent documents, scientific papers, and databases, mentioned in this application are incorporated herein by reference in their entirety for all purposes to the same extent as if each individual publication was individually incorporated by reference. If a definition set forth herein is contrary to or otherwise inconsistent with a definition set forth in patents, applications, published applications, and other publications incorporated herein by reference, the definition set forth herein takes precedence over the definition incorporated herein by reference.

[0098] The section headings used herein are for organizational purposes only and are not to be construed as limiting the subject matter described.

[0099] I. Definitions

[0100] Unless otherwise defined, all specialized terms, symbols, and other technical and scientific terms or terminology used herein are intended to have the same meaning as commonly understood by one of ordinary skill in the art to which the claimed subject matter belongs. In some cases, terms with commonly understood meanings are defined herein for clarity and / or for ease of reference, and the inclusion of such definitions herein should not be construed as indicating a substantial difference from those generally understood in the art.

[0101] As used herein, the singular forms "a," "an," and "the" include plural referents unless the context clearly indicates otherwise. For example, "a" or "an" means "at least one" or "one or more." It should be understood that the aspects and variations described herein include "consisting of" and / or "consisting essentially of" the aspects and variations.

[0102] Throughout this disclosure, various aspects of the claimed subject matter are presented in the format of a range. It should be understood that the description of the range format is only for convenience and brevity and should not be interpreted as a rigid limitation on the scope of the claimed subject matter. Therefore, the description of the range should be considered to have specifically disclosed all possible sub-ranges and single numerical values ​​within the range. For example, in the case of providing a range of values, it should be understood that each intermediate value between the upper and lower limits of the range and any other specified value or intermediate value within the specified range are included in the claimed subject matter. The upper and lower limits of these smaller ranges can be independently included in the smaller range, and are also included in the claimed subject matter, with any clearly excluded limits in the specified range as a condition. When the specified range includes one or two limits, the scope excluding any one or two of the included limits is also included in the claimed subject matter. Regardless of the width of the range, this applies.

[0103] As used herein, the term "about" refers to the usual error range of each value that is readily known. References herein to an "about" value or parameter include (and describe) embodiments for that value or parameter itself. For example, a description referring to "about X" includes a description of "X".

[0104] As used herein, a statement that a cell or cell colony "expresses" a particular marker or is "positive" for a particular marker refers to that a particular marker is detectable on or in a cell. When referring to surface markers, the term refers to, for example, by staining with an antibody specifically bound to the marker and detecting the antibody as detected by flow cytometry, there is surface expression, wherein the staining can be detected by flow cytometry, and its level is substantially higher than the staining detected by the same procedure with an isotype-matched control under other identical conditions, and / or its level is substantially similar to the level of cells known to be positive for the marker, and / or its level is substantially higher than the level of cells known to be negative for the marker. When referring to markers in cells, such as transcription products or translation products, the term refers to, for example, the presence of detectable transcription products or translation products, wherein the level of the product detected is substantially higher than the level detected by the same procedure with a control under other identical conditions, and / or its level is substantially similar to the level of cells known to be positive for the marker, and / or its level is substantially higher than the level of cells known to be negative for the marker.

[0105] As used herein, a statement that a cell or cell colony "does not express" a particular marker or is "negative" for a particular marker refers to that a particular marker is substantially undetectable on or in a cell. When referring to surface markers, the term refers to the absence of surface expression as detected by flow cytometry, such as by staining with an antibody specifically bound to the marker and detecting the antibody, wherein the presence of a particular marker is substantially higher than the level detected by the same procedure with an isotype-matched control under other identical conditions, and / or substantially lower than the level of cells known to be positive for the marker, and / or substantially similar levels compared to the level of cells known to be negative for the marker. When referring to markers in cells, such as transcription products or translation products, the term refers to the absence of detectable transcription products or translation products, wherein the presence of a particular marker is substantially higher than the level detected by the same procedure with a control under other identical conditions, and / or substantially lower than the level of cells known to be positive for the marker, and / or substantially similar levels compared to cells known to be negative for the marker.

[0106] The term "expression" or "expressed" as used herein when referring to a gene refers to the transcription product and / or translation product of the gene. The expression level of a DNA molecule in a cell can be determined based on the amount of the corresponding mRNA present in the cell or the amount of the protein encoded by the DNA produced by the cell (Sambrook et al., 1989, Molecular Cloning: A Laboratory Manual, 18.1-18.88).

[0107] As used herein, the term "stem cell" refers to a cell characterized by the ability to self-renew through cell mitosis and the potential to differentiate into any of a variety of cell types. Among mammalian stem cells, embryonic stem cells and somatic stem cells can be distinguished. Embryonic stem cells are present in blastocysts and produce embryonic tissues, while somatic stem cells are present in adult tissues for the purpose of tissue regeneration and repair.

[0108] "Self-renewal" refers to the ability of a cell to divide and produce at least one daughter cell that has the self-renewal characteristics of the parent cell. The second daughter cell can enter a specific differentiation pathway. For example, a self-renewing hematopoietic stem cell can divide and form a daughter stem cell and another daughter cell that is committed to differentiation in the bone marrow or lymphatic pathway.

[0109] As used herein, the term "progenitor cell" refers to a cell that has the potential to differentiate into any of a variety of cell types but has lost its self-renewal ability relative to stem cells. For example, a progenitor cell can produce two daughter cells after cell division that display a more differentiated (e.g., restricted) phenotype.

[0110] As used herein, the term "non-self-renewing cell" refers to a cell that undergoes cell division to produce daughter cells, none of which have the differentiation potential of the parent cell type, eg, produces differentiated daughter cells.

[0111] As used herein, the term "adult stem cell" refers to undifferentiated cells found in individuals after embryonic development. Adult stem cells multiply by cell division to replenish dead cells and regenerate damaged tissues. Adult stem cells can divide and produce another cell like itself or produce a more differentiated cell. Although adult stem cells are associated with the expression of pluripotency markers such as Rex1, Nanog, Oct4 or Sox2, they do not have the ability of pluripotent stem cells to differentiate into cell types of all three germ layers.

[0112] As used herein, the term "pluripotent" or "pluripotency" refers to cells that are capable of producing progeny that can, under appropriate conditions, differentiate into cell types that collectively exhibit characteristics associated with cell lineages from the three germ layers (endoderm, mesoderm, and ectoderm). Pluripotent stem cells can contribute to tissues of prenatal, postnatal, or adult organisms.

[0113] As used herein, the term "pluripotent stem cell characteristics" refers to cell characteristics that distinguish pluripotent stem cells from other cells. The expression or non-expression of certain combinations of molecular markers is an example of pluripotent stem cell characteristics. More specifically, human pluripotent stem cells can express at least some, optionally all, of the markers from the following non-limiting list: SSEA-3, SSEA-4, TRA-1-60, TRA-1-81, TRA-2-49 / 6E, ALP, Sox2, E-cadherin, UTF-1, Oct4, Lin28, Rex1, and Nanog. Cell morphology associated with pluripotent stem cells is also a characteristic of pluripotent stem cells.

[0114] As used herein, the terms "induced pluripotent stem cells", "iPS" and "iPSC" refer to pluripotent stem cells artificially obtained (e.g., by artificial manipulation) from non-pluripotent cells. "Non-pluripotent cells" can be cells with lower self-renewal and differentiation potentials than pluripotent stem cells. Cells with lower potential can be adult stem cells, tissue-specific precursor cells, or primary cells or secondary cells.

[0115] As provided herein, the term "specification" or "specialized" refers to a narrowing of the fate of a cell or tissue to a limited number of specific cell types. A specialized cell can still change its specific fate until it reaches a directed state. A specialized cell can autonomously differentiate (e.g., differentiate on its own) when placed in an environment that is neutral to a developmental pathway, such as a culture dish or a test tube. During the specialized stage, the cell type may still be able to be changed. If a specialized cell is transplanted into a population of cells that are specialized in a different way, its interaction with the new neighboring cells can change the fate of the transplanted cell.

[0116] As used herein, "directed state" refers to a cell having only one cell type into which it can differentiate. For example, directed dopaminergic cells cannot become other types of neurons, although they themselves may not yet be dopaminergic neurons, and may or may not express clear markers of dopaminergic neurons. Directed cells may also be able to autonomously differentiate when placed in an embryonic region unrelated to the cell. For example, an unrelated region of directed dopaminergic cells is any organ or tissue other than the brain. Directed cells are also able to autonomously differentiate when placed in a cell cluster that is specialized in different ways in a culture dish.

[0117] As used herein, the term "differentiated" or "committed" refers to a cell or cells that have acquired cell type specific functions.

[0118] "Neuronary precursor cells" are cells that have a tendency to differentiate into neurons or glial cells and do not have the multipotent potential of stem cells. Neuronal precursors are cells that are stereotyped as neurons or glial lineages, characterized by expressing one or more marker genes that are specific to the neurons or glial lineages. The terms "neural" and "neuronal" are used according to their common meanings in the art and are used interchangeably throughout this article.

[0119] As used herein, "dopaminergic cells" or "differentiated dopaminergic cells" refer to cells that are capable of synthesizing the neurotransmitter dopamine. In some embodiments, the dopaminergic cells are A9 dopaminergic cells. The term "A9 dopaminergic cells" refers to the most densely packed dopaminergic cell population in the human brain, which is located in the substantia nigra pars compacta in the midbrain of healthy adults.

[0120] As used herein, the term "committed dopaminergic cell" refers to a cell that will differentiate into a dopaminergic neuron but cannot differentiate into a non-dopaminergic cell. A "committed dopaminergic cell" is a cell that can differentiate into a dopaminergic neuron independently of its environment. Committed dopaminergic cells may express Foxa2 or Nurrl. Committed dopaminergic cells may not express serotonin.

[0121] As used herein, the term "reprogramming" refers to a process of dedifferentiating non-pluripotent cells into cells that exhibit characteristics of pluripotent stem cells.

[0122] As used herein, the term "cell culture" may refer to an in vitro cell population that exists outside an organism. Cell cultures may be established from primary cells isolated from a cell bank or an animal, or from secondary cells derived from one of these sources and immortalized to obtain long-term in vitro cultures.

[0123] As used herein, the terms "culture", "culturing", "grow", "growing", "maintain", "maintaining", "expand", "expanding", etc. are used interchangeably when referring to cell culture itself or the culture process, and mean cells maintained in vitro (e.g., ex vivo) under conditions suitable for survival. The cultured cells are allowed to survive, and the culture may cause the cells to grow, differentiate or divide.

[0124] As used herein, a composition refers to any mixture of two or more products, substances or compounds (including cells). It can be a solution, a suspension, a liquid, a powder, a paste, an aqueous solution, a non-aqueous solution or any combination thereof.

[0125] The term "pharmaceutical composition" refers to a composition suitable for pharmaceutical use, such as pharmaceutical use in a mammalian subject (e.g., a human). A pharmaceutical composition typically comprises an effective amount of an active agent (e.g., a cell) and a carrier, excipient, or diluent. The carrier, excipient, or diluent is typically a pharmaceutically acceptable carrier, excipient, or diluent, respectively.

[0126] "Pharmaceutically acceptable carrier" refers to ingredients in a pharmaceutical formulation other than the active ingredient that are non-toxic to the subject. Pharmaceutically acceptable carriers include, but are not limited to, buffers, excipients, stabilizers or preservatives.

[0127] The term "package insert" is used to refer to instructions customarily included in commercial packages of therapeutic products, that contain information about the indications, usage, dosage, administration, combination therapy, contraindications and / or warnings concerning the use of such therapeutic products.

[0128] As used herein, a "subject" is a mammal, such as a human or other animal, and typically is a human.

[0129] II. Methods for Classifying or Identifying Cells

[0130] In some embodiments, provided herein is a method for classifying the differentiation state of an in vitro cell colony. In some embodiments, provided methods are used to identify an in vitro cell colony with a desired differentiation state. In some embodiments, provided methods are used to select an in vitro cell colony with a desired differentiation state.

[0131] In some embodiments, the present invention also provides a method for predicting whether an in vitro cell colony will show neurite outgrowth after being implanted in a brain region. In some embodiments, the method provided is used to identify an in vitro cell colony that will show neurite outgrowth after being implanted in a brain region. In some embodiments, the method provided is used to select an in vitro cell colony that will show neurite outgrowth after being implanted in a brain region.

[0132] In some embodiments, the provided method is a computer-implemented method. In some embodiments, the provided method is performed by a computing device. In some embodiments, the provided method is performed by any one of the provided computing devices (e.g., any one described in Section III).

[0133] In some embodiments, the provided method particularly provides information about whether an in vitro cell colony (e.g., a neuronal cell colony) includes cells that are directed to differentiate into a specific functional cell type (e.g., including directed dopaminergic cells) or whether an in vitro cell colony includes cells from an early stage (e.g., pluripotent stem cells, neuronal precursor cells), cells from a later stage (e.g., committed dopaminergic cells) or other differentiated cell types. In some embodiments, the provided method predicts whether an in vitro cell colony will differentiate into a specific cell type (e.g., differentiate into dopaminergic cells). In some embodiments, cells identified with the provided method are directed to differentiate into specific functional cell types (e.g., differentiate into dopaminergic cells). Whether a cell is directed to differentiate into a specific functional cell type (e.g., whether the cell is a directed dopaminergic cell) can also be confirmed in vitro or in vivo by allowing the cell to fully differentiate. The provided method also encompasses identification cells, which are pluripotent stem cells, specialized cells, neuronal types that are differentiating except directed dopaminergic cells, or other differentiated cell types.

[0134] In some embodiments, the provided methods include receiving a test data set as input, the test data set including features of one or more test cells. Exemplary test cells are described in Section II-C. In some embodiments, the provided methods include receiving a test data set as input, the test data set including expression levels of genes expressed in one or more test cells. Gene expression levels can be assessed using any of the methods described in Section II-D.

[0135] In some embodiments, the provided methods include calculating a first similarity score and a second similarity score. In some embodiments, the first similarity score indicates whether the differentiation state of the test cell is more similar to the first differentiation state or more similar to the second differentiation state. In some embodiments, the second similarity score indicates whether the differentiation state of the test cell is more similar to the second differentiation state or more similar to the third differentiation state. Exemplary methods for calculating the first similarity score and the second similarity score are described in Section II-A. Exemplary first differentiation state, second differentiation state, and third differentiation state are described in Section II-C.

[0136] In some embodiments, the differentiation state of one or more test cells is classified based on one or both of the first similarity score and the second similarity score. In some embodiments, provided methods include classifying the differentiation state of one or more test cells based on one or both of the first similarity score and the second similarity score.

[0137] In some embodiments, the differentiation state of one or more test cells is classified based on the first similarity score and the second similarity score.In some embodiments, provided methods include classifying the differentiation state of one or more test cells based on the first similarity score and the second similarity score.

[0138] In some embodiments, if one or both of the first similarity score and the second similarity score indicate that the differentiation state of the one or more test cells is more similar to the second differentiation state, the differentiation state of the one or more test cells is classified as being in the second differentiation state. In some embodiments, the provided methods include classifying the differentiation state of the one or more test cells as being in the second differentiation state if one or both of the first similarity score and the second similarity score indicate that the differentiation state of the one or more test cells is more similar to the second differentiation state.

[0139] In some embodiments, the classification is based on one of the first similarity score and the second similarity score. In some embodiments, the classification is based on the lower of the first similarity score and the second similarity score. In some embodiments, the classification is based on the higher of the first similarity score and the second similarity score. In some embodiments, the classification is based on the first similarity score. In some embodiments, the classification is based on the second similarity score.

[0140] In some embodiments, if the first similarity score and the second similarity score indicate that the differentiation state of the one or more test cells is more similar to the second differentiation state, the differentiation state of the one or more test cells is classified as being in the second differentiation state. In some embodiments, the provided methods include classifying the differentiation state of the one or more test cells as being in the second differentiation state if the first similarity score and the second similarity score indicate that the differentiation state of the one or more test cells is more similar to the second differentiation state.

[0141] In some embodiments, the differentiation state of one or more test cells is classified as being in a second differentiation state, and the in vitro cell population is identified as having a desired differentiation state. In some embodiments, the differentiation state of one or more test cells is classified as being in a second differentiation state, and the provided methods include identifying the in vitro cell population as having a desired differentiation state.

[0142] In some embodiments, the methods provided include selecting an in vitro cell colony with a desired differentiation state for use in treating a disease or condition in a subject. In some embodiments, an in vitro cell colony with a desired differentiation state is selected for implantation in a subject. In some embodiments, the methods provided include implanting an in vitro cell colony with a desired differentiation state in a subject, e.g., according to any of the methods described in Section VI.

[0143] In some embodiments, the provided method also includes calculating the correlation score using the features of one or more test cells and a control data set. In some embodiments, the classification of the differentiation state of one or more test cells is based on the correlation score and one or both of the first similarity score and the second similarity score. In some embodiments, the classification is based on the correlation score and one of the first similarity score and the second similarity score. In some embodiments, the classification is based on the correlation score and the first similarity score and the second similarity score. The lower one. In some embodiments, the classification is based on the correlation score and the higher one of the first similarity score and the second similarity score. In some embodiments, the classification is based on the correlation score and the first similarity score. In some embodiments, the classification is based on the correlation score and the first similarity score. In some embodiments, the classification is based on the correlation score and the second similarity score. Exemplary methods for calculating correlation scores are described in Section II-B.

[0144] In some embodiments, the provided methods further include calculating a correlation score using the features of the one or more test cells and a control data set. In some embodiments, the classification of the differentiation state of the one or more test cells is based on a first similarity score, a second similarity score, and a correlation score. Exemplary methods for calculating correlation scores are described in Section II-B.

[0145] In some embodiments, the provided method involves the use of a trained machine learning model. In some embodiments, the first machine learning model and the second machine learning model are used to determine the first similarity score and the second similarity score, respectively. In some embodiments, the first similarity score and the second similarity score are determined based on one or more outputs of the first machine learning model and the second machine learning model, respectively. Exemplary model types of the first machine learning model and the second machine learning model are described in Section II-A-4. In some embodiments, the first machine learning model and the second machine learning model are each trained using features (e.g., gene expression levels) of multiple reference cell populations. Exemplary reference cell populations are described in Section II-C.

[0146] In some embodiments, the present invention also provides a method for training a machine learning model that can be used to classify the differentiation state of an in vitro cell population.

[0147] In some embodiments, the method includes training a first machine learning model and a second machine learning model. In some embodiments, the first machine learning model and the second machine learning model are trained using gene expression levels. Exemplary genes included and / or selected for model training are described in Section II-A-3. Exemplary model types for the first machine learning model and the second machine learning model are described in Section II-A-4. Gene expression levels may be assessed according to any of the methods described in Section II-D.

[0148] In some embodiments, the method provided includes obtaining, for a plurality of reference cell populations, gene expression levels of one or more genes differentially expressed between cells in a first differentiation state and cells in a second differentiation state. In some embodiments, the method includes selecting genes differentially expressed between cells in a first differentiation state and cells in a second differentiation state. In some embodiments, the method includes obtaining expression levels of one or more genes in the selected genes of a plurality of reference cell populations. In some embodiments, the gene expression levels of a plurality of reference cell populations are applied as input to train a first machine learning model. In some embodiments, one or more outputs of the trained first machine learning model may be used to classify the differentiation state of one or more test cells. In some embodiments, one or more outputs of the trained first machine learning model may be used to calculate a first similarity score indicating whether the differentiation state of the test cell is more similar to the first differentiation state or more similar to the second differentiation state.

[0149] In some embodiments, the method provided includes obtaining, for a plurality of reference cell populations, the gene expression levels of one or more genes differentially expressed between cells in a second differentiation state and cells in a third differentiation state. In some embodiments, the method includes selecting genes differentially expressed between cells in a second differentiation state and cells in a third differentiation state. In some embodiments, the method includes obtaining the expression levels of one or more genes in the selected genes of a plurality of reference cell populations. In some embodiments, the gene expression levels of a plurality of reference cell populations are applied as input to train a second machine learning model. In some embodiments, one or more outputs of the trained second machine learning model may be used to classify the differentiation state of one or more test cells. In some embodiments, one or more outputs of the trained second machine learning model may be used to calculate a second similarity score indicating whether the differentiation state of the test cell is more similar to the second differentiation state or more similar to the third differentiation state. Exemplary reference cell populations and first differentiation states, second differentiation states, and third differentiation states are described in Section II-C.

[0150] In some embodiments, the method further comprises obtaining, for a plurality of reference cell populations, gene expression levels of one or more genes expressed in cells in a control differentiation state. The control differentiation state may be the same or different from one of the first differentiation state, the second differentiation state, or the third differentiation state. Exemplary control differentiation states are described in Section II-C. In some embodiments, the method further comprises applying the gene expression levels of one or more genes as input to a training control machine learning model. In some embodiments, one or more outputs of the trained control machine learning model may be used to classify the differentiation state of one or more test cells. In some embodiments, one or more outputs of the trained control machine learning model may be used to determine whether the differentiation state of the test cell is similar to the control differentiation state.

[0151] A. Similarity score

[0152] In some embodiments, the methods provided include calculating a first similarity score and a second similarity score. In some embodiments, the differentiation state of one or more test cells is classified based on one or both of the first similarity score and the second similarity score. In some embodiments, if one or both of the first similarity score and the second similarity score indicate that the differentiation state of one or more test cells is similar to the second differentiation state, the differentiation state of one or more test cells is classified as being in the second differentiation state. In some embodiments, the methods provided include if one or both of the first similarity score and the second similarity score indicate that the differentiation state of one or more test cells is similar to the second differentiation state, the differentiation state of one or more test cells is classified as being in the second differentiation state.

[0153] In some embodiments, the classification is based on one of the first similarity score and the second similarity score. In some embodiments, the classification is based on the lower of the first similarity score and the second similarity score. In some embodiments, the classification is based on the higher of the first similarity score and the second similarity score. In some embodiments, the classification is based on the first similarity score. In some embodiments, the classification is based on the second similarity score.

[0154] In some embodiments, the methods provided include calculating a first similarity score and a second similarity score. In some embodiments, the differentiation state of one or more test cells is classified based on the first similarity score and the second similarity score. In some embodiments, if the first similarity score and the second similarity score indicate that the differentiation state of one or more test cells is similar to the second differentiation state, the differentiation state of one or more test cells is classified as being in the second differentiation state. In some embodiments, the methods provided include if the first similarity score and the second similarity score indicate that the differentiation state of one or more test cells is similar to the second differentiation state, the differentiation state of one or more test cells is classified as being in the second differentiation state.

[0155] In some embodiments, the gene expression levels of the test data set are used to calculate the first similarity score and the second similarity score. In some embodiments, the gene expression levels of the test data set are compared with the gene expression levels included in the first reference data set and the second reference data set. In some embodiments, the first similarity score and the second similarity score are calculated using the representations of the gene expression levels included in the first reference data set and the second reference data set, respectively. In some embodiments, these representations are obtained by machine learning. In some embodiments, the first reference data set and the second reference data set include a first machine learning model and a second machine learning model, respectively, and the first similarity score and the second similarity score are calculated by applying the gene expression levels of the test data set as inputs to the first machine learning model and the second machine learning model, respectively, and the first similarity score and the second similarity score are based on one or more outputs of the first machine learning model and the second machine learning model, respectively.

[0156] In some embodiments, one or both of the first similarity score and the second similarity score are binary outputs (e.g., 0 or 1, or -1 or 1) indicating whether the differentiation state of the one or more test cells is the second differentiation state. In some embodiments, one or both of the first similarity score and the second similarity score are non-binary outputs.

[0157] Depending on the type of non-binary output, a first similarity score and a second similarity score that are above or below a predetermined threshold level may indicate that the differentiation state of one or more test cells is a second differentiation state. The predetermined threshold levels may be the same or different for the first similarity score and the second similarity score. Any suitable method for setting a predetermined threshold level may be used. For example, in some embodiments, a predetermined threshold level for a first similarity score is set based on a plurality of first similarity scores calculated using gene expression levels of a plurality of reference cell populations, the plurality of reference cell populations being used to obtain a representation of gene expression levels of a first reference data set, such as for training a first machine learning model for a first reference data set. In some embodiments, the predetermined threshold level is set to a value that separates a first similarity score of a reference cell population comprising cells of a first differentiation state from a first similarity score of a reference cell population comprising cells of a second differentiation state by an accuracy metric (e.g., accuracy, recall, precision, or F1 score) of at least 0.5, 0.6, 0.7, 0.8, 0.85, 0.9, 0.91, 0.92, 0.93, 0.94, 0.95, 0.96, 0.97, 0.98, or 0.99. Similarly, in some embodiments, a predetermined threshold level of a second similarity score is set based on a plurality of second similarity scores calculated using gene expression levels of a plurality of reference cell populations, the plurality of reference cell populations being used to obtain a representation of gene expression levels of a second reference data set, e.g., for training a second machine learning model for the second reference data set. In some embodiments, the predetermined threshold level is set to a value that separates a second similarity score for a reference cell population comprising cells in a second differentiation state from a second similarity score for a reference cell population comprising cells in a third differentiation state by an accuracy metric (e.g., accuracy, recall, precision, or F1 score) of at least 0.5, 0.6, 0.7, 0.8, 0.85, 0.9, 0.91, 0.92, 0.93, 0.94, 0.95, 0.96, 0.97, 0.98, or 0.99.

[0158] In some embodiments, one or both of the first similarity score and the second similarity score is the probability that the differentiation state of one or more test cells is the second differentiation state. In some embodiments, the probability of exceeding a predetermined probability threshold level indicates that the differentiation state of one or more test cells is the second differentiation state. For the first similarity score and the second similarity score, the predetermined probability threshold level may be the same or different. In some embodiments, the predetermined probability threshold level is, is about, is greater than, or is greater than about 0.5. In some embodiments, the predetermined probability threshold level is, is about, is greater than, or is greater than about 0.55. In some embodiments, the predetermined probability threshold level is, is about, is greater than, or is greater than about 0.6. In some embodiments, the predetermined probability threshold level is, is about, is greater than, or is greater than about 0.65. In some embodiments, the predetermined probability threshold level is, is about, is greater than, or is greater than about 0.7. In some embodiments, the predetermined probability threshold level is, is about, is greater than, or is greater than about 0.75. In some embodiments, the predetermined probability threshold level is, is about, is greater than, or is greater than about 0.8. In some embodiments, the predetermined probability threshold level is, is about, is greater than, or is greater than about 0.85. In some embodiments, the predetermined probability threshold level is, is about, is greater than, or is greater than about 0.9. In some embodiments, the predetermined probability threshold level is, is about, is greater than, or is greater than about 0.91. In some embodiments, the predetermined probability threshold level is, is about, is greater than, or is greater than about 0.92. In some embodiments, the predetermined probability threshold level is, is about, is greater than, or is greater than about 0.93. In some embodiments, the predetermined probability threshold level is, is about, is greater than, or is greater than about 0.94. In some embodiments, the predetermined probability threshold level is, is about, is greater than, or is greater than about 0.95. In some embodiments, the predetermined probability threshold level is, is about, is greater than, or is greater than about 0.96. In some embodiments, the predetermined probability threshold level is, is about, is greater than, or is greater than about 0.97. In some embodiments, the predetermined probability threshold level is, is about, is greater than, or is greater than about 0.98. In some embodiments, the predetermined probability threshold level is, is about, is greater than, or is greater than about 0.99.

[0159] In some embodiments, one or both of the first similarity score and the second similarity score are each compared to a predetermined threshold level. In some embodiments, one of the first similarity score and the second similarity score is compared to a predetermined threshold level. In some embodiments, the similarity score compared to its predetermined threshold level is based on which similarity score is closest to its predetermined threshold level. In some aspects, the similarity score compared to its predetermined threshold level is selected so that if the selected similarity score indicates that the differentiation state of the test cell is more similar to the second differentiation state, it is expected that another similarity score will also indicate that the differentiation state of the test cell is more similar to the second differentiation state.

[0160] 1. First similarity score

[0161] In some aspects, provided methods involve calculating a first similarity score indicating whether the differentiation state of the test cell is more similar to a first differentiation state or more similar to a second differentiation state. In some embodiments, the first similarity score is calculated using a first reference data set that includes gene expression levels of one or more genes differentially expressed between cells in the first differentiation state and cells in the second differentiation state. In some embodiments, the first similarity score is calculated using a first reference data set that includes representations of gene expression levels of one or more genes differentially expressed between cells in the first differentiation state and cells in the second differentiation state.

[0162] In some embodiments, the first reference data set includes gene expression levels of one or more genes differentially expressed between cells in a first differentiation state and cells in a second differentiation state. In some embodiments, the gene expression levels are normalized gene expression levels. In some embodiments, a first similarity score is obtained by comparing the gene expression levels of the first reference data set with the gene expression levels of the test data set.

[0163] In some embodiments, the first reference data set includes a representation of gene expression levels of one or more genes differentially expressed between cells in a first differentiation state and cells in a second differentiation state. In some embodiments, the representation of gene expression levels is obtained by machine learning. In some embodiments, the representation of gene expression levels is obtained by training a first machine learning model using gene expression levels of one or more genes.

[0164] In some embodiments, a first similarity score is calculated using a first reference data set including a first machine learning model, which is trained using gene expression levels of one or more genes differentially expressed between cells in a first differentiation state and cells in a second differentiation state. In some embodiments, the first similarity score is calculated by providing the gene expression levels of a test data set as input to a first machine learning model or a process including a first machine learning model. For example, in some embodiments, the gene expression levels of the test data set are normalized or converted before being provided as input to the first machine learning model. In some embodiments, the first similarity score is an output of the first machine learning model. In some embodiments, the first similarity score is calculated using one or more outputs of the first machine learning model.

[0165] In some embodiments, the gene expression levels of multiple reference cell populations are used to obtain a representation of the gene expression levels of the first reference data set. In some embodiments, the gene expression levels of multiple reference cell populations are used to train the first machine learning model. In some embodiments, multiple reference cell populations include at least one reference cell population comprising cells of the first differentiation state and at least one reference cell population comprising cells of the second differentiation state. In some embodiments, multiple reference cell populations include multiple reference cell populations comprising cells of the first differentiation state and multiple reference cell populations comprising cells of the second differentiation state. In some embodiments, reference cell populations, such as those cell populations comprising cells of the first differentiation state or the second differentiation state, are any cell populations as described in Section II-C.

[0166] 2. Second similarity score

[0167] In some aspects, the provided methods involve calculating a second similarity score indicating whether the differentiation state of the test cell is more similar to the second differentiation state or more similar to the third differentiation state. In some embodiments, the second similarity score is calculated using a second reference data set that includes gene expression levels of one or more genes differentially expressed between cells in the second differentiation state and cells in the third differentiation state. In some embodiments, the second similarity score is calculated using a second reference data set that includes representations of gene expression levels of one or more genes differentially expressed between cells in the second differentiation state and cells in the third differentiation state.

[0168] In some embodiments, the second reference data set includes gene expression levels of one or more genes differentially expressed between cells in the second differentiation state and cells in the third differentiation state. In some embodiments, the gene expression levels are normalized gene expression levels. In some embodiments, a second similarity score is obtained by comparing the gene expression levels of the second reference data set with the gene expression levels of the test data set.

[0169] In some embodiments, the second reference data set includes a representation of gene expression levels of one or more genes differentially expressed between cells in the second differentiation state and cells in the third differentiation state. In some embodiments, the representation of gene expression levels is obtained by machine learning. In some embodiments, the representation of gene expression levels is obtained by training a second machine learning model using gene expression levels of one or more genes.

[0170] In some embodiments, a second similarity score is calculated using a second reference data set including a second machine learning model, which is trained using gene expression levels of one or more genes differentially expressed between cells in a second differentiation state and cells in a third differentiation state. In some embodiments, the second similarity score is calculated by providing the gene expression levels of the test data set as input to a second machine learning model or a process including a second machine learning model. For example, in some embodiments, the gene expression levels of the test data set are normalized or converted before being provided as input to the second machine learning model. In some embodiments, the second similarity score is the output of the second machine learning model. In some embodiments, the second similarity score is calculated using one or more outputs of the second machine learning model.

[0171] In some embodiments, the gene expression levels of multiple reference cell populations are used to obtain a representation of the gene expression levels of the second reference data set. In some embodiments, the gene expression levels of multiple reference cell populations are used to train the second machine learning model. In some embodiments, multiple reference cell populations include at least one reference cell population comprising cells of the second differentiation state and at least one reference cell population comprising cells of the third differentiation state. In some embodiments, multiple reference cell populations include multiple reference cell populations comprising cells of the second differentiation state and multiple reference cell populations comprising cells of the third differentiation state. In some embodiments, reference cell populations, such as those cell populations comprising cells of the second differentiation state or the third differentiation state, are any cell populations as described in Section II-C.

[0172] 3. Exemplary genes

[0173] One or more genes of the first reference data set and / or the second reference data set may be selected based on any suitable criteria, such as one or more genes for training the first machine learning model and / or the second machine learning model, including in any of the methods provided involving training the first machine learning model and / or the second machine learning model. The criteria may include the expression of one or more genes above a minimum threshold level in a relevant cell population, such as a reference cell population comprising cells in a first differentiation state, a second differentiation state, and / or a third differentiation state, or in any combination of these reference cell populations. The criteria may also include differential expression of one or more genes between relevant cell populations (e.g., between cells in a first differentiation state and a second differentiation state, or between cells in a second differentiation state and a third differentiation state), such as differential expression at a threshold fold change level with a particular statistical significance, or making each of the one or more genes individually predict the differentiation state.

[0174] In some embodiments, one or more genes of the first reference data set or one or more genes selected for training the first machine learning model include genes whose expression levels increase from the first differentiation state to the second differentiation state. In some embodiments, one or more genes of the first reference data set or one or more genes selected for training the first machine learning model include genes whose expression levels decrease from the first differentiation state to the second differentiation state. In some embodiments, one or more genes of the first reference data set or one or more genes selected for training the first machine learning model include genes whose expression levels increase from the first differentiation state to the second differentiation state and genes whose expression levels decrease from the first differentiation state to the second differentiation state.

[0175] In some embodiments, one or more genes of the second reference data set or one or more genes selected for training the second machine learning model include genes whose expression levels increase from the second differentiation state to the third differentiation state. In some embodiments, one or more genes of the second reference data set or one or more genes selected for training the second machine learning model include genes whose expression levels decrease from the second differentiation state to the third differentiation state. In some embodiments, one or more genes of the second reference data set or one or more genes selected for training the second machine learning model include genes whose expression levels increase from the second differentiation state to the third differentiation state and genes whose expression levels decrease from the second differentiation state to the third differentiation state.

[0176] In some embodiments, one or more genes of the first reference dataset or one or more genes selected for training the first machine learning model are the same as one or more genes of the second reference dataset or one or more genes selected for training the second machine learning model. In some embodiments, one or more genes of the first reference dataset or one or more genes selected for training the first machine learning model are different from one or more genes of the second reference dataset or one or more genes selected for training the second machine learning model. In some embodiments, some of the one or more genes of the first reference dataset or one or more genes selected for training the first machine learning model are included in one or more genes of the second reference dataset or one or more genes selected for training the second machine learning model. In some embodiments, none of the one or more genes of the first reference dataset or one or more genes selected for training the first machine learning model are included in one or more genes of the second reference dataset or one or more genes selected for training the second machine learning model.

[0177] In some embodiments, the one or more genes of the first reference data set and / or the second reference data set or the one or more genes selected for training the first machine learning model and / or the second machine learning model include multiple genes. In some embodiments, the multiple genes include, include about, include greater than, or include greater than about 2 genes. In some embodiments, the multiple genes include, include about, include greater than, or include greater than about 3 genes. In some embodiments, the multiple genes include, include about, include greater than, or include greater than about 4 genes. In some embodiments, the multiple genes include, include about, include greater than, or include greater than about 5 genes. In some embodiments, the multiple genes include, include about, include greater than, or include greater than about 6 genes. In some embodiments, the multiple genes include, include about, include greater than, or include greater than about 7 genes. In some embodiments, the multiple genes include, include about, include greater than, or include greater than about 8 genes. In some embodiments, the multiple genes include, include about, include greater than, or include greater than about 9 genes. In some embodiments, the multiple genes include, include about, include greater than, or include greater than about 10 genes. In some embodiments, the plurality of genes include, include about, include greater than, or include greater than about 12 genes. In some embodiments, the plurality of genes include, include about, include greater than, or include greater than about 14 genes. In some embodiments, the plurality of genes include, include about, include greater than, or include greater than about 16 genes. In some embodiments, the plurality of genes include, include about, include greater than, or include greater than about 18 genes. In some embodiments, the plurality of genes include, include about, include greater than, or include greater than about 20 genes. In some embodiments, the plurality of genes include, include about, include greater than, or include greater than about 25 genes. In some embodiments, the plurality of genes include, include about, include greater than, or include greater than about 30 genes. In some embodiments, the plurality of genes include, include about, include greater than, or include greater than about 35 genes. In some embodiments, the plurality of genes include, include about, include greater than, or include greater than about 40 genes. In some embodiments, the plurality of genes include, include about, include greater than, or include greater than about 45 genes. In some embodiments, the plurality of genes include, include about, include greater than, or include greater than about 55 genes. In some embodiments, the plurality of genes include, include about, include greater than, or include greater than about 60 genes. In some embodiments, the plurality of genes include, include about, include greater than, or include greater than about 62 genes. In some embodiments, the plurality of genes include, include about, include greater than, or include greater than about 64 genes. In some embodiments, the plurality of genes include, include about, include greater than, or include greater than about 66 genes. In some embodiments, the plurality of genes include, include about, include greater than, or include greater than about 68 genes.In some embodiments, the plurality of genes include, include about, include greater than or include greater than about 70 genes. In some embodiments, the plurality of genes include, include about, include greater than or include greater than about 80 genes. In some embodiments, the plurality of genes include, include about, include greater than or include greater than about 90 genes. In some embodiments, the plurality of genes include, include about, include greater than or include greater than about 100 genes. In some embodiments, the plurality of genes include, include about, include greater than or include greater than about 110 genes. In some embodiments, the plurality of genes include, include about, include greater than or include greater than about 120 genes. In some embodiments, the plurality of genes include, include about, include greater than or include greater than about 130 genes. In some embodiments, the plurality of genes include, include about, include greater than or include greater than about 140 genes. In some embodiments, the plurality of genes include, include about, include greater than or include greater than about 150 genes. In some embodiments, the plurality of genes include, include about, include greater than or include greater than about 160 genes. In some embodiments, the plurality of genes include, include about, include greater than or include greater than about 170 genes. In some embodiments, the plurality of genes include, include about, include greater than or include greater than about 180 genes. In some embodiments, the plurality of genes include, include about, include greater than or include greater than about 190 genes. In some embodiments, the plurality of genes include, include about, include greater than or include greater than about 200 genes. In some embodiments, the plurality of genes include, include about, include greater than or include greater than about 250 genes. In some embodiments, the plurality of genes include, include about, include greater than or include greater than about 300 genes. In some embodiments, the plurality of genes include, include about, include greater than or include greater than about 350 genes. In some embodiments, the plurality of genes include, include about, include greater than or include greater than about 400 genes. In some embodiments, the plurality of genes include, include about, include greater than or include greater than about 450 genes. In some embodiments, the plurality of genes include, include about, include greater than or include greater than about 500 genes.

[0178] In some embodiments, one or more genes of the first reference data set and / or the second reference data set include genes with the lowest expression level in cells of the first differentiation state, the second differentiation state, and / or the third differentiation state. In some embodiments, one or more genes selected for training the first machine learning model and / or the second machine learning model are selected for having the lowest expression level in cells of the first differentiation state, the second differentiation state, and / or the third differentiation state. In some embodiments, the one or more genes include genes with at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 14, 16, 18, or 20 read counts (e.g., counts per million mapped reads (CPM) or log2CPM).

[0179] In some embodiments, one or more genes of the first reference data set and / or the second reference data set are genes that are differentially expressed with a specific statistical significance. In some embodiments, one or more genes selected for training the first machine learning model and / or the second machine learning model are selected for differential expression with a specific statistical significance. In some embodiments, the one or more genes are genes that are differentially expressed with a relevant p value of less than 0.05. In some embodiments, the one or more genes are genes that are differentially expressed with a relevant p value of less than 0.01. In some embodiments, the one or more genes are genes that are differentially expressed with a relevant p value of less than 0.001. In some embodiments, the one or more genes are genes that are differentially expressed with a relevant p value of less than 0.0001. In some embodiments, the p value is an adjusted p value. In some embodiments, the p value has been adjusted for multiple comparisons. Any suitable multiple comparison program can be used. In some embodiments, the p value is a Bonferroni-corrected p value. In some embodiments, the p value is a p value adjusted by the false discovery rate (FDR). In some embodiments, the p value is a Holm-Bonferroni-corrected p value.

[0180] In some embodiments, one or more genes of the first reference data set or one or more genes selected for training the first machine learning model are selected from the genes listed in Table E1. In some embodiments, the one or more genes include 10 or more genes selected from the genes listed in Table E1. In some embodiments, the one or more genes include 20 or more genes selected from the genes listed in Table E1. In some embodiments, the one or more genes include 30 or more genes selected from the genes listed in Table E1. In some embodiments, the one or more genes include 40 or more genes selected from the genes listed in Table E1. In some embodiments, the one or more genes include 50 or more genes selected from the genes listed in Table E1. In some embodiments, the one or more genes include 60 or more genes selected from the genes listed in Table E1. In some embodiments, the one or more genes include 70 or more genes selected from the genes listed in Table E1. In some embodiments, the one or more genes include 80 or more genes selected from the genes listed in Table E1. In some embodiments, the one or more genes include 90 or more genes selected from the genes listed in Table E1. In some embodiments, the one or more genes include 100 or more genes selected from the genes listed in Table E1. In some embodiments, the one or more genes include 200 or more genes selected from the genes listed in Table E1. In some embodiments, the one or more genes include 300 or more genes selected from the genes listed in Table E1. In some embodiments, the one or more genes include 400 or more genes selected from the genes listed in Table E1. In some embodiments, the one or more genes include 500 or more genes selected from the genes listed in Table E1.

[0181] In some embodiments, one or more genes of the second reference data set or one or more genes selected for training the second machine learning model are selected from the genes listed in Table E2. In some embodiments, the one or more genes include 10 or more genes selected from the genes listed in Table E2. In some embodiments, the one or more genes include 20 or more genes selected from the genes listed in Table E2. In some embodiments, the one or more genes include 30 or more genes selected from the genes listed in Table E2. In some embodiments, the one or more genes include 40 or more genes selected from the genes listed in Table E2. In some embodiments, the one or more genes include 50 or more genes selected from the genes listed in Table E2. In some embodiments, the one or more genes include 60 or more genes selected from the genes listed in Table E2. In some embodiments, the one or more genes include 70 or more genes selected from the genes listed in Table E2. In some embodiments, the one or more genes include 80 or more genes selected from the genes listed in Table E2. In some embodiments, the one or more genes include 90 or more genes selected from the genes listed in Table E2. In some embodiments, the one or more genes include 100 or more genes selected from the genes listed in Table E2. In some embodiments, the one or more genes include 200 or more genes selected from the genes listed in Table E2. In some embodiments, the one or more genes include 300 or more genes selected from the genes listed in Table E2. In some embodiments, the one or more genes include 400 or more genes selected from the genes listed in Table E2. In some embodiments, the one or more genes include 500 or more genes selected from the genes listed in Table E2.

[0182] In some embodiments, one or more genes of the first reference data set and / or the second reference data set are genes that are differentially expressed by at least a certain amount. In some embodiments, one or more genes selected for training the first machine learning model and / or the second machine learning model are selected for differential expression by at least a certain amount. In some embodiments, the one or more genes are genes that show at least a threshold multiple increase or decrease in gene expression levels. In some embodiments, the one or more genes are genes that show at least a threshold multiple increase or decrease in gene expression levels with a specific statistical significance (e.g., with any relevant p value described herein). In some embodiments, the one or more genes are genes that show at least 1 times increase or decrease in gene expression levels. In some embodiments, the one or more genes are genes that show at least 2 times increase or decrease in gene expression levels. In some embodiments, the one or more genes are genes that show at least 3 times increase or decrease in gene expression levels. In some embodiments, the one or more genes are genes that show at least 4 times increase or decrease in gene expression levels. In some embodiments, the one or more genes are genes that show at least 5 times increase or decrease in gene expression levels. In some embodiments, the one or more genes are genes that show at least 6 times increase or decrease in gene expression levels. In some embodiments, the one or more genes are genes that exhibit at least a 7-fold increase or decrease in gene expression levels. In some embodiments, the one or more genes are genes that exhibit at least an 8-fold increase or decrease in gene expression levels. In some embodiments, the one or more genes are genes that exhibit at least a 9-fold increase or decrease in gene expression levels. In some embodiments, the one or more genes are genes that exhibit at least a 10-fold increase or decrease in gene expression levels.

[0183] In some embodiments, one or more genes of the first reference data set and / or the second reference data set are genes that individually predict that a cell has one differentiation state or another differentiation state (e.g., the first differentiation state or the second differentiation state of the first reference data set, and the second differentiation state or the third differentiation state of the second reference data set). In some embodiments, one or more genes selected for training the first machine learning model and / or the second machine learning model are genes selected for individually predicting that a cell has one differentiation state or another differentiation state (e.g., the first differentiation state or the second differentiation state of the first machine learning model, and the second differentiation state or the third differentiation state of the second machine learning model). The prediction of a gene can be evaluated using any suitable accuracy metric, such as accuracy, recall, precision, or F1 score. In some embodiments, the predictability of a gene is its accuracy in classifying the differentiation state of a cell based on a threshold expression level of the gene, wherein one or more cells having a gene expression level above the threshold are classified as having a differentiation state, and one or more cells having a gene expression level below the threshold are classified as having another differentiation state. In some embodiments, the accuracy is at least 80%. In some embodiments, the accuracy is at least 82%. In some embodiments, the accuracy is at least 84%. In some embodiments, the accuracy is at least 86%. In some embodiments, the accuracy is at least 88%. In some embodiments, the accuracy is at least 90%. In some embodiments, the accuracy is at least 92%. In some embodiments, the accuracy is at least 94%. In some embodiments, the accuracy is at least 96%. In some embodiments, the accuracy is at least 98%. In some embodiments, the accuracy is 100%.

[0184] 4. Example Machine Learning Model

[0185] Various machine learning models are suitable for classifying the differentiation state of cells based on gene expression levels and are within the scope of the present disclosure. In some embodiments, the machine learning models of the first reference data set and the second reference data set are the same type of machine learning models, for example, both are logistic regression models. In some embodiments, the machine learning models of the first reference data set and the second reference data set are different types of machine learning models, for example, a logistic regression model and a support vector machine classifier. Similarly, the first machine learning model and the second machine learning model trained according to any of the provided methods can be the same or different types of machine learning models.

[0186] Any suitable method for training a machine learning model can be used, including any method described in Hastie et al., The Elements of Statistical Learning (2016); and Abu-Mostafa et al., Learning from Data (2012). Exemplary machine learning models are also described in Hastie et al., The Elements of Statistical Learning (2016); and Abu-Mostafa et al., Learning from Data (2012).

[0187] Further exemplary machine learning models are provided in this section. The machine learning models of the first reference data set and the second reference data set or the first machine learning model and the second machine learning model trained according to any of the provided methods can be any exemplary machine learning model described herein.

[0188] In some embodiments, the machine learning model comprises a supervised machine learning model. In some embodiments, the machine learning model comprises an unsupervised machine learning model. In some embodiments, the machine learning model comprises a semi-supervised machine learning model. In some embodiments, the machine learning model comprises a clustering method.

[0189] In some embodiments, the machine learning model comprises a regression model. In some embodiments, the machine learning model comprises a classification model. In some embodiments, the machine learning model comprises a binary classification model. In some embodiments, the machine learning model comprises a multi-class classification model.

[0190] In some embodiments, the machine learning model comprises a linear model. In some embodiments, the machine learning model comprises a nonlinear model.

[0191] In some embodiments, the machine learning model comprises a logistic regression model. In some embodiments, the machine learning model comprises a linear regression model. In some embodiments, the machine learning model comprises a multivariate linear regression model. In some embodiments, the machine learning model comprises a polynomial regression model. In some embodiments, the machine learning model comprises a quantile regression model. In some embodiments, the machine learning model comprises a principal component regression model. In some embodiments, the machine learning model comprises a partial minimum regression model. In some embodiments, the machine learning model comprises a support vector regression model. In some embodiments, the machine learning model comprises an ordered regression model. In some embodiments, the machine learning model comprises a Poisson regression model. In some embodiments, the machine learning model comprises a negative binomial regression model. In some embodiments, the machine learning model comprises a quasi-Poisson regression model. In some embodiments, the machine learning model comprises a linear discriminant analysis (LDA) model. In some embodiments, the machine learning model comprises a naive Bayes classifier. In some embodiments, the machine learning model comprises a perceptron. In some embodiments, the machine learning model comprises a support vector machine (SVM). In some embodiments, the machine learning model comprises a quadratic classifier. In some embodiments, the machine learning model comprises a decision tree. In some embodiments, the machine learning model comprises a random forest. In some embodiments, the machine learning model comprises a neural network.

[0192] In some embodiments, the machine learning model includes a connectivity-based clustering method. In some embodiments, the machine learning model includes hierarchical clustering. In some embodiments, the machine learning model includes a centroid-based clustering method. In some embodiments, the machine learning model includes k-means clustering. In some embodiments, the machine learning model includes a distribution-based clustering method. In some embodiments, the machine learning model includes Gaussian mixture modeling. In some embodiments, the machine learning model includes a density-based clustering method. In some embodiments, the machine learning model includes DBSCAN. In some embodiments, the machine learning model includes OPTICS. In some embodiments, the machine learning model includes a grid-based clustering method. In some embodiments, the machine learning model includes STING. In some embodiments, the machine learning model includes CLIQUE.

[0193] In some embodiments, the machine learning model comprises factor analysis. In some embodiments, the machine learning model comprises network component analysis. In some embodiments, the machine learning model comprises linear discriminant analysis. In some embodiments, the machine learning model comprises independent component analysis (ICA). In some embodiments, the machine learning model comprises principal component analysis (PCA). In some embodiments, the machine learning model comprises sparse PCA. In some embodiments, the machine learning model comprises robust PCA.

[0194] In some embodiments, the machine learning model comprises non-negative matrix factorization (NMF). In some embodiments, the machine learning model comprises traditional NMF. In some embodiments, the machine learning model comprises discriminative NMF. In some embodiments, the machine learning model comprises regularized NMF. In some embodiments, the machine learning model comprises graph regularized NMF. In some embodiments, the machine learning model comprises guided sparse NMF.

[0195] In some embodiments, the machine learning model includes kernel PCA. In some embodiments, the machine learning model includes generalized discriminant analysis (GDA). In some embodiments, the machine learning model includes an autoencoder. In some embodiments, the machine learning model includes T-distributed stochastic neighbor embedding (t-SNE). In some embodiments, the machine learning model includes manifold learning techniques. In some embodiments, the machine learning model includes isometric mapping. In some embodiments, the machine learning model includes local linear embedding (LLE). In some embodiments, the machine learning model includes Hessian LLE. In some embodiments, the machine learning model includes Laplace eigenmaps. In some embodiments, the machine learning model includes a graph-based kernel PCA. In some embodiments, the machine learning model includes unified manifold approximation and projection (UMAP).

[0196] In some embodiments, the machine learning model comprises a penalized machine learning model. In some embodiments, the machine learning comprises a penalized version of any of the foregoing models. A penalized machine learning model is a model that regularizes or constrains coefficient estimates to zero. In some embodiments, the machine learning model comprises a ridge regression model. In some embodiments, the machine learning model comprises a lasso regression model. In some embodiments, the machine learning model comprises an elastic net regression model.

[0197] In some embodiments, the machine learning model comprises an ensemble model. In some embodiments, the ensemble model involves a boosting algorithm. In some embodiments, the ensemble model involves a bagging algorithm.

[0198] In some embodiments, the machine learning model includes an integrated model that includes any combination of multiple of any of the aforementioned models.

[0199] B. Relevance score

[0200] In some embodiments, the test data set includes gene expression levels of one or more genes whose expression levels are included in the control data set. In some embodiments, the test data set includes gene expression levels of one or more genes having a representation of the expression levels included in the control data set.

[0201] In some embodiments, the provided methods further comprise calculating a correlation score. In some embodiments, the correlation score indicates the similarity of the gene expression levels in the test data set to the gene expression levels in the control data set. In some embodiments, the correlation score indicates the similarity of the representations of the gene expression levels in the test data set to the gene expression levels in the control data set.

[0202] In some embodiments, the calculation of the correlation score includes calculating the degree of correlation between the gene expression level in the control data set or its representation and the gene expression level in the test data set. Any suitable measure indicating the degree of correlation can be used, including Pearson's correlation coefficient, Spearman's rank correlation and mutual information.

[0203] In some embodiments, the differentiation state of one or more test cells is classified based on the correlation score and one or both of the first similarity score and the second similarity score. In some embodiments, the provided methods also include classifying the differentiation state of one or more test cells based on the correlation score and one or both of the first similarity score and the second similarity score.

[0204] In some embodiments, the classification is based on the relevance score and one of the first similarity score and the second similarity score. In some embodiments, the classification is based on the relevance score and the lower of the first similarity score and the second similarity score. In some embodiments, the classification is based on the relevance score and the higher of the first similarity score and the second similarity score. In some embodiments, the classification is based on the relevance score and the first similarity score. In some embodiments, the classification is based on the relevance score and the second similarity score.

[0205] In some embodiments, if the correlation score indicates a difference between the gene expression level in the test data set and the gene expression level in the control data set or its representation, the differentiation state of one or more test cells is not classified as being in a desired differentiation state. In some embodiments, if the degree of correlation does not exceed a predetermined cutoff value, the differentiation state of one or more test cells is not classified as being in a desired differentiation state. In some embodiments, if the correlation score indicates that the correlation or explained variance between the gene expression level of the control data set or its representation and the gene expression level of the test data set is less than or less than about 0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9 or 0.95, the differentiation state of one or more test cells is not classified as being in a desired differentiation state.

[0206] In some embodiments, the differentiation state of one or more test cells is classified based on the first similarity score, the second similarity score and the correlation score. In some embodiments, the method provided also includes classifying the differentiation state of one or more test cells based on the first similarity score, the second similarity score and the correlation score. In some embodiments, if the correlation score indicates the difference between the gene expression level in the test data set and the gene expression level in the control data set or its representation, the differentiation state of one or more test cells is not classified as being in the desired differentiation state. In some embodiments, if the degree of correlation does not exceed a predetermined cutoff value, the differentiation state of one or more test cells is not classified as being in the desired differentiation state. In some embodiments, if the correlation score indicates that the correlation or explained variance between the gene expression level of the control data set or its representation and the gene expression level of the test data set is less than or less than about 0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9 or 0.95, the differentiation state of one or more test cells is not classified as being in the desired differentiation state.

[0207] In some embodiments, the correlation score is calculated before, simultaneously with, or after calculating the first similarity score and the second similarity score. In some embodiments, the correlation score is calculated before calculating the first similarity score and the second similarity score. In some embodiments, if the correlation score indicates a difference between the gene expression level in the test data set and the gene expression level in the control data set, or a representation thereof, the provided method is terminated. In some embodiments, if the degree of correlation does not exceed a predetermined cutoff value, the provided method is terminated.

[0208] In some embodiments, the one or more genes of the control data set include genes expressed in cells in a control differentiation state. In some embodiments, the control differentiation state is any differentiation state described in Section II-C. In some embodiments, the control differentiation state is the same as one of the first differentiation state, the second differentiation state, and the third differentiation state. In some embodiments, the control differentiation state is different from the first differentiation state, the second differentiation state, and the third differentiation state.

[0209] In some embodiments, the one or more genes of the control data set include genes expressed in cells in any one of a plurality of control differentiation states. In some embodiments, the one or more genes of the control data set include genes expressed in cells in each of a plurality of control differentiation states. In some embodiments, each of a plurality of control differentiation states is independently selected from any differentiation state described in Section II-C. In some embodiments, a plurality of control differentiation states include a first differentiation state, a second differentiation state, and a third differentiation state.

[0210] In some embodiments, the gene expression levels in the control data set or its representation are based on the gene expression levels of multiple reference cell populations. In some embodiments, the multiple reference cell populations include reference cell populations whose gene expression levels are used to train the first machine learning model and the second machine learning model, or include reference cell populations similar to those used to train the first machine learning model and the second machine learning model, such as those of the same cell type or from the same stem cell differentiation pathway. Therefore, in some aspects, the calculation of the correlation score allows the test data set to be compared with the gene expression levels of cells across the first differentiation state, the second differentiation state, and / or the third differentiation state.

[0211] In some embodiments, multiple reference cell populations are different from (e.g., do not include) reference cell populations whose gene expression levels are used to train the first machine learning model and the second machine learning model. For example, in some embodiments, multiple reference cell populations include cell types and / or differentiation states that are different from the reference cell populations whose gene expression levels are used to train the first machine learning model and the second machine learning model. Therefore, in some aspects, the calculation of the correlation score allows the test data set to be compared with the gene expression levels of cells other than cells in the first differentiation state, the second differentiation state, and / or the third differentiation state.

[0212] In some embodiments, one or more genes of the control data set include genes with at least a minimum expression level in cells of a control differentiation state. In some embodiments, one or more genes of the control data set include genes with at least a minimum expression level in cells of any one of a plurality of control differentiation states. In some embodiments, one or more genes of the control data set include genes with at least a minimum expression level in cells of each of a plurality of control differentiation states. In some embodiments, one or more genes of the control data set are expressed at an average of at least a minimum expression level in a plurality of cell populations of the control differentiation state or a plurality of control differentiation states.

[0213] In some embodiments, one or more genes of the control data set include genes whose expression levels exceed a threshold value. In some embodiments, one or more genes of the control data set are filtered to include only genes whose expression levels exceed a threshold value. In some embodiments, one or more genes of the control data set include genes whose expression levels exceed the threshold value on average in multiple cell populations of the control differentiation state or multiple control differentiation states. In some embodiments, the threshold value is a threshold CPM value. In some embodiments, the threshold value is a threshold log2CPM value. In some embodiments, the threshold value is or is about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 14, 16, 18 or 20. In some embodiments, the threshold value is or is about 10CPM. In some embodiments, the threshold value is or is about 10log2CPM.

[0214] In some embodiments, the gene expression level of the control data set includes a representation of the gene expression level of one or more genes. In some embodiments, the gene expression level of the control data set includes a normalized gene expression level. In some embodiments, the gene expression level of the control data set is normalized by CPM, for example, CPM expression level. In some embodiments, the gene expression level of the control data set is logarithmically transformed. In some embodiments, the gene expression level of the control data set is log2 transformed.

[0215] In some embodiments, the gene expression level of the control data set or its representation includes the average gene expression level of multiple reference cell populations. In some embodiments, the degree of correlation between the gene expression level in the test data set and the average gene expression level included in the control data set is calculated. In some embodiments, the average gene expression level includes the centroid of the gene expression level. In some embodiments, the degree of correlation between the gene expression level in the test data set and the centroid of the gene expression level in the control data set is calculated.

[0216] In some embodiments, the gene expression level of the control data set also includes a deviation measure of the gene expression level of a plurality of reference cell populations. Any suitable deviation measure can be used, including standard deviation, range, interquartile range, mean absolute difference, median absolute deviation, mean absolute deviation, distance standard deviation, coefficient of variation (CV), interquartile coefficient of variation, relative mean difference, entropy, variance, and variance mean ratio. In some embodiments, the deviation measure is standard deviation. In some embodiments, the deviation measure is coefficient of variation (CV).

[0217] In some embodiments, the degree of correlation is a weighted correlation value. In some embodiments, the correlation value is weighted by a measure of deviation. In some embodiments, the correlation value is weighted by the inverse of the measure of deviation. In some embodiments, the degree of correlation is a 1 / CV weighted correlation value. In some embodiments, the degree of correlation is a 1 / CV weighted correlation value calculated between the centroid values ​​of the gene expression levels in the test data set and the gene expression levels in the control data set.

[0218] C. Cell Population

[0219] In some embodiments, the method provided relates to classifying the differentiation state of the test cell. In some embodiments, the differentiation state of the test cell is classified based on a representation of gene expression levels (e.g., a machine learning model based on gene expression levels from multiple reference cell populations). In some embodiments, the machine learning model used in the method provided is trained using gene expression levels from multiple reference cell populations. In some embodiments, the machine learning model is trained using gene expression levels from multiple reference cell populations to classify the differentiation state of the test cell.

[0220] 1. Reference Cell Population

[0221] In some aspects, multiple reference cell populations include cells of known identity, such as cells of known cell type and / or differentiation state. For example, in some embodiments, multiple reference cell populations used to train a first machine learning model include cells known to have a first differentiation state or a second differentiation state. Similarly, in some embodiments, multiple reference cell populations used to train a second machine learning model include cells known to have a second differentiation state or a third differentiation state. In some embodiments, information about the known identities of multiple reference cell populations is used to train a machine learning model, or to establish criteria to determine whether a first similarity score and a second similarity score indicate whether the differentiation state of a test cell is more similar to a second differentiation state.

[0222] In some embodiments, multiple reference cell populations are derived from cell cultures differentiated from pluripotent cells subjected to appropriate differentiation conditions. The provided methods can be performed with reference cell populations generated according to any differentiation method. Exemplary differentiation methods are described in Section II-C.

[0223] In some embodiments, the plurality of reference cell populations comprises cells differentiated under conditions to become dopaminergic neurons. In some embodiments, the plurality of reference cell populations comprises cells differentiated according to any of the methods described in Section II-C.

[0224] In some embodiments, the pluripotent stem cell is an induced pluripotent stem cell (iPSC). In some embodiments, iPSC is produced by fibroblasts collected from healthy human subjects. In some embodiments, iPSC is produced by fibroblasts collected from human subjects suffering from Parkinson's disease. Exemplary methods for iPSC generation are described in Section II-C.

[0225] In some embodiments, the cell of the reference cell colony includes pluripotent stem cells. In some embodiments, the pluripotent stem cell is an induced pluripotent stem cell (iPSC). In some embodiments, iPSC is produced by fibroblasts collected from healthy human subjects. In some embodiments, iPSC is produced by fibroblasts collected from human subjects suffering from Parkinson's disease. In some embodiments, iPSC is produced by fibroblasts collected from human subjects susceptible to Parkinson's disease. Exemplary methods for iPSC generation are described in Section II-C.

[0226] In some embodiments, the cells of the reference cell colony include cells differentiated under conditions that become neuronal cells, such as midbrain floor plate precursor cells, directed dopaminergic cells, or dopaminergic neurons. In some embodiments, the cells of the reference cell colony include cells differentiated according to any method described in Section II-C. In some embodiments, the cells of the reference cell colony include directed dopaminergic cells. In some embodiments, the cells of the reference cell colony include dopaminergic neurons, such as stereotyped dopaminergic neurons. In some embodiments, the cells of the reference cell colony include cells derived from iPSCs (e.g., iPSCs as described above) that have been cultured under conditions that promote differentiation into dopaminergic neurons.

[0227] In some embodiments, the cells of the reference cell population include dopaminergic neurons expressing markers of midbrain dopaminergic neurons, such as expressing FOXA2 or tyrosine hydroxylase (TH). In some embodiments, the cells of the reference cell population include cells expressing TH (TH+). In some embodiments, the cells of the reference cell population include cells expressing FOXA2 (FOXA2+). In some embodiments, the cells of the reference cell population include cells expressing TH and FOXA2 (TH+FOXA2+).

[0228] In some embodiments, the cells of the reference cell population include cells that are directed to or can become dopaminergic neurons, that is, directed dopaminergic cells, such as one or more characteristics based on indicating that the cells can have the functional activity of dopaminergic neurons but may not yet express markers of dopaminergic neurons or may not express the marker at a high level. For example, the cell may show a lower level of TH than dopaminergic neurons, but still show one or more characteristics of directed dopaminergic cells, indicating that the cell can have the functional activity of dopaminergic neurons. In some embodiments, one or more characteristics include the activity of surviving, colonizing and / or dominating other cells when administered to, for example, an animal model in vivo. In some embodiments, the cells of the reference cell population include cells that can dominate host tissues after being transplanted into an animal or human subject. In some embodiments, the cells of the reference cell population include cells that show neurite outgrowth after being transplanted into an animal or human subject. In some embodiments, the cells of the reference cell population include cells that survive after being transplanted into an animal or human subject. In some embodiments, the cells of the reference cell population include cells that are colonized after being transplanted into an animal or human subject.

[0229] In some embodiments, the cells of the reference cell colony include cells having a therapeutic effect for treating a neurodegenerative disease. In some embodiments, these cells improve or reverse the symptoms of a neurodegenerative disease when implanted. In some embodiments, the neurodegenerative disease is Parkinson's disease. In some embodiments, these cells improve Parkinson's disease symptoms when implanted in the substantia nigra of a subject (e.g., a patient) in need thereof.

[0230] In some embodiments, the cells of the reference cell colony include cells screened for their therapeutic effects on treating neurodegenerative diseases, such as those determined in animal models of neurodegenerative diseases. In some embodiments, the neurodegenerative disease is Parkinson's disease. In some embodiments, an animal model of Parkinson's disease is used to screen reference cells. Any suitable animal model of Parkinson's disease can be used for screening. In some embodiments, the animal model is an injury model, in which the animal receives a unilateral stereotaxic injection of 6-hydroxydopamine (6-OHDA) into the substantia nigra. In some embodiments, the animal model is an injury model, in which the animal receives a unilateral stereotaxic injection of 6-OHDA into the medial forebrain bundle. In some embodiments, these cells are implanted into the substantia nigra of the animal model. In some embodiments, behavioral assays are performed to screen the therapeutic effects of implantation on the animal model. In some embodiments, behavioral assays include monitoring amphetamine-induced circling behavior. In some embodiments, it is determined that these cells can alleviate, reduce or reverse Parkinson's disease model brain damage in the model. In some embodiments, these cells may include cells that fail to alleviate, reduce, or reverse brain damage in a Parkinson's disease model in this model.

[0231] A reference cell population can include various cells that exhibit variable or different therapeutic effects in treating a neurodegenerative disease, such as in an animal model.

[0232] 2. Test cells

[0233] In some aspects, the test cell is a cell of unknown identity, such as a cell of unknown cell type and / or differentiation state. In some embodiments, the test cell is known or suspected to have a specific stem cell differentiation pathway, but the differentiation state in the pathway is unknown. In some aspects, the provided method allows the cell type and / or differentiation state of the test cell to be determined based on the gene expression level of the test cell. Based on this determination, the in vitro cell colony containing the test cell can be classified as having a specific cell type and / or differentiation state.

[0234] In some embodiments, the in vitro cell population containing the test cells is derived from a cell culture differentiated from a pluripotent cell subjected to suitable differentiation conditions. The provided methods can be performed with test cells produced according to any differentiation method. Exemplary differentiation methods and in vitro cell populations are described in Section II-C.

[0235] In some embodiments, these cells are stem cell derived neuronal cells. In some embodiments, the test cells include cells differentiated under conditions that become dopaminergic neurons. In some embodiments, the test cells include cells differentiated according to any of the methods described in Section II-C.

[0236] In some embodiments, the pluripotent stem cell is an induced pluripotent stem cell (iPSC). In some embodiments, iPSC is produced by fibroblasts collected from healthy human subjects. In some embodiments, iPSC is produced by fibroblasts collected from human subjects suffering from Parkinson's disease. Exemplary methods for iPSC generation are described in Section II-C.

[0237] In some embodiments, the test cell is from an in vitro cell population that is or is suspected of being in a different differentiation pathway than the reference cell population. In some embodiments, the test cell is from an in vitro cell population that has been or is suspected of having been generated using a differentiation method different from those used to generate the reference cell population.

[0238] In some embodiments, the test cells are from an in vitro cell population that is or is suspected of being in the same differentiation pathway as the reference cell population. In some embodiments, the test cells are from an in vitro cell population that has been or is suspected of having been produced using the same differentiation methods as those used to generate the reference cell population. In some embodiments, the test cells are from an in vitro cell population that is or is suspected of being in the same differentiation pathway as the reference cell population, but has been or is suspected of having been produced using a differentiation method different from those used to generate the reference cell population.

[0239] 3. Exemplary Differentiation Pathways, Methods, and States

[0240] In some embodiments, the first differentiation state is earlier than the second differentiation state in the stem cell differentiation pathway. In some embodiments, the first differentiation state is later than the second differentiation state in the stem cell differentiation pathway. In some embodiments, the first differentiation state and the second differentiation state are from different stem cell differentiation pathways. In some embodiments, the first differentiation state is in a cell differentiation pathway parallel to the cell differentiation pathway of the second differentiation state. In some embodiments, the cell differentiation pathways are those that fork, such as making the first differentiation state and the second differentiation state different cell types.

[0241] In some embodiments, the second differentiation state is earlier in the stem cell differentiation pathway than the third differentiation state. In some embodiments, the second differentiation state is later in the stem cell differentiation pathway than the third differentiation state. In some embodiments, the second differentiation state and the third differentiation state are from different stem cell differentiation pathways. In some embodiments, the second differentiation state is in a cell differentiation pathway parallel to the cell differentiation pathway of the third differentiation state. In some embodiments, the cell differentiation pathways are those that fork, such as making the second differentiation state and the third differentiation state different cell types.

[0242] In some embodiments, the first differentiation state, the second differentiation state, and the third differentiation state are all in the same stem cell differentiation pathway. In some embodiments, the first differentiation state, the second differentiation state, and the third differentiation state are in different stem cell differentiation pathways. In some embodiments, the first differentiation state and the second differentiation state are in one stem cell differentiation pathway, and the first differentiation state and the third differentiation state are in another stem cell differentiation pathway, such as a pathway in which a cell in the first differentiation state is a precursor cell that can differentiate into a cell in the second differentiation state or the third differentiation state. In some embodiments, the second differentiation state and the third differentiation state are different cell types.

[0243] In some embodiments, the first differentiation state, the second differentiation state, and the third differentiation state are all in the same stem cell differentiation pathway. In some embodiments, the second differentiation state is an intermediate differentiation state between the first differentiation state and the third differentiation state.

[0244] Exemplary in vitro cell colonies and differentiation states are provided in this section. This section is organized based on specific stem cell differentiation pathways, but the combination of the first differentiation state, the second differentiation state, the third differentiation state, and the control differentiation state across multiple cell types or pathways is also considered and disclosed herein. For example, in some embodiments, the first differentiation state, the second differentiation state, and the third differentiation state are all the same cell type (e.g., neurons). In other embodiments, at least one of the first differentiation state, the second differentiation state, and the third differentiation state can be a cell type different from the remaining differentiation states (e.g., the first differentiation state is a differentiation state of neuronal cells, and the second differentiation state and the third differentiation state are differentiation states of cardiomyocytes).

[0245] In some embodiments, the test cells are from an in vitro stem cell-derived cardiomyocyte population (see, e.g., Le and Chong, Cell Death Discovery 2: 16052 (2016)). In some embodiments, stem cell-derived cardiomyocytes express Nkx2.5 and / or Isl-1. Exemplary methods for in vitro differentiation of stem cell-derived cardiomyocytes are described in US9234176, US20170058263, Vahdat et al., Scientific Reports 9: 16006 (2019), Laflamme et al. (2007) Nature Biotechnology 25: 1015-24, and Wu et al. (2021) Biosci Rep 41 (6): BSR20200833. In some embodiments, the first differentiation state, the second differentiation state, the third differentiation state, and / or the control differentiation state are differentiation states of cardiac precursor cells or directed or stereotyped cardiomyocytes, endothelial cells, vascular smooth muscle cells, or cardiac fibroblasts. In some embodiments, the first differentiation state is a differentiation state of a myocardial precursor cell; the second differentiation state is a differentiation state of a directed myocardial cell, an endothelial cell, a vascular smooth muscle cell, or a cardiac fibroblast; and the third differentiation state is a stereotyped differentiation state corresponding to the second differentiation state. In some embodiments, the second differentiation state is a differentiation state of a directed myocardial cell. In some embodiments, the third differentiation state is a differentiation state of a stereotyped myocardial cell. In some embodiments, cells identified as having a desired differentiation state (e.g., having a second differentiation state) can be used to treat degenerative diseases, such as ischemic cardiomyopathy and conduction system diseases (such as sinus node dysfunction and atrioventricular block), or congenital heart disease, such as atrial or ventricular septal defects.

[0246] In some embodiments, the test cell is from an in vitro population derived from a cell culture differentiated from a pluripotent cell subjected to a differentiation protocol for inducing PSC (e.g., iPSC) differentiation into cardiomyocytes, such as according to any of the methods described herein, e.g., as described in Laflamme et al. (2007) Nature Biotechnology 25: 1015-24 or Wu et al. (2021) Biosci Rep 41(6): BSR20200833. In some embodiments, the cells in the second differentiation state are at any day of the 14th to 21st day of the differentiation protocol. In some embodiments, the cells in the first differentiation state are at day 13 or earlier, day 12 or earlier, day 11 or earlier, or day 10 or earlier of the differentiation protocol. In some embodiments, the cells in the third differentiation state are at day 22 or later, day 30 or later, day 40 or later, day 50 or later, day 60 or later, or day 70 or later of the differentiation protocol. In some embodiments, the cells in the second differentiation state are at any day 14 to 21 of the differentiation protocol; and the cells in the third differentiation state are at day 22 or later, day 30 or later, day 40 or later, day 50 or later, day 60 or later, or day 70 or later of the differentiation protocol. In some embodiments, the cells of the first differentiation state are at day 13 or earlier, day 12 or earlier, day 11 or earlier, or day 10 or earlier of the differentiation protocol; the cells of the second differentiation state are at any day of day 14 to 21 of the differentiation protocol; and the cells of the third differentiation state are at day 22 or later, day 30 or later, day 40 or later, day 50 or later, day 60 or later, or day 70 or later of the differentiation protocol. In some embodiments, the cells of the third differentiation state are at any day of day 70 to 126 of the differentiation protocol.

[0247] In some embodiments, the test cells are from an in vitro stem cell-derived skeletal muscle cell population (see, e.g., Relaix et al., Nature Communications 12: 692 (2021)). In some embodiments, stem cell-derived skeletal muscle cells express PAX7 and / or PAX3. Exemplary methods for in vitro differentiation of stem cell-derived skeletal muscle cells are described in WO2001011011 and US9789136. In some embodiments, the first differentiation state, the second differentiation state, the third differentiation state, and / or the control differentiation state are differentiation states of skeletal muscle precursor cells, committed skeletal muscle cells, or directed skeletal muscle cells. In some embodiments, the first differentiation state is a differentiation state of skeletal muscle precursor cells; the second differentiation state is a differentiation state of directed skeletal muscle cells; and the third differentiation state is a differentiation state of committed skeletal muscle cells. In some embodiments, cells identified as having a desired differentiation state (e.g., having a second differentiation state) can be used to treat a muscle disorder, such as a myopathy, e.g., polymyositis, dermatomyositis, Duchenne muscular dystrophy; fibrositis; myasthenia gravis; rhabdomyolysis; amyotrophic lateral sclerosis; or sarcopenia.

[0248] In some embodiments, the test cell is from an in vitro stem cell-derived smooth muscle cell colony. An exemplary method for in vitro differentiation of stem cell-derived smooth muscle cells is described in US7531355. In some embodiments, the first differentiation state, the second differentiation state, the third differentiation state and / or the control differentiation state are the differentiation states of smooth muscle precursor cells, committed smooth muscle cells or oriented smooth muscle cells. In some embodiments, the first differentiation state is the differentiation state of smooth muscle precursor cells; the second differentiation state is the differentiation state of oriented smooth muscle cells; and the third differentiation state is the differentiation state of committed smooth muscle cells. In some embodiments, cells identified as having a desired differentiation state (e.g., having a second differentiation state) can be used to rebuild tissues (such as urinary tract, epithelial pathways or bladder) containing smooth muscle-derived cells or for treating disorders affecting smooth muscle function, for example, urinary incontinence, bladder disease, vascular disorders, intestinal disorders, vesicoureteral reflux or other smooth muscle function disorders.

[0249] In some embodiments, the test cell is from an in vitro stem cell-derived vascular endothelial cell colony. Exemplary methods for in vitro differentiation of stem cell-derived vascular endothelial cells are described in U.S. Patent Nos. 10,041,036, 10,563,175, 10,828,337, 10,767,161, 9,938,499, and 10,947506. In some embodiments, the first differentiation state, the second differentiation state, the third differentiation state, and / or the control differentiation state are differentiation states of vascular endothelial precursor cells, stereotyped vascular endothelial cells, or directional vascular endothelial cells. In some embodiments, the first differentiation state is the differentiation state of vascular endothelial precursor cells; the second differentiation state is the differentiation state of directional vascular endothelial cells; and the third differentiation state is the differentiation state of stereotyped vascular endothelial cells.

[0250] In some embodiments, the test cells are from an in vitro stem cell-derived tubular cell population (see, e.g., Chambers and Wingert, World J Stem Cells 2016; 8(11): 367-375). Exemplary methods for in vitro differentiation of stem cell-derived tubular cells are described in Ribeiro et al., Stem Cells Int. 2020: 8894590. In some embodiments, the first differentiation state, the second differentiation state, the third differentiation state, and / or the control differentiation state are tubular precursor cells or committed or directed podocytes, proximal S1 cells, proximal S2 cells, proximal S3 cells, proximal tubular cells, DTL1 type cells, DTL 2 type cells, DTL 3 type cells, ascending limb thin segment cells, Henlein ascending limb thick segment cells, CTAL cells, macula densa cells, distal convoluted tubule cells, CNT cells, PC (CCD) cells, PC (OMCD) cells, type A IC cells, type B IC cells, or IMCD differentiation states. In some embodiments, the first differentiation state is the differentiation state of tubular precursor cells; the second differentiation state is a directional podocyte, a proximal S1 cell, a proximal S2 cell, a proximal S3 cell, a proximal tubule cell, a DTL type 1 cell, a DTL type 2 cell, a DTL type 3 cell, a thin segment cell of the ascending limb, a thick segment cell of the ascending limb of the loop of Henle, a CTAL cell, a dense macula cell, a distal convoluted tubule cell, a CNT cell, a PC (CCD) cell, a PC (OMCD) cell, a type A IC cell, a type B IC cell, or a IMCD cell differentiation state; and the third differentiation state is a stereotyped differentiation state corresponding to the second differentiation state. In some embodiments, cells identified as having a desired differentiation state (e.g., having a second differentiation state) can be used to treat acute kidney injury, chronic kidney disease, refractory systemic lupus erythematosus or lupus nephritis or for renal transplantation (see, e.g., Wong, World J Stem Cells, 2021; 13 (7): 914-933).

[0251] In some embodiments, the test cell is from an in vitro stem cell-derived erythrocyte colony. Exemplary methods for in vitro differentiation of stem cell-derived erythrocytes are described in US1027211. In some embodiments, the first differentiation state, the second differentiation state, the third differentiation state and / or the control differentiation state are differentiation states of erythrocyte precursor cells, committed erythrocytes or directed erythrocytes. In some embodiments, the first differentiation state is the differentiation state of erythrocyte precursor cells; the second differentiation state is the differentiation state of directed erythrocytes; and the third differentiation state is the differentiation state of committed erythrocytes. In some embodiments, cells identified as having a desired differentiation state (e.g., having a second differentiation state) can be used to treat a disorder characterized by erythrocyte defects, such as treating subjects suffering from autoimmune disorders, immunodeficiency or any other diseases or disorders that can benefit from transfusions.

[0252] In some embodiments, the test cell is from an in vitro stem cell-derived lung cell population (see, e.g., Leeman et al., Curr Top Dev Biol 2014; 107: 207-233). In some embodiments, stem cell-derived lung cells express Nkx2.1. Exemplary methods for in vitro differentiation of stem cell-derived lung cells are described in US11214769 and WO2015108893. In some embodiments, the first differentiation state, the second differentiation state, the third differentiation state, and / or the control differentiation state are differentiation states of lung precursor cells or committed or oriented airway epithelial cells, such as goblet cells, ciliated cells, Clara cells, neuroendocrine cells (neuroendocrine bodies), basal cells, intermediate cells (or parabasal cells), serous cells, brush cells, tumor cells, non-ciliated columnar cells, metaplastic cells (e.g., squamous or Clara-mucous cells, bronchial metaplasia); or alveolar cells, such as type 1 or type 2 pneumocytes or cuboidal non-ciliated cells. In some embodiments, the first differentiation state is a differentiation state of a lung precursor cell; the second differentiation state is a differentiation state of a committed airway epithelial cell, such as a committed goblet cell, a ciliated cell, a Clara cell, a neuroendocrine cell (neuroendocrine body), a basal cell, an intermediate cell (or a parabasal cell), a serous cell, a brush cell, a tumor cell, a non-ciliated columnar cell, a metaplastic cell (e.g., a squamous or Clara-mucous cell, a bronchiolar metaplasia); or a committed alveolar cell, such as a committed type 1 or type 2 pneumocyte or a cuboidal non-ciliated cell; and the third differentiation state is a committed differentiation state corresponding to the second differentiation state. In some embodiments, cells identified as having a desired differentiation state (e.g., having a second differentiation state) can be used to treat a respiratory disorder, such as cystic fibrosis, respiratory distress syndrome, acute respiratory distress syndrome, tuberculosis, cough, bronchial asthma, cough based on increased airway hyperresponsiveness (bronchitis, influenza syndrome, asthma, obstructive lung disease, etc.), influenza syndrome, cough suppression, airway hyperresponsiveness, tuberculosis, asthma (airway inflammatory cell infiltration, increased airway hyperresponsiveness, bronchoconstriction, mucus hypersecretion), chronic obstructive pulmonary disease, emphysema, pulmonary fibrosis, idiopathic pulmonary fibrosis, cough, reversible airway obstruction, adult respiratory disease syndrome, pigeon fancier's disease, farmer's lung, bronchopulmonary dysplasia, airway disorders, emphysema, allergic bronchopulmonary aspergillosis, allergic bronchitis bronchiectasis, occupational asthma, reactive airway disease syndrome, interstitial lung disease, or pulmonary parasitic diseases.

[0253] In some embodiments, the test cell is from an in vitro stem cell-derived thyroid cell population. In some embodiments, stem cell-derived thyroid cells express Pax-8 and / or NKX2-1. Exemplary methods for in vitro differentiation of stem cell-derived thyroid cells are described in Fierabracci, Journal of Endocrinology 213 (1): 1-13 (2012). In some embodiments, the first differentiation state, the second differentiation state, the third differentiation state, and / or the control differentiation state are differentiation states of thyroid precursor cells or committed or directed follicular cells or C cells. In some embodiments, the first differentiation state is a differentiation state of thyroid precursor cells; the second differentiation state is a differentiation state of directed follicular cells or C cells; and the third differentiation state is a committed differentiation state corresponding to the second differentiation state. In some embodiments, cells identified as having a desired differentiation state (e.g., having a second differentiation state) can be used to treat thyroid disorders, such as goiter, adenoma, hypothyroidism, or autoimmune diseases.

[0254] In some embodiments, the test cell is from an in vitro stem cell-derived pancreatic cell colony. In some embodiments, stem cell-derived pancreatic cells express Pdx1. In some embodiments, stem cell-derived pancreatic cells are endocrine cells. In some embodiments, stem cell-derived pancreatic cells are exocrine cells. Exemplary methods for in vitro differentiation of stem cell-derived pancreatic cells are described in US8859286, WO2011011300, WO2014105543, WO2013095953, US9157062, and Balboa et al. (2022) Nature Biotechnology 40: 1042-55. In some embodiments, the first differentiation state, the second differentiation state, the third differentiation state, and / or the control differentiation state are differentiation states of pancreatic precursor cells or stereotyped or directed exocrine cells (e.g., acinar or ductal cells) or endocrine cells (e.g., β, α, δ or PP cells). In some embodiments, the first differentiation state is a differentiation state of a pancreatic precursor cell; the second differentiation state is a differentiation state of a committed exocrine cell (e.g., an acinar or ductal cell) or an endocrine cell (e.g., a β, α, δ, or PP cell); and the third differentiation state is a committed differentiation state corresponding to the second differentiation state. In some embodiments, the second differentiation state is a differentiation state of a committed β cell. In some embodiments, the third differentiation state is a differentiation state of a committed β cell. In some embodiments, cells identified as having a desired differentiation state (e.g., having a second differentiation state) can be used to treat pancreatic disorders, metabolic disorders, or diseases involving improper production or use of insulin, such as type 1 diabetes.

[0255] In some embodiments, the test cell is from an in vitro population, which is from a cell culture differentiated from a pluripotent cell, which is subjected to a differentiation protocol for inducing PSC (e.g., iPSC) to differentiate into β cells, such as according to any method described herein, for example, as described in Balboa et al. (2022) Nature Biotechnology 40: 1042-55. In some embodiments, the cells in the second differentiation state are in any day of the 21st to 35th day of the differentiation protocol. In some embodiments, the cells in the first differentiation state are in the 20th day or earlier, the 19th day or earlier, the 18th day or earlier, or the 17th day or earlier of the differentiation protocol. In some embodiments, the cells in the third differentiation state are in the 36th day or later, the 38th day or later, the 40th day or later, the 42nd day or later, or the 44th day or later of the differentiation protocol. In some embodiments, the cells of the second differentiation state are at any day of 21 to 35 of the differentiation protocol; and the cells of the third differentiation state are at day 36 or later, day 38 or later, day 40 or later, day 42 or later, or day 44 or later of the differentiation protocol. In some embodiments, the cells of the first differentiation state are at day 20 or earlier, day 19 or earlier, day 18 or earlier, or day 17 or earlier of the differentiation protocol; the cells of the second differentiation state are at any day of 21 to 35 of the differentiation protocol; and the cells of the third differentiation state are at day 36 or later, day 38 or later, day 40 or later, day 42 or later, or day 44 or later of the differentiation protocol. In some embodiments, the cells of the third differentiation state are at any day of 56 to 98 of the differentiation protocol.

[0256] In some embodiments, the test cells are from an in vitro stem cell-derived epidermal cell population (see, e.g., Jackson et al., Stem Cell Research & Therapy 8: 155 (2017)). Exemplary methods for in vitro differentiation of stem cell-derived epidermal cells are described in US9404122. In some embodiments, the first differentiation state, the second differentiation state, the third differentiation state, and / or the control differentiation state are differentiation states of epidermal precursor cells or committed or directed keratinocytes, melanocytes, or Langerhans cells. In some embodiments, the first differentiation state is a differentiation state of epidermal precursor cells; the second differentiation state is a differentiation state of directed keratinocytes, melanocytes, or Langerhans cells; and the third differentiation state is a committed differentiation state corresponding to the second differentiation state. In some embodiments, cells identified as having a desired differentiation state (e.g., having a second differentiation state) can be used to treat skin injuries or conditions, such as burns, chronic wounds, or stable vitiligo.

[0257] In some embodiments, the test cell is from an in vitro stem cell-derived pigment cell colony. In some embodiments, the stem cell-derived pigment cell is a retinal pigment cell. In some embodiments, the stem cell-derived pigment cell is a melanocyte. Exemplary methods for in vitro differentiation of stem cell-derived pigment cells are described in WO2005070011, WO2011149762, WO2014121077, WO2009051671, and WO2008129554. In some embodiments, the first differentiation state, the second differentiation state, the third differentiation state, and / or the control differentiation state are the differentiation states of pigment precursor cells or stereotyped or directed retinal pigment cells or melanocytes. In some embodiments, the first differentiation state is the differentiation state of pigment precursor cells; the second differentiation state is the differentiation state of directed retinal pigment cells or melanocytes; and the third differentiation state is the stereotyped differentiation state corresponding to the second differentiation state. In some embodiments, cells identified as having a desired differentiation state (e.g., having a second differentiation state) can be used to treat degenerative diseases, such as retinal degenerative diseases, such as macular degeneration.

[0258] a. Neuronal cells

[0259] In some embodiments, test cell is from in vitro stem cell derived neuron cell colony.Exemplary methods for in vitro differentiation stem cell derived neuron cells are described in WO2014176606, US8460931, US10273453, WO2012095730, US9309495, US20190249140, US20180298326, WO2009148170, WO2021146349, WO2021216623, WO2021216622, WO2013104752, WO2010096496, WO2013067362, WO2016196661, WO2015143342 and US20160348070.

[0260] In some embodiments, the method of differentiating stem cell-derived neuronal cells can be a method of differentiating pluripotent stem cells (e.g., iPSC) into any neural cell type using any available or known method for inducing pluripotent stem cells (e.g., iPSC) differentiation. In some embodiments, the method induces pluripotent stem cells to differentiate into midbrain floor plate precursor cells, directed dopaminergic cells and / or dopaminergic neurons. Any available and known method for inducing pluripotent stem cells to differentiate into midbrain floor plate precursor cells, directed dopaminergic cells and / or dopaminergic neurons can be used.

[0261] In some embodiments, the method induces differentiation of pluripotent stem cells into glial cells.In some embodiments, the glial cells are selected from the group consisting of microglia, astrocytes, oligodendrocytes, and ependymal cells.

[0262] In some embodiments, the test cell is from an in vitro stem cell-derived microglia population. In some embodiments, the method induces pluripotent stem cells to differentiate into microglia or microglia-like cells. Any available and known method for inducing pluripotent stem cells to differentiate into microglia or microglia-like cells can be used. Exemplary methods for inducing pluripotent stem cells to differentiate into microglia or microglia-like cells can be found in, for example, McQuade et al. (2018) Molecular Neurodegeneration 13: 67; Abud et al., Neuron (2017), Vol. 94: 278-293; Douvaras et al., Stem Cell Reports (2017), Vol. 8: 1516-1524; Muffat et al., Nature Medicine (2016), Vol. 22, No. 11: 1358-1367; and Pandya et al., Nature Neuroscience (2017), Vol. 20, No. 5: 753-759. In some embodiments, the first differentiation state, the second differentiation state, the third differentiation state and / or the control differentiation state are the differentiation states of iPSC, hematopoietic progenitor cells or microglia. In some embodiments, the first differentiation state is the differentiation state of iPSC; the second differentiation state is the differentiation state of hematopoietic progenitor cells; and the third differentiation state is the differentiation state of microglia. In some embodiments, cells identified as having a desired differentiation state (e.g., having a second differentiation state) can be used to treat Parkinson's disease, Parkinson's syndrome, age-related neurodegenerative diseases, Alzheimer's disease or frontotemporal dementia.

[0263] In some embodiments, the test cell is from an in vitro colony, and the in vitro colony is from a cell culture differentiated from a pluripotent cell, and the pluripotent cell is subjected to a differentiation scheme for inducing PSC (e.g., iPSC) to differentiate into microglia, such as according to any method described herein. In some embodiments, the cell of the second differentiation state is in any day of the 28th to 35th day of the differentiation scheme. In some embodiments, the cell of the first differentiation state is in the 27th day or earlier, the 26th day or earlier, the 25th day or earlier, or the 24th day or earlier of the differentiation scheme. In some embodiments, the cell of the third differentiation state is in the 36th day or later, the 37th day or later, the 38th day or later, the 39th day or later, the 40th day or later, the 41st day or later, the 42nd day or later, or the 43rd day or later of the differentiation scheme. In some embodiments, the cells of the second differentiation state are at any day of 28 to 35 of the differentiation protocol; and the cells of the third differentiation state are at day 36 or later, 37 or later, 38 or later, 39 or later, 40 or later, 41 or later, 42 or later, or 43 or later of the differentiation protocol. In some embodiments, the cells of the first differentiation state are at day 27 or earlier, 26 or earlier, 25 or earlier, or 24 or earlier of the differentiation protocol; the cells of the second differentiation state are at any day of 28 to 35 of the differentiation protocol; and the cells of the third differentiation state are at day 36 or later, 37 or later, 38 or later, 39 or later, 40 or later, 41 or later, 42 or later, or 43 or later of the differentiation protocol. In some embodiments, the cells in the third differentiation state are on any day 49 to 63 of a differentiation protocol.

[0264] In some embodiments, the method induces pluripotent stem cells to differentiate into astrocytes. Any available and known method for inducing pluripotent stem cells to differentiate into astrocytes can be used. Exemplary methods for inducing pluripotent stem cells to differentiate into astrocytes can be found in, for example, TCW et al., Stem Cell Reports (2017), Vol. 9: 600-614, including methods described in references cited therein, such as in Table 1. In some embodiments, exemplary methods for inducing pluripotent stem cells to differentiate into astrocytes can include the use of commercially available kits and methods for using such kits provided, including, for example, astrocyte culture medium, catalog number 1801 (ScienCell Research Laboratories, Carlsbad, CA); astrocyte culture medium, catalog number A1261301 (ThermoFisherScientific Inc, Waltham, MA); and AGM astrocyte growth medium kit, catalog number CC-3186 (Lonza, Basel, Switzerland).

[0265] In some embodiments, the method induces pluripotent stem cells to differentiate into oligodendrocytes. Any available and known method for inducing pluripotent stem cells to differentiate into oligodendrocytes can be used. Exemplary methods for inducing pluripotent stem cells to differentiate into oligodendrocytes can be found in, for example, Ehrlich et al., PNAS (2017), Vol. 114, No. 11: E2243-E2252; Douvaras et al., Stem Cell Reports (2014), Vol. 3, No. 2: 250-259; Stacpoole et al., Stem Cell Reports (2013), Vol. 1, No. 5: 437-450; Wang et al., Cell Stem Cell (2013), Vol. 12, No. 2: 252-264; and Piao et al., Cell Stem Cell (2015), Vol. 16, No. 2: 198-210.

[0266] In some embodiments, the test cell is from an in vitro stem cell-derived GABA neuron cell colony. The exemplary method for in vitro differentiation of stem cell-derived GABA neuron cells is described in Maroof et al. (2013) Cell Stem Cell 12 (5): 573-586, US2020 / 0002679A1, US20110183912A1 and US20140248696A1. In some embodiments, the first differentiation state, the second differentiation state, the third differentiation state and / or the control differentiation state are the differentiation states of GABA neuron precursor cells, stereotyped GABA neuron cells or directional GABA neuron cells. In some embodiments, the first differentiation state is the differentiation state of GABA neuron precursor cells; the second differentiation state is the differentiation state of directional GABA neuron cells; and the third differentiation state is the differentiation state of stereotyped GABA neuron cells. In some embodiments, cells identified as having a desired differentiation state (e.g., having a second differentiation state) can be used to treat epilepsy.

[0267] In some embodiments, the test cell is from an in vitro colony, which is from a cell culture differentiated from a pluripotent cell, which is subjected to a differentiation protocol for inducing PSC (e.g., iPSC) to differentiate into inhibitory neurons (e.g., GABAergic neuron cells). Exemplary methods for in vitro differentiation of inhibitory neurons are described in Kang et al. (2017) Sci Rep 7:12233 and Nicholas et al. (2013) Cell Stem Cell 12 (5):573-86. In some embodiments, the cells of the second differentiation state are in any week of the 5th to 10th week of the differentiation protocol. In some embodiments, the cells of the first differentiation state are in the 4th week or earlier, the 3rd week or earlier, or the 2nd week or earlier of the differentiation protocol. In some embodiments, the cells of the third differentiation state are in the 12th week or later, the 14th week or later, the 16th week or later, the 18th week or later, or the 20th week or later of the differentiation protocol. In some embodiments, the cells of the second differentiation state are in any week of the 5th to 10th week of the differentiation protocol; and the cells of the third differentiation state are in the 12th week or later, the 14th week or later, the 16th week or later, the 18th week or later, or the 20th week or later of the differentiation protocol. In some embodiments, the cells of the first differentiation state are in the 4th week or earlier, the 3rd week or earlier, or the 2nd week or earlier of the differentiation protocol; the cells of the second differentiation state are in any week of the 5th to 10th week of the differentiation protocol; and the cells of the third differentiation state are in the 12th week or later, the 14th week or later, the 16th week or later, the 18th week or later, or the 20th week or later of the differentiation protocol. In some embodiments, the cells of the third differentiation state are in any week of the 20th to 30th week of the differentiation protocol.

[0268] In some embodiments, the method induces iPSC to differentiate into midbrain floor plate precursor cells, directed dopaminergic cells and / or dopaminergic neurons. In some embodiments, the method includes (a) performing a first incubation, including culturing pluripotent stem cells under conditions that produce cell spheres in a non-adherent culture vessel, wherein at the beginning of the first incubation (day 0), the cells are exposed to (i) TGF-β / activated Nodal signaling inhibitor; (ii) at least one sonic hedgehog (SHH) signaling activator; (iii) bone morphogenetic protein (BMP) signaling inhibitor; and (iv) glycogen synthase kinase 3β (GSK3β) signaling inhibitor; and (b) performing a second incubation, including culturing the cells of the sphere in a substrate-coated culture vessel under conditions that cause the cells to differentiate neurally.

[0269] In some embodiments, the method involves exposing pluripotent stem cells to (a) a bone morphogenetic protein (BMP) signaling inhibitor; (b) a TGF-β / Nodal signaling inhibitor; and (c) at least one sonic hedgehog (SHH) signaling activator. In some embodiments, the method further comprises exposing the pluripotent stem cells to at least one GSK3β signaling inhibitor. In some embodiments, exposure to the BMP signaling inhibitor and the TGF-β / Nodal signaling inhibitor occurs while the pluripotent stem cells are attached to a substrate. In some embodiments, during exposure to the BMP signaling inhibitor, the TGF-β / Nodal signaling inhibitor, and at least one SHH signaling activator, the pluripotent stem cells are attached to a substrate. In some embodiments, during exposure to at least one GSK3β signaling inhibitor, the pluripotent stem cells are attached to a substrate. In some embodiments, during exposure to the BMP signaling inhibitor, the TGF-β / Nodal signaling inhibitor, and at least one SHH signaling activator, the pluripotent stem cells are in a non-adherent culture vessel under conditions that produce cell spheres. In some embodiments, the pluripotent stem cells are maintained in a non-adherent culture vessel under conditions that produce spheroids during exposure to the at least one GSK3β signaling inhibitor.

[0270] In some embodiments, non-adherent culture vessels allow three-dimensional formation of cell aggregates. In some embodiments, iPSC is cultured in non-adherent culture vessels, such as multi-well plates, to produce cell aggregates (e.g., spheres). In some embodiments, iPSC is cultured in non-adherent culture vessels, such as multi-well plates, to produce cell aggregates (e.g., spheres) on about the 7th day of the method. In some embodiments, cell aggregates (e.g., spheres) express at least one of PAX6 and OTX2 on or before about the 7th day of the method.

[0271] In some embodiments, the first incubation is from about day 0 to about day 6. In some embodiments, the first incubation comprises culturing the pluripotent stem cells in a culture medium ("medium"). In some embodiments, the first incubation comprises culturing the pluripotent stem cells in a culture medium from about day 0 to about day 6. In some embodiments, the first incubation comprises culturing the pluripotent stem cells in a culture medium to induce differentiation of the PSCs into midbrain floor plate precursor cells.

[0272] In some embodiments, the culture medium is also supplemented with a serum replacement that contains minimal components of non-human origin (e.g., KnockOut TMSerum replacement). In some embodiments, during at least a portion of the first incubation, a 5% (v / v) serum replacement is provided in the culture medium. In some embodiments, on days 0 and 1, a 5% (v / v) serum replacement is provided in the culture medium. In some embodiments, during at least a portion of the first incubation, a 2% (v / v) serum replacement is provided in the culture medium. In some embodiments, from day 2 to day 6, a 2% (v / v) serum replacement is provided in the culture medium. In some embodiments, on days 0 and 1, a 5% (v / v) serum replacement is provided in the culture medium and from day 2 to day 6, a 2% (v / v) serum replacement is provided.

[0273] In some embodiments, the culture medium is also supplemented with a small molecule, such as any of the small molecules described above. In some embodiments, the small molecule is selected from the group consisting of: a Rho-associated protein kinase (ROCK) inhibitor, a TGF-β / activated Nodal signaling inhibitor, at least one sonic hedgehog (SHH) signaling activator, a bone morphogenetic protein (BMP) signaling inhibitor, a glycogen synthase kinase 3β (GSK3β) signaling inhibitor, and a combination thereof.

[0274] In some embodiments, on one or more days when the cells are passaged, the culture medium is supplemented with a Rho-associated protein kinase (ROCK) inhibitor. In some embodiments, on each day of the cell passage, the culture medium is supplemented with a ROCK inhibitor. In some embodiments, the culture medium is supplemented with a ROCK inhibitor at day 0.

[0275] In some embodiments, the ROCK inhibitor is selected from the group consisting of: Fasudil, Ripasudil, Netarsudil, RKI-1447, Y-27632, GSK429286A, Y-30141, and combinations thereof. In some embodiments, the ROCK inhibitor is a small molecule. In some embodiments, the ROCK inhibitor selectively inhibits p160ROCK. In some embodiments, the ROCK inhibitor is Y-27632, having the formula:

[0276]

[0277] In some embodiments, the culture medium is supplemented with TGF-β / activation of Nodal signaling inhibitors. In some embodiments, the culture medium is supplemented with TGF-β / activation of Nodal signaling inhibitors until about day 7 (e.g., day 6 or day 7). In some embodiments, from about day 0 to day 6, including every day, the culture medium is supplemented with TGF-β / activation of Nodal signaling inhibitors.

[0278] In some embodiments, the TGF-β / Nodal signaling inhibitor is a small molecule. In some embodiments, the TGF-β / Nodal signaling inhibitor is capable of reducing or blocking transforming growth factor β (TGFβ) / activator Nodal signaling. In some embodiments, the TGF-β / Nodal signaling inhibitor inhibits ALK4, ALK5, ALK7, or a combination thereof. In some embodiments, the TGF-β / Nodal signaling inhibitor inhibits ALK4, ALK5, and ALK7. In some embodiments, the TGF-β / Nodal signaling inhibitor does not inhibit ALK2, ALK3, ALK6, or a combination thereof. In some embodiments, the inhibitor does not inhibit ALK2, ALK3, or ALK6. In some embodiments, the TGF-β / activated Nodal signaling inhibitor is SB431542 (e.g., CAS 301836-41-9, with a molecular formula of C22H18N4O3 and a name of 4-[4-(1,3-benzodioxol-5-yl)-5-(2-pyridinyl)-1H-imidazol-2-yl]-benzamide), having the following formula:

[0279]

[0280] In some embodiments, the TGF-β / Nodal signaling inhibitor is a small molecule. In some embodiments, the TGF-β / Nodal signaling inhibitor is capable of reducing or blocking transforming growth factor β (TGFβ) / activator Nodal signaling. In some embodiments, the TGF-β / Nodal signaling inhibitor inhibits ALK4, ALK5, ALK7, or a combination thereof. In some embodiments, the TGF-β / Nodal signaling inhibitor inhibits ALK4, ALK5, and ALK7. In some embodiments, the TGF-β / Nodal signaling inhibitor does not inhibit ALK2, ALK3, ALK6, or a combination thereof. In some embodiments, the inhibitor does not inhibit ALK2, ALK3, or ALK6. In some embodiments, the TGF-β / activated Nodal signaling inhibitor is SB431542 (e.g., CAS 301836-41-9, with a molecular formula of C22H18N4O3 and a name of 4-[4-(1,3-benzodioxol-5-yl)-5-(2-pyridinyl)-1H-imidazol-2-yl]-benzamide), having the following formula:

[0281]

[0282] In some embodiments, the at least one SHH signaling activator is an activator of the hedgehog receptor Smoothened. In some embodiments, the at least one SHH signaling activator is a small molecule. In some embodiments, the at least one SHH signaling activator is purmorphamine (eg, CAS 483367-10-8), having the following formula:

[0283]

[0284] In some embodiments, the cells are exposed to purmorphamine at a concentration of about 10 μM. In some embodiments, the cells are exposed to purmorphamine at a concentration of about 10 μM until day 7 (e.g., day 6 or day 7). In some embodiments, the cells are exposed to purmorphamine at a concentration of about 10 μM from about day 0 to about day 6, including every day.

[0285] In some embodiments, the at least one SHH signaling activator is SHH protein and purmorphamine. In some embodiments, cells are exposed to a certain concentration of SHH protein and purmorphamine until about the 7th day (e.g., the 6th day or the 7th day). In some embodiments, cells are exposed to SHH protein and purmorphamine from about day 0 to about day 6, including every day. In some embodiments, cells are exposed to a certain concentration of 100ng / mL SHH protein and 10μM purmorphamine until about day 7 (e.g., the 6th day or the 7th day). In some embodiments, cells are exposed to 100ng / mL SHH protein and 10μM purmorphamine from about day 0 to about day 6, including every day.

[0286] In some embodiments, the culture medium is supplemented with a BMP signaling inhibitor. In some embodiments, the culture medium is supplemented with a BMP signaling inhibitor until about day 7 (e.g., day 6 or day 7). In some embodiments, from about day 0 to day 6, including every day, the culture medium is supplemented with a BMP signaling inhibitor.

[0287] In some embodiments, the BMP signaling inhibitor is a small molecule. In some embodiments, the BMP signaling inhibitor is selected from LDN193189 or K02288. In some embodiments, the BMP signaling inhibitor is capable of inhibiting "small mother antagonism to fruit fly skin growth factor" SMAD signaling. In some embodiments, the BMP signaling inhibitor inhibits ALK1, ALK2, ALK3, ALK6, or a combination thereof. In some embodiments, the BMP signaling inhibitor inhibits ALK1, ALK2, ALK3, and ALK6. In some embodiments, the BMP signaling inhibitor inhibits BMP2, BMP4, BMP6, BMP7, and activin cytokine signals, and subsequently inhibits SMAD phosphorylation of Smad1, Smad5, and Smad8. In some embodiments, the BMP signaling inhibitor is LDN193189. In some embodiments, the BMP signaling inhibitor is LDN193189 (e.g., IUPAC name 4-(6-(4-(piperazin-1-yl)phenyl)pyrazolo[1,5-a]pyrimidin-3-yl)quinoline, chemical formula C25H22N6), having the following formula:

[0288]

[0289] In some embodiments, the cells are exposed to LDN193189 at a concentration of about 0.1 μM. In some embodiments, the cells are exposed to LDN193189 at a concentration of about 0.1 μM until about day 7 (e.g., day 6 or day 7). In some embodiments, the cells are exposed to LDN193189 at a concentration of about 0.1 μM from about day 0 to about day 6, including every day.

[0290] In some embodiments, the culture medium is supplemented with a GSK3β signaling inhibitor. In some embodiments, the culture medium is supplemented with a GSK3β signaling inhibitor until about day 7 (e.g., day 6 or day 7). In some embodiments, from about day 0 to day 6, including every day, the culture medium is supplemented with a GSK3β signaling inhibitor.

[0291] In some embodiments, the GSK3β signaling inhibitor is selected from the group consisting of: lithium ion, valproic acid, iodinated tuberculocidin, naproxen, famotidine, curcumin, olanzapine, CHIR99012, and combinations thereof. In some embodiments, the GSK3β signaling inhibitor is a small molecule. In some embodiments, the GSK3β signaling inhibitor inhibits glycogen synthase kinase 3β enzyme. In some embodiments, the GSK3β signaling inhibitor inhibits GSK3α. In some embodiments, the GSK3β signaling inhibitor regulates TGF-β and MAPK signaling. In some embodiments, the GSK3β signaling inhibitor is a wingless / integrated (Wnt) signaling agonist. In some embodiments, the GSK3β signaling inhibitor has an IC50 of 6.7nM for human GSK3β. In some embodiments, the GSK3β signaling inhibitor is CHIR99021 (e.g., "3-[3-(2-carboxyethyl)-4-methylpyrrole-2-methylene]-2-indolone" or the IUPAC name 6-(2-(4-(2,4-dichlorophenyl)-5-(4-methyl-1H-imidazol-2-yl)pyrimidin-2-ylamino)ethylamino)nicotinonitrile), having the formula:

[0292]

[0293] In some embodiments, the cells are exposed to CHIR99021 at a concentration of about 2.0 μM. In some embodiments, the cells are exposed to CHIR99021 at a concentration of about 2.0 μM until about day 7 (e.g., day 6 or day 7). In some embodiments, the cells are exposed to CHIR99021 at a concentration of about 2.0 μM from about day 0 to about day 6, including every day.

[0294] In some embodiments, at least about 50% of the culture medium is replaced every day from about day 2 to about day 6. In some embodiments, about 50% of the culture medium is replaced every day, every other day, or every three days from about day 2 to about day 6. In some embodiments, about 50% of the culture medium is replaced every day from about day 2 to about day 6. In some embodiments, at least about 75% of the culture medium is replaced on day 1. In some embodiments, about 100% of the culture medium is replaced on day 1. In some embodiments, the replacement culture medium contains about twice as concentrated small molecule as compared to the concentration of small molecule in the culture medium on day 0.

[0295] In some embodiments, the first incubation comprises culturing the pluripotent stem cells in a "basal induction medium". In some embodiments, the first incubation comprises culturing the pluripotent stem cells in a basal induction medium from about day 0 to about day 6. In some embodiments, the first incubation comprises culturing the pluripotent stem cells in a basal induction medium to induce differentiation of the PSCs into midbrain floor plate progenitor cells.

[0296] In some embodiments, the basal induction medium is formulated to contain Neurobasal TM culture medium and DMEM / F12 medium supplemented with N-2 and B27 supplements, non-essential amino acids (NEAA), GlutaMAX TM , L-glutamine, β-mercaptoethanol and insulin. In some embodiments, the basal induction medium is further supplemented with any of the above-mentioned small molecules.

[0297] In some embodiments, cell aggregates (eg, spheroids) generated after a first incubation of culturing pluripotent stem cells in a non-adherent culture vessel are transferred or dissociated prior to a second incubation of cells on a substrate (adherent culture).

[0298] In some embodiments, the first incubation is performed to produce cell aggregates (e.g., spheres) expressing at least one of PAX6 and OTX2. In some embodiments, the first incubation produces cell aggregates (e.g., spheres) expressing PAX6 and OTX2. In some embodiments, the first incubation produces cell aggregates (e.g., spheres) at or before about day 7 of these methods. In some embodiments, the first incubation produces cell aggregates (e.g., spheres) expressing at least one of PAX6 and OTX2 at or before about day 7 of these methods. In some embodiments, the first incubation produces cell aggregates (e.g., spheres) expressing at least one of PAX6 and OTX2. In some embodiments, the first incubation produces cell aggregates (e.g., spheres) expressing PAX6 and OTX2 at or before about day 7 of these methods.

[0299] In some embodiments, before the cells are incubated for the second time on the substrate, the cell aggregates (e.g., spheres) produced by the first incubation are dissociated. In some embodiments, the cell aggregates (e.g., spheres) produced by the first incubation are dissociated to produce a cell suspension. In some embodiments, the cell suspension produced by dissociation is a single cell suspension. In some embodiments, dissociation is performed when the spheroid cells express at least one of PAX6 and OTX2. In some embodiments, dissociation is performed when the spheroid cells express PAX6 and OTX2. In some embodiments, dissociation is performed on about the 7th day. In some embodiments, cell aggregates (e.g., spheres) are dissociated by enzymatic dissociation. In some embodiments, the enzyme is selected from the group consisting of the following items: accutase, dispase, collagenase, and combinations thereof. In some embodiments, the enzyme includes accutase. In some embodiments, the enzyme is accutase. In some embodiments, the enzyme is dispase. In some embodiments, the enzyme is collagenase.

[0300] In some embodiments, the cell aggregates or the cell suspension produced therefrom are transferred to a substrate-coated culture vessel for a second incubation. In some embodiments, the cell aggregates or the cell suspension produced therefrom are transferred to a substrate-coated culture vessel after the cell aggregates (e.g., spheroids) are dissociated. In some embodiments, the transfer is performed immediately after the dissociation. In some embodiments, the transfer is performed on about the 7th day.

[0301] In some embodiments, the cell aggregates (e.g., spheres) are not dissociated before the second incubation. In some embodiments, the cell aggregates (e.g., spheres) are transferred as a whole to a substrate-coated culture vessel for a second incubation. In some embodiments, the transfer is performed when the sphere cells express at least one of PAX6 and OTX2. In some embodiments, the transfer is performed when the sphere cells express PAX6 and OTX2. In some embodiments, the transfer is performed on about the 7th day.

[0302] In some embodiments, the second incubation includes culturing cells of the sphere in a culture vessel coated with a substrate comprising laminin, collagen, nestin, heparin sulfate proteoglycans, and combinations thereof, wherein starting from day 7, the cells are exposed to (i) a BMP signaling inhibitor and (ii) a GSK3β signaling inhibitor; and starting from day 11, the cells are exposed to (i) brain-derived neurotrophic factor (BDNF); (ii) ascorbic acid; (iii) glial cell-derived neurotrophic factor (GDNF); (iv) dibutyryl cyclic AMP (dbcAMP); (v) transforming growth factor β-3 (TGFβ3); and (vi) a Notch signaling inhibitor. In some embodiments, the method further includes harvesting the differentiated cells.

[0303] In some embodiments, the substrate-coated culture vessel is a culture vessel with a surface to which cells can attach. In some embodiments, the substrate-coated culture vessel is a culture vessel with a surface to which a large number of cells attach. In some embodiments, the substrate is a basement membrane protein. In some embodiments, the substrate is laminin, collagen, entactin, heparin sulfate proteoglycans, or a combination thereof. In some embodiments, the substrate is laminin. In some embodiments, the substrate is collagen. In some embodiments, the substrate is entactin. In some embodiments, the substrate is heparin sulfate proteoglycans. In some embodiments, the substrate is a recombinant protein. In some embodiments, the substrate is a recombinant laminin. In some embodiments, the substrate-coated culture vessel is exposed to poly-L-ornithine. In some embodiments, the substrate-coated culture vessel is exposed to poly-L-ornithine before being used for cell culture.

[0304] In some embodiments, the substrate-coated culture vessel allows monolayer cell culture. In some embodiments, the cell aggregate (such as spheroid) derived from the first incubation is cultured in the form of monolayer culture on the substrate-coated plate. In some embodiments, the cell aggregate (such as spheroid) derived from the first incubation is cultured to produce a positive monolayer cell culture for one or more of LMX1A, FOXA2, EN1, CORIN and their combination. In some embodiments, the cell aggregate (such as spheroid) derived from the first incubation is cultured to produce a monolayer cell culture, wherein at least some cells are positive for EN1 and CORIN. In some embodiments, the cell aggregate (such as spheroid) derived from the first incubation is cultured to produce a monolayer cell culture, wherein at least some cells are TH+. In some embodiments, at least some cells are TH+ before about the 25th day or at about the 25th day. In some embodiments, cells derived from cell aggregates (e.g., spheres) produced by the first incubation are cultured to produce a monolayer cell culture in which at least some of the cells are TH+FOXA2+. In some embodiments, at least some of the cells are TH+FOXA2+ before or at about day 25.

[0305] In the method, the second incubation involves culturing the cells of the sphere in the substrate-coated culture vessel under conditions that induce neural differentiation of the cells. In some embodiments, at about day 7, the cells of the sphere are plated on the substrate-coated culture vessel.

[0306] In some embodiments, the second incubation starts from about day 7 until the cells are harvested. In some embodiments, the cells are harvested on or after about day 16. In some embodiments, the cells are harvested between about day 16 and about day 30. In some embodiments, the cells are harvested between about day 18 and about day 25. In some embodiments, the cells are harvested on about day 18. In some embodiments, the cells are harvested on about day 25. In some embodiments, the second incubation is from about day 7 until about day 18. In some embodiments, the second incubation is from about day 7 until about day 25.

[0307] In some embodiments, the second incubation comprises culturing cells derived from cell aggregates (eg, spheroids) in culture medium ("medium").

[0308] In some embodiments, the second incubation comprises culturing the cells in the culture medium starting from about day 7 until harvesting or collecting. In some embodiments, the cells are cultured in the culture medium to produce committed dopaminergic cells or dopaminergic neurons.

[0309] In some embodiments, the culture medium is also supplemented with a serum replacement that contains minimal components of non-human origin (e.g., KnockOut TM In some embodiments, from about day 7 to about day 10, the culture medium is supplemented with a serum replacement. In some embodiments, the culture medium is supplemented with about 2% (v / v) serum replacement. In some embodiments, from about day 7 to about day 10, the culture medium is supplemented with about 2% (v / v) serum replacement.

[0310] In some embodiments, the culture medium is further supplemented with a small molecule. In some embodiments, the small molecule is selected from the group consisting of a Rho-associated protein kinase (ROCK) inhibitor, a bone morphogenetic protein (BMP) signaling inhibitor, a glycogen synthase kinase 3β (GSK3β) signaling inhibitor, and combinations thereof.

[0311] In some embodiments, on one or more days when the cells are passaged, the culture medium is supplemented with a Rho-associated protein kinase (ROCK) inhibitor. In some embodiments, on each day of the cell passage, the culture medium is supplemented with a ROCK inhibitor. In some embodiments, the culture medium is supplemented with a ROCK inhibitor on the 7th day, the 16th day, the 20th day, or a combination thereof. In some embodiments, the culture medium is supplemented with a ROCK inhibitor on the 7th day. In some embodiments, the culture medium is supplemented with a ROCK inhibitor on the 16th day. In some embodiments, the culture medium is supplemented with a ROCK inhibitor on the 20th day. In some embodiments, the culture medium is supplemented with a ROCK inhibitor on the 7th and 16th days. In some embodiments, the culture medium is supplemented with a ROCK inhibitor on the 16th and 20th days. In some embodiments, the culture medium is supplemented with a ROCK inhibitor on the 7th, 16th, and 20th days.

[0312] In some embodiments, the ROCK inhibitor is fasudil, ripasudil, naftadil, RKI-1447, Y-27632, GSK429286A, Y-30141, or a combination thereof. In some embodiments, the ROCK inhibitor is a small molecule. In some embodiments, the ROCK inhibitor selectively inhibits p160ROCK. In some embodiments, the ROCK inhibitor is Y-27632, having the following formula:

[0313]

[0314] In some embodiments, the cells are exposed to Y-27632 at a concentration of about 105 μM. In some embodiments, the cells are exposed to Y-27632 at a concentration of about 10 μM on day 7, day 16, day 20, or a combination thereof. In some embodiments, the cells are exposed to Y-27632 at a concentration of about 10 μM on day 7. In some embodiments, the cells are exposed to Y-27632 at a concentration of about 10 μM on day 16. In some embodiments, the cells are exposed to Y-27632 at a concentration of about 10 μM on day 20. In some embodiments, the cells are exposed to Y-27632 at a concentration of about 10 μM on day 7 and day 16. In some embodiments, the cells are exposed to Y-27632 at a concentration of about 10 μM on day 16 and day 20. In some embodiments, the cells are exposed to Y-27632 at a concentration of about 10 μM on day 7, day 16, and day 20.

[0315] In some embodiments, the culture medium is supplemented with a BMP signaling inhibitor. In some embodiments, the culture medium is supplemented with a BMP signaling inhibitor from about day 7 until about day 11 (e.g., day 10 or day 11). In some embodiments, from about day 7 to day 10, including every day, the culture medium is supplemented with a BMP signaling inhibitor.

[0316] In some embodiments, the BMP signaling inhibitor is a small molecule. In some embodiments, the BMP signaling inhibitor is LDN193189 or K02288. In some embodiments, the BMP signaling inhibitor is capable of inhibiting "small maternal antagonism to Drosophila skin growth factor" SMAD signaling. In some embodiments, the BMP signaling inhibitor inhibits ALK1, ALK2, ALK3, ALK6, or a combination thereof. In some embodiments, the BMP signaling inhibitor inhibits ALK1, ALK2, ALK3, and ALK6. In some embodiments, the BMP signaling inhibitor inhibits BMP2, BMP4, BMP6, BMP7, and activin cytokine signals, and subsequently inhibits SMAD phosphorylation of Smad1, Smad5, and Smad8. In some embodiments, the BMP signaling inhibitor is LDN193189. In some embodiments, the BMP signaling inhibitor is LDN193189 (e.g., IUPAC name 4-(6-(4-(piperazin-1-yl)phenyl)pyrazolo[1,5-a]pyrimidin-3-yl)quinoline, chemical formula C25H22N6), having the following formula:

[0317]

[0318] In some embodiments, the cells are exposed to LDN193189 at a concentration of about 0.1 μM. In some embodiments, the cells are exposed to LDN193189 at a concentration of about 0.1 μM starting from about day 7 until about day 11 (e.g., day 10 or day 11). In some embodiments, the cells are exposed to LDN193189 at a concentration of about 0.1 μM from about day 7 to about day 10, including every day.

[0319] In some embodiments, the culture medium is supplemented with a GSK3β signaling inhibitor. In some embodiments, the culture medium is supplemented with a GSK3β signaling inhibitor from about day 7 until about day 13 (e.g., day 12 or day 13). In some embodiments, from about day 7 to day 12, including every day, the culture medium is supplemented with a GSK3β signaling inhibitor.

[0320] In some embodiments, the GSK3β signaling inhibitor is selected from lithium ions, valproic acid, iodinated tuberculocidin, naproxen, famotidine, curcumin, olanzapine, CHIR99012 or a combination thereof. In some embodiments, the GSK3β signaling inhibitor is a small molecule. In some embodiments, the GSK3β signaling inhibitor inhibits glycogen synthase kinase 3β enzyme. In some embodiments, the GSK3β signaling inhibitor inhibits GSK3α. In some embodiments, the GSK3β signaling inhibitor regulates TGF-β and MAPK signaling. In some embodiments, the GSK3β signaling inhibitor is a wingless / integrated (Wnt) signaling agonist. In some embodiments, the GSK3β signaling inhibitor has an IC50 of 6.7nM for human GSK3β. In some embodiments, the GSK3β signaling inhibitor is CHIR99021 (e.g., "3-[3-(2-carboxyethyl)-4-methylpyrrole-2-methylene]-2-indolone" or the IUPAC name 6-(2-(4-(2,4-dichlorophenyl)-5-(4-methyl-1H-imidazol-2-yl)pyrimidin-2-ylamino)ethylamino)nicotinonitrile), having the formula:

[0321]

[0322] In some embodiments, the cells are exposed to CHIR99021 at a concentration of about 2.0 μM. In some embodiments, the cells are exposed to CHIR99021 at a concentration of about 2.0 μM starting from about day 7 until about day 13 (e.g., day 12 or day 13). In some embodiments, the cells are exposed to CHIR99021 at a concentration of about 2.0 μM from about day 7 to about day 12, including every day.

[0323] In some embodiments, the culture medium is supplemented with brain-derived neurotrophic factor (BDNF). In some embodiments, the culture medium is supplemented with BDNF starting from about day 11. In some embodiments, the culture medium is supplemented with BDNF starting from about day 11 until harvest or collection. In some embodiments, the culture medium is supplemented with BDNF from about day 11 to day 18. In some embodiments, the culture medium is supplemented with BDNF from about day 11 to day 25.

[0324] In some embodiments, the culture medium is supplemented with 20 ng / mL BDNF starting at about day 11. In some embodiments, the culture medium is supplemented with 20 ng / mL BDNF starting at about day 11 until harvest or collection. In some embodiments, the culture medium is supplemented with about 20 ng / mL BDNF from about day 11 to day 18. In some embodiments, the culture medium is supplemented with about 20 ng / mL BDNF from about day 11 to day 25.

[0325] In some embodiments, the culture medium is supplemented with glial cell line derived neurotrophic factor (GDNF). In some embodiments, the culture medium is supplemented with GDNF starting from about day 11. In some embodiments, the culture medium is supplemented with GDNF starting from about day 11 until harvest or collection. In some embodiments, the culture medium is supplemented with GDNF from about day 11 to day 18. In some embodiments, the culture medium is supplemented with GDNF from about day 11 to day 25.

[0326] In some embodiments, the culture medium is supplemented with 20 ng / mL GDNF starting at about day 11. In some embodiments, the culture medium is supplemented with 20 ng / mL GDNF starting at about day 11 until harvest or collection. In some embodiments, the culture medium is supplemented with about 20 ng / mL GDNF from about day 11 to day 18. In some embodiments, the culture medium is supplemented with about 20 ng / mL GDNF from about day 11 to day 25.

[0327] In some embodiments, the culture medium is supplemented with ascorbic acid. In some embodiments, the culture medium is supplemented with ascorbic acid starting from about day 11. In some embodiments, the culture medium is supplemented with ascorbic acid starting from about day 11 until harvest or collection. In some embodiments, the culture medium is supplemented with ascorbic acid from about day 11 to day 18. In some embodiments, the culture medium is supplemented with ascorbic acid from about day 11 to day 25.

[0328] In some embodiments, the culture medium is supplemented with about 0.2 mM ascorbic acid starting at about day 11. In some embodiments, the culture medium is supplemented with 0.2 mM ascorbic acid starting at about day 11 until harvest or collection. In some embodiments, the culture medium is supplemented with about 0.2 mM ascorbic acid from about day 11 to day 18. In some embodiments, the culture medium is supplemented with about 0.2 mM ascorbic acid from about day 11 to day 25.

[0329] In some embodiments, the culture medium is supplemented with dibutyryl cyclic AMP (dbcAMP). In some embodiments, the culture medium is supplemented with dbcAMP starting from about day 11. In some embodiments, the culture medium is supplemented with dbcAMP starting from about day 11 until harvest or collection. In some embodiments, the culture medium is supplemented with dbcAMP from about day 11 to day 18. In some embodiments, the culture medium is supplemented with dbcAMP from about day 11 to day 25.

[0330] In some embodiments, the culture medium is supplemented with about 0.5 mM dbcAMP starting at about day 11. In some embodiments, the culture medium is supplemented with 0.5 mM dbcAMP starting at about day 11 until harvest or collection. In some embodiments, the culture medium is supplemented with about 0.5 mM dbcAMP from about day 11 to day 18. In some embodiments, the culture medium is supplemented with about 0.5 mM dbcAMP from about day 11 to day 25.

[0331] In some embodiments, the culture medium is supplemented with transforming growth factor β3 (TGFβ3). In some embodiments, the culture medium is supplemented with TGFβ3 starting from about day 11. In some embodiments, the culture medium is supplemented with TGFβ3 starting from about day 11 until harvest or collection. In some embodiments, the culture medium is supplemented with TGFβ3 from about day 11 to day 18. In some embodiments, the culture medium is supplemented with TGFβ3 from about day 11 to day 25.

[0332] In some embodiments, the culture medium is supplemented with about 1 ng / mL TGFβ3 starting on about day 11. In some embodiments, the culture medium is supplemented with 1 ng / mL TGFβ3 starting on about day 11 until harvest or collection. In some embodiments, the culture medium is supplemented with about 1 ng / mL TGFβ3 from about day 11 to day 18. In some embodiments, the culture medium is supplemented with about 1 ng / mL TGFβ3 from about day 11 to day 25.

[0333] In some embodiments, the culture medium is supplemented with a Notch signaling inhibitor. In some embodiments, the culture medium is supplemented with a Notch signaling inhibitor starting from about day 11. In some embodiments, the culture medium is supplemented with a Notch signaling inhibitor starting from about day 11 until harvest or collection. In some embodiments, the culture medium is supplemented with a Notch signaling inhibitor from about day 11 to day 18. In some embodiments, the culture medium is supplemented with a Notch signaling inhibitor from about day 11 to day 25.

[0334]

[0335] In some embodiments, the Notch signaling inhibitor is selected from cowanin, PF-03084014, L685458, LY3039478, DAPT, or a combination thereof. In some embodiments, the Notch signaling inhibitor inhibits gamma secretase. In some embodiments, the Notch signaling inhibitor is a small molecule. In some embodiments, the Notch signaling inhibitor is DAPT, having the following formula:

[0336] In some embodiments, the culture medium is supplemented with about 10 μM DAPT starting on about day 11. In some embodiments, the culture medium is supplemented with 10 μM DAPT starting on about day 11 until harvest or collection. In some embodiments, the culture medium is supplemented with about 10 μM DAPT from about day 11 to day 18. In some embodiments, the culture medium is supplemented with about 10 μM DAPT from about day 11 to day 25.

[0337] In some embodiments, starting from about day 11, the culture medium is supplemented with about 20 ng / mL BDNF, about 20 ng / mL GDNF, about 0.2 mM ascorbic acid, about 0.5 mM dbcAMP, about 1 ng / mL TGFβ3, and about 10 μM DAPT. In some embodiments, starting from about day 11 until harvest or collection, the culture medium is supplemented with about 20 ng / mL BDNF, about 20 ng / mL GDNF, about 0.2 mM ascorbic acid, about 0.5 mM dbcAMP, about 1 ng / mL TGFβ3, and about 10 μM DAPT. In some embodiments, from about day 11 to day 18, the culture medium is supplemented with about 20 ng / mL BDNF, about 20 ng / mL GDNF, about 0.2 mM ascorbic acid, about 0.5 mM dbcAMP, about 1 ng / mL TGFβ3, and about 10 μM DAPT. In some embodiments, from about day 11 to day 25, the culture medium is supplemented with about 20 ng / mL BDNF, about 20 ng / mL GDNF, about 0.2 mM ascorbic acid, about 0.5 mM dbcAMP, about 1 ng / mL TGFβ3, and about 10 μM DAPT.

[0338] In some embodiments, serum replacement is provided in the culture medium from about day 7 to about day 10. In some embodiments, serum replacement is provided in the culture medium at 2% (v / v) from day 7 to day 10.

[0339] In some embodiments, from about day 7 to about day 16, at least about 50% of the culture medium is replaced every day. In some embodiments, from about day 7 to about day 16, about 50% of the culture medium is replaced every day, every other day, or every three days. In some embodiments, from about day 7 to about day 16, about 50% of the culture medium is replaced every day. In some embodiments, starting from about day 17, at least about 50% of the culture medium is replaced every day, every other day, or every three days. In some embodiments, starting from about day 17, at least about 50% of the culture medium is replaced every other day. In some embodiments, starting from about day 17, about 50% of the culture medium is replaced every other day. In some embodiments, starting from about day 17, about 50% of the culture medium is replaced every other day. In some embodiments, starting from about day 17, about 50% of the culture medium is replaced every other day. In some embodiments, the replacement culture medium contains about twice as concentrated small molecules as compared to the concentration of small molecules in the culture medium at day 0.

[0340] In some embodiments, the second incubation comprises culturing cells derived from cell aggregates (e.g., spheres) in a "basal induction medium." In some embodiments, the second incubation comprises culturing cells derived from cell aggregates (e.g., spheres) in a "maturation medium." In some embodiments, the second incubation comprises culturing cells derived from cell aggregates (e.g., spheres) in a basal induction medium and then in a maturation medium.

[0341] In some embodiments, the second incubation comprises culturing the cells in a basal induction medium from about day 7 to about day 10. In some embodiments, the second incubation comprises culturing the cells in a maturation medium starting from about day 11. In some embodiments, the second incubation comprises culturing the cells in a basal induction medium from about day 7 to about day 10, and then culturing the cells in a maturation medium starting from about day 11. In some embodiments, the cells are cultured in a maturation medium to produce committed dopaminergic cells or dopaminergic neurons.

[0342] In some embodiments, the basal induction medium is formulated to contain Neurobasal TM culture medium and DMEM / F12 medium supplemented with N-2 and B27 supplements, non-essential amino acids (NEAA), GlutaMAX TM , L-glutamine, β-mercaptoethanol, and insulin.

[0343] In some embodiments, the maturation medium is formulated to contain N-2 and B27 supplements, non-essential amino acids (NEAA), and GlutaMAX TM Neurobasal TM Culture medium.

[0344] In some embodiments, cells are cultured in a basal induction medium from about the 7th day to about the 11th day (e.g., the 10th day or the 11th day). In some embodiments, cells are cultured in a basal induction medium from about the 7th day to the 10th day, including every day. In some embodiments, cells are cultured in a maturation medium from about the 11th day. In some embodiments, cells are cultured in a basal induction medium from about the 7th day to about the 10th day, and then cells are cultured in a maturation medium from about the 11th day. In some embodiments, cells are cultured in a maturation medium from about the 11th day until harvest or cells are collected. In some embodiments, cells are harvested between the 16th day and the 27th day. In some embodiments, cells are harvested between the 18th day and the 25th day. In some embodiments, cells are harvested on the 18th day. In some embodiments, cells are harvested on the 25th day.

[0345] In some embodiments, the test cell is from an in vitro population derived from a cell culture differentiated from a pluripotent cell subjected to a differentiation protocol for inducing differentiation of PSCs (e.g., iPSCs) into dopaminergic neurons, such as according to any of the methods described herein.

[0346] In some embodiments, the cells of the second differentiation state are at any day of the 15th to 21st day of the differentiation protocol. In some embodiments, the cells of the first differentiation state are at day 14 or earlier, day 13 or earlier, day 12 or earlier, or day 11 or earlier of the differentiation protocol. In some embodiments, the cells of the third differentiation state are at day 22 or later, day 23 or later, day 24 or later, or day 25 or later of the differentiation protocol. In some embodiments, the cells of the first differentiation state are at day 14 or earlier, day 13 or earlier, day 12 or earlier, or day 11 or earlier of the differentiation protocol; the cells of the second differentiation state are at any day of the 15th to 21st day of the differentiation protocol; and the cells of the third differentiation state are at day 22 or later, day 23 or later, day 24 or later, or day 25 or later of the differentiation protocol. In some embodiments, the cells of the first differentiation state are at any day of the 10th to 14th day of the differentiation protocol. In some embodiments, the cells in the third differentiation state are on day 25 or later of a differentiation protocol.

[0347] In some embodiments, the cells in the second differentiation state are at any day 16 to 18 of the differentiation protocol. In some embodiments, the cells in the first differentiation state are at day 15 or earlier, day 14 or earlier, day 13 or earlier, day 12 or earlier, or day 11 or earlier of the differentiation protocol. In some embodiments, the cells in the third differentiation state are at day 19 or later, day 20 or later, day 11 or later, day 22 or later, day 23 or later, day 24 or later, or day 25 or later of the differentiation protocol. In some embodiments, the cells of the first differentiation state are at day 15 or earlier, day 14 or earlier, day 13 or earlier, day 12 or earlier, or day 11 or earlier of the differentiation protocol; the cells of the second differentiation state are at any day of day 16 to 18 of the differentiation protocol; and the cells of the third differentiation state are at day 19 or later, day 20 or later, day 21 or later, day 22 or later, day 23 or later, day 24 or later, or day 25 or later of the differentiation protocol. In some embodiments, the cells of the first differentiation state are at any day of day 11 to 13 of the differentiation protocol. In some embodiments, the cells of the third differentiation state are at day 25 or later of the differentiation protocol.

[0348] In some embodiments, the cells in the second differentiation state are at any day 17 to 19 of the differentiation protocol. In some embodiments, the cells in the first differentiation state are at day 16 or earlier, day 15 or earlier, day 14 or earlier, day 13 or earlier, day 12 or earlier, or day 11 or earlier of the differentiation protocol. In some embodiments, the cells in the third differentiation state are at day 20 or later, day 11 or later, day 22 or later, day 23 or later, day 24 or later, or day 25 or later of the differentiation protocol. In some embodiments, the cells of the first differentiation state are at day 16 or earlier, day 15 or earlier, day 14 or earlier, day 13 or earlier, day 12 or earlier, or day 11 or earlier of the differentiation protocol; the cells of the second differentiation state are at any day of day 17 to 19 of the differentiation protocol; and the cells of the third differentiation state are at day 20 or later, day 21 or later, day 22 or later, day 23 or later, day 24 or later, or day 25 or later of the differentiation protocol. In some embodiments, the cells of the first differentiation state are at any day of day 12 to 14 of the differentiation protocol. In some embodiments, the cells of the third differentiation state are at day 25 or later of the differentiation protocol.

[0349] In some embodiments, the cells in the second differentiation state are at any day 15 to 17 of the differentiation protocol. In some embodiments, the cells in the first differentiation state are at day 14 or earlier, day 13 or earlier, day 12 or earlier, or day 11 or earlier of the differentiation protocol. In some embodiments, the cells in the third differentiation state are at day 18 or later, day 19 or later, day 20 or later, day 11 or later, day 22 or later, day 23 or later, day 24 or later, or day 25 or later of the differentiation protocol. In some embodiments, the cells of the first differentiation state are at day 14 or earlier, day 13 or earlier, day 12 or earlier, or day 11 or earlier of the differentiation protocol; the cells of the second differentiation state are at any day of day 15 to 17 of the differentiation protocol; and the cells of the third differentiation state are at day 18 or later, day 19 or later, day 20 or later, day 21 or later, day 22 or later, day 23 or later, day 24 or later, or day 25 or later of the differentiation protocol. In some embodiments, the cells of the first differentiation state are at any day of day 10 to 12 of the differentiation protocol. In some embodiments, the cells of the third differentiation state are at day 30 or later of the differentiation protocol.

[0350] 4. Pluripotent stem cells

[0351] In some embodiments, test cells and / or reference cell colonies are produced by pluripotent stem cells. Various sources of pluripotent stem cells can be used, including embryonic stem (ES) cells and induced pluripotent stem cells (iPSC). In some embodiments, pluripotent stem cells are iPSC. iPSC can be produced by a process called reprogramming, in which non-pluripotent cells are effectively "dedifferentiated" into embryonic stem cell-like states (Takahashi and Yamanaka Cell (2006) 126: 663-76) by being engineered to express genes such as OCT4, SOX2 and KLF4. In some embodiments, pluripotent stem cells are iPSCs artificially derived from non-pluripotent cells of a subject. In some embodiments, non-pluripotent cells are fibroblasts. In some embodiments, a subject is a person. In some embodiments, a subject is a person suffering from Parkinson's disease.

[0352] In some aspects, pluripotency refers to cells that can produce offspring, which can be differentiated into cell types that jointly show characteristics related to cell lineages from three germ layers (endoderm, mesoderm and ectoderm) under appropriate conditions. Pluripotent stem cells can contribute to the tissue of prenatal, postnatal or adult organisms. Standard tests accepted in the art, such as the ability to form teratomas in 8-12 week-old SCID mice, can be used to establish the pluripotency of cell colonies. However, the identification of various pluripotent stem cell characteristics can also be used to identify pluripotent cells. In some aspects, pluripotent stem cells can be distinguished from other cells by specific characteristics, including by expressing or not expressing a combination of certain molecular markers. More specifically, human pluripotent stem cells can express at least some of the markers from the following non-limiting list, optionally all: SSEA-3, SSEA-4, TRA-1-60, TRA-1-81, TRA-2-49 / 6E, ALP, Sox2, E-cadherin, UTF-1, Oct4, Lin28, Rex1 and Nanog. In some aspects, the pluripotent stem cell characteristic is a cell morphology associated with pluripotent stem cells.

[0353] The method of generating iPSC is known. For example, in 2006 (Takahashi and Yamanaka) reported mouse iPSC, and in late 2007 (Takahashi et al. and Yu et al.) reported human iPSC. Mouse iPSC shows the important characteristics of pluripotent stem cells, including the expression of stem cell markers, the formation of tumors containing cells from all three germ layers, and the ability to facilitate many different tissues when injected into mouse embryos at the very early stage of development. Human iPSC also expresses stem cell markers and can produce cell characteristics of all three germ layers.

[0354] In some embodiments, non-pluripotent cells (e.g., fibroblasts) derived from patients with Parkinson's disease (PD) are reprogrammed into iPSCs before differentiation into neuronal cells. In some embodiments, fibroblasts can be reprogrammed into iPSCs by transforming fibroblasts with genes (OCT4, SOX2, NANOG, LIN28, and KLF4) cloned into plasmids (e.g., see Yu et al., Science DOI: 10.1126 / science.1172482). In some embodiments, non-pluripotent fibroblasts derived from patients with PD are reprogrammed to differentiate into directed dopaminergic cells and / or dopaminergic neurons, such as by reprogramming cells using non-integrating Sendai viruses (e.g., using CTS TM CytoTune TM-iPS 2.1 Sendai Reprogramming Kit). In some embodiments, the resulting differentiated cells are then administered to the patient from whom they were obtained in an autologous stem cell transplant. In some embodiments, the PSCs (e.g., iPSCs) are allogeneic to the subject to be treated, i.e., the PSCs are derived from an individual different from the subject to which the differentiated cells are to be administered. In some embodiments, non-pluripotent cells (e.g., fibroblasts) derived from another individual (e.g., an individual who does not suffer from a neurodegenerative disorder such as Parkinson's disease) are reprogrammed into iPSCs prior to differentiation into committed dopaminergic cells and / or dopaminergic neurons. In some embodiments, the cells are reprogrammed using a non-integrating Sendai virus (e.g., using a CTS TM CytoTune TM -iPS 2.1 Sendai Reprogramming Kit), at least partially achieving reprogramming. In some embodiments, the resulting differentiated cells are then administered to an individual who is not the same individual from whom the differentiated cells were obtained (e.g., allogeneic cell therapy or allogeneic cell transplantation).

[0355] In any of the embodiments provided, the PSC described herein may be genetically engineered to be low immunogenic. Methods for reducing immunogenicity are known and include eliminating polymorphic HLA-A / -B / -C and HLA class II molecule expression and introducing immunomodulatory factors PD-L1, HLA-G and CD47 into the AAVS1 safe harbor locus in differentiated cells (Han et al., PNAS (2019) 116 (21): 10441-46). Therefore, in some embodiments, the PSC described herein is engineered to delete highly polymorphic HLA-A / -B / -C genes and introduce immunomodulatory factors, such as PD-L1, HLA-G and / or CD47 into the AAVS1 safe harbor locus.

[0356] In some embodiments, PSCs (e.g., iPSCs) are cultured in the absence of feeder cells until they reach 80% to 90% confluence, at which time they are harvested and further cultured for differentiation (day 0). In some aspects, once the iPSCs reach 80% to 90% confluence, they are washed in phosphate buffered saline (PBS) and enzymatically dissociated, such as with Accutase TM Enzymatic dissociation is performed until the cells are easily dislodged from the surface of the culture vessel. The dissociated iPSCs are then resuspended in culture medium for downstream differentiation into desired cell types, such as committed dopaminergic cells and / or dopaminergic neurons.

[0357] In some embodiments, PSCs are resuspended in a basal induction medium. In some embodiments, the basal induction medium is formulated to contain a 1:1 ratio of NeurobasalTM culture medium and DMEM / F12 medium supplemented with N-2 and B27 supplements, non-essential amino acids (NEAA), GlutaMAX TM , L-glutamine, β-mercaptoethanol and insulin. In some embodiments, the basal induction medium is further supplemented with serum replacement, Rho-associated protein kinase (ROCK) inhibitors and various small molecules for differentiation. In some embodiments, PSCs are resuspended in the same medium in which they will be cultured for at least a portion of the first incubation.

[0358] 5. Exemplary Characteristics of Classified Cells

[0359] In some embodiments, the cells of the in vitro cell colonies identified as having a desired differentiation state (e.g., a second differentiation state) can survive when administered to, for example, an animal model in vivo. In some embodiments, the cells of the identified in vitro colonies survive after being transplanted into an animal or human subject. In some embodiments, the cells of the identified in vitro cell colonies have a therapeutic effect for treating a disease or condition of an animal model. In some embodiments, the cells of the identified in vitro cell colonies have a therapeutic effect for treating a disease or condition of a human patient. In some embodiments, these cells improve or reverse the symptoms of the disease or condition when implanted.

[0360] In some embodiments, cells of an in vitro cell population identified as having a desired differentiation state (e.g., a second differentiation state, which may be a differentiation state of directed dopaminergic neuronal cells) express markers of midbrain dopaminergic neurons, such as FOXA2 or tyrosine hydroxylase (TH). In some embodiments, these cells express TH (TH+). In some embodiments, these cells express FOXA2 (FOXA2+). In some embodiments, these cells express TH and FOXA2 (TH+FOXA2+).

[0361] In some embodiments, the cells of the identified in vitro cell colonies are directed to or can become dopaminergic neurons, that is, directed dopaminergic cells, as determined based on one or more features of the markers that indicate that the cells can have the functional activity of dopaminergic neurons but may not yet express dopaminergic neurons or may not express the markers at high levels. For example, the cell may show a lower level of TH than dopaminergic neurons, but still show one or more features of directed dopaminergic cells, indicating that the cell can have the functional activity of dopaminergic neurons. In some embodiments, one or more features include the activity of surviving, implanting and / or dominating other cells when administered to, for example, an animal model in vivo. In some embodiments, the cells of the identified in vitro colonies can dominate host tissues after being transplanted into an animal or human subject. In some embodiments, the cells of the identified in vitro colonies show neurite outgrowth after being transplanted into an animal or human subject. In some embodiments, the cells of the identified in vitro colonies survive after being transplanted into an animal or human subject. In some embodiments, the cells of the identified in vitro colonies are implanted after being transplanted into an animal or human subject.

[0362] In some embodiments, the cells of the identified in vitro cell colonies have a therapeutic effect for treating a neurodegenerative disease in an animal model of a neurodegenerative disease. In some embodiments, the neurodegenerative disease is Parkinson's disease. Any suitable animal model of Parkinson's disease can be used for screening. In some embodiments, the animal model is an injury model, in which the animal receives a unilateral stereotaxic injection of 6-hydroxydopamine (6-OHDA) to the substantia nigra. In some embodiments, the animal model is an injury model, in which the animal receives a unilateral stereotaxic injection of 6-OHDA to the medial forebrain bundle. In some embodiments, these cells are implanted in the substantia nigra of the animal model. In some embodiments, behavioral assays are performed to screen the therapeutic effects of implantation on the animal model. In some embodiments, behavioral assays include monitoring amphetamine-induced circling behavior. In some embodiments, these cells alleviate, reduce or reverse Parkinson's disease model brain damage in the model.

[0363] In some embodiments, the cells of the identified in vitro cell colonies have a therapeutic effect for treating neurodegenerative diseases (including in human patients). In some embodiments, these cells improve or reverse the symptoms of neurodegenerative diseases when implanted. In some embodiments, the neurodegenerative disease is Parkinson's disease. In some embodiments, these cells improve Parkinson's disease symptoms when implanted in the substantia nigra of a subject (e.g., a patient) in need thereof.

[0364] D.Gene expression level

[0365] In some embodiments, the gene expression level of any test cell or reference cell colony described herein is determined based on the level of the gene product synthesized by the information of one or more genes. In some embodiments, the gene product is any biomolecule assembled, generated and / or synthesized with the information of the gene encoding, and may include polynucleotides and / or polypeptides. In some embodiments, assessing, measuring and / or determining gene expression includes determining or measuring the level, amount or concentration of the gene product. In some embodiments, the level, amount or concentration of the gene product can be converted (e.g., normalized) or directly analyzed (e.g., original).

[0366] In some embodiments, gene products include proteins encoded and / or expressed by genes, i.e., polypeptides. In specific embodiments, gene product encoding is confined and / or exposed to the cell surface protein. In some embodiments, protein is soluble protein. In certain embodiments, protein is secreted by cells. In specific embodiments, gene expression is the amount, level and / or concentration of proteins encoded by genes. In certain embodiments, one or more protein gene products are measured by any suitable method. Suitable methods for assessing, measuring, determining and / or quantifying the level, amount or concentration of one or more protein gene products include immunoassays, nucleic acid-based or protein-based aptamer technology, HPLC (high-precision liquid chromatography), peptide sequencing (such as Edman degradation sequencing or mass spectrometry (e.g., MS / MS), optionally in combination with HPLC), and any of the aforementioned microarray adaptations (including nucleic acid, antibody or protein-protein (i.e., non-antibody) arrays) for detection. In some embodiments, immunoassays are or include methods or assays based on immune response, such as detection of proteins by detecting antibodies or antigen-binding antibody fragments and gene products. Immunoassays include quantitative immunocytochemistry or immunohistochemistry, ELISA (including direct, indirect, sandwich, competitive, multiplex and portable ELISA (see, e.g., U.S. Pat. No. 7,510,687), Western blot (including one-, two- or higher-dimensional blots or other chromatographic means, optionally including peptide sequencing), enzyme immunoassay (EIA), RIA (radioimmunoassay) and SPR (surface plasmon resonance).

[0367] In certain embodiments, gene product is a polynucleotide (e.g., mRNA) or protein encoded by a gene. In some embodiments, gene product is a polynucleotide expressed and / or encoded by a gene. In certain embodiments, polynucleotide is RNA. In some embodiments, gene product is messenger RNA (mRNA), transfer RNA (tRNA), ribosomal RNA, small nuclear RNA, small nucleolar RNA, antisense RNA, long non-coding RNA, microRNA, Piwi interaction RNA, small interfering RNA and / or short hairpin RNA. In specific embodiments, gene product is mRNA.

[0368] In a specific embodiment, the amount or level of the assessing, measuring, determining and / or quantifying RNA gene product comprises the step of generating, polymerizing and / or deriving cDNA polynucleotides and / or cDNA oligonucleotides from the RNA gene product. In certain embodiments, the RNA gene product is assessed, measured, determined and / or quantified by directly assessing, measuring, determining and / or quantifying cDNA polynucleotides and / or cDNA oligonucleotides derived from the RNA gene product.

[0369] In certain embodiments, the amount or level of a polynucleotide in a sample can be assessed, measured, determined, and / or quantified by any suitable method. For example, in some embodiments, the amount or level of a polynucleotide gene product can be assessed, measured, determined, and / or quantified by polymerase chain reaction (PCR), including reverse transcriptase (rt) PCR, droplet digital PCR, and real-time and quantitative PCR (qPCR) methods (including, for example, Molecular beacons, LIGHTUP TM SCORPION TM , See, e.g., U.S. Pat. Nos. 5,538,848; 5,925,517; 6,174,670; 6,329,144; 6,326,145 and 6,635,427); northern blotting; e.g., Southern blotting of reverse transcription products and derivatives; array-based methods, including blot arrays, microarrays, or in situ synthesis arrays; and sequencing, e.g., sequencing by synthesis, pyrosequencing, dideoxy sequencing, or sequencing by ligation, or other methods, such as those discussed in Shendure et al., Nat. Rev. Genet. 5:335-44 (2004) or Nowrousian, Euk. Cell 9(9):1300-1310 (2010), including methods such as 454, ABI and In certain embodiments, the level of nucleic acid gene products is measured by quantitative PCR (qPCR) methods, such as qRT-PCR. In some embodiments, qRT-PCR uses three sets of nucleic acids per gene, where the three nucleic acids include a primer pair and a probe that binds between the target nucleic acid regions to which the primers bind - commercially known as Determination method.

[0370] In certain embodiments, the expression of two or more genes is measured or evaluated simultaneously. In certain embodiments, multiplex PCR, such as multiplex rt-PCR evaluation or multiplex quantitative PCR (qPCR), is used to measure, determine and / or quantify the level, amount or concentration of two or more gene products. In some embodiments, a microarray (e.g. and In some embodiments, microarrays are used to assess, measure, determine and / or quantify the level, amount or concentration of two or more gene products. In some embodiments, microarrays are used to assess, measure, determine and / or quantify the level, amount or concentration of cDNA polynucleotides derived from RNA gene products. In some embodiments, the expression of one or more gene products (e.g., polynucleotide gene products) is determined by sequencing gene products and / or by sequencing cDNA polynucleotides derived from gene products. In some embodiments, sequencing is performed by non-Sanger sequencing methods and / or next generation sequencing (NGS) technology. The example of next generation sequencing technology includes massively parallel signature sequencing (MPSS), Polony sequencing, pyrophosphate sequencing, reversible dye terminator sequencing, SOLiD sequencing, ion semiconductor sequencing, DNA nanoball sequencing, Helioscope single molecule sequencing, single molecule real-time (SMRT) sequencing, single molecule real-time (RNAP) sequencing and nanopore DNA sequencing.

[0371] In some embodiments, NGS technology is RNA sequencing (RNA-Seq). In specific embodiments, the expression of one or more polynucleotide gene products is measured, determined and / or quantified by RNA-Seq. RNA-Seq, also known as whole transcriptome shotgun sequencing, determines the presence and quantity of RNA in a sample. RNA sequencing methods have been adapted to the most common DNA sequencing platforms [such as HiSeq system (Illumina), 454 genome sequencer FLX system (Roche), Applied Biosystems SOLiD (Life Technologies) and IonTorrent (Life Technologies)]. These platforms require RNA to be initially reverse transcribed into cDNA. In contrast, the single-molecule sequencer HeliScope (Helicos BioSciences) can use RNA as a template for sequencing. The principle verification of direct RNA sequencing on the PacBio RS platform has also been demonstrated (PacificBioscience). In some embodiments, one or more RNA gene products are evaluated, measured, determined and / or quantified by RNA-seq. In some embodiments, RNA-seq is RNA-seq based on tags. In tag-based methods, each transcript is represented by a unique tag. Initially, the tag-based method was developed as a sequence-based method for measuring transcript abundance and identifying differentially expressed genes, assuming that the number (count) of tags directly corresponds to the abundance of mRNA molecules. The complexity of obtaining sample reduction by sequencing a limited area is crucial for making the Sanger-based method affordable. When NGS technology becomes available, a large number of reads that can be generated promote the analysis of differential gene expression. In the tag-based method, the quantitative transcript length deviation of the gene expression level observed for the shotgun method will not be encountered. By definition, all tag-based methods are chain-specific. In a specific embodiment, one or more RNA gene products are evaluated, measured, determined and / or quantitatively measured by RNA-seq based on tags.

[0372] In some embodiments, RNA-seq is shotgun RNA-seq. Many protocols have been described for shotgun RNA-seq, but they share many common steps: fragmentation (which can occur at the RNA level or the cDNA level), conversion of RNA to cDNA (performed by oligo dT or random primers), second strand synthesis, ligation of adapter sequences at the 3' and 5' ends (at the RNA or DNA level), and final amplification. In some embodiments, RNA-seq can focus only on polyadenylated RNA molecules (mainly mRNA, but also some lncRNA, snoRNA, pseudogenes, and histones) if poly(A)+ RNA is selected before fragmentation, or can also include non-polyadenylated RNA if no selection is performed. In the latter case, ribosomal RNA (more than 80% of the total RNA pool) needs to be depleted before fragmentation. Therefore, it is clear that differences in capturing the mRNA portion of the transcriptome result in partial overlap in the detected transcript types. Moreover, different protocols can affect the abundance and distribution of sequencing reads. This makes it difficult to compare the results of experiments performed with different library preparation protocols.

[0373] In some embodiments, RNA is obtained from each sample, fragmented and used to generate complementary DNA (cDNA) samples, such as cDNA libraries for sequencing. Reads can be processed and compared with the human genome, and the expected mapping number of each gene / isotype is estimated and used to determine the read count. In some embodiments, read counts are normalized according to the length of gene / isotype and the number of reads in the library, to obtain, for example, the FPKM normalized according to the length of gene / isotype and the number of reads in the library, to obtain the number of fragments (FPKM) of exons per thousand bases in every million mapped reads according to gene length and total mapping reads. In some aspects, normalization between samples is realized by normalization, such as the 75th quantile normalization, wherein each sample is scaled by the median value of the 75th quantile from all samples, for example, to obtain quantile-normalized FPKM (FPKQ) values. FPKQ values ​​can be logarithmically converted (log2).

[0374] In some embodiments, RNA is obtained from each sample, fragmented and used to generate complementary DNA (cDNA) samples, such as cDNA libraries for sequencing. The reads can be processed and compared with the human genome, and the expected mapping number of each gene / isoform is estimated and used to determine the read count. In some embodiments, the read count is normalized according to the length of the gene / isoform and the number of reads in the library. In some embodiments, the read count is provided in the form of counts per million (CPM). In some embodiments, the CPM read count is logarithmically converted (e.g., log2).

[0375] In some embodiments, relative gene expression is measured by comparing the CPM of the target gene to the CPM of a housekeeping gene. In some embodiments, the housekeeping gene is GAPDH. In some embodiments, the relative gene expression of the target gene is determined as the CPM of the target gene compared to the CPM of a housekeeping gene (e.g.

[0376] GAPDH) CPM ratio.

[0377] In some embodiments, gene expression levels are obtained using microarray analysis. In some embodiments, gene expression levels are obtained using RNA sequencing. In some embodiments, gene expression levels are obtained using both microarray analysis and RNA sequencing. In some embodiments, RNA sequencing is performed on a large amount of RNA from multiple cells. In some embodiments, a large amount of RNA sequencing data is obtained from the combined RNA of multiple cells. In some embodiments, RNA sequencing is performed on a single cell. In some embodiments, RNA sequencing is performed on a large amount of RNA from multiple cells and a single cell.

[0378] Any suitable method for obtaining a large amount of RNA sequencing data can be used (e.g., see Chao et al., 2019, BMC Genomics 20: 571, which is incorporated herein by reference in its entirety). For example, TRIZOL can be used to separate total RNA from a sample (e.g., a plurality of cells from a cell population), treated with DNase I and purified. Before the preparation of a library of total RNA or mRNA, the concentration and quality of the isolated RNA can be measured and checked. For library preparation, total RNA or mRNA can be fragmented and converted to cDNA using reverse transcription. After double-stranded cDNA construction, amplification, and optional barcoding, any suitable library preparation technology, sequencing platform, and genome alignment tool can be used to process the library for next-generation sequencing.

[0379] In some embodiments, single cell RNA sequencing is used to obtain gene expression levels. In some embodiments, the use of single cell RNA sequencing data provides certain advantages. In some embodiments, the use of single cell RNA sequencing data allows characterization of cell subpopulations, such as directional dopaminergic cells in larger cell populations. In some embodiments, the use of single cell RNA sequencing data reduces the number of cells required for use in the method provided herein, for example, reduces the number of cells required for obtaining data for training machine learning models. In some embodiments, the use of single cell RNA sequencing data improves the characterization of biological variability across cells. In some embodiments, the use of single cell RNA sequencing data allows easier verification and interpretation of gene expression levels.

[0380] Any suitable method for single-cell RNA sequencing can be used (e.g., see Zheng et al., 2017 (Nature Communications 8:14049) and Haque et al., 2017 (Genome Medicine 9:75, which is incorporated herein by reference in its entirety). For single RNA sequencing, single cells can be isolated from samples (e.g., in vitro cell colonies) using flow cytometry cell sorting, microfluidic platforms, or droplet-based methods. The isolated cells are lysed to allow capture of RNA molecules. Poly[T] primers can be specifically used to analyze polyadenylated mRNA molecules, and reverse transcription is used to convert the initiated mRNA molecules into cDNA. In some cases, unique molecular identifiers can be used to mark single mRNA molecules based on cell origin. The cDNA library can then be amplified, optionally barcoded, and sequenced, such as using next generation sequencing (NGS) and library preparation technology, sequencing platforms, and genome alignment tools (similar to those for a large number of RNA samples). In some cases, unbiased cell type classification within a mixed population of different cell types can be achieved with as few as 10,000 to 50,000 reads / cells, and single cell libraries from various common schemes can be close to saturation when sequenced to a depth of 1,000,000 reads.

[0381] In some embodiments, the gene expression level comprises a large amount of RNA sequencing data and single cell RNA sequencing data. In some embodiments, a large amount of RNA sequencing data and single cell RNA sequencing data are obtained from the same cell population. In some embodiments, the single cell RNA sequencing data can be used to estimate a large amount of RNA sequencing data obtained from the same cell population. In some embodiments, the estimated large amount of RNA sequencing data is obtained by averaging the single cell RNA sequencing data from cells in the same cell population. In some embodiments, the gene expression level comprises a large amount of RNA sequencing data estimated.

[0382] III. Computing Devices

[0383] In some embodiments, provided herein are computational devices for classifying the differentiation state of an in vitro cell population. In some embodiments, provided computational devices are used to identify an in vitro cell population having a desired differentiation state.

[0384] In some embodiments, the computing device includes a memory including a first reference data set and a second reference data set. Exemplary first reference data sets and second reference data sets are described in Section II-A. In some embodiments, the first reference data set and the second reference data set are any reference data sets as described in Section II-A.

[0385] In some embodiments, the memory further comprises one or more additional reference data sets. In some embodiments, the one or more additional reference data sets comprise any one of the first reference data set and the second reference data set described in Section II-A.

[0386] In some embodiments, the memory further comprises a control data set. Exemplary control data sets are described in Section II-A. In some embodiments, the control data set is any data set as described in Section II-A.

[0387] In some embodiments, the computing device includes an instruction stored in a memory for performing any of the methods provided. In some embodiments, the computing device also includes a processor that implements the instruction stored in the memory. In some embodiments, the processor includes one or more processing elements communicated with a system data repository (SDS) including one or more storage elements. In some embodiments, the processor includes one or more processing elements, such as CELERON, PENTIUM, XEON, CORE 2DUO or CORE 2QUAD level microprocessors (Intel Corp., SantaClara, Calif.), or SEMPRON, PHENM, OPTERON, ATHLON X2 or ATHLON 64X2 (AMD Corp., Sunnyvale, Calif.), but other general-purpose processors may also be used. In some embodiments, the functionality may be distributed across multiple processing elements. The term processing element may refer to (1) a process running on a specific piece of hardware or across multiple specific pieces of hardware, (2) a specific piece of hardware, or any one of (1) or (2) (as the context allows). Some implementations may include one or more limited special-purpose processors, such as a digital signal processor (DSP), an application-specific integrated circuit (ASIC), or a field-programmable gate array (FPGA). In addition, some implementations may use a combination of general-purpose and special-purpose processors.

[0388] In some embodiments, the computing device includes one or more input devices for receiving input from a user and / or a software application. In some embodiments, the input includes a test data set. Exemplary test data sets are described in Section II-A. In some embodiments, the test data set is any data set as described in Section II-A.

[0389] In some embodiments, the computing device includes one or more output devices for presenting output to a user and / or software application. In some embodiments, the output device presents the output of any of the provided methods. In some embodiments, the output device includes a monitor capable of displaying a graphical representation of the output to the user.

[0390] In some embodiments, the computing device also includes an SDS, which may include various primary storage elements and auxiliary storage elements. In a specific implementation, the SDS will include registers and RAM as part of the primary storage device. In some specific implementations, the primary storage device may include other forms of memory, such as cache memory or non-volatile memory (e.g., FLASH, ROM or EPROM). The SDS may also include an auxiliary storage device, including a single, multiple and / or different server and storage element. For example, the SDS may use an internal storage device connected to a system processor. In a specific implementation where a single processing element supports all functionality, a local hard drive may be used as an auxiliary storage device of the SDS, and a disk operating system executed on such a single processing element may be used as a data server for receiving and serving data requests.

[0391] Those skilled in the art will appreciate that different information used in the systems and methods disclosed herein may be logically or physically isolated within a single device used as a secondary storage device for an SDS; multiple related data repositories accessible through a unified management system, which together serve as an SDS; or multiple independent data repositories individually accessible through different management systems, which in some implementations may be collectively considered an SDS. The various storage elements comprising the physical architecture of the SDS may be centrally located or distributed across a variety of different locations.

[0392] In addition or alternatively, the functionality and methods discussed above, or portions thereof, may be embodied in instructions executable by a computer, wherein such instructions are stored in and / or on one or more computer-readable storage media. Such media may include primary storage and / or secondary storage devices, such as RAM and / or disks, integrated with and / or within the computer, and / or may be separable from the computer, such as on a solid-state device or a removable disk or optical disk. The medium may use any technology, including ROM, RAM, magnetic, optical, paper, and / or solid-state media technology. In some embodiments, the computing device may be a multi-purpose machine having modules and / or components dedicated to performing the disclosed methods.

[0393] IV. Compositions and Formulations

[0394] In some embodiments, provided herein are pharmaceutical compositions containing a cell population that includes a population of cells (e.g., stem cell-derived cells) identified as having a desired differentiation state by any of the provided methods, such as any of the methods described in Section II.

[0395] In some embodiments, the cells in the therapeutic compositions provided include stem cell-derived neuronal cells. In some embodiments, stem cell-derived neuronal cells are suitable for treating neurodegenerative diseases when implanted in the brain of a subject in need of such treatment. In some embodiments, the cells in the therapeutic compositions provided include directed dopaminergic (DA) neuronal cells. In some embodiments, the cells in the therapeutic compositions provided are stem cell-derived neuronal cells that are capable of colonizing in a brain region after implantation.

[0396] In some embodiments, the cell in the composition is an in vitro stem cell-derived neuronal cell colony. In some embodiments, the in vitro stem cell-derived neuronal cell colony is characterized by expressing cells of one or more genes selected from the group consisting of the following items: CCNB2, AURKB, PTTG1, TOP2A, NEUROG2, HES1, REST, E2F4, FOXM1, SIN3A, NFYA, LIN28A, FLRT3, ITGA5, NES, SOX2, SOX9 and RFX4. In some embodiments, the cells in the colony are characterized by expressing only one gene in the above-mentioned genes. In some embodiments, the cells in the colony are characterized by expressing 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17 or 18 genes in the above-mentioned genes. In some embodiments, at least one of the one or more genes is REST.

[0397] In some embodiments, at least 50% of cells in an in vitro stem cell-derived neuronal cell population express the one or more genes. In some embodiments, at least 60% of cells in an in vitro stem cell-derived neuronal cell population express the one or more genes. In some embodiments, at least 70% of cells in an in vitro stem cell-derived neuronal cell population express the one or more genes. In some embodiments, at least 80% of cells in an in vitro stem cell-derived neuronal cell population express the one or more genes. In some embodiments, at least 90% of cells in an in vitro stem cell-derived neuronal cell population express the one or more genes.

[0398] In some aspects, the expression of the one or more genes is RNA expression. In some embodiments, RNA expression is measured by RNA sequencing. In other aspects, the expression of the one or more genes is protein expression.

[0399] In some embodiments, the cell populations in provided compositions have been differentiated in vitro from pluripotent stem cells (PSCs). The differentiation can be performed by any of the methods described in Section C. In certain embodiments, the method involves differentiating iPSCs into neuronal progenitor cells, including for the generation of directed dopaminergic neurons.

[0400] In some embodiments of any embodiment, the one or more genes are genes that are overexpressed in the cells of the colony compared with iPSC. In some embodiments, the one or more genes are genes that are overexpressed in the cells of the colony compared with the cells of the precursor colony differentiated from iPSC. For example, in some embodiments, the one or more genes are genes that are overexpressed compared with the cells of the cell precursor colony in the differentiation stage before the cell is or may be suspected to be a directed dopaminergic neuron. For example, the precursor colony of the cell can be the 13th day cell of the dopaminergic differentiation scheme as described herein. In some embodiments, the one or more genes are genes that are overexpressed in the cells of the colony compared with the cells of the mature stereotyped dopaminergic neuron cell colony differentiated from iPSC. For example, in some embodiments, the one or more genes are genes that are overexpressed compared with the mature or stereotyped cell colony in the differentiation stage before the cell is or may be suspected to be a directed dopaminergic neuron. For example, mature stereotyped cells can be the 25th day cells of the dopaminergic differentiation scheme as described herein. In some embodiments, mature stereotyped dopaminergic neuron cells express LMX1A and / or NR4A2 (NURR1). In some embodiments, at least 40%, at least 50%, at least 60%, at least 70%, or at least 80% of the cells in a population of committed dopaminergic neuronal cells express LMX1A and / or NR4A2. In some embodiments, overexpression is a positive log2 fold change of greater than or greater than about 1.5, 2.0, 3.0, 4.0, or 5 times.

[0401] In some embodiments of any embodiment of the in vitro stem cell derived neuron cell colony, the one or more genes are genes that are expressed reduced in the cells of the colony compared with iPSC. In some embodiments, one or more genes are genes that are expressed reduced in the cells of the colony compared with the cells of the precursor colony differentiated from iPSC. For example, in some embodiments, the one or more genes are genes that are expressed reduced compared with the cells of the cell precursor colony in the differentiation stage before the cell is or may be suspected to be the oriented dopaminergic neuron. For example, the precursor colony of the cell can be the 13th day cell of the dopaminergic differentiation scheme as described herein. In some embodiments, the one or more genes are genes that are expressed reduced in the cells of the colony compared with the cells of the mature stereotyped dopaminergic neuron cell colony differentiated from iPSC. For example, in some embodiments, the one or more genes are genes that are expressed reduced compared with the mature or stereotyped cell colony in the differentiation stage before the cell is or may be suspected to be the oriented dopaminergic neuron. For example, mature stereotyped cells can be the 25th day cells of the dopaminergic differentiation scheme as described herein. In some embodiments, mature committed dopaminergic neuron cells express LMX1A and / or NR4A2 (NURR1). In some embodiments, at least 40%, at least 50%, at least 60%, at least 70%, or at least 80% of cells in a population of committed dopaminergic neuron cells express LMX1A and / or NR4A2. In some embodiments, the reduced expression is a negative log2 fold change of greater than or greater than about 1.5 times, 2.0 times, 3.0 times, 4.0 times, or 5 times.

[0402] In some embodiments of any of the embodiments of the in vitro stem cell-derived neuronal cell population, less than 30%, less than 20%, or less than 10% of the cells in the population express LMX1A and / or NR4A2.

[0403] In some embodiments of any embodiment of the in vitro stem cell derived neuron cell colony, the cells in the colony can be colonized and dominate other cells in vivo. In some embodiments, the cells in the colony can show neurite outgrowth when administered to the brain of the subject. In some embodiments, the cells in the colony can produce dopamine. In some embodiments, the cells in the colony do not produce or substantially do not produce norepinephrine.

[0404] In some embodiments, the cells in the provided therapeutic compositions are capable of producing dopamine (DA). In some embodiments, the cells in the provided therapeutic compositions do not produce or do not substantially produce norepinephrine (NE). Thus, in some embodiments, the cells in the provided therapeutic compositions are capable of producing DA, but do not produce or do not substantially produce NE.

[0405] In some embodiments, the committed DA neuron cells express EN1. In some embodiments, at least about 20%, at least about 25%, at least about 30%, at least about 35%, at least about 40%, at least about 45%, at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, or at least about 80% of the total cells in the therapeutic composition express EN1. In some embodiments, at least about 20% of the cells in the therapeutic composition express EN1. In some embodiments, at least about 25% of the cells in the therapeutic composition express EN1. In some embodiments, at least about 30% of the cells in the therapeutic composition express EN1. In some embodiments, at least about 35% of the cells in the therapeutic composition express EN1. In some embodiments, at least about 40% of the cells in the therapeutic composition express EN1. In some embodiments, at least about 45% of the cells in the therapeutic composition express EN1. In some embodiments, at least about 50% of the cells in the therapeutic composition express EN1. In some embodiments, at least about 55% of the cells in the therapeutic composition express EN1. In some embodiments, at least about 60% of the cells in the therapeutic composition express EN1. In some embodiments, at least about 65% of the cells in the therapeutic composition express EN1. In some embodiments, at least about 70% of the cells in the therapeutic composition express EN1. In some embodiments, at least about 75% of the cells in the therapeutic composition express EN1. In some embodiments, at least about 80% of the cells in the therapeutic composition express EN1.

[0406] In some embodiments, the therapeutic composition exhibits a -4 In some embodiments, the ratio of CPM EN1 to CPM GAPDH is between about 1.5×10 -3 Up to 1×10 -2 between.

[0407] In some embodiments, the committed DA neuron cells express CORIN. In some embodiments, at least about 20%, at least about 25%, at least about 30%, at least about 35%, at least about 40%, at least about 45%, at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, or at least about 80% of the total cells in the therapeutic composition express CORIN. In some embodiments, at least about 20% of the cells in the therapeutic composition express CORIN. In some embodiments, at least about 25% of the cells in the therapeutic composition express CORIN. In some embodiments, at least about 30% of the cells in the therapeutic composition express CORIN. In some embodiments, at least about 35% of the cells in the therapeutic composition express CORIN. In some embodiments, at least about 40% of the cells in the therapeutic composition express CORIN. In some embodiments, at least about 45% of the cells in the therapeutic composition express CORIN. In some embodiments, at least about 50% of the cells in the therapeutic composition express CORIN. In some embodiments, at least about 55% of the cells in the therapeutic composition express CORIN. In some embodiments, at least about 60% of the cells in the therapeutic composition express CORIN. In some embodiments, at least about 65% of the cells in the therapeutic composition express CORIN. In some embodiments, at least about 70% of the cells in the therapeutic composition express CORIN. In some embodiments, at least about 75% of the cells in the therapeutic composition express CORIN. In some embodiments, at least about 80% of the cells in the therapeutic composition express CORIN.

[0408] In some embodiments, the therapeutic composition exhibits a -4 In some embodiments, the ratio of CPM CORIN to CPM GAPDH is between about 5×10 -2 Up to 5×10 -1 between.

[0409] In some embodiments, the committed DA neuron cells express EN1 and CORIN. In some embodiments, at least about 20%, at least about 25%, at least about 30%, at least about 35%, at least about 40%, at least about 45%, at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, or at least about 80% of the total cells in the therapeutic composition express EN1 and CORIN. In some embodiments, at least about 20% of the cells in the therapeutic composition express EN1 and CORIN. In some embodiments, at least about 25% of the cells in the therapeutic composition express EN1 and CORIN. In some embodiments, at least about 30% of the cells in the therapeutic composition express EN1 and CORIN. In some embodiments, at least about 35% of the cells in the therapeutic composition express EN1 and CORIN. In some embodiments, at least about 40% of the cells in the therapeutic composition express EN1 and CORIN. In some embodiments, at least about 45% of the cells in the therapeutic composition express EN1 and CORIN. In some embodiments, at least about 50% of the cells in the therapeutic composition express EN1 and CORIN. In some embodiments, at least about 55% of the cells in the therapeutic composition express EN1 and CORIN. In some embodiments, at least about 60% of the cells in the therapeutic composition express EN1 and CORIN. In some embodiments, at least about 65% of the cells in the therapeutic composition express EN1 and CORIN. In some embodiments, at least about 70% of the cells in the therapeutic composition express EN1 and CORIN. In some embodiments, at least about 75% of the cells in the therapeutic composition express EN1 and CORIN. In some embodiments, at least about 80% of the cells in the therapeutic composition express EN1 and CORIN.

[0410] In some embodiments, the therapeutic composition exhibits (a) greater than about 1×10 -4 10-10 counts per million (CPM) EN1 to CPM GAPDH ratio; and (b) greater than about 2×10 -2 In some embodiments, the ratio of CPM EN1 to CPM GAPDH is between about 1.5×10 -3 Up to 1×10 -2 and the ratio of CPM CORIN to CPMGAPDH is between about 5×10 -2 Up to 5×10 -1 between.

[0411] In some embodiments, less than 10% of the committed DA neuron cells express TH. In some embodiments, committed DA neuron cells express low levels of TH. In some embodiments, committed DA neuron cells do not express TH. In some embodiments, committed DA neuron cells express TH at a lower level than cells harvested or collected on other days. In some embodiments, some committed DA neuron cells express EN1 and CORIN, and less than 10% of the committed DA neuron cells express TH. In some embodiments, less than 8% of the committed DA neuron cells express TH. In some embodiments, less than 5% of the committed DA neuron cells express TH.

[0412] In some embodiments, about 2% to 10%, about 2% to 8%, about 2% to 6%, about 2% to 4%, about 4% to 10%, about 4% to 8%, about 4% to 6%, about 6% to 10%, about 6% to 8%, or about 8% to 10% of the total cells in the therapeutic composition express TH.

[0413] In some embodiments, the therapeutic composition exhibits less than about 3×10 -2 In some embodiments, the ratio of CPM TH to CPM GAPDH is between about 1×10 -3 Up to 2.5×10 -2 between.

[0414] In some embodiments, less than 10% of the total cells in the therapeutic composition express TH, and at least about 20% of the cells in the therapeutic composition express EN1. In some embodiments, less than 10% of the total cells in the therapeutic composition express TH, and at least about 25% of the cells in the therapeutic composition express EN1. In some embodiments, less than 10% of the total cells in the therapeutic composition express TH, and at least about 30% of the cells in the therapeutic composition express EN1. In some embodiments, less than 10% of the total cells in the therapeutic composition express TH, and at least about 35% of the cells in the therapeutic composition express EN1. In some embodiments, less than 10% of the total cells in the therapeutic composition express TH, and at least about 40% of the cells in the therapeutic composition express EN1. In some embodiments, less than 10% of the total cells in the therapeutic composition express TH, and at least about 45% of the cells in the therapeutic composition express EN1. In some embodiments, less than 10% of the total cells in the therapeutic composition express TH, and at least about 50% of the cells in the therapeutic composition express EN1. In some embodiments, less than 10% of the total cells in the therapeutic composition express TH, and at least about 55% of the cells in the therapeutic composition express EN1. In some embodiments, less than 10% of the total cells in the therapeutic composition express TH, and at least about 60% of the cells in the therapeutic composition express EN1. In some embodiments, less than 10% of the total cells in the therapeutic composition express TH, and at least about 65% of the cells in the therapeutic composition express EN1. In some embodiments, less than 10% of the total cells in the therapeutic composition express TH, and at least about 70% of the cells in the therapeutic composition express EN1. In some embodiments, less than 10% of the total cells in the therapeutic composition express TH, and at least about 75% of the cells in the therapeutic composition express EN1. In some embodiments, less than 10% of the total cells in the therapeutic composition express TH, and at least about 80% of the cells in the therapeutic composition express EN1.

[0415] In some embodiments, less than 10% of the total cells in the therapeutic composition express TH, and at least about 20% of the cells in the therapeutic composition express CORIN. In some embodiments, less than 10% of the total cells in the therapeutic composition express TH, and at least about 25% of the cells in the therapeutic composition express CORIN. In some embodiments, less than 10% of the total cells in the therapeutic composition express TH, and at least about 30% of the cells in the therapeutic composition express CORIN. In some embodiments, less than 10% of the total cells in the therapeutic composition express TH, and at least about 35% of the cells in the therapeutic composition express CORIN. In some embodiments, less than 10% of the total cells in the therapeutic composition express TH, and at least about 40% of the cells in the therapeutic composition express CORIN. In some embodiments, less than 10% of the total cells in the therapeutic ...

Claims

1. A computing device for classifying a differentiation state of an in vitro cell population, the device comprising a memory, the memory comprising: a first reference data set comprising representations of gene expression levels of one or more genes differentially expressed between cells in a first differentiation state and cells in a second differentiation state; as well as A second reference data set comprising representations of gene expression levels of one or more genes differentially expressed between cells in the second differentiation state and cells in a third differentiation state.

2. The computing device of claim 1 , further comprising a processor that implements instructions stored in the memory to perform a method, the method comprising: (a) receiving as input a test dataset comprising expression levels of genes expressed in one or more test cells contained in an in vitro cell population, wherein the expression levels in the test dataset comprise expression levels of: (i) one or more genes among the genes whose representations of expression levels are included in the first reference dataset, and (ii) one or more genes among the genes whose representations of expression levels are included in the second reference dataset; (b) calculating a first similarity score indicating whether the differentiation state of the test cell is more similar to the first differentiation state or more similar to the second differentiation state using the test data set and the first reference data set; (c) calculating a second similarity score indicating whether the differentiation state of the test cell is more similar to the second differentiation state or more similar to the third differentiation state using the test data set and the second reference data set; as well as (d) classifying the differentiation state of the one or more test cells based on one or both of the first similarity score and the second similarity score.

3. A computing device according to claim 1 or claim 2, wherein the memory also includes a control data set, the control data set including a representation of the gene expression level of one or more genes expressed in cells in one or more control differentiation states, and the control differentiation state may be the same or different from one of the first differentiation state, the second differentiation state, or the third differentiation state.

4. The computing device of claim 3, wherein: The test data set includes gene expression levels of one or more genes among the genes included in the control data set, which are represented by expression levels; The instructions include calculating a degree of correlation between the representation of gene expression levels of one or more genes in the control dataset and the gene expression levels of the one or more genes in the test dataset to calculate a correlation score; and The classification of the differentiation state of the one or more test cells is based on the relevance score and one or both of the first similarity score and the second similarity score.

5. The computing device of claim 4, wherein the relevance score is calculated before calculating the first similarity score and the second similarity score, and the method is terminated if the relevance score of the test cell does not meet a predefined cutoff value.

6. A computing device according to any one of claims 3 to 5, wherein the control data set includes gene expression levels, which are normalized by counts per million mapped reads (CPM) and filtered to include only gene expression levels exceeding a threshold CPM value.

7. The computing device of any one of claims 3 to 6, wherein the control data set comprises the centroid of the gene expression levels of the one or more genes in the control data set.

8. A computing device according to claim 7, wherein the correlation score is calculated by normalizing the gene expression levels of the one or more genes in the test data set and calculating the correlation between the gene expression levels of the one or more genes in the test data set and the centroid.

9. A computing device according to claim 8, wherein the control data set includes coefficient of variation (CV) values ​​of gene expression levels of the one or more genes in the control data set, and the correlation with the centroid is weighted by the inverse of the CV value.

10. The computing device of any one of claims 1 to 9, wherein the cell population is from a cell culture differentiated from pluripotent cells subjected to suitable differentiation conditions.

11. The computing device of any one of claims 1 to 10, wherein the first differentiation state is earlier in the stem cell differentiation pathway than the second differentiation state.

12. The computing device of any one of claims 1 to 11, wherein the second differentiation state is earlier in the stem cell differentiation pathway than the third differentiation state.

13. The computing device of any one of claims 1 to 10, wherein the first differentiation state is in a cell differentiation pathway that is parallel to a cell differentiation pathway of the second differentiation state.

14. A computing device according to any one of claims 1 to 13, wherein the cell population is selected from the group consisting of: stem cell-derived cardiomyocytes, stem cell-derived skeletal muscle cells, stem cell-derived tubular cells, stem cell-derived erythrocytes, stem cell-derived smooth muscle cells, stem cell-derived lung cells, stem cell-derived thyroid cells, stem cell-derived pancreatic cells, stem cell-derived epidermal cells, stem cell-derived pigment cells, and stem cell-derived neuronal cells.

15. The computing device of any one of claims 1 to 14, wherein the cell population is stem cell-derived neuronal cells.

16. The computing device of any one of claims 1 to 15, wherein the second differentiation state is a differentiation state of committed dopaminergic neuronal cells.

17. The computing device of any one of claims 1 to 15, wherein the second differentiation state is a differentiation state of a hematopoietic progenitor cell.

18. The computing device of any one of claims 1 to 17, wherein the first reference data set comprises a representation of gene expression levels of one or more genes selected from Table E1.

19. A computing device according to any one of claims 1 to 18, wherein the second reference data set comprises a representation of gene expression levels of one or more genes selected from Table E2.

20. The computing device of any one of claims 1 to 19, wherein the first reference data set comprises a representation of gene expression levels of at least 20 genes selected from Table E1.

21. The computing device of any one of claims 1 to 20, wherein the second reference data set comprises a representation of gene expression levels of at least 20 genes selected from Table E2.

22. The computing device of any one of claims 1 to 21, wherein the first reference data set comprises a representation of gene expression levels of at least 50 genes selected from Table E1.

23. A computing device according to any one of claims 1 to 22, wherein the second reference data set comprises a representation of gene expression levels of at least 50 genes selected from Table E2.

24. The computing device of any one of claims 1 to 23, wherein at least one of the first differentiation state, the second differentiation state, and the third differentiation state is characterized using an in vitro assay.

25. The computing device of any one of claims 1 to 24, wherein at least one of the first differentiation state, the second differentiation state, and the third differentiation state is characterized using an in vivo assay.

26. The computing device of claim 25, wherein the in vivo assay comprises determining whether a reference cell is able to survive, colonize, and / or innervate a tissue when administered to an animal or human subject.

27. The computing device of claim 25 or claim 26, wherein the in vivo assay comprises determining whether the reference cells improve or reverse symptoms of a neurodegenerative disease when implanted into an animal or human subject.

28. The computing device of claim 26 or claim 27, wherein the animal subject comprises an animal model of Parkinson's disease.

29. The computing device of any one of claims 1 to 28, wherein the memory further comprises one or more additional reference data sets, wherein each of the additional reference data sets comprises a representation of gene expression levels of one or more genes differentially expressed between cells in the second differentiation state and cells in additional differentiation states, wherein: The processor implements instructions to calculate one or more additional similarity scores indicating whether the differentiation state of the test cell is more similar to the second differentiation state or more similar to one of the one or more additional differentiation states using the additional reference data set, and The classification of the differentiation state of the one or more test cells is based on the first similarity score, the second similarity score, and the one or more additional similarity scores.

30. The computing device of any one of claims 1 to 29, wherein the representation of gene expression levels in the first reference dataset and / or the second reference dataset is obtained using machine learning.

31. A computing device according to claim 30, wherein the machine learning includes principal component analysis.

32. A computing device according to any one of claims 1 to 29, wherein the representation of gene expression levels in the first reference data set and / or the second reference data set comprises normalized gene expression levels.

33. A computing device according to any one of claims 1 to 32, wherein if one or both of the first similarity score and the second similarity score indicate that the differentiation state of the one or more test cells is more similar to the second differentiation state, the differentiation state of the one or more test cells is classified as being in the second differentiation state.

34. A method for selecting a cell population having a desired differentiation state, the method comprising: (a) calculating a first similarity score using a test dataset and a first reference dataset, wherein: the first reference data set comprising a representation of gene expression levels of one or more genes differentially expressed between cells in a first differentiation state and cells in a second differentiation state, The test dataset comprises expression levels of genes expressed in one or more test cells contained in an in vitro cell population, wherein the expression levels in the test dataset comprise expression levels of one or more genes among the genes represented by expression levels included in the first reference dataset, and the first similarity score indicates whether the differentiation state of the test cell is more similar to the first differentiation state or more similar to the second differentiation state; (b) calculating a second similarity score using the test data set and the second reference data set, wherein: the second reference data set comprising a representation of gene expression levels of one or more genes differentially expressed between cells in the second differentiation state and cells in a third differentiation state, The expression levels in the test dataset include expression levels of one or more genes among the genes included in the second reference dataset, and the second similarity score indicates whether the differentiation state of the test cell is more similar to the second differentiation state or more similar to the third differentiation state; and (c) classifying the differentiation state of the one or more test cells based on one or both of the first similarity score and the second similarity score.

35. The method of claim 34, wherein: The test data set comprises gene expression levels of one or more genes whose expression levels are represented in a control data set, the control data set comprising a representation of gene expression levels of one or more genes expressed in cells in a control differentiation state, the control differentiation state may be the same as or different from one of the first differentiation state, the second differentiation state, or the third differentiation state; The method further comprises calculating a degree of correlation between the representation of gene expression levels of one or more genes in the control dataset and the gene expression levels of the one or more genes in the test dataset to calculate a correlation score; and The classification of the differentiation state of the one or more test cells is based on the relevance score and one or both of the first similarity score and the second similarity score.

36. The method of claim 35, wherein the relevance score is calculated before calculating the first similarity score and the second similarity score, and the method is terminated if the relevance score of the test cell does not meet a predefined cutoff value.

37. A method according to claim 35 or claim 36, wherein the control data set includes gene expression levels, which are normalized by counts per million mapped reads (CPM) and filtered to include only gene expression levels exceeding a threshold CPM value.

38. The method of any one of claims 35 to 37, wherein the control data set comprises the centroid of the gene expression levels of the one or more genes in the control data set.

39. A method according to claim 38, wherein the correlation score is calculated by normalizing the gene expression levels of the one or more genes in the test data set and calculating the correlation between the gene expression levels of the one or more genes in the test data set and the centroid.

40. The method of claim 39, wherein the control data set comprises coefficient of variation (CV) values ​​of gene expression levels of the one or more genes in the control data set, and the correlation with the centroid is weighted by the inverse of the CV value.

41. The method of any one of claims 34 to 40, wherein the first differentiation state is earlier in the stem cell differentiation pathway than the second differentiation state.

42. The method of any one of claims 34 to 41, wherein the second differentiation state is earlier in the stem cell differentiation pathway than the third differentiation state.

43. The method of any one of claims 34 to 40, wherein the first differentiation state is in a cell differentiation pathway that is parallel to that of the second differentiation state.

44. according to the method described in any one in claim 34 to 43, wherein said cell colony is selected from the group consisting of: stem cell derived cardiomyocytes, stem cell derived skeletal muscle cells, stem cell derived renal tubular cells, stem cell derived erythrocytes, stem cell derived smooth muscle cells, stem cell derived pneumonocytes, stem cell derived thyroid cells, stem cell derived pancreatic cells, stem cell derived epidermal cells, stem cell derived pigment cells and stem cell derived neuronal cells.

45. The method of any one of claims 34 to 44, wherein the cell population is stem cell-derived neuronal cells.

46. ​​The method of any one of claims 34 to 45, wherein the second differentiation state is a differentiation state of committed dopaminergic neuronal cells.

47. The method of any one of claims 34 to 45, wherein the second differentiation state is a differentiation state of a hematopoietic progenitor cell.

48. The method according to any one of claims 34 to 47, wherein the first reference data set comprises a representation of gene expression levels of one or more genes selected from Table E1.

49. The method of any one of claims 34 to 48, wherein the second reference data set comprises a representation of gene expression levels of one or more genes selected from Table E2.

50. The method according to any one of claims 34 to 49, wherein the first reference data set comprises a representation of gene expression levels of at least 20 genes selected from Table E1.

51. The method of any one of claims 34 to 50, wherein the second reference data set comprises a representation of gene expression levels of at least 20 genes selected from Table E2.

52. The method according to any one of claims 34 to 51, wherein the first reference data set comprises a representation of gene expression levels of at least 50 genes selected from Table E1.

53. The method of any one of claims 34 to 52, wherein the second reference data set comprises a representation of gene expression levels of at least 50 genes selected from Table E2.

54. The method of any one of claims 34 to 53, wherein at least one of the first differentiation state, the second differentiation state, and the third differentiation state is characterized using an in vitro assay.

55. The method of any one of claims 34 to 54, wherein at least one of the first differentiation state, the second differentiation state, and the third differentiation state is characterized using an in vivo assay.

56. The method of claim 55, wherein the in vivo assay comprises determining whether a reference cell is able to survive, colonize and / or innervate a tissue when administered to an animal or human subject.

57. The method of claim 55 or claim 56, wherein the in vivo assay comprises determining whether the reference cells improve or reverse symptoms of a neurodegenerative disease when implanted into an animal or human subject.

58. The method of claim 56 or claim 57, wherein the animal subject comprises an animal model of Parkinson's disease.

59. The method of any one of claims 34 to 58, wherein the method further comprises calculating one or more additional similarity scores using one or more additional reference data sets, wherein: each said additional reference data set comprising a representation of gene expression levels of one or more genes differentially expressed between cells in said second differentiation state and cells in additional differentiation states; The one or more additional similarity scores indicate whether the differentiation state of the test cell is more similar to the second differentiation state or more similar to one of the one or more additional differentiation states, and The classification of the differentiation state of the one or more test cells is based on the first similarity score, the second similarity score, and the one or more additional similarity scores.

60. The method of any one of claims 34 to 59, wherein the representation of gene expression levels in the first reference dataset and / or the second reference dataset is obtained using machine learning.

61. The method of claim 60, wherein the machine learning comprises principal component analysis.

62. A method according to any one of claims 34 to 59, wherein the representation of gene expression levels in the first reference data set and / or the second reference data set comprises normalized gene expression levels.

63. A method according to any one of claims 34 to 62, wherein the method further comprises classifying the differentiation state of the one or more test cells as being in the second differentiation state if one or both of the first similarity score and the second similarity score indicate that the differentiation state of the one or more test cells is more similar to the second differentiation state.

64. The method of any one of claims 34 to 63, wherein the method further comprises selecting the in vitro cell population comprising one or more test cells classified as having the second differentiation state as having the desired differentiation state.

65. A method for implanting a population of cells having a desired differentiation state into a subject, the method comprising: (a) selecting a cell population having a desired differentiation state using the method according to any one of claims 34 to 64; as well as (b) implanting the cell population into a subject.

66. The method of claim 65, wherein the cells having the desired differentiation state are committed dopaminergic cells or hematopoietic progenitor cells, and the cell population is implanted into a brain region of the subject.

67. The method of claim 65 or claim 66, wherein the cells having the desired differentiation state are from a cell culture differentiated from pluripotent cells under conditions that cause neural differentiation of the cells.

68. A pharmaceutical composition comprising a pharmaceutical carrier and a population of cells having a desired differentiation state, wherein the cells are selected using a method according to any one of claims 34 to 64.

69. The pharmaceutical composition of claim 68, wherein the cells having the desired differentiation state are neuronal cells suitable for treating a neurodegenerative disease when implanted into the brain of a subject in need of such treatment.

70. The pharmaceutical composition of claim 68 or claim 69, wherein the neuronal cells comprise committed dopaminergic cells.

71. A pharmaceutical composition according to claim 68 or claim 69, wherein the neuronal cells comprise hematopoietic progenitor cells.

72. A method for training a machine learning model for classifying the differentiation state of an in vitro cell population, the method comprising: (a) for a plurality of reference cell populations, obtaining gene expression levels of one or more genes differentially expressed between cells in a first differentiation state and cells in a second differentiation state, and applying the gene expression levels as input to training a first machine learning model to predict whether an in vitro cell population contains one or more test cells having a differentiation state that is more similar to the first differentiation state or the second differentiation state; as well as (b) for multiple reference cell populations, obtaining gene expression levels of one or more genes differentially expressed between cells in the second differentiation state and cells in a third differentiation state, and applying the gene expression levels as input to training a second machine learning model to predict whether the in vitro cell population contains one or more test cells having a differentiation state that is more similar to the second differentiation state or the third differentiation state.

73. A method for training a machine learning model for classifying the differentiation state of an in vitro cell population, the method comprising: (a) selecting one or more genes that are differentially expressed between cells in a first differentiation state and cells in a second differentiation state, and applying the expression levels of the selected genes of a plurality of reference cell populations as input to training a first machine learning model to predict whether an in vitro cell population contains one or more test cells having a differentiation state that is more similar to the first differentiation state or the second differentiation state; as well as (c) selecting one or more genes that are differentially expressed between cells in the second differentiation state and cells in the third differentiation state, and applying the expression levels of the selected genes of multiple reference cell populations as input to training a second machine learning model to predict whether the in vitro cell population contains one or more test cells having a differentiation state that is more similar to the second differentiation state or the third differentiation state.

74. A method according to claim 72 or claim 73, wherein the method further comprises obtaining gene expression levels of one or more genes expressed in cells in a control differentiation state, wherein the control differentiation state may be the same as or different from one of the first differentiation state, the second differentiation state, or the third differentiation state, and applying the gene expression levels as input to training a control machine learning model to predict whether an in vitro cell population contains one or more test cells similar to the cells in the control differentiation state.

75. A method for selecting a cell population having a desired differentiation state, comprising: (a) obtaining a test dataset, the test dataset comprising gene expression levels of one or more genes of one or more test cells contained in an in vitro cell population, the one or more genes being selected from the group consisting of AC010247.2, ANKRD33B, APC2, AQP4, ASCL1, AURKB, BARHL2, CACNA1G, CAPN6, CBLN1, CCNB2, CDH1, CDH20, CHGA, COL1A1, COL1A2, COL22A1, COL4A1, CRABP1, DBX1, DCN, DCX, DDC, DOCK10, E2F4, EDNRB, ESRP1, EZH2, FABP7, FBLN1, FLRT3, FOXA2, FOXM1, GAP43, GFAP, GFRA1, GJA1, GLRA2, HES1, HES2, HES5, ITGA5, JPH4, LDHA, LIN28A, LIX1, LMX 1A, LUM, NCAM1, NES, NEUROG2, NGFR, NKX2-2, NMNAT2, NPTX1, NR4A2, NR4A2(NURR1), NSG2, NFYA, OLFM3, OLIG1, OLIG2, OTX2, P4HA1, PBX1, PDGFRA, PIEZO2, PITX3, PLP1, PMEL, PMP2, POSTN, POU2F2, PPP2R2B, PR TG, PTTG1, REST, RET, RFX4, RFX4, SALL4, SIN3A, SLC16A3, SLC18A2, SLC1A, SLC1A2, SLC1A3, SLC4A4, SMAD4, SNAP25, SOX10, SOX2, SOX9, STMN2, SUZ12, SV2B, SYN1, SYT1, SYT13, TH, TOP2A, TPH1, TPM2, and TXNIP; and (b) applying the gene expression levels as input to a process configured to predict whether the cell population has a desired differentiation state.

76. The method of claim 75, wherein the in vitro cell population comprises stem cell-derived neuronal cells.

77. The method of claim 75 or claim 76, wherein the desired differentiation state is a differentiation state of committed dopaminergic neuronal cells.

78. A method for selecting a cell population predicted to exhibit neurite outgrowth after implantation into a brain region, comprising: (a) obtaining a test dataset, the test dataset comprising gene expression levels of one or more genes of one or more test cells contained in an in vitro cell population, the one or more genes being selected from the group consisting of AC010247.2, ANKRD33B, APC2, AQP4, ASCL1, AURKB, BARHL2, CACNA1G, CAPN6, CBLN1, CCNB2, CDH1, CDH20, CHGA, COL1A1, COL1A2, COL22A1, COL4A1, CRABP1, DBX1, DCN, DCX, DDC, DOCK10, E2F4, EDNRB, ESRP1, EZH2, FABP7, FBLN1, FLRT3, FOXA2, FOXM1, GAP43, GFAP, GFRA1, GJA1, GLRA2, HES1, HES2, HES5, ITGA5, JPH4, LDHA, LIN28A, LIX1, LMX 1A, LUM, NCAM1, NES, NEUR0G2, NGFR, NKX2-2, NMNAT2, NPTX1, NR4A2, NR4A2(NURR1), NSG2, NFYA, OLFM3, OLIG1, OLIG2, OTX2, P4HA1, PBX1, PDGFRA, PIEZO2, PITX3, PLP1, PMEL, PMP2, POSTN, POU2F2, PPP2R2B, PR TG, PTTG1, REST, RET, RFX4, RFX4, SALL4, SIN3A, SLC16A3, SLC18A2, SLC1A, SLC1A2, SLC1A3, SLC4A4, SMAD4, SNAP25, SOX10, SOX2, SOX9, STMN2, SUZ12, SV2B, SYN1, SYT1, SYT13, TH, TOP2A, TPH1, TPM2, and TXNIP; and (b) applying the gene expression level as input to a process configured to predict whether the cell population will exhibit neurite outgrowth upon implantation into a brain region.

79. The method of any one of claims 75 to 78, wherein the one or more genes include at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95 or more of AC010247.2, ANKRD33B, APC2, AQP4, ASC L1, AURKB, BARHL2, CACNA1G, CAPN6, CBLN1, CCNB2, CDH1, CDH20, CHGA, COL1A1, COL1A2, COL22A1, COL4A1, CRABP1, DBX 1. DCN, DCX, DDC, DOCK10, E2F4, EDNRB, ESRP1, EZH2, FABP7, FBLN1, FLRT3, FOXA2, FOXM1, GAP43, GFAP, GFRA1, GJA1, GLR A2, HES1, HES2, HES5, ITGA5, JPH4, LDHA, LIN28A, LIX1, LMX1A, LUM, NCAM1, NES, NEUROG2, NGFR, NKX2-2, NMNAT2, NPTX 1. NR4A2, NR4A2(NURR1), NSG2, NFYA, OLFM3, OLIG1, OLIG2, OTX2, P4HA1, PBX1, PDGFRA, PIEZO2, PITX3, PLP1, PMEL, PMP 2. POSTN, POU2F2, PPP2R2B, PRTG, PTTG1, REST, RET, RFX4, RFX4, SALL4, SIN3A, SLC16A3, SLC18A2, SLC1A, SLC1A2, SLC 1A3, SLC4A4, SMAD4, SNAP25, SOX10, SOX2, SOX9, STMN2, SUZ12, SV2B, SYN1, SYT1, SYT13, TH, TOP2A, TPH1, TPM2 and TXNIP.

80. The method of any one of claims 75 to 79, wherein the process comprises a machine learning model trained using gene expression levels of the one or more genes.

81. The method of claim 80, further comprising classifying the differentiation state of the one or more test cells based on one or more outputs of the machine learning model.

82. The method of claim 80, further comprising predicting whether the test cell will exhibit neurite outgrowth after implantation into a brain region based on one or more outputs of the machine learning model.

83. A pharmaceutical composition comprising a drug carrier and a population of neuronal cells, wherein the cells are selected using a method according to any one of claims 75 to 82.

84. An in vitro stem cell-derived neuronal cell population comprising cells expressing one or more genes selected from the group consisting of CCNB2, AURKB, PTTG1, TOP2A, NEUROG2, HES1, REST, E2F4, FOXM1, SIN3A, NFYA, LIN28A, FLRT3, ITGA5, NES, SOX2, SOX9 and RFX4.

85. The in vitro stem cell-derived neuronal cell population according to claim 84, wherein: (1) at least one gene from the one or more genes is selected from the group consisting of CCNB2, AURKB, PTTG1, TOP2A, NEUROG2, HES1, REST, E2F4, FOXM1, SIN3A, NFYA, LIN28A, FLRT3, and ITGA5; and (2) At least one gene from the one or more genes is selected from the group consisting of: NES, SOX2, SOX9 and RFX4.

86. The in vitro stem cell-derived neuronal cell population of claim 84 or claim 85, wherein at least one of the one or more genes is REST.

87. The in vitro stem cell-derived neuronal cell population according to any one of claims 84 to 86, wherein at least 50% of the cells within the population express the one or more genes.

88. The in vitro stem cell-derived neuronal cell population according to any one of claims 84 to 87, wherein at least 60% of the cells within the population express the one or more genes.

89. The in vitro stem cell-derived neuronal cell population according to any one of claims 84 to 88, wherein at least 70% of the cells within the population express the one or more genes.

90. The in vitro stem cell-derived neuronal cell population according to any one of claims 84 to 89, wherein at least 80% of the cells within the population express the one or more genes.

91. The in vitro stem cell-derived neuronal cell population according to any one of claims 84 to 90, wherein at least 90% of the cells within the population express the one or more genes.

92. The in vitro stem cell-derived neuronal cell population according to any one of claims 84 to 91, wherein the cells in the population express EN1 and CORIN.

93. The in vitro stem cell-derived neuronal cell population according to any one of claims 84 to 92, wherein less than 20% of the total cells in the composition express TH.

94. The in vitro stem cell-derived neuronal cell population according to any one of claims 84 to 93, wherein less than 10% of the total cells in the composition express TH.

95. The in vitro stem cell-derived neuronal cell colony according to any one of claims 84 to 94, wherein the expression is RNA expression.

96. The in vitro stem cell-derived neuronal cell population of claim 95, wherein the RNA expression is measured by RNA sequencing.

97. The in vitro stem cell-derived neuronal cell population according to any one of claims 84 to 96, which has been differentiated in vitro from pluripotent stem cells (PSCs).

98. The in vitro stem cell-derived neuronal cell population of claim 97, wherein the one or more genes are genes that are overexpressed in cells of the population compared to the iPSCs.

99. The in vitro stem cell-derived neuronal cell population of claim 97, wherein the one or more genes are genes that are overexpressed in cells of the population compared to cells of a precursor population differentiated from the iPSC.

100. The in vitro stem cell-derived neuronal cell population of claim 97, wherein the one or more genes are genes that are overexpressed in cells of the population compared to cells of a population of mature committed dopaminergic neuronal cells differentiated from the iPSC.

101. The in vitro stem cell-derived neuronal cell population of any one of claims 98 to 100, wherein the overexpression is a positive log2 fold change of greater than or greater than about 1.5-fold, 2.0-fold, 3.0-fold, 4.0-fold, or 5-fold.

102. The in vitro stem cell-derived neuronal cell population of claim 97, wherein the one or more genes are genes whose expression is reduced in cells of the population compared to the iPSCs.

103. The in vitro stem cell-derived neuronal cell population of claim 97, wherein one or more genes are genes whose expression is reduced in cells of the population compared to cells of a precursor population differentiated from the iPSC.

104. The in vitro stem cell-derived neuronal cell population of claim 97, wherein the one or more genes are genes whose expression is reduced in cells of the population compared to cells of a mature committed dopaminergic neuronal cell population differentiated from the iPSC.

105. The in vitro stem cell-derived neuronal cell population according to claim 100 or claim 104, wherein the mature committed dopaminergic neuronal cells express LMX1A and / or NR4A2 (NURR1).

106. The in vitro stem cell-derived neuronal cell population of claim 105, wherein at least 40%, at least 50%, at least 60%, at least 70% or at least 80% of the cells in the committed dopaminergic neuronal cell population express LMX1A and / or NR4A2.

107. The in vitro stem cell-derived neuronal cell colony of any one of claims 102 to 106, wherein the reduced expression is a negative log2 fold change of greater than or greater than about 1.5-fold, 2.0-fold, 3.0-fold, 4.0-fold or 5-fold.

108. The in vitro stem cell-derived neuronal cell population of any one of claims 84 to 107, wherein less than 30%, less than 20% or less than 10% of the cells in the population express LMX1A and / or NR4A2.

109. The in vitro stem cell-derived neuronal cell population according to any one of claims 84 to 108, wherein the cells in the population are capable of colonizing and innervating other cells in vivo.

110. The in vitro stem cell-derived neuronal cell population according to any one of claims 84 to 109, wherein the cells in the population are capable of exhibiting neurite outgrowth when administered to the brain of a subject.

111. The in vitro stem cell-derived neuronal cell population according to any one of claims 84 to 110, wherein the cells in the population are capable of producing dopamine and optionally do not produce or substantially do not produce norepinephrine.

112. according to the in vitro stem cell-derived neuronal cell colony described in any one in claim 84 to 111, wherein said colony comprises at least 5,000,000 total cells, at least 10,000,000 total cells, at least 15,000,000 total cells, at least 20,000,000 total cells, at least 30,000,000 total cells, at least 40,000,000 total cells, at least 50,000,000 total cells, at least 100,000,000 total cells, at least 150,000,000 total cells or at least 200,000,000 total cells.

113. according to the in vitro stem cell-derived neuronal cell colony described in any one in claim 84 to 112, wherein the colony comprises just or about 5 million total cells to just or about 200 million total cells, just or about 5 million total cells to just or about 150 million total cells, just or about 5 million total cells to just or about 100 million total cells, just or about 5 million total cells to just or about 50 million total cells, just or about 5 million total cells to just or about 25 million total cells, just or about 5 million total cells to just or about 10 million total cells, just or about 10 million total cells to just or about 200 million total cells, just or about 10 million total cells to just or about 150 million total cells, just or about 10 million total cells to just or about 100 million total cells, just or about 10 million total cells to just or about 100 million total cells, to just or about 50 million total cells, just or about 10 million total cells to just or about 25 million total cells, just or about 25 million total cells to just or about 200 million total cells, just or about 25 million total cells to just or about 150 million total cells, just or about 25 million total cells to just or about 100 million total cells, just or about 25 million total cells to just or about 50 million total cells , just or about 50 million total cells to just or about 200 million total cells, just or about 50 million total cells to just or about 150 million total cells, just or about 50 million total cells to just or about 100 million total cells, just or about 100 million total cells to just or about 200 million total cells, just or about 100 million total cells to just or about 150 million total cells, or just or about 150 million total cells to just or about 200 million total cells.

114. The in vitro stem cell-derived neuronal cell population of any one of claims 84 to 113, wherein at least about 70%, 75%, 80%, 85%, 90% or 95% of the total cells in the composition are alive.

115. A pharmaceutical composition comprising a pharmaceutical carrier and an in vitro stem cell-derived neuronal cell population according to any one of claims 84 to 114.

116. The pharmaceutical composition of claim 68, claim 83 or claim 115, wherein the composition comprises a cryoprotectant.

117. The pharmaceutical composition of claim 116, wherein the cryoprotectant is selected from the group consisting of glycerol, propylene glycol, and dimethyl sulfoxide (DMSO).

118. A pharmaceutical composition according to any one of claims 68, 83 and 115 to 117, wherein the composition is used to treat a neurodegenerative disease or condition in a subject, optionally wherein the neurodegenerative disease or condition comprises a loss of dopaminergic neurons or microglia.

119. A pharmaceutical composition according to any one of claims 68, 83, and 115 to 118, wherein the neurodegenerative disease or condition comprises a loss of dopaminergic neurons in the substantia nigra, optionally in the SNc.

120. A pharmaceutical composition according to claim 118 or claim 119, wherein the neurodegenerative disease or condition is Parkinson's disease.

121. A pharmaceutical composition according to any one of claims 118 to 120, wherein the neurodegenerative disease or condition is Parkinson's syndrome.

122. A method of treatment comprising implanting a therapeutically effective amount of a pharmaceutical composition according to any one of claims 68, 83, and 115 to 121 into a brain region of a subject in need thereof.

123. The method of claim 122, wherein the number of cells implanted into the subject is about 0.25 x 10 6 cells to about 20×10 6 cells, about 0.25×10 6 cells to about 15×10 6 cells, about 0.25×10 6 cells to about 10×10 6 cells, about 0.25×10 6 cells to about 5×10 6 cells, about 0.25×10 6 cells to about 1×10 6 cells, about 0.25×10 6 cells to about 0.75×10 6 cells, about 0.25×10 6 cells to about 0.5×10 6 cells, about 0.5×10 6 cells to about 20×10 6 cells, about 0.5×10 6 cells to about 15×10 6 cells, about 0.5×10 6 cells to about 10×10 6 cells, about 0.5×10 6 cells to about 5×10 6 cells, about 0.5×10 6 cells to about 1×10 6 cells, about 0.5×10 6 cells to about 0.75×10 6 cells, about 0.75×10 6 cells to about 20×10 6 cells, about 0.75×10 6 cells to about 15×10 6 cells, about 0.75×10 6 cells to about 10×10 6 cells, about 0.75×10 6 cells to about 5×10 6 cells, about 0.75×10 6 cells to about 1×10 6 cells, about 1×10 6 cells to about 20×10 6 cells, about 1×10 6 cells to about 15×10 6 cells, about 1×10 6 cells to about 10×10 6 cells, about 1×10 6 cells to about 5×10 6 cells, about 5×10 6 cells to about 20×10 6 cells, about 5×10 6 cells to about 15×10 6 cells, about 5×10 6 cells to about 10×10 6 cells, about 10×10 6 cells to about 20×10 6 cells, about 10×10 6 cells to about 15×10 6 cells or about 15×10 6 cells to about 20×10 6 cells.

124. The method of claim 122 or claim 123, wherein the subject suffers from a neurodegenerative disease or condition.

125. The method of any one of claims 122 to 124, wherein the neurodegenerative disease or condition comprises loss of dopaminergic neurons or microglia.

126. The method of any one of claims 122 to 125, wherein the subject has lost at least 50%, at least 60%, at least 70%, or at least 80% of dopaminergic neurons.

127. The method of any one of claims 122 to 126, wherein the subject has lost at least 50%, at least 60%, at least 70% or at least 80% of the dopaminergic neurons in the substantia nigra (SN), optionally in the SN pars compacta (SNc).

128. A method according to any one of 122 to 127, wherein the neurodegenerative disease or condition is Parkinson's syndrome.

129. The method of any one of claims 122 to 128, wherein the neurodegenerative disease or condition is Parkinson's disease.

130. The method of any one of claims 122 to 129, wherein the brain region is the substantia nigra.

131. The method of any one of claims 122 to 130, wherein the implantation is performed by stereotactic injection.

132. The method of any one of claims 122 to 131, wherein the cells of the pharmaceutical composition are autologous to the subject.

Images