Method for identifying dopaminergic neurons and progenitor cells

Abstract

This provides a molecular diagnostic tool useful for the efficient and accurate characterization of the discriminative and functional properties of iPSC-derived dopaminergic neurons. [Solution] A computer-based method for identifying determined dopaminergic progenitor cells within an in vitro population of neural progenitor cells is provided, comprising: receiving a test dataset containing data such as gene expression profile information relating to an in vitro population of neural progenitor cells; querying a gene expression reference database and comparing the test dataset with the gene expression reference database, wherein the gene expression reference database contains gene expression profile information of the desired determined dopaminergic progenitor cells; and outputting a computer-calculated label classification that includes an indication of whether or not the in vitro population of neural progenitor cells contains the determined dopaminergic progenitor cells.

Description

[Technical Field]

[0001] (Cross-reference of related applications) This application claims priority to U.S. Provisional Patent Application No. 62 / 878,701, filed on 25 July 2019, entitled "METHOD OF IDENTIFYING DOPAMINERGIC NEURONS AND PROGENITOR CELLS," the contents of which are incorporated in their entirety by reference for all purposes. [Background technology]

[0002] The present invention includes the establishment of key statistical models and data processing steps that enable the evaluation of expression data derived from cultured neurons derived from induced pluripotent stem cells. This involves comparing the test data with reference datasets derived from, for example, pre-featured neurons, neural progenitor cells, or pluripotent stem cells having known biological characteristics. [Overview of the Initiative]

[0003] In one embodiment, a computer-based method is provided for identifying determined dopaminergic progenitor cells within an in vitro population of neural progenitor cells. This method includes receiving a test dataset containing data such as gene expression profile information for an in vitro population of neural progenitor cells; querying a gene expression reference database and comparing the test dataset with the gene expression reference database, wherein the gene expression reference database contains gene expression profile information for desired determined dopaminergic progenitor cells; and outputting a computer-calculated label classification that includes an indication of whether or not the in vitro population of neural progenitor cells contains determined dopaminergic progenitor cells.

[0004] This specification provides a computer-based method for classifying an in vitro population of neural progenitor cells, the method comprising: receiving a test dataset containing gene expression levels and expression levels of one or more metagenes in cells or groups of cells comprising an in vitro population of neural progenitor cells, wherein one or more metagenes are determined based on correlated gene expression levels of reference cells in a reference database, the reference cells being neurons at one or more different stages of differentiation; applying the expression levels of one or more metagenes as input to a process configured to determine the probability of cells or groups of cells having the determined metagene expression levels of dopaminergic progenitor cells; determining a deviation score for the cells or groups of cells, the deviation score indicating the degree to which the gene expression levels in the test dataset deviate from the gene expression levels of one or more reference cells in the reference database, the one or more reference cells being at a stage of differentiation indicating the determined dopaminergic progenitor cells; and outputting a computer-calculated label classification, which includes an indication of whether the cells or groups of cells from the in vitro population of neural progenitor cells are the determined dopaminergic progenitor cells, based on the probability and the deviation score.

[0005] In some embodiments, the process includes a supervised classification model trained using (i) the expression levels of one or more metagenes of reference cells in a reference database, and (ii) classification labels indicating each of one or more different differentiation stages of reference cells in the reference database, in order to determine the probability of a cell or multiple cells having a determined metagene expression level of dopaminergic progenitor cells.

[0006] Also provided herein is a method implemented by a computer for training a process for determining the probability of a cell or cells having the determined metagene expression level of dopaminergic progenitor cells, the method comprising, for determining the probability of a cell or cells having the determined metagene expression level of dopaminergic progenitor cells, (i) the expression levels of one or more metagenes, wherein the one or more metagenes are determined based on the correlated gene expression levels of reference cells in a reference database, the reference cells being neural cells in one or more different differentiation stages, and (ii) classification labels indicating each of the one or more different differentiation stages of the reference cells in the reference database, using to train a supervised classification model.

[0007] Also provided herein is a computer-implemented method for classifying an in vitro population of neural progenitor cells, the method comprising receiving a test data set comprising gene expression levels and expression levels of one or more meta-genes in cells or a plurality of cells included in the in vitro population of neural progenitor cells, wherein the one or more meta-genes are determined based on correlated gene expression levels of reference cells in a reference database, and the reference cells are neural cells at one or more different differentiation stages, and applying the expression levels of the one or more meta-genes as an input to a process, the process comprising a supervised classification model trained using (i) the expression levels of the one or more meta-genes of reference cells in the reference database, and (ii) classification labels indicating each of the one or more different differentiation stages of the reference cells in the reference database, to determine the probability of cells or a plurality of cells having the determined meta-gene expression levels of dopaminergic progenitor cells, determining a deviation score for the cells or the plurality of cells, the deviation score indicating the degree to which the gene expression levels in the test data set deviate from the gene expression levels in one or more reference cells in the reference database, the one or more reference cells being at the differentiation stage indicating the determined dopaminergic progenitor cells, and outputting a computer-calculated label classification comprising an indication of whether the cells or the plurality of cells from the in vitro population of neural progenitor cells are the determined dopaminergic progenitor cells based on the probability and the deviation score.

[0008] In some embodiments of any of the above, the method comprises identifying the in vitro population of neural progenitor cells as a population comprising the determined dopaminergic progenitor cells based on the computer-calculated label classification.

[0009] In some embodiments of any of the above, the supervised classification model is a logistic regression model.

[0010] In some of the embodiments described above, the reference cells are an in vitro population of neural progenitor cells. In some of the embodiments described above, the in vitro population of neural progenitor cells is formed by culturing one or more induced pluripotent stem cells (iPSCs) in vitro for a period of time under conditions that enable differentiation of one or more iPSCs into neural progenitor cells, and optionally, the neural progenitor cells are one or more of floorplate midbrain progenitor cells, determined dopaminergic progenitor cells, or dopamine (DA) neurons. In some embodiments, the iPSCs are human iPSCs. In some embodiments, the human is a healthy subject. In some embodiments, the human is a subject with Parkinson's disease.

[0011] In some of the embodiments described above, the culture period is approximately 2 to 25 days. In some of the embodiments described above, the iPSC is cultured for 2 days, approximately 2 days, or at least 2 days. In some of the embodiments described above, the iPSC is cultured for 5 days, approximately 5 days, or at least 5 days. In some of the embodiments described above, the iPSC is cultured for 10 days, approximately 10 days, or at least 10 days. In some of the embodiments described above, the iPSC is cultured for 13 days, approximately 13 days, or at least 13 days. In some of the embodiments described above, the iPSC is cultured for 15 days, approximately 15 days, or at least 15 days. In some of the embodiments described above, the iPSC is cultured for 18 days, approximately 18 days, or at least 18 days. In some of the embodiments described above, the iPSC is cultured for 25 days, approximately 25 days, or at least 25 days.

[0012] In some embodiments of the above, the reference database includes gene expression levels determined from one or more reference cell populations, each of which is formed by culturing one or more iPSCs in vitro for different periods under conditions that allow one or more iPSCs to differentiate into neural progenitor cells, and optionally, the neural progenitor cells are one or more of floorplate midbrain progenitor cells, determined dopaminergic progenitor cells, or dopamine (DA) neurons. In some embodiments, the different periods are 2 to 30 days. In some embodiments, the different periods are 11 to 25 days.

[0013] In some embodiments of the foregoing, one or more reference cells at one or more differentiation stages in the reference database are formed by culturing one or more iPSCs in vitro for one or more different periods under conditions that allow one or more iPSCs to differentiate into neural progenitor cells, and optionally, the neural progenitor cells are one or more of floorplate midbrain progenitor cells, determined dopaminergic progenitor cells, or dopamine (DA) neurons, and the different periods are approximately 11 to approximately 25 days, optionally 13 days or approximately 13 days, 18 days or approximately 18 days, or 25 days or approximately 25 days. In some embodiments of the foregoing, at least one of the one or more reference cell populations in the reference database includes gene expression levels determined by culturing iPSCs for approximately 13, 18, or 25 days.

[0014] In some of the embodiments described above, the conditions under which one or more iPSCs can be differentiated into neural progenitor cells include (a) exposing the cells to (i) an inhibitor of TGF-β / activin (activating)-Nodal signaling, (ii) at least one activator of sonic hedgehog (SHH) signaling, (iii) an inhibitor of bone morphogenetic protein (BMP) signaling, and (iv) an inhibitor of glycogen synthase kinase 3β (GSK3β) signaling, under conditions that differentiate the cells into floorplate midbrain progenitor cells. (b) a first incubation, which optionally begins on day 0 of culture, and (b) a second incubation following the first incubation of cells, which includes culturing the cells under conditions that induce neuronal differentiation, which optionally begins about 11 days after the first incubation, and further optionally lasts from about 11 days to about 25 days, to culture the iPSCs. In some embodiments, the conditions for differentiating cells into neurons include exposing the cells 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 β3 (TGFβ3) (collectively "BAGCT"), and (vi) an inhibitor of Notch signaling.

[0015] In some of the embodiments described above, at least one of the one or more reference cell populations in the reference database includes a gene expression level determined by culturing iPSCs for about 13 days. In some of the embodiments described above, at least one of the one or more reference cell populations includes a gene expression level determined by culturing iPSCs for about 18 days. In some of the embodiments described above, at least one of the one or more reference cell populations includes a gene expression level determined by culturing iPSCs for about 25 days.

[0016] In some of the embodiments described above, one or more metagenes and the expression levels of one or more metagenes are determined by using a dimensionality reduction technique on one or more reference cells in one or more reference databases. In some embodiments, the dimensionality reduction technique is used in a reference cell population that includes gene expression levels determined at approximately 13 days of in vitro culture under conditions for differentiating iPSCs into neural progenitor cells. In some of the embodiments described above, the dimensionality reduction technique is used in a reference cell population that includes gene expression levels determined at approximately 18 days of in vitro culture under conditions for differentiating iPSCs into neural progenitor cells. In some of the embodiments described above, the dimensionality reduction technique is used in a reference cell population that includes gene expression levels determined at approximately 25 days of in vitro culture under conditions for differentiating iPSCs into neural progenitor cells. In some of the embodiments described above, the dimensionality reduction technique is used in each of the following reference cell populations, including a reference cell population including a gene expression level determined at approximately 13 days of in vitro culture under conditions for differentiating iPSCs into neural progenitor cells, a reference cell population including a gene expression level determined at approximately 18 days of in vitro culture under conditions for differentiating iPSCs into neural progenitor cells, and a reference cell population including a gene expression level determined at approximately 25 days of in vitro culture under conditions for differentiating iPSCs into neural progenitor cells.

[0017] In some of the embodiments described above, the supervised classification model is trained using the expression levels of one or more metagenes determined from one or more reference cells. In some of the embodiments described above, the supervised classification model is trained using the expression levels of one or more metagenes determined from one or more reference cells, including the expression levels of one or more genes on days 11–25 of in vitro culture under conditions for differentiating iPSCs into neural progenitor cells, and optionally, one or more gene expression levels on days 13, 18, and 25 of in vitro culture under conditions for differentiating iPSCs into neural progenitor cells. In some of the embodiments described above, the supervised classification model is trained using the expression levels of one or more metagenes determined from one or more reference cells, including the gene expression level determined around day 13 of in vitro culture under conditions for differentiating iPSCs into neural progenitor cells. In some of the embodiments described above, the supervised classification model is trained using the expression levels of one or more metagenes determined from one or more reference cells, including the gene expression level determined around day 18 of in vitro culture under conditions for differentiating iPSCs into neural progenitor cells. In some of the embodiments described above, the supervised classification model is trained using the expression levels of one or more metagenes determined from one or more reference cells, including gene expression levels determined at approximately 25 days of in vitro culture under conditions for differentiating iPSCs into neural progenitor cells.

[0018] In some of the embodiments described above, the classification label indicating each of one or more different differentiation stages of the reference cell is either a determined dopaminergic progenitor cell or not a determined dopaminergic progenitor cell.

[0019] In some of the embodiments described above, classification labels indicating each of one or more different differentiation stages of reference cells are determined using an in vivo method. In some embodiments, the in vivo method includes transplanting an in vitro population of neural progenitor cells, including a reference cell population, into a brain region of an animal model of Parkinson's disease, and evaluating the occurrence of a therapeutic effect outcome for the transplantation in the animal model, wherein optionally the outcome is selected from innervation or engraftment of host cells, reduction of brain lesions in the animal model, or recovery of brain lesions in the animal model, and designating the cells as determined dopaminergic progenitor cells if the transplantation results in the occurrence of a therapeutic effect outcome, or designating the cells as not determined dopaminergic progenitor cells if the transplantation does not result in the occurrence of a therapeutic effect outcome. In some embodiments, the brain region is the substantia nigra. In some of the embodiments described above, the in vivo method includes a behavioral test.

[0020] In some of the embodiments described above, classification labels indicating each of one or more different differentiation stages of reference cells are determined using an in vitro method. In some embodiments, the in vitro method includes assessing the dopamine production level of a reference cell population and designating the cells as classification labels if the dopamine production level is elevated compared to pluripotent stem cells. In some of the embodiments described above, the assessment of dopamine production is performed by high-performance liquid chromatography.

[0021] In some embodiments of the above, the in vitro method includes evaluating the level of tyrosine hydroxylase expression in a reference cell population, and if the reference cell population expresses high levels of tyrosine hydroxylase, classifying and labeling them as not being the determined dopaminergic progenitor cells. In some embodiments, the level of tyrosine hydroxylase expression is evaluated using flow cytometry.

[0022] In some of the embodiments described above, the reference database further includes classification labels for one or more reference cells.

[0023] In some of the embodiments described above, the expression level of one or more metagenes in the test dataset is determined based on (i) one or more metagenes determined from one or more reference cells in a reference database, and (ii) the gene expression level in the test dataset. In some embodiments, the expression level of one or more metagenes in the test dataset is determined using regression analysis based on (i) one or more metagenes determined from one or more reference cells in a reference database, and (ii) the gene expression level in the test dataset. In some of the embodiments described above, the expression level of one or more metagenes in the test dataset is determined by merging the gene expression levels in the test dataset with those in the reference database to create an updated reference database, and then applying a dimensionality reduction technique to the updated reference database.

[0024] In some of the embodiments described above, the dimensionality reduction method is conventional non-negative matrix factorization, discriminant non-negative matrix factorization, graph regularized non-negative matrix factorization, bootstrap sparse non-negative matrix factorization, or regularized non-negative matrix factorization. In some of the embodiments described above, the dimensionality reduction method is conventional non-negative matrix factorization.

[0025] In some of the embodiments described above, the number of one or more metagenes is selected based on the performance of a supervised classification model in determining the probability of cells or multiple cells having a determined metagene expression level in dopaminergic progenitor cells. In some of the embodiments described above, the number of one or more metagenes is selected based on evaluating one or more metrics determined by performing a dimensionality reduction technique using a multiple candidate number of metagenes. In some embodiments, one or more metrics include Cophen distance, variance, residuals, sum of squared residuals (RSS), silhouette, and / or sparseness values.

[0026] In some embodiments of the above, the computer-calculated label classification indicates that a cell or group of cells from an in vitro population of neural progenitor cells is a determined dopaminergic progenitor cell if the probability of a cell or group of cells having the metagene expression level of a determined dopaminergic progenitor cell is greater than a probability threshold. In some embodiments, the probability threshold is set so that the determined dopaminergic progenitor cells are identified with a sensitivity greater than approximately 75%, 80%, 85%, 90%, or 95%, and / or the probability threshold is set so that the determined dopaminergic progenitor cells are identified with a specificity greater than approximately 75%, 80%, 85%, 90%, or 95%. In some embodiments, the probability threshold is set so that the determined dopaminergic progenitor cells are identified with a sensitivity greater than approximately 98% and a specificity of 100%. In some embodiments of the above, the probability threshold is determined by using the area under the receiver operational characteristic (ROC) curve based on a supervised classification model. In some of the embodiments described above, the probability threshold is approximately 0.4 to 0.8. In some of the embodiments described above, the probability threshold is approximately 0.4, 0.45, 0.5, 0.55, 0.6, 0.65, 0.7, 0.75, or 0.8.

[0027] In some of the embodiments described above, the deviation score of a cell or a group of cells is determined using a single-gene deviation score for each of one or more genes in the test dataset. In some embodiments, the single-gene deviation score is determined using the difference between the gene expression level in the test dataset and the gene expression level in one or more reference cells in the reference database. In some embodiments, the difference is an absolute difference. In some of the embodiments described above, the single-gene deviation score is determined using the standard deviation of the gene expression level in one or more of the one or more reference cells. In some of the embodiments described above, the single-gene deviation score is a z-value determined using the difference between the gene expression level in the test dataset and the gene expression level in one or more of the reference cells in the reference database, and the standard deviation of the gene expression level in one or more of the one or more reference cells in the reference database.

[0028] In some of the embodiments described above, the gene expression level in one or more reference cells in the reference database is determined based on the mean gene expression level in one or more reference cells in the reference database. In some of the embodiments described above, the gene expression level in one or more reference cells in the reference database is determined based on the expression level of one or more metagenes in the test dataset. In some embodiments, the gene expression level in one or more reference cells in the reference database is determined using regression analysis based on (i) the expression level of one or more metagenes in the test dataset, and (ii) the gene expression level in the test dataset.

[0029] In some of the embodiments described above, the deviation score is a summary statistic based on all single-gene deviation scores. In some of the embodiments described above, the deviation score is a summary statistic based on single-gene deviation scores for one or more marker genes. In some of the embodiments described above, the summary statistic is a sum. In some of the embodiments described above, the summary statistic is a weighted sum. In some embodiments, the single-gene deviation scores of one or more marker genes have higher weights.

[0030] In some of the embodiments described above, the summary statistic is a percentile value. In some embodiments, the percentile value is approximately the 50th percentile to approximately the 100th percentile, and / or the percentile value is approximately the 50th, 60th, 70th, 80th, 90th, or 95th percentile.

[0031] In some embodiments of the above, the marker genes include radial glial cell markers, early neuronal development genes, pluripotency-specific markers, mid-to-late neuronal cell markers, neurofilament light polypeptide chain markers, neurofilament medium polypeptide chain markers, nestin filament markers, early patterning markers, neural progenitor cell markers, early migration markers, stage-specific transcription factors, genes necessary for normal neuronal development, genes that control the development of dopaminergic neurons, genes that regulate the discriminative and fate of neural progenitor cells, dopaminergic neuron markers, astrocyte markers, forebrain markers, hindbrain markers, subthalamic nucleus markers, radial glial markers, cell cycle markers, or any combination thereof. In some of the embodiments described above, the marker genes include WNT1, VIM, TOP2A, TH, SOX2A, SLIT2, RFX4, POU5F1, PITX2, PAX6, OTX2, NR4A2, NHLH2, Neurod4, Neurod1, NES, NEFM, NEFL, NASP, MAP2, LMX1A, LIN28A, HOXA2, HMGB2, HES1, FOXG1, FOXA2, FABP7, DDC, DCX, BARHL2, BARJL1, ASPM, ALDH1A1, or any combination thereof.

[0032] In some of the embodiments described above, the computer-calculated label classification indicates that a cell or group of cells from an in vitro population of neural progenitor cells is a determined dopaminergic progenitor cell if the deviation score indicates that at least approximately 50%, 60%, 70%, 80%, 90%, or 95% of the gene expression levels in the test dataset are within 5 × standard deviations of the gene expression levels of one or more reference cells in the reference database. In some of the embodiments described above, the computer-calculated label classification indicates that a cell or group of cells from an in vitro population of neural progenitor cells is a determined dopaminergic progenitor cell if the deviation score indicates that at least approximately 95% of the gene expression levels in the test dataset are within 10, 9, 8, 7, 6, or 5 × standard deviations of the gene expression levels of one or more reference cells in the reference database. In some of the embodiments described above, the computer-calculated label classification indicates that a cell or group of cells from an in vitro population of neural progenitor cells is a determined dopaminergic progenitor cell if the deviation score indicates that at least about 50%, 50%, 70%, 80%, 90%, or 95% of the marker gene expression levels in the test dataset are within 5 × standard deviations of the gene expression levels of one or more reference cells in the reference database. In some of the embodiments described above, the computer-calculated label classification indicates that a cell or group of cells from an in vitro population of neural progenitor cells is a determined dopaminergic progenitor cell if the deviation score indicates that at least about 95% of the marker gene expression levels in the test dataset are within 10, 9, 8, 7, 6, or 5 × standard deviations of the gene expression levels of one or more reference cells in the reference database.

[0033] In some of the embodiments described above, the computer-calculated label classification indicates that a cell or group of cells from an in vitro population of neural progenitor cells is a determined dopaminergic progenitor cell if the probability of a cell or group of cells having the determined metagene expression level of a dopaminergic progenitor cell is greater than a probability threshold, and the deviation score indicates that at least about 50%, 50%, 70%, 80%, 90%, or 95% of the gene expression levels in the test dataset are within 5 × standard deviations of the gene expression levels of one or more reference cells in the reference database. In some of the embodiments described above, the computer-calculated label classification indicates that a cell or group of cells from an in vitro population of neural progenitor cells is a determined dopaminergic progenitor cell if the probability of a cell or group of cells having the determined metagene expression level of a dopaminergic progenitor cell is greater than a probability threshold, and the deviation score indicates that at least about 50%, 50%, 70%, 80%, 90%, or 95% of the marker gene expression levels in the test dataset are within 5 × standard deviations of the gene expression levels of one or more reference cells in the reference database. In some of the embodiments described above, the computer-calculated label classification indicates that the cells or groups of cells from the in vitro population of neural progenitor cells are the determined dopaminergic progenitor cells if the probability of cells or groups having the determined metagene expression level of dopaminergic progenitor cells is greater than a probability threshold, the deviation score indicates that at least about 50%, 50%, 70%, 80%, 90%, or 95% of the gene expression levels in the test dataset are within 5 × standard deviations of the gene expression levels of one or more reference cells in the reference database, and the deviation score indicates that at least about 50%, 50%, 70%, 80%, 90%, or 95% of the marker gene expression levels in the test dataset are within 5 × standard deviations of the gene expression levels of one or more reference cells in the reference database.

[0034] In some of the embodiments described above, the computer-calculated label classification indicates that the cells or groups of cells from the in vitro population of neural progenitor cells are determined dopaminergic progenitor cells if the difference in marker gene expression between the test dataset and the reference cells in the reference database is not statistically significant based on the multiple-comparison corrected significance level. In some embodiments, the multiple-comparison corrected significance level is the Bonferroni corrected significance level or the false discover rate corrected significance level. In some of the embodiments described above, the multiple-comparison corrected significance level is 0.01, 0.05, or 0.1.

[0035] In some of the embodiments described above, the gene expression level is obtained from microarray analysis of intracellular RNA, RNA sequencing, or both. In some of the embodiments described above, the gene expression level is obtained from RNA sequencing. In some of the embodiments described above, RNA sequencing is performed on bulk RNA from multiple cells or multiple reference cells. In some of the embodiments described above, RNA sequencing is performed on RNA from a single cell or a single reference cell. In some of the embodiments described above, the gene expression level of the reference cell in the reference database includes the expression level determined by RNA sequencing performed on bulk RNA from multiple reference cells and RNA from a single reference cell.

[0036] In some of the embodiments described above, receiving the test dataset includes receiving input from an array analysis system. In some of the embodiments described above, receiving the test dataset includes receiving input via a computer network. In some of the embodiments described above, the one or more reference databases form part of a storage medium.

[0037] In some embodiments of any of the foregoing, the method includes repeating the receive, apply, determine, and output steps if a computer-calculated label classification indicates that the cells or groups of cells are not determined dopaminergic neurons, and optionally, the steps are repeated with the same or different in vitro populations of neural progenitor cells. In some embodiments, the receive, apply, determine, and output steps are repeated about 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 days after a prior iteration of the method.

[0038] In some embodiments of any of the above, the method includes repeating the receive, apply, determine, and output steps if a computer-calculated label classification indicates that the cells or a group of cells are not determined dopaminergic neurons, and this step is repeated using a different in vitro population of neural progenitor cells formed by culturing another iPSC clone under conditions that allow one or more iPSCs to differentiate into neural progenitor cells, optionally the neural progenitor cells being one or more of floorplate midbrain progenitor cells, determined dopaminergic progenitor cells, or dopamine (DA) neurons. In some embodiments, the different in vitro population of neural progenitor cells is formed from the same human subjects as in the prior iterations of the method.

[0039] In some of the embodiments described above, the receiving, applying, determining, and outputting steps are repeated until an output indicates that the cells or the group of cells are determined to be dopaminergic neurons in an in vitro population of neural progenitor cells formed by culturing the iPSCs for different periods and / or under different conditions that allow one or more iPSCs to differentiate into neural progenitor cells.

[0040] Furthermore, a determined population of dopaminergic progenitor cells, identified by the methods of some of the embodiments described above, is also provided herein.

[0041] Also provided herein are therapeutic methods comprising administering a determined population of dopaminergic progenitor cells, according to some of the embodiments described above, to a subject having Parkinson's disease. In some embodiments, the administration is by transplanting the determined population of dopaminergic progenitor cells into one or more brain regions of the subject. In some embodiments, the one or more brain regions include the substantia nigra.

[0042] In some of the embodiments described above, the determined population of dopaminergic progenitor cells is autologous to the subject. In some of the embodiments described above, the determined population of dopaminergic progenitor cells is allogeneic to the subject.

[0043] Also provided herein is a method for treating a subject having Parkinson's disease, comprising transplanting a determined population of dopaminergic progenitor cells into a brain region of the subject having Parkinson's disease, wherein the determined population of dopaminergic progenitor cells is identified using a computer-based method as described in some of the embodiments described above.

[0044] In some embodiments, the determined population of dopaminergic progenitor cells is autologous to the subject. In some embodiments of any of the above, the determined population of dopaminergic progenitor cells is allogeneic to the subject. In some embodiments of any of the above, approximately 1 × 10 6 pieces, or at least 1 × 10 6 The cells are injected into the substantia nigra. In some of the embodiments described above, the cells are injected into both the left and right hemispheres. [Brief explanation of the drawing]

[0045] [Figure 1] This indicates the developmental stage when conventional biomarkers cannot be used for stage identification.

[0046] [Figure 2] An overview of NeuroTest, showing its key components and data flow, is provided. NeuroTest is a computer-based method for identifying determined dopaminergic progenitor cells within an in vitro population of neural progenitor cells. The overview shown in Figure 2 is an exemplary overview of the components and data flow of NeuroTest. In this exemplary embodiment, RNA sequence (RNAseq) data (test sample) derived from an in vitro population of neural progenitor cells is provided to NeuroTest. For each test sample, NeuroTest provides two parameters as output: NeuroScore and Novelty Score. These parameters are used together to determine whether the test sample contains determined dopaminergic progenitor cells.

[0047] [Figure 3A] An example of NeuroTest output is shown: a table of statistical scores. [Figure 3B]An example of NeuroTest output is shown: a histogram with NeuroScore on the y-axis and Novelty on the x-axis. Figure 3B shows induced pluripotent stem cells (iPSCs) and dopaminergic (DA) neurons failing and passing the NeuroTest, respectively. Figure 3B also shows the NeuroScore on the y-axis converted to a percentage value. [Figure 3C] An example of NeuroTest output is shown: a scatter plot with NeuroScore on the y-axis and Novelty on the x-axis. Figure 3C shows induced pluripotent stem cells (iPSCs) and dopaminergic (DA) neurons failing and passing the NeuroTest, respectively. Figure 3C also shows the NeuroScore on the y-axis converted to a percentage value. In Figure 3C, NeuroScore is displayed as "neuri" and Novelty Score is displayed as "deviation".

[0048] [Figure 4] A scatter plot showing NeuroScore (y-axis) and Novelty Score (x-axis) for the validation dataset is shown. The NeuroTest model was validated by first training it with identified genes from microarray data and then supplementing it with gene expression data based on RNA-seq. Here, since the model was trained using Illumina bead array data (using 5-fold cross-validation), RNA-seq data was used for validation. The validation RNA-seq data was generated or downloaded from a public data repository. Samples in the upper left quadrant pass for both high NeuroScore and low Novelty. "Undiff" samples (mainly undifferentiated IPSCs, diamond-shaped) fail because they have lower NeuroScores and higher Novelty levels compared to the reference data model. In Figure 4, NeuroScore is displayed as "N-score".

[0049] [Figure 5]This shows NeuroTest results obtained from the analysis of 86 publicly available neuronal RNA-seq datasets. Data points highlighted with black circles are specifically from the challenge dataset. The blacked-out background data points are from the NeuroTest validation analysis of 695 samples of validation data. These results provide context for the NeuroTest challenge data. The breadth of the challenge data, ranging from iPSCs to cancer cells and neurons, reflects the input data. The tabular output reveals that NeuroTest gave a “pass” score to the DA neuronal preparation. In Figure 5, the NeuroScore is displayed as “N-score”.

[0050] [Figure 6] This paper demonstrates how to use gene expression phenotypes to identify neural progenitor cells using NeuroTest.

[0051] [Figure 7] This report shows the metagene expression levels (metagene contribution) of cell samples at day 18 of a dopaminergic neuron differentiation protocol. Metagenes and their expression levels were obtained by applying conventional non-negative matrix factorization (NMF) to single-cell RNA-seq (scRNA-seq) data. The scRNA-seq data were then integrated to approximate bulk RNA-seq data (bulk from single cells), which were collected from each of four cell lines. Both scRNA-seq and bulk RNA-seq data were collected for each sample from the cell lines.

[0052] [Figure 8] The receiver operational characteristic (ROC) curve shows the classification performance of a logistic regression model trained to identify determined dopaminergic progenitor cells within an in vitro population of neural progenitor cells.

[0053] [Figure 9]This document presents another exemplary workflow for constructing and using NeuroTest. In this exemplary workflow, gene expression data, scRNAseq datasets, and matched bulk RNAseq datasets are collected from publicly available databases for an in vitro population of neural progenitor cells, including determined dopaminergic progenitor cells. These datasets are fed into a process to calculate metagenes and their expression levels (circles 3 and 4). The metagene expression levels are fed as training data for a classification model configured to determine the probability of a sample having the determined dopaminergic progenitor cell metagene expression levels (circle 5). Additional data, e.g., bulk RNAseq data not used to train the model, can be used to validate this model (circle 6). The trained model is then used as part of NeuroTest to test future test samples from other in vitro populations (circle 7). Novelty Scores are also calculated for each training sample, and these scores, along with the trained model, are used to identify NeuroScore and Novelty Score thresholds used to evaluate future test samples (circle 8). For future test samples, RNAseq data are subjected to sequence alignment using Salmon pseudoaligner (Circle 1). The test RNAseq data are then fed into a trained model (Circle 2), and a NeuroScore (Circle 10) and Novelty Score (Circle 11) are output for each test sample. These scores are compared to predetermined thresholds to determine whether the test sample should be transplanted, further screened, or discarded.

[0054] [Figure 10] This shows gene expression deviations in exemplary samples derived from an in vitro population of neural progenitor cells. Gene expression deviations are shown for several individual marker genes and are calculated as normalized residuals indicating how far each gene expression deviates from the expected value, where the expected value is determined from cells with known discriminative properties (e.g., reference cells).

[0055] [Figure 11] The NeuroTest outputs (NeuroScore and Novelty Score) for cell samples at various stages (days) of the dopaminergic neuron differentiation protocol are shown. The horizontal dashed line indicates a NeuroScore of 0. The vertical dashed line indicates a Novelty Score of 5. In this exemplary embodiment, samples with a NeuroScore > 0 and a Novelty Score < 5 are identified as containing the determined dopaminergic progenitor cells. [Modes for carrying out the invention]

[0056] This specification provides a method for classifying whether an in vitro population of neural progenitor cells contains a particular differentiated neuronal cell type. In some embodiments, the method provided classifies whether an in vitro population of differentiated neurons contains determined dopaminergic progenitor cells. In some embodiments, the method provided herein identifies whether an in vitro population of neurons contains determined dopaminergic progenitor cells. In some embodiments, determined dopaminergic progenitor cells are cells that differentiate into dopaminergic neurons and cannot differentiate into non-dopaminergic cells. Using the cell populations classified according to the method provided, cells of interest for therapeutic purposes can be identified, for example. Thus, populations of determined dopaminergic progenitor cells identified by the method provided, and pharmaceutical compositions containing them, are also provided. In some embodiments, determined dopaminergic progenitor cells have therapeutic uses in the treatment of neurodegenerative diseases such as Parkinson's disease.

[0057] The provided method includes receiving a test dataset comprising (1) gene expression levels, and (2) expression levels of one or more metagenes for cells or multiple cells in an in vitro population of neural progenitor cells, where one or more metagene cells are determined based on correlated gene expression levels of reference cells in a reference database. In some embodiments, the in vitro population of neural progenitor cells is a population of cells subjected to a process for differentiating pluripotent stem cells, such as induced pluripotent stem cells (iPSCs), into nerve cells, such as dopaminergic neurons or precursors of determined dopaminergic neurons. In some embodiments, the method includes applying the expression levels of one or more metagenes as input to a process configured to determine the probability of cells or multiple cells in an in vitro population of neural progenitor cells having metagene expression levels of determined dopaminergic progenitor cells. In some embodiments, the method also includes determining a deviation score for one or more cells in an in vitro population of neural progenitor cells, the deviation score indicating the degree to which the gene expression levels in the test dataset deviate from the gene expression levels in one or more reference cells in a reference database, the one or more reference cells being at a differentiation stage indicating determined dopaminergic progenitor cells. In some embodiments, the deviation score is determined using the gene expression levels in the test dataset and the gene expression levels in the reference database. In some embodiments, the method includes outputting a computer-calculated label classification that, based on probability and deviation score, provides an indication of whether the one or more cells from the in vitro population of neural progenitor cells are determined dopaminergic progenitor cells, thereby classifying the in vitro population of neural progenitor cells as a population that is determined dopaminergic progenitor cells or a population that contains them. In some embodiments, this specification can therefore be identified based on the classification of whether the in vitro population of neural progenitor cells is a population that contains determined dopaminergic progenitor cells.

[0058] In some embodiments, a specific population of differentiated neurons derived from pluripotent stem cells, including determined dopaminergic progenitor cells, may be cells at a stage of differentiation where the cells are not identifiable by one or a few features or characteristics. The methods provided herein enable the determination of cell recognizability when one or a few features or characteristics, such as gene expression markers or functional characteristics, are unavailable (e.g., unknown) or cannot be practically used to determine cell recognizability. For example, as shown in Figure 1, cell recognizability can be determined using cells at a stage of differentiation where there are no clear biomarkers. Pluripotent stem cells can be identified as positive by clear biomarkers, e.g., by the expression level of a specific gene, and differentiated cells can be identified as positive based on functional markers, but individual markers for identifying cells at various transitional stages throughout differentiation are unknown. Due to the lack of such markers, it has been difficult to characterize, confirm, and / or identify pre-differentiated cells with a specific cellular phenotype. In some embodiments, the methods provided herein overcome the lack of a single or a few features or characteristics (e.g., biomarkers) by examining a group of relevant genes and their expression levels. Such an approach does not rely on knowledge of individual marker genes, but instead uses a whole transcriptome approach in characterizing and identifying determined dopaminergic progenitor cells.

[0059] Induced pluripotent stem cells (iPSCs) are considered useful as cell therapies because they have the ability to differentiate into at least specific cell types. For example, iPSCs can differentiate into specific cell types that can be used to replace diseased or damaged tissue, similar to pluripotent stem cells. In some cases, iPSCs differentiated into specific neuronal cell types or precursors can be used to treat neurodegenerative diseases, for example, by differentiating the iPSCs and transplanting the differentiated neurons into the brain of a subject with neurodegenerative disease. The inability to determine the identifiability of differentiated cells throughout the differentiation process can make the success of the process uncertain. For example, it may be necessary to complete the differentiation process to determine whether it was successful or not. Therefore, if there is no ability to determine, as needed, whether the differentiating cells will proceed through the transient phases, the differentiation process can become time-consuming and inefficient, and may hinder the treatment of the subject if the differentiation process fails, for example. Furthermore, in some cases, therapeutic procedures may involve administering (e.g., by injection) differentiated cells that have not yet reached the final differentiation stage to the subject.

[0060] In some embodiments, cells at intermediate stages of differentiation are not identifiable or readily identifiable by clear biomarkers. The methods provided herein enable the identification of cells at stages of differentiation when clear features or characteristics are unavailable or can be practically used to determine cell recognizability. In some embodiments, the methods provided herein can be used, for example, to improve the differentiation process by enabling the determination of cell recognizability throughout the differentiation stages and to determine whether cells undergoing the differentiation process are appropriately and / or distinguished according to defined criteria. If it is determined that cells are not appropriately differentiated, in some embodiments the process may be terminated or, optionally, restarted using a different patient-derived iPSC clone.

[0061] In some embodiments, the methods provided herein may be used in combination with processes that involve differentiation from iPSCs to generate neurons useful for treating neurodegenerative diseases such as Parkinson's disease. In some embodiments, the methods provided herein may be used to identify neurons generated by a differentiation process, for example, the process described in Section II, which is useful for treating Parkinson's disease.

[0062] The methods provided herein may be used to determine whether an in vitro population of cells contains predetermined dopaminergic progenitor cells. In some embodiments, the methods provided herein include determining the metagenes and their expression levels of the test cells contained in the in vitro population. In some embodiments, the methods provided herein include determining the probability that test cells have the determined metagene expression levels of dopaminergic progenitor cells. In some embodiments, the probability is determined using a machine learning model. In some embodiments, the methods provided herein include determining a deviation score indicating the degree to which the gene expression level of the test cells deviates from the expected gene expression level. In some embodiments, the expected gene expression level is based on the gene expression level of a reference cell known to be a determined dopaminergic progenitor cell. In some embodiments, the methods provided herein include outputting a computer-calculated label classification based on (i) the probability that test cells have the determined metagene expression levels of dopaminergic progenitor cells, and (ii) the deviation score, or both. In some embodiments, the deviation score is based on a subset of marker genes. In some embodiments, by determining the probability of test cells having the determined metagene expression levels of dopaminergic progenitor cells, it becomes possible to identify cells having a desired phenotype, which lacks individual marker genes. In some embodiments, by determining deviation scores, it becomes possible to identify cells that may contain abnormalities in the expression of, for example, specific marker genes. Thus, the methods provided herein offer a multifaceted approach to determining cells suitable for therapy.

[0063] The following subsections describe exemplary features of a method provided for classifying whether an in vitro population of neural progenitor cells contains a specific differentiated neuronal cell type, and methods for identifying a specific differentiated neuronal cell type. Related compositions, their manufacturing methods, and uses are also described.

[0064] I. Method for determining dopaminergic cells This specification provides, in particular, a method for identifying dopaminergic precursors in an in vitro cell population of neural progenitor cells using gene expression as a phenotype. The method provided herein provides, in particular, information on whether a cell preparation (e.g., a population of neural progenitor cells) contains cells determined to differentiate into a specific functional cell type (e.g., a determined dopaminergic progenitor cell), or whether the cell preparation contains cells from an earlier stage (e.g., pluripotent stem cells, a specified cell), other differentiated neuronal types, and other differentiated cell types.

[0065] Accordingly, one embodiment provides a computer-based method for identifying determined dopaminergic progenitor cells within an in vitro population of neural progenitor cells. This method includes receiving a test dataset containing data such as gene expression profile information relating to an in vitro population of neural progenitor cells; querying a gene expression reference database and comparing the test dataset with the gene expression reference database, wherein the gene expression reference database contains gene expression profile information for desired determined dopaminergic progenitor cells; and outputting a computer-calculated label classification that includes an indication of whether or not the in vitro population of neural progenitor cells contains determined dopaminergic progenitor cells.

[0066] The methods provided herein can define a determined state of cells and predict whether a cell preparation will differentiate into a particular cell type. The reference database provided herein may include gene expression profile information for two cell types. In embodiments, cells identified by the methods provided herein are determined to differentiate into a particular functional cell type. Whether cells are determined to differentiate into a particular functional cell type (e.g., a determined dopaminergic progenitor cell) can be further demonstrated in vitro or in vivo by fully differentiating the cells. In embodiments, cells identified by the methods provided herein are pluripotent stem cells, designated cells, differentiated neuronal types other than dopaminergic progenitors, or other differentiated cell types.

[0067] In embodiments, the computer-based method further includes a machine learning model trained to determine whether the in vitro population of neural progenitor cells contains determined dopaminergic progenitor cells, the machine learning model outputting a computer-calculated label classification. In embodiments, the in vitro population of neural progenitor cells is formed by differentiating induced pluripotent stem cells (iPSCs) in vitro. In embodiments, the iPSCs are human iPSCs. In embodiments, the iPSCs are cultured for at least 15 days under conditions for differentiation into neural progenitor cells. In embodiments, the iPSCs are cultured for about 18 days under conditions for differentiation into neural progenitor cells. The in vitro cell populations of neural progenitor cells provided herein may be formed by methods commonly known in the art for differentiating iPSCs into dopaminergic neurons. Exemplary methods of the differentiation process are described in Section II. Different points in the process of differentiating iPSCs into dopaminergic neurons may result in cells at different stages of differentiation. Therefore, the term "d18" or "day 18" as provided herein refers to day 18 of the differentiation process of iPSCs for the formation of dopaminergic neurons. Similarly, the term "d0" or "day 0" refers to the day on which the differentiation process of iPSCs for the formation of dopaminergic neurons is initiated. Using the provided method, differentiated populations of neurons can be classified and thus identified, which are determined to contain specific neural progenitor cells, such as dopaminergic progenitor cells, based on classification labels according to the provided method.

[0068] In some embodiments, the computer-based method includes a machine learning model trained to determine the probability that a cell or a group of cells in an in vitro population of neural progenitor cells has a determined metagenetic expression level for a dopaminergic progenitor cell. In some embodiments, the machine learning model outputs the probability (also referred herein as NeuroScore) of a cell or a group of cells having a determined metagenetic expression level for a dopaminergic progenitor cell. In some embodiments, the computer-based method further includes determining a deviation score (also referred herein as Novelty Score) for a cell or a group of cells, the deviation score indicating the degree to which the gene expression level of a cell or a group of cells deviates from an expected gene expression level. In some embodiments, the expected gene expression level is based on the gene expression level of a reference cell, e.g., a reference cell known to be a determined dopaminergic progenitor cell. In some embodiments, the computer-based method includes outputting a computer-calculated label classification based on the probability and the deviation score.

[0069] The methods, algorithms, and systems described herein are designed to provide a novel method for defining determined dopaminergic progenitor cells or dopaminergic cells. This novel method is referred to as the computed definition, while previous types of definitions are referred to as the biological definition (functional, structural, or originating). While the computed definition is related to the biological definition, as discussed herein, the computed definition provides a more robust and accurate method for comparing two different cells and determining whether they are the same type of cell or different cell types. In some embodiments, the computed definition provides a more robust and accurate method for identifying cells of unknown identifiability.

[0070] A computed definition refers to the use of computational analysis of information to arrive at a definition. A database of information about one or more cells is disclosed. For example, some of the databases are reference databases. A reference database may include a cell dataset generated from cell data of at least two known cell lines, tissues, or primary cells. A known cell line, tissue, or primary cell means a cell line in which several characteristics, such as phenotypes, such as dopaminergic cells or determined dopaminergic progenitor cells, are identified by conventional biological assays, e.g., induction methods, raw materials, biochemical assays (e.g., enzyme activity, e.g., alkaline phosphatase activity), or markers such as specific identified proteins that are thought to be able to identify a particular cell line. In some embodiments, cells in which several characteristics are known are called reference cells. A computed phenotype may be defined by comprehensive profiling methods (or other molecular profiling methods), such as gene expression, which are then utilized in the methods disclosed herein. Whether a cell is a stem cell or a differentiated cell is determined using a subset of markers or a subset of profiling data such as gene expression, and such biological phenotypes can be used and incorporated into this method in the form of labeled, related biological classes.

[0071] A. Reference cell In some embodiments, the methods provided herein involve the use of reference cells and / or a reference database for identifying (e.g., determining) the presence of determined dopaminergic progenitor cells within an in vitro population of neural progenitor cells. The types of reference cells intended for use in accordance with the methods provided herein include cells with known distinctiveness (e.g., labeled cells) and known characteristics, such as having a distinctive gene expression profile. In some embodiments, the reference database includes reference cell labels and corresponding reference cell characteristics from a plurality of reference cells. In some embodiments, the reference database can be used, for example, in accordance with the methods provided herein, to determine whether cells with unknown distinctiveness (e.g., unlabeled) having a particular characteristic, such as a gene expression pattern, have a particular cell distinctiveness.

[0072] In some embodiments, the reference cells are pluripotent stem cells. In some embodiments, the pluripotent stem cells are induced pluripotent stem cells (iPSCs). In some embodiments, the iPSCs are generated from fibroblasts collected from healthy human subjects. In some embodiments, the iPSCs are generated from fibroblasts collected from human subjects with Parkinson's disease. In some embodiments, the iPSCs are generated from fibroblasts collected from human subjects susceptible to developing Parkinson's disease. Exemplary methods for iPSC generation are described in Section II.

[0073] In some embodiments, the reference cells are cells differentiated under conditions to become neural progenitor cells such as floorplate midbrain progenitor cells, determined dopaminergic progenitor cells, or dopaminergic neurons. In some embodiments, the reference cells are cells differentiated according to any of the methods described in Section II. In some embodiments, the reference cells are determined dopaminergic progenitor cells. In some embodiments, the reference cells are dopaminergic neurons. In some embodiments, the differentiated cells, determined dopaminergic cells, and / or dopaminergic cells are derived from iPSCs, for example, the iPSCs cultured under conditions that promote differentiation into dopaminergic cells.

[0074] In some embodiments, the reference cells are cells that are described, for example, labeled and characterized in a publicly available database.

[0075] In some embodiments, the reference cell has known distinctiveness. Therefore, in some cases, the distinctiveness of the cell can be used as the label for the reference cell. In some embodiments, the reference cell label indicates the cell phenotype. In some embodiments, the reference cell label indicates a cell characteristic, such as gene expression level. In some embodiments, the reference cell label indicates whether or not the reference cell is a pluripotent stem cell. In some embodiments, the reference cell label indicates whether or not the reference cell is a determined dopaminergic progenitor cell. In some embodiments, the reference cell label indicates whether or not the reference cell is a dopaminergic neuron.

[0076] In some embodiments, the reference cell label indicates the differentiation stage of the reference cell. In some embodiments, the reference cell label indicates the period of time the reference cell was cultured under differentiation conditions. In some embodiments, the reference cell label indicates the period of time the reference cell was cultured under differentiation conditions to become a dopaminergic neuron, for example, any of the periods described in Section II.

[0077] In some embodiments, reference cell labeling is based on publicly available annotations of the reference cells. In some embodiments, reference cell labeling is based on an assessment of the dopamine production level of the reference cells. In some embodiments, the dopamine production level is assessed using high-performance liquid chromatography (HPLC). In some embodiments, reference cell labeling is based on an assessment of tyrosine hydroxylase (TH) expression in the reference cells. In some embodiments, TH expression is assessed using cell staining methods. In some embodiments, reference cell labeling is based on an assessment of FOXA2 expression in the reference cells. In some embodiments, FOXA2 expression is assessed using cell staining methods. In some embodiments, TH expression is assessed using flow cytometry.

[0078] In some embodiments, reference cells are characterized as dopaminergic neurons if they express markers of midbrain dopaminergic neurons, such as the expression of FOXA2 or tyrosine hydroxylase (TH). In some embodiments, reference cells express TH (TH+). In some embodiments, reference cells express FOXA2 (FOXA2+). In some embodiments, reference cells express both TH and FOXA2 (TH+FOXA2+).

[0079] In some embodiments, the reference cell is a determined dopaminergic progenitor cell when it is determined to be a dopaminergic neuron or can become a dopaminergic neuron, i.e., the reference cell may have the functional activity of a dopaminergic neuron but may not yet express, or may not express at high levels, markers of a dopaminergic neuron. For example, the reference cell may exhibit lower levels of TH than a dopaminergic neuron but still exhibit one or more characteristics of a determined dopaminergic progenitor cell, indicating that the differentiated cell may have the functional activity of a dopaminergic neuron. In some embodiments, one or more characteristics of the reference cell include the ability to survive, engraft, and / or innervate other cells in vivo, for example, when administered to an animal model. In some embodiments, the reference cell may be able to innervate host tissue upon transplantation into an animal or human subject.

[0080] In some embodiments, the reference cells are cells that have a therapeutic effect for treating neurodegenerative diseases. In some embodiments, when transplanted, the reference cells improve or reverse the symptoms of the neurodegenerative disease. In some embodiments, the neurodegenerative disease is Parkinson's disease. In some embodiments, when transplanted into a target requiring transplantation, such as the substantia nigra of a patient, the reference cells improve Parkinson's symptoms.

[0081] In some embodiments, reference cells are screened for their therapeutic effect to treat a neurodegenerative disease, for example, determined in an animal model of the neurodegenerative disease. In some embodiments, the neurodegenerative disease is Parkinson's disease. In some embodiments, reference cells are screened using an animal model of Parkinson's disease. Any known and available animal model of Parkinson's disease can be used for screening. In some embodiments, the animal model is a lesion model in which 6-hydroxydopamine (6-OHDA) is injected unilaterally and stereotactically into the substantia nigra of an animal. In some embodiments, the animal model is a lesion model in which 6-OHDA is injected unilaterally and stereotactically into the medial forebrain bundle of an animal. In some embodiments, reference cells are transplanted into the substantia nigra of the animal model. In some embodiments, behavioral tests are performed to screen the therapeutic effect of the transplantation into the animal model. In some embodiments, the behavioral tests include monitoring amphetamine-induced spinning behavior. In some embodiments, reference cells are determined to reduce, decrease, or restore brain lesions in the Parkinson's model in this model. In some embodiments, reference cells may be cells that do not reduce, decrease, or restore brain lesions in the Parkinson's model in this model. The reference database may include data from various reference cell populations that exhibit diverse or different therapeutic effects, for example, in animal models for treating neurodegenerative diseases.

[0082] As described above, in some embodiments, any of several reference cell properties of a particular reference cell can be determined, including any one or more properties, characteristics, features, or attributes of the reference cell. In some embodiments, reference cell properties can be used as data to characterize or describe a particular population of reference cells. For example, reference cell properties may include mRNA expression levels, microRNA expression levels, protein expression levels, post-translational protein modification levels, non-coding RNA expression profiles, DNA methylation levels, histone modification levels, transcription factor-DNA site binding profiles, DNA sequence profiles, or any other type of cell property, or any combination thereof. Any one or more of the reference cell properties can be used as data to enter into a reference cell database or to add to a reference cell database.

[0083] In some embodiments, a reference cell characteristic may be protein expression level. In some embodiments, a reference cell characteristic may be post-translational protein modification level. In some embodiments, a reference cell characteristic may be non-coding RNA expression profile. In some embodiments, a reference cell characteristic may be epigenetic profile. In some embodiments, a reference cell characteristic may be transcription profile. In some embodiments, a reference cell characteristic may be gene expression level. In some embodiments, a reference cell database may contain information on one or more of the above reference cell characteristics.

[0084] 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 bulk RNA from multiple cells. In some embodiments, RNA sequencing is performed on a single cell. In some embodiments, RNA sequencing is performed on bulk RNA from multiple cells and on a single cell.

[0085] In some embodiments, a reference database is populated using multiple reference cells having known identifiability, such as labels, and known characteristics, such as gene expression levels. In some embodiments, the multiple reference cells used to populate the reference database have different labels from each other. In some embodiments, a portion of the reference cells used to populate the reference database have the same label. In some embodiments, a portion of the reference cells used to populate the reference database have different labels from other reference cells in the reference database. Thus, in some embodiments, the reference database may contain multiple reference cells, some of which have the same label as other cells in the reference database, and some of which have different labels from other cells in the reference database.

[0086] In some embodiments, the reference cell characteristics of a specific reference cell are included in the reference database. In some embodiments, the reference database includes reference cell labels. In some embodiments, the reference database includes protein expression levels of the reference cell. In some embodiments, the reference database includes epigenetic profiles of the reference cell. In some embodiments, the reference database includes transcription profiles of the reference cell. In some embodiments, the reference database includes gene expression levels of the reference cell. In some embodiments, the reference database includes gene expression data from publicly available databases. In some embodiments, the reference database includes microarray data. In some embodiments, the reference database includes RNA sequence data. In some embodiments, the reference database includes both microarray data and RNA sequence data.

[0087] In some embodiments, the reference database includes bulk RNA sequence data. In some embodiments, the bulk RNA sequence data is obtained from multiple reference cells. In some embodiments, the bulk RNA sequence data is obtained from pooled RNA from multiple reference cells.

[0088] To obtain bulk RNA sequence data, any known and available method can be used (see, for example, Chao et al., 2019, BMC Genomics 20:571, which is incorporated in its entirety herein by reference). For example, total RNA from samples, e.g., from multiple reference cells derived from an in vitro population of cells, can be isolated using TRIZOL, treated with DNase I, and purified. The concentration and quality of the isolated RNA can be measured and confirmed prior to library preparation of total RNA or mRNA. For library preparation, total RNA or mRNA is fragmented and converted to cDNA using reverse transcription. After construction, amplification, and barcoding of any double-stranded cDNA, the library can be processed for next-generation sequencing using any known and available library preparation method, sequencing platform, and genome alignment tool.

[0089] In some embodiments, the reference database includes single-cell RNA sequence data. In some embodiments, the use of single-cell RNA sequence data offers specific advantages. In some embodiments, the use of single-cell RNA sequence data enables the characterization of subpopulations of cells, e.g., determined dopaminergic progenitor cells within a larger in vitro population of cells. In some embodiments, the use of single-cell RNA sequence data reduces the number of reference cells required for use in the methods provided herein. In some embodiments, the use of single-cell RNA sequence data improves the characterization of the biological variability across the reference cells. In some embodiments, the use of single-cell RNA sequence data allows for easier verification and interpretation of gene expression levels.

[0090] Any known and available method for single-cell RNA sequencing can be used (see, for example, Zheng et al., 2017 (Nature Communications 8:14049) and Haque et al., 2017 (Genome Medicine 9:75), which are incorporated herein in their entirety by reference). For single-cell RNA sequencing, a sample, e.g., a single cell derived from an in vitro population of cells, can be isolated using flow cytometry cell sorting, a microfluidic platform, or a droplet-based method. The isolated cells are lysed to allow for the acquisition of RNA molecules. Poly[T]-primers can be used for the specific analysis of polyadenylated mRNA molecules, and the priming mRNA molecules are converted to cDNA using reverse transcription. In some cases, unique molecules Using identifiers, single mRNA molecules can be labeled based on cell origin. The cDNA pool is then amplified, optionally barcoded, and sequenced, for example, using next-generation sequencing (NGS) and library preparation techniques, sequencing platforms, and genome alignment tools similar to those used for bulk RNA samples. In some cases, unbiased cell type classification within a mixed population of distinct cell types can be achieved with as few as 10,000–50,000 reads per cell, and single-cell libraries using various common protocols may approach saturation when sequenced at a read depth of 1,000,000.

[0091] In some embodiments, the reference database includes bulk RNA sequence data and single-cell RNA sequence data. In some embodiments, the bulk RNA sequence data and single-cell RNA sequence data are obtained from the same sample, e.g., an in vitro population of cells. In some embodiments, the single-cell RNA sequence data can be used to approximate the bulk RNA sequence data obtained from the same sample, e.g., an in vitro population of cells. In some embodiments, the approximate bulk RNA sequence data is obtained by averaging the single-cell RNA sequence data derived from reference cells contained in the same sample, e.g., an in vitro population of cells. In some embodiments, the reference database includes the approximate bulk RNA sequence data.

[0092] In the embodiment, the gene expression reference database includes transcriptional profiles of one or more dopaminergic neurons. In the embodiment, the method includes classifying cells by an in vitro population of neural progenitor cells based at least partially on a computer-derived protein-protein network. In the embodiment, the gene expression profile information includes transcriptional profiles. In the embodiment, the gene expression profile information includes transcriptional profiles from single cells. In the embodiment, the gene expression reference database includes known classification labels.

[0093] The reference database consists of cell datasets, and each cell dataset consists of characteristic data. Characteristic data may include, for example, the output from mRNA expression analysis, microRNA expression analysis, protein expression analysis, post-translational protein modification analysis, non-coding RNA expression analysis, DNA methylation pattern analysis, histone modification analysis, transcription factor-DNA site binding analysis, DNA sequence analysis, or any other type of cell characteristics.

[0094] B. Test cells In some embodiments, the methods provided herein enable the determination of whether unknown identifiable cells or a group of cells are determined dopaminergic progenitor cells. In some embodiments, the unknown identifiable cells or a group of cells are test cells. In some embodiments, the test cells are an in vitro population of cells. In some embodiments, the test cells consist of an in vitro population of neural progenitor cells. In some embodiments, the test cells include cells differentiated under conditions to become dopaminergic neurons. In some embodiments, the test cells include cells differentiated according to any of the methods described in Section II. In some embodiments, the test cells include cells differentiated under conditions to become dopaminergic neurons for any of the periods described in Section II. In some embodiments, the cells to be differentiated are pluripotent stem cells. In some embodiments, the pluripotent stem cells are induced pluripotent stem cells (iPSCs). In some embodiments, iPSCs are generated from fibroblasts collected from a healthy human subject. In some embodiments, iPSCs are generated from fibroblasts collected from a human subject with Parkinson's disease. Exemplary methods for iPSC generation are described in Section II.

[0095] In some embodiments, the discriminative nature of the test cells, for example, the determination of whether the test cells are determined dopaminergic progenitor cells, indicates whether the in vitro population of cells includes a population of determined dopaminergic progenitor cells.

[0096] In some embodiments, the test dataset is determined from test cells. In some embodiments, the test dataset is used to determine whether the test cells are determined dopaminergic progenitor cells. In some embodiments, the test dataset is used to determine whether the test cells contain determined dopaminergic progenitor cells.

[0097] A “test dataset” is a dataset generated from cells (e.g., neural progenitor cells) for which a calculated definition is desired. It is generated from characteristic data of unknown cell lines, tissues, or primary cells. In this context, “unknown” means for which a calculated definition is desired. Typically, a test dataset consists of a comprehensive profile, such as those discussed herein, when related to a comprehensive profile in a reference database. A test dataset can be merged with a reference database to form an updated reference database. In certain embodiments, this may be as simple as adding data to an existing spreadsheet. Thus, a test dataset containing gene expression profile information for an in vitro population of neural progenitor cells can be included (merged) into a reference database after determining that the in vitro population of neural progenitor cells includes determined dopaminergic progenitor cells.

[0098] In some embodiments, the test dataset includes characteristics of the test cells. For example, in some cases, the test dataset includes the same types of characteristics determined for the reference cells. In some embodiments, the test dataset may include reference cell characteristics such as mRNA expression levels, microRNA expression levels, protein expression levels, post-translational protein modification levels, non-coding RNA expression profiles, DNA methylation levels, histone modification levels, transcription factor-DNA site binding profiles, DNA sequence profiles, or any other types of cellular characteristics.

[0099] In some embodiments, the test dataset includes protein expression levels. In some embodiments, the test dataset includes post-translational protein modification levels. In some embodiments, the test dataset includes non-coding RNA expression profiles. In some embodiments, the test dataset includes epigenetic profiles. In some embodiments, the test dataset includes transcription profiles. In some embodiments, the test dataset includes gene expression levels.

[0100] 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 bulk RNA derived from multiple cells. In some embodiments, RNA sequencing is performed on a single cell. In some embodiments, RNA sequencing is performed on bulk RNA derived from multiple cells and on a single cell. Exemplary methods for extracting, preparing, and analyzing bulk RNA and single-cell RNA are described in Section IA above.

[0101] In some embodiments, test cell characteristics are included in the test dataset. In some embodiments, the test dataset includes protein expression levels of test cells. In some embodiments, the test dataset includes epigenetic profiles of test cells. In some embodiments, the test dataset includes transcription profiles of test cells. In some embodiments, the test dataset includes gene expression levels of test cells. In some embodiments, the test dataset includes microarray data. In some embodiments, the test dataset includes RNA sequence data. In some embodiments, the test dataset includes microarray data and RNA sequence data. In some embodiments, the test dataset includes bulk RNA sequence data. In some embodiments, the test dataset includes single-cell RNA sequence data. In some embodiments, the test dataset includes bulk RNA sequence data and single-cell RNA sequence data. In some embodiments, the test dataset includes expression levels of one or more metagenes. The determination of metagenes and their expression levels is discussed in Section I C.

[0102] C. metagene In some embodiments, the methods provided herein use metagenes and their expression levels to determine the distinctiveness of test cells. A metagene refers to a pattern of gene expression. For example, a metagene may be a group of genes having correlated gene expression. In some embodiments, a metagene combines information from several individual genes, and the metagene's expression level is calculated based on the expression levels of the individual genes. Multiple metagenes and their expression levels can be determined based on the individual gene expression levels. In some embodiments, the metagene's expression level is based on the combined individual gene expression levels, and the determination of such metagene includes determining the extent to which the expression levels of the individual genes contribute to the metagene's expression level. For example, the metagene's expression level may be a weighted combination of individual gene expression levels, and the determination of such metagene includes determining the weight of the individual genes for each metagene. In some embodiments, the metagene and its expression level reflect the correlated expression levels of the individual genes as a whole. In some embodiments, the metagene and its expression level reflect individual genes co-expressed by cells of the same phenotype (e.g., determined dopaminergic progenitor cells). Exemplary co-expressed genes in determined dopaminergic progenitor cells are discussed in Section III.

[0103] In some embodiments, the methods provided herein use metagene expression levels to determine whether cells contained in a population of cells are determined dopaminergic progenitor cells. In some embodiments, metagene expression levels are used to determine whether a population of cells contains determined dopaminergic progenitor cells. In some embodiments, the use of metagenes reduces the number of features used in determining whether cells are determined dopaminergic progenitor cells, or whether a population of cells contains determined dopaminergic progenitor cells. In some embodiments, reducing the number of features makes such decisions more computer-friendly. In some embodiments, reducing the number of features improves the accuracy of such decisions. For example, because metagenes combine and / or retain information derived from individual genes, the performance of machine learning models trained using metagene expression levels may be higher than those trained on gene expression levels alone.

[0104] 1. Determination of metagenes In some embodiments, metagenes are determined based on the gene expression levels of reference cells. In some embodiments, the gene expression levels of reference cells are included in a reference database. Exemplary reference cells and reference databases are described in Section IA. In some embodiments, metagenes are determined using a reference database containing microarray data. In some embodiments, metagenes are determined using a reference database containing RNA sequence data. In some embodiments, metagenes are determined using a reference database containing microarray data and a reference database containing RNA sequence data. In some embodiments, metagenes are determined using a reference database containing bulk RNA sequence data. In some embodiments, metagenes are determined using a reference database containing single-cell RNA sequence data. In some embodiments, metagenes are determined using a reference database containing bulk RNA sequence data and a reference database containing single-cell RNA sequence data.

[0105] In some embodiments, metagenes are determined by computer. In some embodiments, metagenes are determined using dimensionality reduction techniques. Dimensionality reduction techniques transform data from a high-dimensional space (e.g., individual genes) to a low-dimensional space (e.g., metagenes) such that the lower-dimensional representation of the data still retains meaningful or useful properties of the original data. In some embodiments, metagenes are determined by applying dimensionality reduction techniques to a database.

[0106] In some embodiments, the dimensionality reduction method is a linear method. In some embodiments, the dimensionality reduction method is factor analysis. In some embodiments, the dimensionality reduction method is network element analysis. In some embodiments, the dimensionality reduction method is linear discriminant analysis. In some embodiments, the dimensionality reduction method is independent component analysis (ICA). In some embodiments, the dimensionality reduction method is principal component analysis (PCA). In some embodiments, the dimensionality reduction method is sparse PCA. In some embodiments, the dimensionality reduction method is robust PCA.

[0107] In some embodiments, the dimensionality reduction technique is non-negative matrix factorization (NMF). Using NMF, a matrix can be decomposed into two matrices such that none of the three matrices contain negative elements. This non-negativity makes the resulting matrix easier to examine, for example, if the original matrix itself contains only non-negative values. In some embodiments, the dimensionality reduction technique is conventional NMF. In some embodiments, the dimensionality reduction technique is discriminative NMF. In some embodiments, the dimensionality reduction technique is normalization NMF. In some embodiments, the dimensionality reduction technique is graph normalization NMF. In some embodiments, the dimensionality reduction technique is bootstrap sparse NMF.

[0108] In some embodiments, the dimensionality reduction method is a nonlinear method. In some embodiments, the dimensionality reduction method is kernel PCA. In some embodiments, the dimensionality reduction method is generalized discriminant analysis (GDA). In some embodiments, the dimensionality reduction method is an autoencoder. In some embodiments, the dimensionality reduction method is a T-distributed stochastic neighbor embedding (t-SNE). In some embodiments, the dimensionality reduction method is a manifold learning method. In some embodiments, the dimensionality reduction method is an isomap. In some embodiments, the dimensionality reduction method is locally linear embedding (LLE). In some embodiments, the dimensionality reduction method is a Hessian LLE. In some embodiments, the dimensionality reduction method is a Laplacian eigenmap. In some embodiments, the dimensionality reduction method is a graph-based kernel PCA. In some embodiments, the dimensionality reduction method is uniform manifold approximation and projection (UMAP).

[0109] In some embodiments, the dimensionality reduction method is a clustering method that can be used as a dimensionality reduction method. In some embodiments, the dimensionality reduction method is a connectivity-based clustering method. In some embodiments, the dimensionality reduction method is hierarchical clustering. In some embodiments, the dimensionality reduction method is centroid-based clustering. In some embodiments, the dimensionality reduction method is k-means clustering. In some embodiments, the dimensionality reduction method is a distribution-based clustering method. In some embodiments, the dimensionality reduction method is a Gaussian mixture modeling method. In some embodiments, the dimensionality reduction method is a density-based clustering method. In some embodiments, the dimensionality reduction method is DBSCAN. In some embodiments, the dimensionality reduction method is OPTICS. In some embodiments, the dimensionality reduction method is a grid-based clustering method. In some embodiments, the dimensionality reduction method is STING. In some embodiments, the dimensionality reduction method is CLIQUE.

[0110] 2. Metagene expression levels In some embodiments, the expression level of the determined metagene is calculated. In some embodiments, the metagene expression level is determined using the same reference database used to determine the metagene. In some embodiments, the metagene expression level is determined using a reference database not used to determine the metagene. In some embodiments, the metagene expression level is determined using a test dataset (e.g., any test dataset described in Section IB). Determination of the metagene expression level is possible if the expression levels of the same or similar set of genes are included in the reference database used to determine the metagene, as well as in the reference database and / or test dataset used to determine the metagene expression level.

[0111] In some embodiments, the metagene gene expression level is determined using a reference database containing microarray data. In some embodiments, the metagene gene expression level is determined using a reference database containing RNA sequence data. In some embodiments, the metagene gene expression level is determined using a reference database containing microarray data and a reference database containing RNA sequence data. In some embodiments, the metagene gene expression level is determined using a reference database containing bulk RNA sequence data. In some embodiments, the metagene gene expression level is determined using a reference database containing single-cell RNA sequence data. In some embodiments, the metagene gene expression level is determined using a reference database containing bulk RNA sequence data and a reference database containing single-cell RNA sequence data.

[0112] In some embodiments, the metagene is determined using a reference database containing bulk RNA sequence data, and the metagene expression level is determined using a reference database containing bulk RNA sequence data. In some embodiments, the metagene is determined using a reference database containing bulk RNA sequence data, and the metagene expression level is determined using a reference database containing single-cell RNA sequence data. In some embodiments, the metagene is determined using a reference database containing single-cell RNA sequence data, and the metagene expression level is determined using a reference database containing bulk RNA sequence data. In some embodiments, the metagene is determined using a reference database containing single-cell RNA sequence data, and the metagene expression level is determined using a reference database containing single-cell RNA sequence data. In some embodiments, the metagene is determined using a reference database containing bulk RNA sequence data and a reference database containing single-cell RNA sequence data, and the metagene expression level is determined using a reference database containing bulk RNA sequence data. In some embodiments, the metagene is determined using a reference database containing bulk RNA sequence data and a reference database containing single-cell RNA sequence data, and the metagene expression level is determined using a reference database containing single-cell RNA sequence data.

[0113] In some embodiments, the metagene gene expression level is determined using a test dataset including microarray data. In some embodiments, the metagene gene expression level is determined using a test dataset including RNA sequence data. In some embodiments, the metagene gene expression level is determined using a test dataset including microarray data and RNA sequence data. In some embodiments, the metagene gene expression level is determined using a test dataset including bulk RNA sequence data. In some embodiments, the metagene gene expression level is determined using a test dataset including single-cell RNA sequence data. In some embodiments, the metagene gene expression level is determined using a test dataset including bulk RNA sequence data and single-cell RNA sequence data.

[0114] In some embodiments, the metagene is determined using a reference database containing bulk RNA sequence data, and the metagene expression level is determined using a test dataset containing bulk RNA sequence data. In some embodiments, the metagene is determined using a reference database containing bulk RNA sequence data, and the metagene expression level is determined using a test dataset containing single-cell RNA sequence data. In some embodiments, the metagene is determined using a reference database containing single-cell RNA sequence data, and the metagene expression level is determined using a test dataset containing bulk RNA sequence data. In some embodiments, the metagene is determined using a reference database containing single-cell RNA sequence data, and the metagene expression level is determined using a test dataset containing single-cell RNA sequence data. In some embodiments, the metagene is determined using a reference database containing bulk RNA sequence data and a reference database containing single-cell RNA sequence data, and the metagene expression level is determined using a test dataset containing bulk RNA sequence data. In some embodiments, the metagene is determined using a reference database containing bulk RNA sequence data and a reference database containing single-cell RNA sequence data, and the metagene expression level is determined using a test dataset containing single-cell RNA sequence data.

[0115] In some embodiments, metagenes are determined by applying a dimensionality reduction technique to one or more reference databases. In some embodiments, one or more outputs of the dimensionality reduction technique are used to determine the metagene expression level.

[0116] In some embodiments, one or more outputs of a dimensionality reduction method and a reference database are used to determine metagene expression levels based on the reference database. In some embodiments, one or more outputs of a dimensionality reduction method and a test dataset are used to determine metagene expression levels based on the test dataset.

[0117] In some embodiments, one or more outputs of the dimensionality reduction method include information about how a plurality of individual genes are combined to form a metagene. In some embodiments, one or more outputs of the dimensionality reduction method include information about the extent to which the expression levels of individual genes contribute to the expression level of the metagene. In some embodiments, one or more outputs of the dimensionality reduction method include, for example, the weights of individual genes if the metagene expression level is a weighted combination of the expression levels of individual genes.

[0118] In some embodiments, metagene expression levels are determined using regression analysis. In some embodiments, the regression analysis is linear regression. In some embodiments, the regression analysis is performed using one or more outputs of a dimensionality reduction method and a reference database. In some embodiments, the regression analysis is used to approximate the gene expression levels in the reference database using one or more outputs of a dimensionality reduction method (e.g., the weights of the individual genes contributing to the metagene). In some embodiments, the regression analysis is used to approximate the gene expression levels in the reference database as a weighted combination of the weights of the individual genes contributing to the metagene. In some embodiments, the weights estimated by the regression analysis can be used as the metagene expression levels in the reference database.

[0119] In some embodiments, regression analysis is performed using one or more outputs of a dimensionality reduction method and the test dataset. In some embodiments, regression analysis is used to approximate the gene expression levels of the test dataset using one or more outputs of a dimensionality reduction method (e.g., the weights of individual genes contributing to a metagene). In some embodiments, regression analysis is used to approximate the gene expression levels of the test dataset as a weighted combination of the weights of individual genes contributing to a metagene. In some embodiments, the weights estimated by regression analysis can be used as the metagene expression levels of the test dataset.

[0120] D. Probability evaluation (e.g., NeuroScore) In some embodiments, the methods provided herein involve the use of a machine learning model. In some embodiments, the machine learning model is trained to determine the likelihood of a cell or a group of cells having a determined metagene expression level of a dopaminergic progenitor cell. In some embodiments, the machine learning model is trained to determine the probability of a cell or a group of cells having a determined metagene expression level of a dopaminergic progenitor cell. In some embodiments, the machine learning model is trained to classify a cell or a group of cells as having a determined metagene expression level of a dopaminergic progenitor cell.

[0121] In some embodiments, the machine learning model is trained on the expression levels of one or more metagenes. In some embodiments, the machine learning model is trained on metagene expression levels determined based on a reference database (for example, determined using any of the reference databases described in Section IA and any of the methods described in Section IC).

[0122] In some embodiments, the machine learning model is a supervised classification model. In some embodiments, the machine learning model is trained using reference cell labels contained in a reference database. In some embodiments, the reference cell label indicates whether the corresponding reference cell is a determined dopaminergic progenitor cell. In some embodiments, the reference cell label indicates the period during which the corresponding reference cell differentiates under conditions to become a dopaminergic neuron, for example, one of the periods described in Section II. In some embodiments, the reference cell label indicates whether the period is at least about 18 days. In some embodiments, the reference cell label indicates whether the period is about 18 to 25 days.

[0123] In some embodiments, the supervised classification model is a logistic regression model. In some embodiments, the supervised classification model is a linear discriminant analysis (LDA) model. In some embodiments, the supervised classification model is a Naive Bayes classifier. In some embodiments, the supervised classification model is a perceptron. In some embodiments, the supervised classification model is a support vector machine (SVM). In some embodiments, the supervised classification model is a quadratic classifier. In some embodiments, the supervised classification model is a decision tree. In some embodiments, the supervised classification model is a random forest. In some embodiments, the supervised classification model is a neural network. In some embodiments, the supervised classification model is an ensemble model that includes any of these models.

[0124] In embodiments, the machine learning model is a best-fitting classification model identified by the algorithm most stable to random perturbations. In embodiments, the best-fitting classification model can cluster individual datasets such that each dataset in a cluster is indistinguishable from each other within that cluster. In embodiments, the method includes identifying computer-derived classification labels based solely on biological characteristics. In embodiments, the method includes identifying differences in at least one label between at least two samples in at least two clusters across at least one dataset. In embodiments, the method includes filtering within clusters of samples having similar label profiles. In embodiments, the method includes defining a differentially regulated protein-protein network. In embodiments, the method includes using the protein-protein network to define or manipulate class membership or to define the biological function of the neural progenitor cells. In embodiments, the best-fitting classification model can cluster individual datasets such that each dataset in a cluster is distinct from each other.

[0125] At some point after the reference database has been received, the method may include performing unsupervised classification. This means that a new screening of the data is performed, with no prior conception about the outcome of the screening. The screening is typically performed multiple times, for example, at least 5, 10, 20, 50, 100, 200, 300, or 500 times. The screening results are analyzed for stable results, which means that the screening results provide the same results, or results that are similar (at least 80%, 85%, 90%, 95%, 97%, 99%, or 100%) to the previous results. The re-screening of the data can be done entirely de novo or can be initiated using specific assumptions.

[0126] In some embodiments, the metagene expression level of test cells is determined based on a test dataset (e.g., using any of the test datasets described in Section IB and any of the methods described in Section IC), and the metagene expression level is applied as input to a trained machine learning model. In some embodiments, the machine learning model outputs a binary prediction of test cells having the determined dopaminergic progenitor cell metagene expression level. In some embodiments, the machine learning model outputs the probability of test cells having the determined dopaminergic progenitor cell metagene expression level. In some embodiments, the machine learning model outputs the probability of test cells having the determined dopaminergic progenitor cell metagene expression level. The outputs (e.g., binary prediction, probability, probability) are also referred to herein as “NeuroScore”.

[0127] In some embodiments, the NeuroScore output of the test cells, for example, the probability of a test cell having the determined metagene expression level of a dopaminergic progenitor cell, is compared to a predetermined threshold. In some embodiments, the method provided herein outputs a computer-calculated label classification, which, if it exceeds a predetermined threshold, indicates that the test cell contains the determined dopaminergic progenitor cell.

[0128] A predetermined threshold for NeuroScore can be set using various methods and criteria. For example, a predetermined threshold can be set to optimize the specificity and / or sensitivity when predicting whether a test cell has a determined metagene expression level for dopaminergic progenitor cells. In some embodiments, the predetermined threshold is set so that test cells having a determined metagene expression level for dopaminergic progenitor cells are identified with a sensitivity of approximately 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, or greater than 99%. In some embodiments, the predetermined threshold is set so that test cells having a determined metagene expression level for dopaminergic progenitor cells are identified with a specificity of approximately 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, or greater than 99%. In some embodiments, a predetermined threshold is set so that test cells having the determined metagene expression level of dopaminergic progenitor cells are identified with a sensitivity of over 98% and a specificity of 100%.

[0129] In some embodiments, a predetermined threshold is set based on a NeuroScore calculated from a reference database. In some embodiments, the reference database includes gene expression levels of reference cells differentiated according to any of the methods described in Section II. In some embodiments, the predetermined threshold is set such that reference cells differentiated for at least about 18 days have a NeuroScore above the predetermined threshold. In some embodiments, the predetermined threshold is set such that reference cells differentiated for about 18 to 25 days have a NeuroScore above the predetermined threshold. In some embodiments, the predetermined threshold is set such that reference cells known to have a clinical effect, e.g., reduction or recovery of symptoms of Parkinson's disease, have a NeuroScore above the predetermined threshold.

[0130] In some embodiments, if the NeuroScore of a test cell indicates a probability greater than approximately 0.4 that the test cell has the determined metagene expression level of a dopaminergic progenitor cell, the computer-calculated label classification indicates that the test cell is or contains the determined dopaminergic progenitor cell. In some embodiments, if the NeuroScore of a test cell indicates a probability greater than approximately 0.45 that the test cell has the determined metagene expression level of a dopaminergic progenitor cell, the computer-calculated label classification indicates that the test cell is or contains the determined dopaminergic progenitor cell. In some embodiments, if the NeuroScore of a test cell indicates a probability greater than approximately 0.5 that the test cell has the determined metagene expression level of a dopaminergic progenitor cell, the computer-calculated label classification indicates that the test cell is or contains the determined dopaminergic progenitor cell. In some embodiments, if the NeuroScore of a test cell indicates a probability greater than approximately 0.55 that the test cell has the determined metagene expression level of a dopaminergic progenitor cell, the computer-calculated label classification indicates that the test cell is or contains the determined dopaminergic progenitor cell. In some embodiments, if the NeuroScore of a test cell indicates a probability greater than approximately 0.6 that the test cell has the determined metagene expression level of a dopaminergic progenitor cell, the computer-calculated label classification indicates that the test cell is or contains the determined dopaminergic progenitor cell. In some embodiments, if the NeuroScore of a test cell indicates a probability greater than approximately 0.65 that the test cell has the determined metagene expression level of a dopaminergic progenitor cell, the computer-calculated label classification indicates that the test cell is or contains the determined dopaminergic progenitor cell.In some embodiments, if the NeuroScore of a test cell indicates a probability greater than approximately 0.7 that the test cell has the determined metagene expression level of a dopaminergic progenitor cell, the computer-calculated label classification indicates that the test cell is or contains the determined dopaminergic progenitor cell. In some embodiments, if the NeuroScore of a test cell indicates a probability greater than approximately 0.75 that the test cell has the determined metagene expression level of a dopaminergic progenitor cell, the computer-calculated label classification indicates that the test cell is or contains the determined dopaminergic progenitor cell. In some embodiments, if the NeuroScore of a test cell indicates a probability greater than approximately 0.8 that the test cell has the determined metagene expression level of a dopaminergic progenitor cell, the computer-calculated label classification indicates that the test cell is or contains the determined dopaminergic progenitor cell. In some embodiments, if the NeuroScore of a test cell indicates a probability greater than approximately 0.85 that the test cell has the determined metagene expression level of a dopaminergic progenitor cell, the computer-calculated label classification indicates that the test cell is or contains the determined dopaminergic progenitor cell. In some embodiments, if the NeuroScore of a test cell indicates a probability greater than approximately 0.9 that the test cell has the determined metagene expression level of a dopaminergic progenitor cell, the computer-calculated label classification indicates that the test cell is or contains the determined dopaminergic progenitor cell. In some embodiments, if the NeuroScore of a test cell indicates a probability greater than approximately 0.95 that the test cell has the determined metagene expression level of a dopaminergic progenitor cell, the computer-calculated label classification indicates that the test cell is or contains the determined dopaminergic progenitor cell.

[0131] In some embodiments, if the NeuroScore of the test cells exceeds an approximate probability threshold, the computer-calculated label classification indicates that the test cells are or contain the determined dopaminergic progenitor cells. In some embodiments, the probability threshold is approximately 0.4 to 1. In some embodiments, the probability threshold is approximately 0.4 to 0.9. In some embodiments, the probability threshold is approximately 0.4 to 0.8. In some embodiments, the probability threshold is approximately 0.4 to 0.7. In some embodiments, the probability threshold is approximately 0.4 to 0.6. In some embodiments, the probability threshold is approximately 0.5 to 0.8. In some embodiments, the probability threshold is approximately 0.5 to 0.7. In some embodiments, the probability threshold is approximately 0.5 to 0.6.

[0132] In some embodiments, the probability threshold is approximately 0.4. In some embodiments, the probability threshold is approximately 0.45. In some embodiments, the probability threshold is approximately 0.5. In some embodiments, the probability threshold is approximately 0.55. In some embodiments, the probability threshold is approximately 0.6. In some embodiments, the probability threshold is approximately 0.65. In some embodiments, the probability threshold is approximately 0.7. In some embodiments, the probability threshold is approximately 0.75. In some embodiments, the probability threshold is approximately 0.8. In some embodiments, the probability threshold is approximately 0.85. In some embodiments, the probability threshold is approximately 0.9. In some embodiments, the probability threshold is approximately 0.95.

[0133] E. Standard score (e.g., Novelty Score) In some embodiments, the methods provided herein include calculating a deviation score. The deviation score, also referred to herein as the Novelty Score, indicates the degree to which the gene expression levels in a test dataset (e.g., any of those described in Section IB) differ from the expected gene expression levels. The expected gene expression levels can be determined using a variety of methods. In some embodiments, the expected gene expression levels are based on the gene expression levels in a reference database, e.g., any of those exemplified in Section IA. In some embodiments, the expected gene expression levels are based on the average gene expression levels in the reference database.

[0134] In some embodiments, the expected gene expression level is based on the expression level of one or more metagenes determined for the test dataset, for example, using one of the exemplary methods described in Section I C of this specification. In some embodiments, the expected gene expression level is calculated based on the gene expression levels in the test dataset, as well as the metagenes and their expression levels determined for the test dataset. Any method can be used to calculate the predicted value (e.g., expected gene expression level) based on the relationship between one or more predictors (e.g., metagene expression levels in the test dataset) and a dependency (e.g., gene expression levels in the test dataset). In some embodiments, regression analysis is used to calculate the expected gene expression level for the test dataset.

[0135] In some embodiments, the deviation score is based on the expression levels of all genes included in the test dataset. In some embodiments, the deviation score is based on the expression levels of a subset of genes included in the test dataset.

[0136] In some embodiments, the deviation score is based on a pre-selected set of marker genes. In some embodiments, the marker genes are selected based on their diagnostic ability, for example, if their expression levels can be used to distinguish cell types (e.g., determined dopaminergic progenitor cells from other cell types). In some embodiments, the marker genes include radial glial cell markers, early neuronal development genes, pluripotency-specific markers, mid-to-late neuronal cell markers, neurofilament polypeptide light chain markers, neurofilament polypeptide medium chain markers, nestin filament markers, early patterning markers, neural progenitor cell markers, early migration markers, stage-specific transcription factors, genes necessary for normal neuronal development, genes that control the development of dopaminergic neurons, genes that regulate the discriminative and fate of neural progenitor cells, dopaminergic neuron markers, astrocytic cell markers, forebrain markers, hindbrain markers, subthalamic nucleus markers, radial glial markers, cell cycle markers, or any combination of any of these. In some embodiments, the marker genes include genes that are not expected to be expressed by determined dopaminergic progenitor cells. In some embodiments, the marker gene includes one or more of the genes listed in Table E1.

[0137] In some embodiments, a preliminary deviation score is calculated, and the maximum preliminary deviation score is output as the deviation score. In some embodiments, the first deviation score is calculated based on the expression levels of all genes included in the test dataset, and the second deviation score is calculated based on a subset of genes. In some embodiments, the first deviation score is calculated based on the expression levels of all genes included in the test dataset, and the second deviation score is calculated based on a pre-selected set of marker genes. In some embodiments, the deviation score is the maximum of the preliminary deviation scores.

[0138] In some embodiments, the deviation of a single gene is calculated as the residual (i.e., difference) between the gene expression level in the test dataset and the gene expression level in one or more reference cells. In some embodiments, the one or more reference cells are the stage of differentiation that represents the determined dopaminergic progenitor cell. In some embodiments, the residual is normalized. In some embodiments, the residual is normalized by dividing it by the variance of the gene expression level in a reference database, e.g., one of those described in Section IA. In some embodiments, the residual is normalized by dividing it by the standard deviation of the gene expression level in the reference database.

[0139] In some embodiments, the deviation score is a summary statistic of one or more single-gene deviation scores. Any known summary statistic can be used. In some embodiments, the deviation score is the mean single-gene deviation score. In some embodiments, the deviation score is the sum of the single-gene deviation scores. In some embodiments, the deviation score is a weighted sum of the single-gene deviation scores. In some embodiments, the single-gene deviation score of a particular gene (e.g., a marker gene such as those listed in Table E1 herein) is weighted more heavily than the single-gene deviation scores of other genes. In some embodiments, the deviation score is a single-gene deviation score corresponding to a percentile of one or more single-gene deviation scores. In some embodiments, the percentile is approximately the 50th percentile to approximately the 100th percentile. In some embodiments, the percentile is approximately the 60th percentile to approximately the 100th percentile. In some embodiments, the percentile is approximately the 70th percentile to approximately the 100th percentile. In some embodiments, the percentile is approximately the 80th percentile to approximately the 100th percentile. In some embodiments, the percentile is approximately the 90th percentile to the 100th percentile. In some embodiments, the percentile is approximately the 95th percentile.

[0140] In some embodiments, the Novelty Score output of the test cells is compared to a predetermined threshold. In some embodiments, the method provided herein outputs a computer-calculated label classification, and if the computer-calculated label classification does not exceed a predetermined threshold, it indicates that the test cells are or contain determined dopaminergic progenitor cells.

[0141] A predetermined threshold for the Novelty Score can be set using various methods and criteria. In some embodiments, the predetermined threshold is set based on a Novelty Score calculated based on a reference database. In some embodiments, the reference database includes gene expression levels of reference cells differentiated according to any of the methods described in Section II.

[0142] In some embodiments, the computer-calculated label classification indicates that the test cells are or contain determined dopaminergic progenitor cells if the Novelty Score of the test cells indicates that at least approximately 50% of the gene expression levels in the test dataset are within 5 × standard deviations of the expected gene expression levels. In some embodiments, the computer-calculated label classification indicates that the test cells are or contain determined dopaminergic progenitor cells if the Novelty Score of the test cells indicates that at least approximately 60% of the gene expression levels in the test dataset are within 5 × standard deviations of the expected gene expression levels. In some embodiments, the computer-calculated label classification indicates that the test cells are or contain determined dopaminergic progenitor cells if the Novelty Score of the test cells indicates that at least approximately 70% of the gene expression levels in the test dataset are within 5 × standard deviations of the expected gene expression levels. In some embodiments, the computer-calculated label classification indicates that the test cells are or contain determined dopaminergic progenitor cells if the Novelty Score of the test cells indicates that at least approximately 80% of the gene expression levels in the test dataset are within 5 × standard deviations of the expected gene expression levels. In some embodiments, the computer-calculated label classification indicates that the test cells are or contain determined dopaminergic progenitor cells if the Novelty Score of the test cells indicates that at least approximately 90% of the gene expression levels in the test dataset are within 5 × standard deviations of the expected gene expression levels. In some embodiments, the computer-calculated label classification indicates that the test cells are or contain determined dopaminergic progenitor cells if the Novelty Score of the test cells indicates that at least approximately 95% of the gene expression levels in the test dataset are within 5 × standard deviations of the expected gene expression levels.

[0143] In some embodiments, the computer-calculated label classification indicates that the test cells are or contain determined dopaminergic progenitor cells if the Novelty Score of the test cells indicates that at least approximately 95% of the gene expression levels in the test dataset are within 10 × standard deviations of the expected gene expression levels. In some embodiments, the computer-calculated label classification indicates that the test cells are or contain determined dopaminergic progenitor cells if the Novelty Score of the test cells indicates that at least approximately 95% of the gene expression levels in the test dataset are within 9 × standard deviations of the expected gene expression levels. In some embodiments, the computer-calculated label classification indicates that the test cells are or contain determined dopaminergic progenitor cells if the Novelty Score of the test cells indicates that at least approximately 95% of the gene expression levels in the test dataset are within 8 × standard deviations of the expected gene expression levels. In some embodiments, the computer-calculated label classification indicates that the test cells are or contain determined dopaminergic progenitor cells if the Novelty Score of the test cells indicates that at least approximately 95% of the gene expression levels in the test dataset are within 7 × standard deviations of the expected gene expression levels. In some embodiments, the computer-calculated label classification indicates that the test cells are or contain determined dopaminergic progenitor cells if the Novelty Score of the test cells indicates that at least approximately 95% of the gene expression levels in the test dataset are within 6 × standard deviations of the expected gene expression levels. In some embodiments, the computer-calculated label classification indicates that the test cells are or contain determined dopaminergic progenitor cells if the Novelty Score of the test cells indicates that at least approximately 95% of the gene expression levels in the test dataset are within 5 × standard deviations of the expected gene expression levels.

[0144] In some embodiments, the computer-calculated label classification indicates that the test cells are or contain determined dopaminergic progenitor cells if the Novelty Score of the test cells indicates that at least about 50% of the marker gene expression levels in the test dataset are within 5 × standard deviations of the expected gene expression levels. In some embodiments, the computer-calculated label classification indicates that the test cells are or contain determined dopaminergic progenitor cells if the Novelty Score of the test cells indicates that at least about 60% of the marker gene expression levels in the test dataset are within 5 × standard deviations of the expected gene expression levels. In some embodiments, the computer-calculated label classification indicates that the test cells are or contain determined dopaminergic progenitor cells if the Novelty Score of the test cells indicates that at least about 70% of the marker gene expression levels in the test dataset are within 5 × standard deviations of the expected gene expression levels. In some embodiments, the computer-calculated label classification indicates that the test cells are or contain determined dopaminergic progenitor cells if the Novelty Score of the test cells indicates that at least approximately 80% of the marker gene expression levels in the test dataset are within 5 × standard deviations of the expected gene expression levels. In some embodiments, the computer-calculated label classification indicates that the test cells are or contain determined dopaminergic progenitor cells if the Novelty Score of the test cells indicates that at least approximately 90% of the marker gene expression levels in the test dataset are within 5 × standard deviations of the expected gene expression levels. In some embodiments, the computer-calculated label classification indicates that the test cells are or contain determined dopaminergic progenitor cells if the Novelty Score of the test cells indicates that at least approximately 95% of the gene expression levels in the test dataset are within 5 × standard deviations of the expected gene expression levels.

[0145] In some embodiments, the computer-calculated label classification indicates that the test cells are or contain determined dopaminergic progenitor cells if the Novelty Score of the test cells indicates that at least approximately 95% of the marker gene expression levels in the test dataset are within 10 × standard deviations of the expected gene expression levels. In some embodiments, the computer-calculated label classification indicates that the test cells are or contain determined dopaminergic progenitor cells if the Novelty Score of the test cells indicates that at least approximately 95% of the marker gene expression levels in the test dataset are within 9 × standard deviations of the expected gene expression levels. In some embodiments, the computer-calculated label classification indicates that the test cells are or contain determined dopaminergic progenitor cells if the Novelty Score of the test cells indicates that at least approximately 95% of the marker gene expression levels in the test dataset are within 8 × standard deviations of the expected gene expression levels. In some embodiments, the computer-calculated label classification indicates that the test cells are or contain determined dopaminergic progenitor cells if the Novelty Score of the test cells indicates that at least approximately 95% of the marker gene expression levels in the test dataset are within 7 × standard deviations of the expected gene expression levels. In some embodiments, the computer-calculated label classification indicates that the test cells are or contain determined dopaminergic progenitor cells if the Novelty Score of the test cells indicates that at least approximately 95% of the marker gene expression levels in the test dataset are within 6 × standard deviations of the expected gene expression levels. In some embodiments, the computer-calculated label classification indicates that the test cells are or contain determined dopaminergic progenitor cells if the Novelty Score of the test cells indicates that at least approximately 95% of the marker gene expression levels in the test dataset are within 5 × standard deviations of the expected gene expression levels.

[0146] In some embodiments, the computer-calculated label classification indicates that if the Novelty Score of the test cells is less than approximately 10, the test cells are determined to be dopaminergic progenitor cells or contain them. In some embodiments, the computer-calculated label classification indicates that if the Novelty Score of the test cells is less than approximately 9, the test cells are determined to be dopaminergic progenitor cells or contain them. In some embodiments, the computer-calculated label classification indicates that if the Novelty Score of the test cells is less than approximately 8, the test cells are determined to be dopaminergic progenitor cells or contain them. In some embodiments, the computer-calculated label classification indicates that if the Novelty Score of the test cells is less than approximately 7, the test cells are determined to be dopaminergic progenitor cells or contain them. In some embodiments, the computer-calculated label classification indicates that if the Novelty Score of the test cells is less than approximately 6, the test cells are determined to be dopaminergic progenitor cells or contain them. In some embodiments, a computer-calculated label classification indicates that the test cells are or contain determined dopaminergic progenitor cells if the Novelty Score of the test cells is less than approximately 5.

[0147] F. Exemplary Methods In some embodiments, the methods provided herein are used to determine whether a population of test cells, for example, neural progenitor cells produced by a differentiation process from iPSCs, is or contains determined dopaminergic progenitor cells. In some embodiments, the ability of any of the methods provided herein to determine whether a population of test cells contains determined dopaminergic progenitor cells can be used to verify the release of cells for use in subsequent applications. In some embodiments, subsequent applications may include therapeutic applications of determined dopaminergic progenitor cells, such as use in the treatment of neurogenic diseases. In some embodiments, the therapeutic application includes transplantation of test cells for the treatment of neurodegenerative diseases. In some embodiments, the neurodegenerative disease is Parkinson's disease. In some embodiments, the test cells are transplanted into the substantia nigra to treat the neurodegenerative disease, for example, Parkinson's disease.

[0148] An exemplary process following the provided method is shown in Figure 9. In some embodiments, a reference database containing gene expression levels from publicly available databases is used. In some embodiments, a reference database containing gene expression levels obtained from single-cell RNA sequences is used. In some embodiments, a reference database containing gene expression levels obtained from bulk RNA sequences is used. In some embodiments, a reference database is used to determine metagenes (circles 3 and 4). In some embodiments, metagene expression levels are calculated for the reference database and used to train a machine learning model to determine the probability of test cells having the determined metagene expression levels of dopaminergic progenitor cells (circle 5). In some embodiments, additional data, such as bulk RNA sequence data not used to train the model, can be used to validate the machine learning model (circle 6).

[0149] In some embodiments, trained machine learning is used as part of the method provided herein (circle 7) to classify test cells. In some embodiments, the Novelty Score is calculated based on a reference database. In some embodiments, the Novelty Score based on the reference database is used to identify the NeuroScore and Novelty Score threshold (circle 8).

[0150] In some embodiments, test cells are used to generate a test dataset containing the gene expression levels of the test cells. In some embodiments, the gene expression levels of the test cells are obtained using RNA sequencing. In some embodiments, the gene expression levels are subjected to sequencing alignment (circle 1). In some embodiments, sequencing alignment is performed using a Salmon pseudoaligner. In some embodiments, the test dataset is fed to a trained model (circle 2). In some embodiments, the NeuroScore (circle 10) and Novelty Score (circle 11) are outputs for the test dataset. In some embodiments, the NeuroScore and Novelty Score are compared to predetermined NeuroScore and Novelty Score thresholds. In some embodiments, the test cells are transplanted and / or screened, for example, if both thresholds are met. In some embodiments, for example, if the thresholds are not met, the test cells are discarded.

[0151] In some embodiments, reference cells and a reference database are generated, for example, according to one of the methods described in Sections IA and II. In some embodiments, reference cells are generated using iPSCs produced from subjects having Parkinson's disease. In some embodiments, the reference database includes gene expression levels of reference cells that can be differentiated from iPSCs over various periods of culture, for example, 13, 18, and 25 days, or about 13, about 18, and about 25 days, or at least 13, 18, and 25 days, under conditions that differentiate iPSCs into neurons. In some embodiments, the reference database includes bulk RNA sequence data. In some embodiments, the reference database includes single-cell RNA sequence data. In some embodiments, the reference database includes reference cell labels indicating whether the reference cells exhibit the characteristics of a determined dopaminergic progenitor cell, as determined by a functional assay, such as using an animal model of neurodegenerative disease. In some embodiments, the reference database includes reference cell labels of a population of cells differentiated from iPSCs into neurons for 18 days, about 18 days, or at least 18 days. The method of differentiation may include any of those described in Section II.

[0152] In some embodiments, metagenes are determined using a reference database containing single-cell RNA sequence data, for example, using one of the methods described in Section I C1. In some embodiments, based on the determined metagenes, metagene expression levels are determined using a reference database containing bulk RNA sequence data, for example, using one of the methods described in Section I C2.

[0153] In some embodiments, metagene expression levels are used to train a machine learning model, for example, one of those described in Section I D. In some embodiments, the machine learning model is a supervised classification model. In some embodiments, the machine learning model is a logistic regression model. In some embodiments, the machine learning model is trained using reference cell labels contained in a reference database.

[0154] In some embodiments, test cells and test datasets are generated, for example, using any of the methods described in Section IB and Section II. In some embodiments, test cells are generated using iPSCs produced from patients with Parkinson's disease. In some embodiments, test datasets are used to determine the metagene expression levels of test cells, for example, using any of the methods described in Section IC2. In some embodiments, test cells are comprised of an in vitro population of cells. In some embodiments, test cells are comprised of an in vitro population of neural progenitor cells.

[0155] In some embodiments, metagene expression levels determined from the test dataset are supplied as input to a machine learning model. In some embodiments, the machine learning model outputs a NeuroScore (e.g., one of those exemplified in Section I D). In some embodiments, a Novelty Score is determined using the test dataset, for example, according to one of the methods described in Section I E. In some embodiments, the NeuroScore and Novelty Score are determined for the test cells.

[0156] In some embodiments, the NeuroScore of the test cells is compared to a predetermined threshold (e.g., any of those described in Section I, D). In some embodiments, the Novelty Score of the test cells is compared to a predetermined threshold (e.g., any of those described in Section I, E). In some embodiments, both the NeuroScore and Novelty Score of the test cells are compared to a predetermined threshold.

[0157] In some embodiments, the method provided herein includes outputting a computer-calculated label classification, which includes an indication of whether the test cells contain determined dopaminergic progenitor cells. In some embodiments, the computer-calculated label classification is based on NeuroScores against their corresponding predetermined thresholds and their comparisons. In some embodiments, the computer-calculated label classification is based on Novelty Scores against their corresponding predetermined thresholds and their comparisons. In some embodiments, the computer-calculated label classification is based on both NeuroScores against their corresponding predetermined thresholds and their comparisons, and Novelty Scores against their corresponding predetermined thresholds and their comparisons.

[0158] In some embodiments, if the NeuroScore of the test cells indicates a probability of greater than approximately 0.5 that the test cells have a predetermined metagene expression level for dopaminergic progenitor cells, the computer-calculated label classification indicates that the test cells are or contain the determined dopaminergic progenitor cells. In some embodiments, if the Novelty Score of the test cells indicates that at least approximately 95% of the gene expression levels in the test dataset are within 5 × standard deviations of the expected gene expression levels, the computer-calculated label classification indicates that the test cells are or contain the determined dopaminergic progenitor cells. In some embodiments, the computer-calculated label classification indicates that a test cell is or contains a determined dopaminergic progenitor cell if (i) the NeuroScore of the test cell indicates a probability greater than approximately 0.5 that the test cell has the metagenetic expression level of the determined dopaminergic progenitor cell, and (ii) the Novelty Score of the test cell indicates that at least approximately 95% of the gene expression levels in the test dataset are within 5 × standard deviations of the expected gene expression levels.

[0159] In some embodiments, a computer-calculated label classification of the test cells indicates that the test cells are or contain determined dopaminergic progenitor cells. In some embodiments, an in vitro population of cells containing test cells identified as determined dopaminergic progenitor cells is selected for use. In some embodiments, an in vitro population of cells containing test cells identified as determined dopaminergic progenitor cells is selected for transplantation, for example, according to one of the methods described in Section V.

[0160] In some embodiments, the computer-calculated label classification of the test cells indicates that the test cells do not contain determined dopaminergic progenitor cells. In some embodiments, the Novelty Score of the test cells indicates that less than approximately 95% of the gene expression levels in the test dataset were within 5 × standard deviations of the expected gene expression levels. In some embodiments, the in vitro population of cells containing test cells not identified as determined dopaminergic progenitor cells will not differentiate in the future. In some embodiments, the in vitro population of cells containing test cells not identified as determined dopaminergic progenitor cells is discarded. In some embodiments, the method provided herein is repeated by generating an additional set of test cells and another test dataset. In some embodiments, the additional set of test cells is generated from the same subject having Parkinson's disease. In some embodiments, the additional set of test cells is generated from the same population of iPSCs from which the first set of test cells was generated. In some embodiments, the computer-calculated label classification is the output for the additional set of test cells.

[0161] In some embodiments, the computer-calculated label classification of the test cells indicates that the test cells do not contain the determined dopaminergic progenitor cells. In some embodiments, the NeuroScore of the test cells indicates a probability of less than approximately 0.5 that the test cells have the metagene expression level of the determined dopaminergic progenitor cells. In some embodiments, the Novelty Score of the test cells indicates that more than approximately 95% of the gene expression levels in the test dataset were within 5 × standard deviations from the expected gene expression level. In some embodiments, the in vitro population of cells containing test cells not identified as determined dopaminergic progenitor cells is allowed to continue differentiation. In some embodiments, an additional set of test cells and a test dataset from the same in vitro population of cells are collected. In some embodiments, the computer-calculated label classification is the output for the additional set of test cells.

[0162] In some embodiments, an additional set of test cells is collected and tested according to the method provided herein, approximately 1 to 30 days after testing the first set of test cells. In some embodiments, an additional set of test cells is collected and tested according to the method provided herein, approximately 1 to 25 days after testing the first set of test cells. In some embodiments, an additional set of test cells is collected and tested according to the method provided herein, approximately 1 to 20 days after testing the first set of test cells. In some embodiments, an additional set of test cells is collected and tested according to the method provided herein, approximately 1 to 15 days after testing the first set of test cells. In some embodiments, an additional set of test cells is collected and tested according to the method provided herein, approximately 1 to 10 days after testing the first set of test cells. In some embodiments, an additional set of test cells is collected and tested according to the method provided herein, approximately 1 to 5 days after testing the first set of test cells. In some embodiments, an additional set of test cells is collected and tested according to the method provided herein, approximately 1 to 3 days after testing the first set of test cells.

[0163] In some embodiments, the method provided herein is repeated until a computer-calculated label classification is provided indicating that the test cells generated from the subject are or contain the determined dopaminergic progenitor cells.

[0164] In the embodiments, the computer-generated label classification is an unsupervised classification of an updated reference database, which includes clustering RNA, DNA, and / or protein profiles. In the embodiments, gene expression profile information is obtained from microarray analysis of intracellular RNA. In the embodiments, gene expression profile information is obtained from microarray analysis of intracellular RNA derived from a single cell. In the embodiments, the computer-generated label classification is an unsupervised machine classification that includes bootstrap sparse non-negative matrix factorization.

[0165] In the embodiment, the gene expression reference database forms part of the storage medium. In the embodiment, receiving a test dataset includes receiving input from an array analysis system. In the embodiment, receiving a test dataset includes receiving input via a computer network. In the embodiment, the data in the reference database is associated with one or more labeled relevant biological classes of cells.

[0166] II. Methods for differentiating cells In some embodiments, the methods provided herein involve the use of reference cells and / or test cells, which are products of a method for differentiating cells. In some embodiments, the reference cells and / or test cells described in Sections IA and IB are products of a method for differentiating pluripotent stem cells. Various sources of pluripotent stem cells can be used, including embryonic stem (ES) cells and induced pluripotent stem cells (iPSCs). In some embodiments, the cells are iPSCs. In some embodiments, the pluripotent stem cells are iPSCs. In some embodiments, the pluripotent stem cells are iPSCs artificially induced from non-pluripotent cells. iPSCs can be generated by a process known as reprogramming, in which non-pluripotent cells are efficiently “dedifferentiated” to an embryonic stem cell-like state by manipulating them to express genes such as OCT4, SOX2, and KLF4. Takahashi and Yamanaka Cell (2006) 126:663-76.

[0167] In some embodiments, the cells are pluripotent stem cells. In some embodiments, the cells are pluripotent stem cells artificially induced from non-pluripotent cells of the subject. In some embodiments, the non-pluripotent cells are fibroblasts. In some embodiments, the subject is human. In some embodiments, the subject is human with Parkinson's disease. In some embodiments, the pluripotent stem cells are iPSCs.

[0168] Pluripotency of a cell population can be established using standard, art-acceptable tests, such as the ability to form teratomas in 8-12 week old SCID mice. However, the identification of various pluripotent stem cell characteristics can also be used to identify pluripotent cells. In some embodiments, pluripotent stem cells can be distinguished from other cells by specific characteristics, including the expression or non-expression of specific combinations of molecular markers. More specifically, human pluripotent stem cells may express at least some, or any, of any markers from a non-limiting list consisting of 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 embodiments, pluripotent stem cell characteristics are cell morphologies associated with pluripotent stem cells.

[0169] Methods for generating iPSCs are known. For example, mouse iPSCs were reported in 2006 (Takahashi and Yamanaka), and human iPSCs were reported in late 2007 (Takahashi et al. and Yu et al.). Mouse iPSCs exhibit key pluripotent stem cell characteristics, including the expression of stem cell markers, the formation of tumors containing cells derived from all three germ layers, and the ability to contribute to many different tissues when injected into mouse embryos at a very early stage of development. Human iPSCs also express stem cell markers and can generate cells characteristic of all three germ layers.

[0170] In some embodiments, the reference cells and / or test cells are neurons differentiated from pluripotent stem cells. In some embodiments, the cells are differentiated using any available or known method for inducing cell differentiation, such as a method for differentiating cells, e.g., iPSCs, into any neuronal cell type. As is understood, specific differentiation protocols and culture timings may result in differentiated neurons of different states. In some embodiments, differentiation is carried out by culturing pluripotent stem cells, e.g., iPSCs, under conditions that result in cells that are constrained to become neurons, or neural progenitor cells containing them. In some embodiments, the iPSCs are differentiated under conditions that result in floorplate midbrain progenitor cells, determined dopaminergic progenitor cells, and / or dopamine (DA) neurons. In some embodiments, the iPSCs are cultured under conditions for differentiation into determined dopaminergic progenitor cells. In some embodiments, the iPSCs are cultured under conditions for differentiation into dopaminergic neurons. Cells, such as pluripotent stem cells, can be differentiated using any available and known method for floorplate midbrain progenitor cells, determined dopaminergic progenitor cells, and / or dopamine (DA) neurons. Exemplary methods for differentiation into neurons can be found, for example, in International Publications 2013104752, 2010096496, 2013067362, 2014176606, 2016196661, 2015143342, and U.S. Patent Application Publication 20160348070, the contents of which are incorporated herein by reference in their entirety. In some embodiments, iPSCs can be differentiated in culture as part of the differentiation into neurons. In some embodiments, cells are cultured or incubated in the presence of one or more factors that can induce or promote the differentiation of iPSCs into neurons.In some embodiments, iPSCs are cultured in the presence of one or more of the following: (i) an inhibitor of TGF-β / activin-Nodal signaling, (ii) at least one activator of Sonic Hedgehog (SHH) signaling, (iii) an inhibitor of bone morphogenetic protein (BMP) signaling, and (iv) an inhibitor of glycogen synthase kinase 3β (GSK3β) signaling. In some embodiments, the TGF-β / activin-Nodal signaling inhibitor is SB431542 (e.g., about 1 μM to about 20 μM, 10 μM, etc.). In some embodiments, at least one activator of SHH signaling is SHH (e.g., about 10 ng / mL to about 500 ng / mL, 100 ng / mL, etc.) or palmorfamine (e.g., about 0.1 μM to about 10 μM, 2 μM, etc.). In some embodiments, at least one activator of SHH signaling is SHH protein (e.g., about 10 ng / mL to about 500 ng / mL, 100 ng / mL, etc.) and palmorfamine (e.g., about 0.1 μM to about 10 μM, 2 μM, etc.). In some embodiments, the inhibitor of BMP signaling is LDN193189 (e.g., about 0.01 μM to about 5 μM, 0.1 μM, etc.). In some embodiments, the inhibitor of GSK3β signaling is CHIR99021 (e.g., about 0.1 μM to about 10 μM, 2 μM, etc.).

[0171] In some embodiments, iPSCs are exposed to one or more factors or agents at the start of culture or incubation (day 0). In some embodiments, the presence of one or more factors or agents can be independently maintained in the culture for the duration of the culture or for part of the culture duration. In some embodiments, one or more factors or agents are independently present in the culture for a period that allows for differentiation of the iPSCs into midbrain floorplate precursors, or until they exhibit the characteristics of midbrain floorplate precursors as determined by classification labeling according to the provided method. In some embodiments, one or more factors or agents are independently present in the culture until day 5, day 6, day 7, day 8, day 9, day 10, day 11, day 12, or day 13 of the culture. For example, an exemplary protocol involves culturing iPSCs under conditions for differentiation into nerve cells, initiating a first incubation on approximately day 0, the first incubation comprising culturing pluripotent stem cells and exposing the cells daily from day 0 to day 10 to (i) an inhibitor of TGF-β / activin-Nodal signaling, (ii) at least one activator of sonic hedgehog (SHH) signaling, from day 1 to day 6, (iii) an inhibitor of bone morphogenetic protein (BMP) signaling, from day 0 to day 10, and (iv) an inhibitor of glycogen synthase kinase 3β (GSK3β) signaling, from day 0 to day 12.

[0172] In some embodiments, a second culture or incubation may be performed on cells differentiated in the first culture, wherein the second culture or incubation is carried out in the presence of one or more additional agents or factors under conditions that further differentiate the cells into neurons. In some embodiments, the second culture or initiation may be initiated when the cells in the first culture have differentiated into midbrain floorplate precursors, or before or after such cells exhibit the characteristics of midbrain floorplate precursors as determined by classification labeling according to the provided method. In some embodiments, the one or more additional agents or factors may include any one or more of the one or more factors present in the first culture. In some embodiments, one or more additional agents or factors may include one or more of the following: (i) brain-derived neurotrophic factor (BDNF), (ii) ascorbic acid, (iii) glial cell-derived neurotrophic factor (GDNF), (iv) cyclic AMP (cAMP), for example, dibutyryl cyclic AMP (dbcAMP), (v) transforming growth factor β3 (TGFβ3) (collectively "BAGCT"), and (vi) Notch inhibitors. In some embodiments, cells are exposed to BDNF at a concentration of approximately 1 ng / mL to 100 ng / mL (e.g., 20 ng / mL). In some embodiments, cells are exposed to ascorbic acid at a concentration of approximately 0.05 mM to 5 mM, e.g., 0.2 mM. In some embodiments, cells are exposed to GDNF at a concentration of 1 ng / mL to 100 ng / mL, e.g., 20 ng / mL. In some embodiments, cells are exposed to cAMP, e.g., dibutyryl cyclic AMP (dbcAMP), at a concentration of approximately 0.05 mM to 5 mM, e.g., approximately 0.5 mM.In some embodiments, cells are exposed to transforming growth factor β3 (TGFβ3) at a concentration of approximately 0.1 ng / mL to 10 ng / mL, for example, 1 ng / mL.

[0173] In some embodiments, a second culture or incubation may be performed for the duration of the time it takes for the cells to differentiate into determined dopaminergic progenitor cells, or until such cells exhibit the characteristics of dopaminergic neurons as determined by classification labels according to the provided method. In some embodiments, a second culture or incubation may be performed for the duration it takes for the cells to differentiate into dopaminergic neurons, or until such cells exhibit the characteristics of dopaminergic neurons as determined by classification labels according to the provided method. In some embodiments, the second culture or incubation is performed up to about 30 days after the start of the first culture or incubation. In some embodiments, the second culture or incubation is performed from about 11 to 25 days after the start of the first culture or incubation, for example, from day 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, or 25. In some embodiments, the second culture or incubation is performed until approximately 18 days after the start of the first culture. In some embodiments, the second culture is performed until approximately 25 days after the start of the first culture.

[0174] In some embodiments, the cells of the culture are exposed to one or more additional factors or agents for the duration of the culture or over a certain period of time. In some embodiments, the presence of one or more additional factors or agents can be maintained in the culture independently for the duration of the culture or for part of the culture period. In some embodiments, one or more additional factors or agents are present in the culture independently for the duration of the differentiation of the cells into determined dopaminergic progenitor cells, or until such cells exhibit the characteristics of dopaminergic neurons as determined by classification labeling according to the provided method. In some embodiments, one or more additional factors or agents are present in the culture independently for the duration of the differentiation of the cells into dopaminergic neurons, or until such cells exhibit the characteristics of dopaminergic neurons as determined by classification labeling according to the provided method. In some embodiments, a second culture or incubation is performed up to approximately 30 days after the start of the first culture or incubation. In some embodiments, one or more additional agents or factors are present in the culture independently from the start of the second culture until approximately 11 to 25 days after the start of the first culture or incubation, for example, on day 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, or 25. In some embodiments, one or more additional agents or factors are present in the culture independently from the start of the second culture until approximately 18 days after the start of the first culture. In some embodiments, one or more additional agents or factors are present in the culture independently from the start of the second culture until approximately 25 days after the start of the first culture.For example, in an exemplary protocol, culturing iPSCs under conditions for differentiation into neurons further comprises a second incubation in which the cells from the first incubation are further cultured by exposing the cells 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 β3 (TGFβ3) (collectively "BAGCT"), and (vi) Notch's inhibitor from day 11. In some embodiments, the cells are exposed to BAGCT until differentiated neurons are harvested, for example, up to day 18 or day 25. In some embodiments, the second incubation may further comprise culturing the cells by exposing them daily from day 11 to day 12 to an inhibitor of GSK3β signaling.

[0175] In some embodiments, incubation may include culturing by exposing cells to a Rho-related protein kinase (ROCK) signaling inhibitor one or more times during culture, for example, on about day 0, day 7, day 16, and / or day 20 from the start of the first culture. In some embodiments, the ROCK inhibitor is Y-27632 (e.g., about 1 μM to about 20 μM, about 10 μM, etc.).

[0176] In some embodiments, the culture of iPSCs under conditions for differentiation into neurons can span a period from the start of culture to the harvesting of differentiated cells at 10 to 30 days. It is understood that specific timing may be selected based on the desired differentiation state of the cells, for example, to be determined empirically by a functional or other phenotypic assay, or based on the classification label of the differentiated cells determined according to a provided method. In some embodiments, reference cells are differentiated by culture over a specific or defined period. In some embodiments, reference cells are differentiated by culture over the entire period, for example, as described in Section IA, to determine that the cells exhibit desired functional or phenotypic attributes or characteristics. In some embodiments, test cells are differentiated by culture over the entire period. In some embodiments, test cells are differentiated by culture over the entire period, during which it is determined that the test cells exhibit a desired classification label according to a provided method. In some embodiments, a provided method may be used to assess whether the test cells have been cultured under conditions for differentiation into desired neurons, e.g., determined dopaminergic progenitor cells, by their classification label, as determined according to one of the provided methods.

[0177] In one embodiment, iPSCs are cultured for at least 10 days for differentiation into nerve cells. In another embodiment, iPSCs are cultured for at least 11 days for differentiation into nerve cells. In another embodiment, iPSCs are cultured for at least 12 days for differentiation into nerve cells. In another embodiment, iPSCs are cultured for at least 13 days for differentiation into nerve cells. In another embodiment, iPSCs are cultured for at least 14 days for differentiation into nerve cells. In another embodiment, iPSCs are cultured for at least 15 days for differentiation into nerve cells. In another embodiment, iPSCs are cultured for at least 16 days for differentiation into nerve cells. In another embodiment, iPSCs are cultured for at least 17 days for differentiation into nerve cells. In another embodiment, iPSCs are cultured for at least 18 days for differentiation into nerve cells. In another embodiment, iPSCs are cultured for at least 19 days for differentiation into nerve cells. In another embodiment, iPSCs are cultured for at least 20 days for differentiation into nerve cells.

[0178] In one embodiment, iPSCs are cultured for approximately 10 days for differentiation into nerve cells. In another embodiment, iPSCs are cultured for approximately 11 days for differentiation into nerve cells. In another embodiment, iPSCs are cultured for approximately 12 days for differentiation into nerve cells. In another embodiment, iPSCs are cultured for approximately 13 days for differentiation into nerve cells. In another embodiment, iPSCs are cultured for approximately 14 days for differentiation into nerve cells. In another embodiment, iPSCs are cultured for approximately 15 days for differentiation into nerve cells. In another embodiment, iPSCs are cultured for approximately 16 days for differentiation into nerve cells. In another embodiment, iPSCs are cultured for approximately 17 days for differentiation into nerve cells. In another embodiment, iPSCs are cultured for approximately 18 days for differentiation into nerve cells. In another embodiment, iPSCs are cultured for approximately 19 days for differentiation into nerve cells. In another embodiment, iPSCs are cultured for approximately 20 days for differentiation into nerve cells. In one embodiment, iPSCs are cultured for approximately 21 days for differentiation into nerve cells. In another embodiment, iPSCs are cultured for approximately 22 days for differentiation into nerve cells. In yet another embodiment, iPSCs are cultured for approximately 23 days for differentiation into nerve cells. In yet another embodiment, iPSCs are cultured for approximately 24 days for differentiation into nerve cells. In yet another embodiment, iPSCs are cultured for approximately 25 days for differentiation into nerve cells.

[0179] In some embodiments, reference cells, for example, as described in Section IA, undergo the differentiation method described herein. In some embodiments, test cells, for example, as described in Section IB, undergo the differentiation method described herein. In some embodiments, both reference cells and test cells undergo the same differentiation method provided herein.

[0180] III. Exemplary Characteristics of Determined Dopaminergic Neurons In some embodiments, the determined dopaminergic progenitor cells identified by the methods provided herein have some degree of increased and / or decreased gene expression levels compared to pluripotent stem cells. In some embodiments, an in vitro population of neural progenitor cells having some degree of increased and / or decreased gene expression levels compared to pluripotent stem cells includes an in vitro population containing the desired determined dopaminergic progenitor cells.

[0181] In the embodiment, the desired determined gene expression profile information for dopaminergic progenitor cells includes increased gene expression levels for pluripotent stem cells in a first gene set, the first gene set including at least one increased gene in one or more first gene ontologies in Table 1.

[0182] In the embodiment, the desired determined gene expression profile information for dopaminergic progenitor cells includes increased gene expression levels for pluripotent stem cells in a first gene set, the first gene set comprising at least one increased gene in one or more first gene ontologies selected from the group consisting of the gene ontologies in Table 1.

[0183] In the embodiment, the desired determined gene expression profile information for dopaminergic progenitor cells includes increased gene expression levels for pluripotent stem cells in a first gene set, the first gene set being GO:0007399, GO:0120025, GO:0042995, GO:0032502, GO:0044767, GO:0048856, GO:0048731, GO:0022008, GO:0048699, GO:0007275, GO:0030030, GO:0032501, GO:0044707, GO:0050874, G O:0048468, GO:0120036, GO:0120038, GO:0044463, GO:0097458, GO:0045202, GO:0030182, GO:0030154, GO:0048869, GO:0051960, GO:0007156, GO:0 005929, GO:0072372, GO:0035082, GO:0035083, GO:0035084, GO:0060284, GO:0050767, GO:0001578, GO:0016339, GO:0043005, GO:0044456, GO:00987 42, GO:0045664, GO:0006928, GO:0099699, GO:0048666, GO:0003341, GO:0036142, GO:0005509, GO:0097060, GO:0031514, GO:0009434, GO:0031512, GO:0007155, GO:0098602, GO:0010975, GO:0098794, GO:0022610, GO:0030424, GO:0099240, GO:0032989, GO:0120035, GO:0000902, GO:0007148, GO:0 045790, GO:0045791, GO:0048812, GO:0036477, GO:0031344, GO:0120039, GO:0061564, GO:0048858, GO:0099055, GO:0009653, GO:0098609, GO:0016 337, GO:0031175, GO:0005930, GO:0035085, GO:0035086, GO:0010720, GO:0007416, GO:0097014, GO:0032990, GO:0098936, GO:0043025, GO:0050768,GO:0051962、GO:0050808、GO:0007409、GO:0007410、GO:2000026、GO:0045597、GO:0044441、GO:0044442、GO:0007417、GO:0048667、GO:0010721、GO:0044459、GO:0060322、GO:0045211、GO:0045666、GO:0032838、GO:0099056、GO:0051961、GO:0044297、GO:0007018、GO:0050769、GO:0040011、GO:0050793、GO:0051094、GO:0005874、GO:0000904、GO:0010976、GO:0045595、GO:0050770、GO:0099536、GO:0098889、GO:0051239、GO:0007420、GO:0099537、GO:0031346、GO:0007268、GO:0098916、GO:0097485、GO:0044782、GO:0031226、GO:0060285、GO:0071974、GO:0010769、GO:0001539、GO:0050804、GO:0099177、GO:0005887、GO:0098984、GO:0045665、GO:0050919、GO:0007411、GO:0008040、GO:0030425、GO:0061387、GO:0097447、GO:0050803、GO:0042734、GO:0042391、GO:0001764、GO:0032279、GO:0010770、GO:0021953、GO:0099572、GO:0098590、GO:0044447、GO:0098978、GO:0014069、GO:0097481、GO:0097483、GO:0033267、GO:0010977、GO:0007017、GO:0150034、GO:0034702、GO:0034703、GO:0050807、GO:0060271、GO:0042384、GO:0051240、GO:0050772、GO:0120031、GO:0007626、GO:0008092、GO:0005886、GO:0005904、GO:0007610、GO:0044708、GO:0098793、GO:0022604、GO:0007267、GO:0071944、GO:0099060, GO:0022836, GO:0030031, GO:0042220, GO:0019226, GO:0030516, GO:0035637, GO:0045596, GO:0021954, GO:0022832, GO:0005244, GO:1902495, GO:0050771, GO:0048513, GO:0022839, GO:0098948, GO:0001508, GO:0099568, GO:0008484, GO:0051966, GO:0003358, GO:0033602, GO:0005261, GO:0015281, GO:0015338, GO:0022603, GO:1990351, GO:0097729, GO:0015631, GO:0051270, GO:0005216, GO:0016043, GO:0044235, GO:0071842, GO:0031345, GO:0005856, GO:0022838, GO:0099061, GO:0098982, GO:0051674, GO:0048870, GO:0060294, GO:0072359, GO:0099634, GO:0015630, GO:0036126, GO:1990939, GO:0072347, GO:0015267, GO:0015249, GO:0015268, GO:0022803, GO:0022814, GO:0008045, It includes at least one increased gene within one or more first gene ontologies, which are GO:0098797, GO:0060160, GO:0099146, GO:0010771, GO:0000226, GO:0045503, GO:0005578, GO:0030334, GO:0044304, GO:0010463, GO:0010646, GO:0008574, GO:0043279, or any combination thereof.

[0184] In the embodiment, the desired determined gene expression profile information for dopaminergic progenitor cells includes increased gene expression levels for pluripotent stem cells in a first gene set, the first gene set being GO:0007399, GO:0120025, GO:0042995, GO:0032502, GO:0044767, GO:0048856, GO:0048731, GO:0022008, GO:0048699, GO:0007275, GO:0030030, GO:0032501, GO:0044707, GO:0050874, G O:0048468, GO:0120036, GO:0120038, GO:0044463, GO:0097458, GO:0045202, GO:0030182, GO:0030154, GO:0048869, GO:0051960, GO:0007156, GO:0 005929, GO:0072372, GO:0035082, GO:0035083, GO:0035084, GO:0060284, GO:0050767, GO:0001578, GO:0016339, GO:0043005, GO:0044456, GO:00987 42, GO:0045664, GO:0006928, GO:0099699, GO:0048666, GO:0003341, GO:0036142, GO:0005509, GO:0097060, GO:0031514, GO:0009434, GO:0031512, GO:0007155, GO:0098602, GO:0010975, GO:0098794, GO:0022610, GO:0030424, GO:0099240, GO:0032989, GO:0120035, GO:0000902, GO:0007148, GO:0 045790, GO:0045791, GO:0048812, GO:0036477, GO:0031344, GO:0120039, GO:0061564, GO:0048858, GO:0099055, GO:0009653, GO:0098609, GO:0016 337, GO:0031175, GO:0005930, GO:0035085, GO:0035086, GO:0010720, GO:0007416, GO:0097014, GO:0032990, GO:0098936, GO:0043025, GO:0050768,GO:0051962、GO:0050808、GO:0007409、GO:0007410、GO:2000026、GO:0045597、GO:0044441、GO:0044442、GO:0007417、GO:0048667、GO:0010721、GO:0044459、GO:0060322、GO:0045211、GO:0045666、GO:0032838、GO:0099056、GO:0051961、GO:0044297、GO:0007018、GO:0050769、GO:0040011、GO:0050793、GO:0051094、GO:0005874、GO:0000904、GO:0010976、GO:0045595、GO:0050770、GO:0099536、GO:0098889、GO:0051239、GO:0007420、GO:0099537、GO:0031346、GO:0007268、GO:0098916、GO:0097485、GO:0044782、GO:0031226、GO:0060285、GO:0071974、GO:0010769、GO:0001539、GO:0050804、GO:0099177、GO:0005887、GO:0098984、GO:0045665、GO:0050919、GO:0007411、GO:0008040、GO:0030425、GO:0061387、GO:0097447、GO:0050803、GO:0042734、GO:0042391、GO:0001764、GO:0032279、GO:0010770、GO:0021953、GO:0099572、GO:0098590、GO:0044447、GO:0098978、GO:0014069、GO:0097481、GO:0097483、GO:0033267、GO:0010977、GO:0007017、GO:0150034、GO:0034702、GO:0034703、GO:0050807、GO:0060271、GO:0042384、GO:0051240、GO:0050772、GO:0120031、GO:0007626、GO:0008092、GO:0005886、GO:0005904、GO:0007610、GO:0044708、GO:0098793、GO:0022604、GO:0007267、GO:0071944、GO:0099060, GO:0022836, GO:0030031, GO:0042220, GO:0019226, GO:0030516, GO:0035637, GO:0045596, GO:0021954, G O:0022832, GO:0005244, GO:1902495, GO:0050771, GO:0048513, GO:0022839, GO:0098948, GO:0001508, GO:0099568, GO :0008484, GO:0051966, GO:0003358, GO:0033602, GO:0005261, GO:0015281, GO:0015338, GO:0022603, GO:1990351, GO: 0097729, GO:0015631, GO:0051270, GO:0005216, GO:0016043, GO:0044235, GO:0071842, GO:0031345, GO:0005856, GO:00 22838, GO:0099061, GO:0098982, GO:0051674, GO:0048870, GO:0060294, GO:0072359, GO:0099634, GO:0015630, GO:003 6126, GO:1990939, GO:0072347, GO:0015267, GO:0015249, GO:0015268, GO:0022803, GO:0022814, GO:0008045, GO:0098 It includes at least one increased gene in one or more first gene ontologies selected from the group consisting of 797, GO:0060160, GO:0099146, GO:0010771, GO:0000226, GO:0045503, GO:0005578, GO:0030334, GO:0044304, GO:0010463, GO:0010646, GO:0008574, GO:0043279, and any combination thereof.

[0185] In the embodiment, the first gene set includes approximately 1 to 500 increased genes within one or more of the first gene ontologies. In the embodiment, the first gene set includes approximately 2 to 500 increased genes within one or more of the first gene ontologies. In the embodiment, the first gene set includes approximately 3 to 500 increased genes within one or more of the first gene ontologies. In the embodiment, the first gene set includes approximately 4 to 500 increased genes within one or more of the first gene ontologies. In the embodiment, the first gene set includes approximately 5 to 500 increased genes within one or more of the first gene ontologies. In the embodiment, the first gene set includes approximately 6 to 500 increased genes within one or more of the first gene ontologies. In the embodiment, the first gene set includes approximately 7 to 500 increased genes within one or more of the first gene ontologies. In the embodiment, the first gene set includes approximately 8 to 500 increased genes within one or more of the first gene ontologies. In the embodiment, the first gene set includes approximately 9 to 500 increased genes within one or more of the first gene ontologies. In the embodiment, the first gene set includes approximately 10 to 500 increased genes within one or more of the first gene ontologies.

[0186] In the embodiment, the first gene set contains approximately 15 to 500 increased genes within one or more of the first gene ontologies. In the embodiment, the first gene set contains approximately 20 to 500 increased genes within one or more of the first gene ontologies. In the embodiment, the first gene set contains approximately 25 to 500 increased genes within one or more of the first gene ontologies. In the embodiment, the first gene set contains approximately 30 to 500 increased genes within one or more of the first gene ontologies. In the embodiment, the first gene set contains approximately 35 to 500 increased genes within one or more of the first gene ontologies. In the embodiment, the first gene set contains approximately 40 to 500 increased genes within one or more of the first gene ontologies. In the embodiment, the first gene set contains approximately 45 to 500 increased genes within one or more of the first gene ontologies. In the embodiment, the first gene set contains approximately 50 to 500 increased genes within one or more of the first gene ontologies. In the embodiment, the first gene set contains approximately 55 to 500 increased genes within one or more of the first gene ontologies. In the embodiment, the first gene set contains approximately 60 to 500 increased genes within one or more of the first gene ontologies. In the embodiment, the first gene set contains approximately 65 to 500 increased genes within one or more of the first gene ontologies. In the embodiment, the first gene set contains approximately 70 to 500 increased genes within one or more of the first gene ontologies. In the embodiment, the first gene set contains approximately 75 to 500 increased genes within one or more of the first gene ontologies. In the embodiment, the first gene set contains approximately 80 to 500 increased genes within one or more of the first gene ontologies. In the embodiment, the first gene set contains approximately 85 to 500 increased genes within one or more of the first gene ontologies. In the embodiment, the first gene set contains approximately 90 to 500 increased genes within one or more of the first gene ontologies.In one embodiment, the first gene set contains approximately 95 to 500 increased genes within one or more of the first gene ontologies. In another embodiment, the first gene set contains approximately 100 to 500 increased genes within one or more of the first gene ontologies.

[0187] In the embodiment, the first gene set includes approximately 105 to 500 increased genes within one or more of the first gene ontologies. In the embodiment, the first gene set includes approximately 115 to 500 increased genes within one or more of the first gene ontologies. In the embodiment, the first gene set includes approximately 120 to 500 increased genes within one or more of the first gene ontologies. In the embodiment, the first gene set includes approximately 125 to 500 increased genes within one or more of the first gene ontologies. In the embodiment, the first gene set includes approximately 130 to 500 increased genes within one or more of the first gene ontologies. In the embodiment, the first gene set includes approximately 135 to 500 increased genes within one or more of the first gene ontologies. In the embodiment, the first gene set includes approximately 140 to 500 increased genes within one or more of the first gene ontologies. In the embodiment, the first gene set includes approximately 145 to 500 increased genes within one or more of the first gene ontologies. In the embodiment, the first gene set includes approximately 150 to 500 increased genes within one or more of the first gene ontologies. In the embodiment, the first gene set includes approximately 155 to 500 increased genes within one or more of the first gene ontologies. In the embodiment, the first gene set includes approximately 160 to 500 increased genes within one or more of the first gene ontologies. In the embodiment, the first gene set includes approximately 165 to 500 increased genes within one or more of the first gene ontologies. In the embodiment, the first gene set includes approximately 170 to 500 increased genes within one or more of the first gene ontologies. In the embodiment, the first gene set includes approximately 175 to 500 increased genes within one or more of the first gene ontologies. In one embodiment, the first gene set contains approximately 180 to 500 increased genes within one or more of the first gene ontologies. In another embodiment, the first gene set contains approximately 185 to 500 increased genes within one or more of the first gene ontologies.In the embodiment, the first gene set includes approximately 190 to 500 increased genes within one or more of the first gene ontologies. In the embodiment, the first gene set includes approximately 195 to 500 increased genes within one or more of the first gene ontologies. In the embodiment, the first gene set includes approximately 200 to 500 increased genes within one or more of the first gene ontologies.

[0188] In the embodiment, the first gene set contains approximately 205 to 500 increased genes within one or more of the first gene ontologies. In the embodiment, the first gene set contains approximately 215 to 500 increased genes within one or more of the first gene ontologies. In the embodiment, the first gene set contains approximately 220 to 500 increased genes within one or more of the first gene ontologies. In the embodiment, the first gene set contains approximately 225 to 500 increased genes within one or more of the first gene ontologies. In the embodiment, the first gene set contains approximately 230 to 500 increased genes within one or more of the first gene ontologies. In the embodiment, the first gene set contains approximately 235 to 500 increased genes within one or more of the first gene ontologies. In the embodiment, the first gene set contains approximately 240 to 500 increased genes within one or more of the first gene ontologies. In the embodiment, the first gene set includes approximately 245 to 500 increased genes within one or more of the first gene ontologies. In the embodiment, the first gene set includes approximately 250 to 500 increased genes within one or more of the first gene ontologies. In the embodiment, the first gene set includes approximately 255 to 500 increased genes within one or more of the first gene ontologies. In the embodiment, the first gene set includes approximately 260 to 500 increased genes within one or more of the first gene ontologies. In the embodiment, the first gene set includes approximately 265 to 500 increased genes within one or more of the first gene ontologies. In the embodiment, the first gene set includes approximately 270 to 500 increased genes within one or more of the first gene ontologies. In the embodiment, the first gene set includes approximately 275 to 500 increased genes within one or more of the first gene ontologies. In the embodiment, the first gene set contains approximately 280 to 500 increased genes within one or more of the first gene ontologies. In the embodiment, the first gene set contains approximately 285 to 500 increased genes within one or more of the first gene ontologies.In the embodiment, the first gene set contains approximately 290 to 500 increased genes within one or more of the first gene ontologies. In the embodiment, the first gene set contains approximately 295 to 500 increased genes within one or more of the first gene ontologies. In the embodiment, the first gene set contains approximately 300 to 500 increased genes within one or more of the first gene ontologies.

[0189] In the embodiment, the first gene set contains approximately 305 to 500 increased genes within one or more of the first gene ontologies. In the embodiment, the first gene set contains approximately 315 to 500 increased genes within one or more of the first gene ontologies. In the embodiment, the first gene set contains approximately 320 to 500 increased genes within one or more of the first gene ontologies. In the embodiment, the first gene set contains approximately 325 to 500 increased genes within one or more of the first gene ontologies. In the embodiment, the first gene set contains approximately 330 to 500 increased genes within one or more of the first gene ontologies. In the embodiment, the first gene set contains approximately 335 to 500 increased genes within one or more of the first gene ontologies. In the embodiment, the first gene set contains approximately 340 to 500 increased genes within one or more of the first gene ontologies. In the embodiment, the first gene set includes approximately 345 to 500 increased genes within one or more of the first gene ontologies. In the embodiment, the first gene set includes approximately 350 to 500 increased genes within one or more of the first gene ontologies. In the embodiment, the first gene set includes approximately 355 to 500 increased genes within one or more of the first gene ontologies. In the embodiment, the first gene set includes approximately 360 to 500 increased genes within one or more of the first gene ontologies. In the embodiment, the first gene set includes approximately 365 to 500 increased genes within one or more of the first gene ontologies. In the embodiment, the first gene set includes approximately 370 to 500 increased genes within one or more of the first gene ontologies. In the embodiment, the first gene set includes approximately 375 to 500 increased genes within one or more of the first gene ontologies. In the embodiment, the first gene set contains approximately 380 to 500 increased genes within one or more of the first gene ontologies. In the embodiment, the first gene set contains approximately 385 to 500 increased genes within one or more of the first gene ontologies.In the embodiment, the first gene set contains approximately 390 to 500 increased genes within one or more of the first gene ontologies. In the embodiment, the first gene set contains approximately 395 to 500 increased genes within one or more of the first gene ontologies. In the embodiment, the first gene set contains approximately 400 to 500 increased genes within one or more of the first gene ontologies.

[0190] In the embodiment, the first gene set contains approximately 405 to 500 increased genes within one or more of the first gene ontologies. In the embodiment, the first gene set contains approximately 415 to 500 increased genes within one or more of the first gene ontologies. In the embodiment, the first gene set contains approximately 420 to 500 increased genes within one or more of the first gene ontologies. In the embodiment, the first gene set contains approximately 425 to 500 increased genes within one or more of the first gene ontologies. In the embodiment, the first gene set contains approximately 430 to 500 increased genes within one or more of the first gene ontologies. In the embodiment, the first gene set contains approximately 435 to 500 increased genes within one or more of the first gene ontologies. In the embodiment, the first gene set contains approximately 440 to 500 increased genes within one or more of the first gene ontologies. In the embodiment, the first gene set contains approximately 445 to 500 increased genes within one or more of the first gene ontologies. In the embodiment, the first gene set contains approximately 450 to 500 increased genes within one or more of the first gene ontologies. In the embodiment, the first gene set contains approximately 455 to 500 increased genes within one or more of the first gene ontologies. In the embodiment, the first gene set contains approximately 460 to 500 increased genes within one or more of the first gene ontologies. In the embodiment, the first gene set contains approximately 465 to 500 increased genes within one or more of the first gene ontologies. In the embodiment, the first gene set contains approximately 470 to 500 increased genes within one or more of the first gene ontologies. In the embodiment, the first gene set contains approximately 475 to 500 increased genes within one or more of the first gene ontologies. In the embodiment, the first gene set contains approximately 480 to 500 increased genes within one or more of the first gene ontologies. In the embodiment, the first gene set contains approximately 485 to 500 increased genes within one or more of the first gene ontologies.In one embodiment, the first gene set contains approximately 490 to 500 increased genes within one or more of the first gene ontologies. In another embodiment, the first gene set contains approximately 495 to 500 increased genes within one or more of the first gene ontologies.

[0191] In this embodiment, the first gene set contains 1, 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, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73 within one or more of the first gene ontologies. ,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,11 1, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142 ,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 2 05, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 23 6, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267,268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, ​​383, 384, 385, 386, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, Contains a total of 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 492, 493, 494, 495, 496, 497, 498, 499, or 500 genes.

[0192] The desired determined gene expression profile information for dopaminergic progenitor cells includes increased gene expression levels for pluripotent stem cells in a first gene set, the first gene set may include at least one increased gene in one or more first gene ontologies listed in Table 1. In the context of a first gene ontology, “one or more” as used herein means at least one of the first gene ontologies, e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, etc. In embodiments, a first gene set includes about 1 to 500 increased genes within 1 to 300 first gene ontologies. In embodiments, a first gene set includes about 1 to 500 increased genes within 10 to 300 first gene ontologies. In the embodiment, the first gene set includes approximately 1 to 500 additional genes within 20 to 300 genes in the first gene ontology. In the embodiment, the first gene set includes approximately 1 to 500 additional genes within 30 to 300 genes in the first gene ontology. In the embodiment, the first gene set includes approximately 1 to 500 additional genes within 40 to 300 genes in the first gene ontology. In the embodiment, the first gene set includes approximately 1 to 500 additional genes within 50 to 300 genes in the first gene ontology. In the embodiment, the first gene set includes approximately 1 to 500 additional genes within 60 to 300 genes in the first gene ontology. In the embodiment, the first gene set includes approximately 1 to 500 additional genes within 70 to 300 genes in the first gene ontology. In the embodiment, the first gene set includes approximately 1 to 500 additional genes within the 80 to 300 genes of the first gene ontology. In the embodiment, the first gene set includes approximately 1 to 500 additional genes within the 90 to 300 genes of the first gene ontology. In the embodiment, the first gene set includes approximately 1 to 500 additional genes within the 100 to 300 genes of the first gene ontology. In the embodiment, the first gene set includes approximately 1 to 500 additional genes within the 110 to 300 genes of the first gene ontology.In the embodiment, the first gene set includes approximately 1 to 500 additional genes within 120 to 300 genes in the first gene ontology. In the embodiment, the first gene set includes approximately 1 to 500 additional genes within 130 to 300 genes in the first gene ontology. In the embodiment, the first gene set includes approximately 1 to 500 additional genes within 140 to 300 genes in the first gene ontology. In the embodiment, the first gene set includes approximately 1 to 500 additional genes within 150 to 300 genes in the first gene ontology. In the embodiment, the first gene set includes approximately 1 to 500 additional genes within 160 to 300 genes in the first gene ontology. In the embodiment, the first gene set includes approximately 1 to 500 additional genes within 170 to 300 genes in the first gene ontology. In the embodiment, the first gene set includes approximately 1 to 500 additional genes within 180 to 300 genes in the first gene ontology. In the embodiment, the first gene set includes approximately 1 to 500 additional genes within 190 to 300 genes in the first gene ontology. In the embodiment, the first gene set includes approximately 1 to 500 additional genes within 200 to 300 genes in the first gene ontology. In the embodiment, the first gene set includes approximately 1 to 500 additional genes within 210 to 300 genes in the first gene ontology. In the embodiment, the first gene set includes approximately 1 to 500 additional genes within 220 to 300 genes in the first gene ontology. In the embodiment, the first gene set includes approximately 1 to 500 additional genes within 230 to 300 genes in the first gene ontology. In the embodiment, the first gene set includes approximately 1 to 500 additional genes within the 240 to 300 genes of the first gene ontology. In the embodiment, the first gene set includes approximately 1 to 500 additional genes within the 250 to 300 genes of the first gene ontology. In the embodiment, the first gene set includes approximately 1 to 500 additional genes within the 260 to 300 genes of the first gene ontology. In the embodiment, the first gene set includes approximately 1 to 500 additional genes within the 270 to 300 genes of the first gene ontology.In one embodiment, the first gene set includes approximately 1 to 500 additional genes within the 280 to 300 genes of the first gene ontology. In another embodiment, the first gene set includes approximately 1 to 500 additional genes within the 290 to 300 genes of the first gene ontology.

[0193] In the embodiment, the first gene set includes approximately 1 to 500 additional genes within 1 to 290 genes in the first gene ontology. In the embodiment, the first gene set includes approximately 1 to 500 additional genes within 1 to 280 genes in the first gene ontology. In the embodiment, the first gene set includes approximately 1 to 500 additional genes within 1 to 270 genes in the first gene ontology. In the embodiment, the first gene set includes approximately 1 to 500 additional genes within 1 to 260 genes in the first gene ontology. In the embodiment, the first gene set includes approximately 1 to 500 additional genes within 1 to 250 genes in the first gene ontology. In the embodiment, the first gene set includes approximately 1 to 500 additional genes within 1 to 240 genes in the first gene ontology. In the embodiment, the first gene set includes approximately 1 to 500 additional genes within 1 to 230 genes in the first gene ontology. In the embodiment, the first gene set includes approximately 1 to 500 additional genes within 1 to 220 genes in the first gene ontology. In the embodiment, the first gene set includes approximately 1 to 500 additional genes within 1 to 210 genes in the first gene ontology. In the embodiment, the first gene set includes approximately 1 to 500 additional genes within 1 to 200 genes in the first gene ontology. In the embodiment, the first gene set includes approximately 1 to 500 additional genes within 1 to 190 genes in the first gene ontology. In the embodiment, the first gene set includes approximately 1 to 500 additional genes within 1 to 180 genes in the first gene ontology. In the embodiment, the first gene set includes approximately 1 to 500 additional genes within 1 to 170 genes in the first gene ontology. In the embodiment, the first gene set includes approximately 1 to 500 additional genes within 1 to 160 genes in the first gene ontology. In the embodiment, the first gene set includes approximately 1 to 500 additional genes within 1 to 150 genes in the first gene ontology. In the embodiment, the first gene set includes approximately 1 to 500 additional genes within 1 to 140 genes in the first gene ontology.In the embodiment, the first gene set includes approximately 1 to 500 additional genes within 1 to 130 of the first gene ontology. In the embodiment, the first gene set includes approximately 1 to 500 additional genes within 1 to 120 of the first gene ontology. In the embodiment, the first gene set includes approximately 1 to 500 additional genes within 1 to 110 of the first gene ontology. In the embodiment, the first gene set includes approximately 1 to 500 additional genes within 1 to 100 of the first gene ontology. In the embodiment, the first gene set includes approximately 1 to 500 additional genes within 1 to 90 of the first gene ontology. In the embodiment, the first gene set includes approximately 1 to 500 additional genes within 1 to 80 of the first gene ontology. In the embodiment, the first gene set includes approximately 1 to 500 increased genes within 1 to 70 of the first gene ontology. In the embodiment, the first gene set includes approximately 1 to 500 increased genes within 1 to 60 of the first gene ontology. In the embodiment, the first gene set includes approximately 1 to 500 increased genes within 1 to 50 of the first gene ontology. In the embodiment, the first gene set includes approximately 1 to 500 increased genes within 1 to 40 of the first gene ontology. In the embodiment, the first gene set includes approximately 1 to 500 increased genes within 1 to 30 of the first gene ontology. In the embodiment, the first gene set includes approximately 1 to 500 increased genes within 1 to 20 of the first gene ontology. In the embodiment, the first gene set includes approximately 1 to 500 increased genes within 1 to 10 of the first gene ontology. In this embodiment, the first gene set includes approximately 1 to 500 additional genes within 1 to 5 of the first gene ontology.

[0194] In this embodiment, the first gene set is as follows: 1, 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, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61 ,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,1 20, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 1 The first gene ontology of 69, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, or 208 genes contains at least one increased gene.

[0195] In this embodiment, the first gene set is as follows: 1, 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, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 1 16, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 14 7, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178 , 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, or 208 first gene ontologies, 1, 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, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82,83、84、85、86、87、88、89、90、91、92、93、94、95、96、97、98、99、100、101、102、103、104、105、106、107、108、109、110、111、112、113、114、115、116、117、118、119、120、121、122、123、124、125、126、127、128、129、130、131、132、133、134、135、136、137、138、139、140、141、142、143、144、145、146、147、148、149、150、151、152、153、154、155、156、157、158、159、160、161、162、163、164、165、166、167、168、169、170、171、172、173、174、175、176、177、178、179、180、181、182、183、184、185、186、187、188、189、190、191、192、193、194、195、196、197、198、199、200、201、202、203、204、205、206、207、208、209、210、211、212、213、214、215、216、217、218、219、220、221、222、223、224、225、226、227、228、229、230、231、232、233、234、235、236、237、238、239、240、241、242、243、244、245、246、247、248、249、250、251、252、253、254、255、256、257、258、259、260、261、262、263、264、265、266、267、268、269、270、271、272、273、274、275、276、277、278、279、280、281、282、283、284、285、286、287、288、289、290、291、292、293、294、295、296、297、298、299、300、301、302、303、304、305、306、307、308、309、310、311、312、313、314、315、316、317、318、319、320、321、322、323、324、325、326、327、328、329、330、331、332、333、334、335、336、337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378 ,379,380,381,382,383,384,385,386,387,388,389,390,391,392,393,394,395,396,397,398,399,400,401,402,403,404,405,406,407,408,409,410,411,412,413,414,415,416,417,418,419,42 0, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 4 Contains 62, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 492, 493, 494, 495, 496, 497, 498, 499, or 500 counted genes.

[0196] In this embodiment, the first gene ontology is one of the gene ontologies listed in Table 1. In this embodiment, the first gene ontology is GO:0007399, GO:0120025, GO:0042995, GO:0032502, GO:0044767, GO:0048856, GO:0048731, GO:0022008, GO:0048699, GO:0007275, GO:0030030, GO:0032501, GO:0044707, GO:0050874, GO:0048468, GO:0120036, GO:0120038, GO:0044463, GO:0097458, GO:004 5202, GO:0030182, GO:0030154, GO:0048869, GO:0051960, GO:0007156, GO:0005929, GO:0072372, GO:0035082, GO:0035083, GO:0035084, GO: 0060284, GO:0050767, GO:0001578, GO:0016339, GO:0043005, GO:0044456, GO:0098742, GO:0045664, GO:0006928, GO:0099699, GO:0048666, GO:0003341, GO:0036142, GO:0005509, GO:0097060, GO:0031514, GO:0009434, GO:0031512, GO:0007155, GO:0098602, GO:0010975, GO:00987 94, GO:0022610, GO:0030424, GO:0099240, GO:0032989, GO:0120035, GO:0000902, GO:0007148, GO:0045790, GO:0045791, GO:0048812, GO:00 36477, GO:0031344, GO:0120039, GO:0061564, GO:0048858, GO:0099055, GO:0009653, GO:0098609, GO:0016337, GO:0031175, GO:0005930, GO :0035085, GO:0035086, GO:0010720, GO:0007416, GO:0097014, GO:0032990, GO:0098936, GO:0043025, GO:0050768, GO:0051962, GO:0050808,GO:0007409、GO:0007410、GO:2000026、GO:0045597、GO:0044441、GO:0044442、GO:0007417、GO:0048667、GO:0010721、GO:0044459、GO:0060322、GO:0045211、GO:0045666、GO:0032838、GO:0099056、GO:0051961、GO:0044297、GO:0007018、GO:0050769、GO:0040011、GO:0050793、GO:0051094、GO:0005874、GO:0000904、GO:0010976、GO:0045595、GO:0050770、GO:0099536、GO:0098889、GO:0051239、GO:0007420、GO:0099537、GO:0031346、GO:0007268、GO:0098916、GO:0097485、GO:0044782、GO:0031226、GO:0060285、GO:0071974、GO:0010769、GO:0001539、GO:0050804、GO:0099177、GO:0005887、GO:0098984、GO:0045665、GO:0050919、GO:0007411、GO:0008040、GO:0030425、GO:0061387、GO:0097447、GO:0050803、GO:0042734、GO:0042391、GO:0001764、GO:0032279、GO:0010770、GO:0021953、GO:0099572、GO:0098590、GO:0044447、GO:0098978、GO:0014069、GO:0097481、GO:0097483、GO:0033267、GO:0010977、GO:0007017、GO:0150034、GO:0034702、GO:0034703、GO:0050807、GO:0060271、GO:0042384、GO:0051240、GO:0050772、GO:0120031、GO:0007626、GO:0008092、GO:0005886、GO:0005904、GO:0007610、GO:0044708、GO:0098793、GO:0022604、GO:0007267、GO:0071944、GO:0099060、GO:0022836、GO:0030031, GO:0042220, GO:0019226, GO:0030516, GO:0035637, GO:0045596, GO:0021954, GO:0022832, GO:0 005244, GO:1902495, GO:0050771, GO:0048513, GO:0022839, GO:0098948, GO:0001508, GO:0099568, GO:000848 4, GO:0051966, GO:0003358, GO:0033602, GO:0005261, GO:0015281, GO:0015338, GO:0022603, GO:1990351, GO: 0097729, GO:0015631, GO:0051270, GO:0005216, GO:0016043, GO:0044235, GO:0071842, GO:0031345, GO:00058 56, GO:0022838, GO:0099061, GO:0098982, GO:0051674, GO:0048870, GO:0060294, GO:0072359, GO:0099634, GO :0015630, GO:0036126, GO:1990939, GO:0072347, GO:0015267, GO:0015249, GO:0015268, GO:0022803, GO:0022 814, GO:0008045, GO:0098797, GO:0060160, GO:0099146, GO:0010771, GO:0000226, GO:0045503, GO:0005578, GO:0030334, GO:0044304, GO:0010463, GO:0010646, GO:0008574, GO:0043279, or any one of these combinations.

[0197] In one embodiment, the desired determined gene expression profile information for dopaminergic progenitor cells includes increased gene expression levels for pluripotent stem cells in a first gene set, the first gene set comprising at least one increased gene in one or more first gene ontologies selected from the group consisting of GO0005509, GO0016339, GO0007416, and GO0048731. In one embodiment, the desired determined gene expression profile information for dopaminergic progenitor cells includes increased gene expression levels for pluripotent stem cells in a first gene set, the first gene set comprising at least one increased gene in one or more first gene ontologies selected from the group consisting of GO0048699, GO0050767, GO0060160, GO0097458, GO0010975, GO0022008, and any combination thereof.

[0198] In the embodiment, the first gene set includes at least one (e.g., 1, 2, 3, 4, 5, 6, etc.) augmented gene from Table 2, Table 3, Table 4, Table 5, Table 6, or Table 7 or any combination thereof.

[0199] In the embodiment, the first gene set includes at least one (e.g., 1, 2, 3, 4, 5, 6, etc.) augmented genes from Table 2. In the embodiment, the at least one (e.g., 1, 2, 3, 4, 5, 6, etc.) augmented genes are GPM6A, DRD2, BMP7, EFNB3, SEMA3C, FSCN2, LGI1, SRCIN1, WNT4, SLIT2, NRG1, TTBK1, RNF165, CDH2, ELAVL4, ONECUT2, KREMEN1, SCRT1, KIAA1024, DSCAM, MAP2, FAT4, PAK3, NGF, S EMA6D, STMN2, ZFHX3, LRP2, APOA1, CAMK2B, MDGA1, ISLR2, SNAP25, NEUROD4, PHOX2B, DCX, MAGI2, PIK3R1, NCAM1, N TRK3, PITX3, MYT1L, AVIL, CDK5R2, INSM1, SOX21, IL6ST, KIF5C, SYNJ1, KALRN, GFRA1, TCTN1, CELSR1, IRX5, PMP22, RUNX1, DPYSL4, NRCAM, ZNF521, MDGA2, PROX1, ZNF536, MAP1A, NEGR1, PLXNA4, EPB41L3, GAP43, EPHA7, DLL3, VSTM2 L, ID4, NRN1, SPOCK1, DUSP10, COL3A1, CX3CL1, SLIT3, MAPK8IP2, FAIM2, TCF12, BMP6, NRBP2, NCAM2, HIPK2, CDH11, These are ADGRL3, ZNF804A, ULK2, CCKAR, SARM1, PLXNA3, ENC1, ASCL1, UNCX, MEIS1, ARX, SRRM4, TRIM67, ALCAM, NTN1, ZNF365, GFI1, ADCYAP1, CNR1, ANKRD1, ALK, STMN4, MAPT, RUFY3, PLXNA2, PLXNC1, MAP1B, DPYSL5, PTPRO, FZD1, or DLX5.

[0200] In the embodiment, at least one (e.g., 1, 2, 3, 4, 5, 6, etc.) of the increased genes are GPM6A, DRD2, BMP7, EFNB3, SEMA3C, FSCN2, LGI1, SRCIN1, WNT4, SLIT2, NRG1, TTBK1, RNF165, CDH2, ELAVL4, ONECUT2, KREMEN1, SCRT1, KIAA1024, DSCAM, MAP2, FAT4, PAK3, NGF, SEM A6D, STMN2, ZFHX3, LRP2, APOA1, CAMK2B, MDGA1, ISLR2, SNAP25, NEUROD4, PHOX2B, DCX, MAGI2, PIK3R1, NCAM1, NTRK3 , PITX3, MYT1L, AVIL, CDK5R2, INSM1, SOX21, IL6ST, KIF5C, SYNJ1, KALRN, GFRA1, TCTN1, CELSR1, IRX5, PMP22, RUNX1 , DPYSL4, NRCAM, ZNF521, MDGA2, PROX1, ZNF536, MAPIA, NEGR1, PLXNA4, EPB41L3, GAP43, EPHA7, DLL3, VSTM2L, ID4, NRN1, SPOCK1, DUSP10, COL3A1, CX3CL1, SLIT3, MAPK8IP2, FAIM2, TCF12, BMP6, NRBP2, NCAM2, HIPK2, CDH11, ADGRL3, The group is selected from ZNF804A, ULK2, CCKAR, SARM1, PLXNA3, ENC1, ASCL1, UNCX, MEIS1, ARX, SRRM4, TRIM67, ALCAM, NTN1, ZNF365, GFI1, ADCYAP1, CNR1, ANKRD1, ALK, STMN4, MAPT, RUFY3, PLXNA2, PLXNC1, MAP1B, DPYSL5, PTPRO, FZD1, and DLX5.

[0201] In the embodiment, the first gene set includes at least one (e.g., 1, 2, 3, 4, 5, 6, etc.) augmented genes from Table 3. In the embodiment, the at least one (e.g., 1, 2, 3, 4, 5, 6, etc.) augmented genes are DRD2, BMP7, EFNB3, SEMA3C, SRCIN1, SLIT2, NRG1, TTBK1, CDH2, KREMEN1, SCRT1, KIAA1024, DSCAM, MAP2, PAK3, NGF, SEMA6D, STMN2, ZFHX3, LRP2, CAMK2B, ISLR2, SNAP25, PHOX2B, MAGI2, NTRK3, PITX3, AVIL, IL6ST, SYNJ1, KALRN These are PMP22, NRCAM, PROX1, ZNF536, NEGR1, PLXNA4, EPHA7, DLL3, ID4, SPOCK1, DUSP10, COL3A1, CX3CL1, TCF12, BMP6, ZNF804A, ULK2, SARM1, PLXNA3, ENC1, ASCL1, MEIS1, TRIM67, NTN1, ZNF365, GFI1, ADCYAP1, CNR1, ANKRD1, ALK, MAPT, RUFY3, PLXNA2, PLXNC1, MAP1B, PTPRO, or FZD1.

[0202] In the embodiment, at least one (e.g., 1, 2, 3, 4, 5, 6, etc.) of the increased genes are DRD2, BMP7, EFNB3, SEMA3C, SRCIN1, SLIT2, NRG1, TTBK1, CDH2, KREMEN1, SCRT1, KIAA1024, DSCAM, MAP2, PAK3, NGF, SEMA6D, STMN2, ZFHX3, LRP2, CAMK2B, ISLR2, SNAP25, PHOX2B, MAGI2, NTRK3, PITX3, AVIL, IL6ST, SYNJ1, KALRN, PMP2 2. Selected from the group consisting of NRCAM, PROX1, ZNF536, NEGR1, PLXNA4, EPHA7, DLL3, ID4, SPOCK1, DUSP10, COL3A1, CX3CL1, TCF12, BMP6, ZNF804A, ULK2, SARM1, PLXNA3, ENC1, ASCL1, MEIS1, TRIM67, NTN1, ZNF365, GFI1, ADCYAP1, CNR1, ANKRD1, ALK, MAPT, RUFY3, PLXNA2, PLXNC1, MAP1B, PTPRO, and FZD1.

[0203] In the embodiment, the first gene set includes at least one (e.g., 1, 2, 3, 4, 5, 6, etc.) augmented gene from Table 4. In the embodiment, the at least one (e.g., 1, 2, 3, 4, 5, 6, etc.) augmented gene is DRD2, RGS4, or PALM.

[0204] In the embodiment, at least one (e.g., 1, 2, 3, 4, 5, 6, etc.) of the increased genes is selected from the group consisting of DRD2, RGS4, and PALM.

[0205] In the embodiment, the first gene set includes at least one (e.g., 1, 2, 3, 4, 5, 6, etc.) augmented genes from Table 5. In the embodiment, the at least one (e.g., 1, 2, 3, 4, 5, 6, etc.) augmented genes are GPM6A, KIFAP3, DRD2, EFNB3, FSCN2, SLC8A1, SCGN, SRCIN1, PACRG, TRIM9, NRG1, TTBK1, HTR2A, SLC18A1, CERKF, CDH2, PAEMD, KREMEN1, TANC2, MAPK10, SCN3A, LRRC4, DSCAM, TGFB3, MAP2, ELFN 1, PAK3, NGF, CPEB2, DDN, STMN2, LRP2, CAMK2B, SVOP, SRR, SNAP25, PPFIA2, KCNA2, SYT5, BAIAP3, CADM2, CHRM2, DCX, MAGI2 , KLHL1, NTRK3, PITX3, P2RX3, ADGRA1, AVIL, CADM3, CDK5R2, IL6ST, KIF5C, SYNJ1, TSPOAP1, DRP2, TMPRSS3, SYBU, HMP19, S NAP91, SCN11A, PALM, SLC1A4, NRCAM, CACNG4, CNIH2, DGKI, CLSTN2, MAPIA, GLRA2, CUBN, SCN7A, EPB41L3, BSN, GAP43, EPHA 7, VSTM2L, SPOCK1, CX3CL1, MAPK8IP2, CAMK2N1, PDE1C, NCAM2, SLC17A6, SLC18A3, KCNC1, ADGRL3, ZNF804A, SARM1, GRIK4, These are ENC1, ASCL1, DMTN, KNCN, TMEM163, CLDN5, KCND3, PCDHB13, GABRR2, ALCAM, SV2B, KCTD16, ADCYAP1, APBA1, CNR1, STMN4, CADPS, MAPT, RUFY3, TP63, NRSN1, MAP1B, PCSK2, DPYSL5, GRM3, SLC6A1, ABAT, CACNA1C, CACNG2, PTPRO, CHRNA5, or CDH10.

[0206] In the embodiment, the first gene set includes at least one (e.g., 1, 2, 3, 4, 5, 6, etc.) augmented genes from Table 5. In the embodiment, the at least one (e.g., 1, 2, 3, 4, 5, 6, etc.) augmented genes are GPM6A, KIFAP3, DRD2, EFNB3, FSCN2, SLC8A1, SCGN, SRCIN1, PACRG, TRIM9, NRG1, TTBK1, HTR2A, SLC18A1, CERKL, CDH2, PALMD, KREMEN1, TANC2, MAPK10, SCN3A, LRRC4, DSCAM, TGFB3, MAP2, ELFN1, P AK3, NGF, CPEB2, DDN, STMN2, LRP2, CAMK2B, SVOP, SRR, SNAP25, PPFIA2, KCNA2, SYT5, BAIAP3, CADM2, CHRM2, DCX, MAGI2, KLH L1, NTRK3, PITX3, P2RX3, ADGRA1, AVIL, CADM3, CDK5R2, IL6ST, KIF5C, SYNJ1, TSPOAP1, DRP2, TMPRSS3, SYBU, HMP19, SNAP91 , SCN11A, PALM, SLC1A4, NRCAM, CACNG4, CNIH2, DGKI, CLSTN2, MAP1A, GLRA2, CUBN, SCN7A, EPB41L3, BSN, GAP43, EPHA7, VSTM 2L, SPOCK1, CX3CL1, MAPK8IP2, CAMK2N1, PDE1C, NCAM2, SLC17A6, SLC18A3, KCNC1, ADGRL3, ZNF804A, SARM1, GRIK4, ENC1, AS The group is selected from CL1, DMTN, KNCN, TMEM163, CLDN5, KCND3, PCDHB13, GABRR2, ALCAM, SV2B, KCTD16, ADCYAP1, APBA1, CNR1, STMN4, CADPS, MAPT, RUFY3, TP63, NRSN1, MAP1B, PCSK2, DPYSL5, GRM3, SLC6A1, ABAT, CACNA1C, CACNG2, PTPRO, CHRNA5, and CDH10.

[0207] In the embodiment, the first gene set includes at least one (e.g., 1, 2, 3, 4, 5, 6, etc.) augmented genes from Table 6. In the embodiment, at least one (e.g., 1, 2, 3, 4, 5, 6, etc.) of the increased genes is EFNB3, SEMA3C, SRCIN1, SLIT2, CDH2, KREMEN1, KIAA1024, DSCAM, MAP2, PAK3, NGF, SEMA6D, STMN2, CAMK2B, ISLR2, SNAP25, MAGI2, NTRK3, AVIL, KALRN, PMP22, NRCAM, NEGR1, PLXNA4, EPHA7, SPOCK1, CX3CL1, ZNF804A, ULK2, SARM1, PLXNA3, ENC1, TRIM67, NTN1, ZNF365, GFI1, ADCYAP1, CNR1, ANKRD1, MAPT, RUFY3, PLXNA2, PLXNC1, MAP1B, PTPRO, or FZD1.

[0208] In the embodiment, at least one (e.g., 1, 2, 3, 4, 5, 6, etc.) of the increased genes is selected from the group consisting of EFNB3, SEMA3C, SRCIN1, SLIT2, CDH2, KREMEN1, KIAA1024, DSCAM, MAP2, PAK3, NGF, SEMA6D, STMN2, CAMK2B, ISLR2, SNAP25, MAGI2, NTRK3, AVIL, KALRN, PMP22, NRCAM, NEGR1, PLXNA4, EPHA7, SPOCK1, CX3CL1, ZNF804A, ULK2, SARM1, PLXNA3, ENC1, TRIM67, NTN1, ZNF365, GFI1, ADCYAP1, CNR1, ANKRD1, MAPT, RUFY3, PLXNA2, PLXNC1, MAP1B, PTPRO, and FZD1.

[0209] In the embodiment, the first gene set includes at least one (e.g., 1, 2, 3, 4, 5, 6, etc.) augmented genes from Table 7. In the embodiment, the at least one (e.g., 1, 2, 3, 4, 5, 6, etc.) augmented genes are GPM6A, DRD2, BMP7, EFNB3, SEMA3C, FSCN2, LGI1, SRCIN1, WNT4, SLIT2, NAV3, NRG1, TTBK1, RNF165, PRDM16, CDH2, ELAVL4, ONECUT2, KREMEN1, SCRT1, KIAA1024, DSCAM, MAP2, PRDM8, FAT4, PAK3, NGF, SEMA6D, STMN2, ZFHX3, LRP2, APOA1, CAMK2B, MDGA1, ISLR2, SNAP25, NEUROD4, PHOX2B, DCX, MAGI2, PIK3R1, NC AM1, NTRK3, PITX3, MYT1L, AVIL, CDK5R2, INSM1, SOX21, IL6ST, KIF5C, SYNJ1, KALRN, GFRA1, TCTN1, CELSR1, IRX5, PMP22 , SOX6, RUNX1, DPYSL4, NRCAM, ZNF521, MDGA2, PROX1, FGF5, ZNF536, MAPIA, DCHS1, NEGR1, PLXNA4, EPB41L3, GAP43, EPHA 7, DLL3, VSTM2L, ID4, NRN1, SPOCK1, DUSP10, COL3A1, CX3CL1, SLIT3, MAPK8IP2, FAIM2, TCF12, BMP6, NRBP2, NCAM2, HIPK 2, CDH11, ADGRL3, ZNF804A, ULK2, CCKAR, SARM1, PLXNA3, ENC1, ASCL1, UNCX, MEIS1, ARX, SRRM4, TRIM67, ALCAM, NTN1, ZNF365, GFI1, ADCYAP1, CNR1, ANKRD1, ALK, STMN4, MAPT, RUFY3, PLXNA2, PLXNC1, MAP1B, DPYSL5, PTPRO, FZD1, or DLX5.

[0210] In the embodiment, at least one (e.g., 1, 2, 3, 4, 5, 6, etc.) of the increased genes are GPM6A, DRD2, BMP7, EFNB3, SEMA3C, FSCN2, LGI1, SRCIN1, WNT4, SLIT2, NAV3, NRG1, TTBK1, RNF165, PRDM16, CDH2, ELAVL4, ONECUT2, KREMEN1, SCRT1, KIAA1024, DSCAM, MAP2, PRDM8, FAT4, PAK 3, NGF, SEMA6D, STMN2, ZFHX3, LRP2, APOA1, CAMK2B, MDGA1, ISLR2, SNAP25, NEUROD4, PHOX2B, DCX, MAGI2, PIK3R1, NCAM1, NTRK3, PITX3, MYT1L, AVIL, CDK5R2, INSM1, SOX21, IL6ST, KIF5C, SYNJ1, KALRN, GFRA1, TCTN1, CELSR1, IRX5, PMP22, SOX6 , RUNX1, DPYSL4, NRCAM, ZNF521, MDGA2, PROX1, FGF5, ZNF536, MAP1A, DCHS1, NEGR1, PLXNA4, EPB41L3, GAP43, EPHA7, DLL3 , VSTM2L, ID4, NRN1, SPOCK1, DUSP10, COL3A1, CX3CL1, SLIT3, MAPK8IP2, FAIM2, TCF12, BMP6, NRBP2, NCAM2, HIPK2, CDH11 The group is selected from ADGRL3, ZNF804A, ULK2, CCKAR, SARM1, PLXNA3, ENC1, ASCL1, UNCX, MEIS1, ARX, SRRM4, TRIM67, ALCAM, NTN1, ZNF365, GFI1, ADCYAP1, CNR1, ANKRD1, ALK, STMN4, MAPT, RUFY3, PLXNA2, PLXNC1, MAP1B, DPYSL5, PTPRO, FZD1, and DLX5.

[0211] In the embodiment, at least one augmented gene is selected from the group consisting of CAPN14, FAT3, FAT4, PCDHGC4, SLC8A1, SLIT2, CEMIP2, CDHR3, CDH2, DRD2, EPHB2, MAGI2, PCDHB11, PCDHB13, PCDHB14, PCDHB16, PCDHB2, ADGRG6, ELF5, EPHA7, FOXP1, GDF7, HOXA1, MINAR1, MSX1, NRBP2, NRIP1, PITX3, POU6F2, PTPRO, SLC35D1, TCF12, ZFHX3, and ZNF703. In the embodiment, at least one augmented gene is CAPN14, FAT3, FAT4, PCDHGC4, SLC8A1, SLIT2, CEMIP2, CDHR3, CDH2, DRD2, EPHB2, MAGI2, PCDHB11, PCDHB13, PCDHB14, PCDHB16, PCDHB2, ADGRG6, ELF5, EPHA7, FOXP1, GDF7, HOXA1, MINAR1, MSX1, NRBP2, NRIP1, PITX3, POU6F2, PTPRO, SLC35D1, TCF12, ZFHX3, or ZNF703.

[0212] In the embodiment, the increase in expression level is at least 4 times higher than that of pluripotent stem cells. In the embodiment, the increase in expression level is about 4 times higher than that of pluripotent stem cells. In the embodiment, the increase in expression level is at least 5 times higher than that of pluripotent stem cells. In the embodiment, the increase in expression level is about 5 times higher than that of pluripotent stem cells. In the embodiment, the increase in expression level is at least 6 times higher than that of pluripotent stem cells. In the embodiment, the increase in expression level is about 6 times higher than that of pluripotent stem cells. In the embodiment, the increase in expression level is at least 7 times higher than that of pluripotent stem cells. In the embodiment, the increase in expression level is about 7 times higher than that of pluripotent stem cells. In the embodiment, the increase in expression level is at least 8 times higher than that of pluripotent stem cells. In the embodiment, the increase in expression level is about 8 times higher than that of pluripotent stem cells. In the embodiment, the increase in expression level is at least 9 times higher than that of pluripotent stem cells. In the embodiment, the increase in expression level is about 9 times higher than that of pluripotent stem cells. In the embodiment, the increase in expression level is at least 10 times higher than that of pluripotent stem cells. In this embodiment, the increase in expression level is approximately 10 times higher than that of pluripotent stem cells.

[0213] In one embodiment, the increase in expression level is at least 11 times higher than that of pluripotent stem cells. In one embodiment, the increase in expression level is about 11 times higher than that of pluripotent stem cells. In one embodiment, the increase in expression level is at least 12 times higher than that of pluripotent stem cells. In one embodiment, the increase in expression level is about 12 times higher than that of pluripotent stem cells. In one embodiment, the increase in expression level is at least 13 times higher than that of pluripotent stem cells. In one embodiment, the increase in expression level is about 13 times higher than that of pluripotent stem cells. In one embodiment, the increase in expression level is at least 14 times higher than that of pluripotent stem cells. In one embodiment, the increase in expression level is about 14 times higher than that of pluripotent stem cells. In one embodiment, the increase in expression level is at least 15 times higher than that of pluripotent stem cells. In one embodiment, the increase in expression level is about 15 times higher than that of pluripotent stem cells. In one embodiment, the increase in expression level is at least 16 times higher than that of pluripotent stem cells. In one embodiment, the increase in expression level is about 16 times higher than that of pluripotent stem cells. In one embodiment, the increase in expression level is at least 17 times higher than that of pluripotent stem cells. In another embodiment, the increase in expression level is about 17 times higher than that of pluripotent stem cells. In one embodiment, the increase in expression level is at least 18 times higher than that of pluripotent stem cells. In another embodiment, the increase in expression level is about 18 times higher than that of pluripotent stem cells. In another embodiment, the increase in expression level is at least 19 times higher than that of pluripotent stem cells. In another embodiment, the increase in expression level is about 19 times higher than that of pluripotent stem cells. In another embodiment, the increase in expression level is at least 20 times higher than that of pluripotent stem cells. In another embodiment, the increase in expression level is about 20 times higher than that of pluripotent stem cells.

[0214] In the embodiment, the increase in expression level is approximately 4 to 100 times higher than that of pluripotent stem cells. In the embodiment, the increase in expression level is 4 to 100 times higher than that of pluripotent stem cells. In the embodiment, the increase in expression level is approximately 6 to 100 times higher than that of pluripotent stem cells. In the embodiment, the increase in expression level is 6 to 100 times higher than that of pluripotent stem cells. In the embodiment, the increase in expression level is approximately 8 to 100 times higher than that of pluripotent stem cells. In the embodiment, the increase in expression level is 8 to 100 times higher than that of pluripotent stem cells. In the embodiment, the increase in expression level is approximately 10 to 100 times higher than that of pluripotent stem cells. In the embodiment, the increase in expression level is 10 to 100 times higher than that of pluripotent stem cells. In the embodiment, the increase in expression level is approximately 20 to 100 times higher than that of pluripotent stem cells. In the embodiment, the increase in expression level is 20 to 100 times higher than that of pluripotent stem cells. In the embodiment, the increase in expression level is approximately 30 to 100 times higher than that of pluripotent stem cells. In one embodiment, the increase in expression level is 30 to 100 times higher than that of pluripotent stem cells. In another embodiment, the increase in expression level is approximately 40 to 100 times higher than that of pluripotent stem cells. In another embodiment, the increase in expression level is 40 to 100 times higher than that of pluripotent stem cells. In another embodiment, the increase in expression level is approximately 50 to 100 times higher than that of pluripotent stem cells. In another embodiment, the increase in expression level is 50 to 100 times higher than that of pluripotent stem cells. In another embodiment, the increase in expression level is approximately 60 to 100 times higher than that of pluripotent stem cells. In another embodiment, the increase in expression level is 60 to 100 times higher than that of pluripotent stem cells. In another embodiment, the increase in expression level is approximately 70 to 100 times higher than that of pluripotent stem cells. In another embodiment, the increase in expression level is 70 to 100 times higher than that of pluripotent stem cells. In yet another embodiment, the increase in expression level is approximately 80 to 100 times higher than that of pluripotent stem cells. In the embodiment, the increase in expression level is 80 to 100 times higher than that of pluripotent stem cells. In the embodiment, the increase in expression level is approximately 90 to 100 times higher than that of pluripotent stem cells. In the embodiment, the increase in expression level is 90 to 100 times higher than that of pluripotent stem cells.

[0215] In the embodiment, the increase in expression level is approximately 4 to 90 times higher than that of pluripotent stem cells. In the embodiment, the increase in expression level is 4 to 90 times higher than that of pluripotent stem cells. In the embodiment, the increase in expression level is approximately 4 to 80 times higher than that of pluripotent stem cells. In the embodiment, the increase in expression level is 4 to 80 times higher than that of pluripotent stem cells. In the embodiment, the increase in expression level is approximately 4 to 70 times higher than that of pluripotent stem cells. In the embodiment, the increase in expression level is 4 to 70 times higher than that of pluripotent stem cells. In the embodiment, the increase in expression level is approximately 4 to 60 times higher than that of pluripotent stem cells. In the embodiment, the increase in expression level is 4 to 60 times higher than that of pluripotent stem cells. In the embodiment, the increase in expression level is approximately 4 to 50 times higher than that of pluripotent stem cells. In the embodiment, the increase in expression level is 4 to 50 times higher than that of pluripotent stem cells. In the embodiment, the increase in expression level is approximately 4 to 40 times higher than that of pluripotent stem cells. In the embodiment, the increase in expression level is 4 to 40 times higher compared to pluripotent stem cells. In the embodiment, the increase in expression level is approximately 4 to 30 times higher than that of pluripotent stem cells. In the embodiment, the increase in expression level is 4 to 30 times higher than that of pluripotent stem cells. In the embodiment, the increase in expression level is approximately 4 to 20 times higher than that of pluripotent stem cells. In the embodiment, the increase in expression level is 4 to 20 times higher than that of pluripotent stem cells. In the embodiment, the increase in expression level is approximately 4 to 10 times higher than that of pluripotent stem cells. In the embodiment, the increase in expression level is 4 to 10 times higher than that of pluripotent stem cells. In the embodiment, the increase in expression level is approximately 4 to 8 times higher than that of pluripotent stem cells. In the embodiment, the increase in expression level is approximately 4 to 6 times higher than that of pluripotent stem cells. In the embodiment, the increase in expression level is 4 to 6 times higher than that of pluripotent stem cells.

[0216] In the embodiment, the desired determined gene expression profile information for dopaminergic progenitor cells includes a reduction in gene expression levels for pluripotent stem cells for a second gene set, the second gene set including at least one reduced gene in one or more second gene ontologies in Table 8.

[0217] In the embodiment, the desired determined gene expression profile information of dopaminergic progenitor cells includes a reduction in gene expression levels for pluripotent stem cells for a second gene set, the second gene set including at least one reduced gene in one or more second gene ontologies selected from the group consisting of the gene ontologies in Table 8.

[0218] In the embodiment, the desired determined gene expression profile information for dopaminergic progenitor cells includes a decrease in gene expression levels for pluripotent stem cells for a second gene set, the second gene set being GO:0044459, GO:0071944, GO:0005886, GO:0005904, GO:0031226, GO:0005887, GO:0042127, GO:0005576, GO:0044421, GO:0070887, GO:0034097, GO:0050896, GO:0051869, GO:0071345, G O:0048856, GO:0010033, GO:0044425, GO:0007166, GO:0032501, GO:0044707, GO:0050874, GO:0023052, GO:0023046, GO:0044700, GO:0031982, GO:0 031988, GO:0032502, GO:0044767, GO:0007154, GO:0071310, GO:0005615, GO:0042221, GO:0031224, GO:0051049, GO:0019221, GO:0048583, GO:00082 84, GO:0007275, GO:0023051, GO:0010646, GO:0048584, GO:0051239, GO:0032879, GO:0006954, GO:0007165, GO:0023033, GO:0043230, GO:0098771, GO:0055065, GO:0016021, GO:1903561, GO:0009966, GO:0035466, GO:0050801, GO:0010647, GO:0006811, GO:0065008, GO:0051240, GO:0098590, GO:0 055082, GO:0055080, GO:0023056, GO:0006875, GO:0070062, GO:0051716, GO:0048878, GO:0043269, GO:0065009, GO:0051050, GO:0050865, GO:0098 857, GO:0006873, GO:0048518, GO:0043119, GO:0030003, GO:0048731, GO:0042592, GO:0045121, GO:0006952, GO:0002217, GO:0042829, GO:0048522,GO:0051242、GO:0046903、GO:0005102、GO:0030154、GO:0019725、GO:0001775、GO:0009967、GO:0035468、GO:0002376、GO:0072503、GO:0045321、GO:0050863、GO:0050878、GO:0048869、GO:0002703、GO:0050670、GO:0022407、GO:0032944、GO:0016020、GO:1902533、GO:0010740、GO:0043270、GO:0045785、GO:0072507、GO:0009888、GO:0022409、GO:0042493、GO:0017035、GO:0002682、GO:0006874、GO:0032101、GO:0070663、GO:0007204、GO:1902531、GO:0010627、GO:1903039、GO:1903037、GO:0002694、GO:0031012、GO:0009605、GO:0044281、GO:2000021、GO:0055074、GO:0035296、GO:0097746、GO:0042312、GO:0044093、GO:0002685、GO:0098589、GO:0051480、GO:0003013、GO:0008015、GO:0070261、GO:1901700、GO:0007187、GO:0030155、GO:0003006、GO:0034220、GO:0050870、GO:0009611、GO:0002245、GO:0008217、GO:1903524、GO:0042129、GO:0033993、GO:0050880、GO:0007188、GO:0051704、GO:0051706、GO:0035150、GO:0030198、GO:0032103、GO:0043062、GO:0050867、GO:0040017、GO:0002687、GO:0022857、GO:0005386、GO:0015563、GO:0015646、GO:0022891、GO:0022892、GO:0048608、GO:0015267、GO:0015249、GO:0015268、GO:0002274、GO:0001890、GO:0048513、GO:0022803、GO:0022814、GO:0002684、GO:0050776、GO:0002819、GO:0045937、GO:0010562、GO:0002366、GO:0061458、GO:0051094、GO:0034762、GO:2000147、GO:0030141、GO:0002263、GO:0006955、GO:0015075、GO:0099503、GO:0000003、GO:0019952、GO:0050876、GO:0098772、GO:0002252、GO:0009653、GO:0050900、GO:1901701、GO:0042802、GO:0043085、GO:0048554、GO:0030335、GO:0005215、GO:0005478、GO:0022414、GO:0044702、GO:0051241、GO:0002696、GO:0046873、GO:0042060、GO:0003018、GO:0032940、GO:0031410、GO:0016023、GO:0002822、GO:0046394、GO:0051272、GO:0097708、GO:0009986、GO:0009928、GO:0009929、GO:0016053、GO:0051928、GO:0042327、GO:0031225、GO:0010469、GO:0009987、GO:0008151、GO:0044763、GO:0050875、GO:0006950、GO:0043207、GO:0002886、GO:0051249、GO:0098655、GO:0005575、GO:0008372、GO:0002697、GO:0019935、GO:0007267、GO:0032496、GO:0070160、GO:0005216、GO:0034765、GO:0006820、GO:0006822、GO:0005911、GO:0019933、GO:0004252、GO:0048545、GO:0051924、GO:0006812、GO:0006819、GO:0015674、GO:0019932、GO:0051707、GO:0009613、GO:0042828、GO:0001934、GO:0022838、GO:1902105、GO:0006636、GO:0071624、GO:0055085、GO:0010959、GO:0005923、GO:0030001、GO:0002237、GO:0009607、GO:0002699、GO:0005261、GO:0015281、GO:0015338、GO:1903522、GO:0043408、GO:0008324、GO:0015711、GO:0071622、GO:0070665、GO:0002683、GO:0010543、GO:0050730、GO:0007189、GO:0010579、GO:0010580、GO:0016338、GO:0050671、GO:0015318、GO:0050777、GO:0050793、GO:0030054、GO:0022610、GO:0032946、GO:0043300、GO:0042102、GO:0001817、GO:0002275、GO:0032844、GO:0060429、GO:0001653、GO:0031347、GO:0048646、GO:0042981、GO:0051345、GO:0002690、GO:0043302、GO:0098660、GO:0009719、GO:0048018、GO:0071884、GO:0009116、GO:0043168、GO:0002444、GO:0043296、GO:0065007、GO:0098662、GO:0043299、GO:0030193、GO:0042119、GO:0050921、GO:0002688、GO:0043410、GO:0022836、GO:0090022、GO:0002888、GO:0002821、GO:1900046、GO:0042509、GO:0042510、GO:0042513、GO:0042516、GO:0042519、GO:0042522、GO:0042525、GO:0042528、GO:0035295、GO:0043235、GO:0022839、GO:0090023、GO:0043065、GO:0046718、GO:0019063、GO:0043067、GO:0043070、GO:0030545、GO:0001816、GO:0003382、GO:0044409、GO:0051806、GO:0030260、GO:0051828、GO:0036230、GO:0010941、GO:0009725、GO:0002476、GO:0002526、GO:0051384、GO:0050790、GO:0048552、GO:0051247、GO:0008285、GO:0097755、GO:0045909、GO:0031960、GO:0070374、GO:0002824、GO:0030728、GO:0007155、GO:0098602、GO:0035556、GO:0007242、GO:0007243、GO:0023013、GO:0023034、GO:0010942、GO:0070372、GO:0051046、GO:0043068、GO:0043071、GO:1902107、GO:0002283、GO:0005509、GO:0050818、GO:0051336、GO:0009119、GO:0003073、GO:0036018、GO:0046635、GO:2000026、GO:0006082、GO:0001819、GO:0004175、GO:0016809、GO:0050764、GO:0043436、GO:0005201、GO:0097028、GO:0008528、GO:0045055、GO:0016477、GO:0030168、GO:0035239、GO:0070820、GO:0031349、GO:0001932、GO:0098797、GO:0045137、GO:0043312、GO:0002446、GO:0052547、GO:0048585、GO:0009070、GO:0009113、GO:0034764、GO:0022600、GO:0016323、GO:0045597、GO:0042803、GO:0016324、GO:0045177、GO:0008406、GO:0006887、GO:0016194、GO:0016195、GO:0008236、GO:0072358、GO:0001944、GO:0002521、GO:1902624、GO:0044283、GO:0048519、GO:0043118、GO:0045684、GO:0006690、GO:0010522、GO:0022890、GO:0015082、GO:0019752、GO:0071396、GO:0001525、GO:0050731、GO:0036017、GO:0042609、GO:0050817、GO:0070252、GO:0060670、GO:0019369、GO:0019229、GO:0009164、GO:0017171、GO:0045907、GO:0008289、GO:1902622、GO:0050920、GO:0051047、GO:0046649、GO:0032270、GO:0009991、GO:0033628、 GO:0004715、GO:0045776、GO:0042454、GO:0005515、GO:0001948、GO:0045308、GO:0002706、GO:1903530、GO:1901657、GO:0030322、GO:0042270、GO:0045088、GO:0046717、GO:0016661、GO:0008584、GO:0002428、GO:1901568、GO:0042325、GO:0044433、GO:0044057、GO:0031638、GO:0006953、GO:0050729、GO:0046546、GO:0042531、GO:0042511、GO:0042515、GO:0042517、GO:0042520、GO:0042523、GO:0042526、GO:0042529、GO:0046850、GO:0005178、GO:0048514、GO:0045682、GO:0003674、GO:0005554、GO:0046634、GO:0061041、GO:0008016、GO:0043407、GO:0046456、GO:0007596、GO:0045606、GO:0014070、GO:0048870、GO:0051674、GO:0002704、GO:0007584、GO:0070228、GO:0002675、GO:0052548、GO:0001664、GO:0090330、GO:0045117、GO:0034340、GO:0044853、GO:0032587、GO:0007586、GO:0097529、GO:0045595、GO:0040012、GO:0050866、GO:0010035、GO:0034767、GO:0098801、GO:0015079、GO:0015388、GO:0022817、GO:0044706、GO:1901605、GO:0009636、GO:0007599、GO:0002705、GO:2000145、GO:0034103、GO:0032642、GO:0098805、GO:0051209、GO:1901137、GO:0090066、GO:0098641、GO:0032409、GO:0007589、GO:0046128、GO:0061134、GO:0015893、GO:0001726、GO:0001893、GO:0030334、GO:0042398, or any combination thereof, including at least one reduced gene within a second gene ontology.

[0219] In the embodiment, the desired determined gene expression profile information for dopaminergic progenitor cells includes a decrease in gene expression levels for pluripotent stem cells for a second gene set, the second gene set being GO:0044459, GO:0071944, GO:0005886, GO:0005904, GO:0031226, GO:0005887, GO:0042127, GO:0005576, GO:0044421, GO:0070887, GO:0034097, GO:0050896, GO:0051869, GO:0071345, G O:0048856, GO:0010033, GO:0044425, GO:0007166, GO:0032501, GO:0044707, GO:0050874, GO:0023052, GO:0023046, GO:0044700, GO:0031982, GO:0 031988, GO:0032502, GO:0044767, GO:0007154, GO:0071310, GO:0005615, GO:0042221, GO:0031224, GO:0051049, GO:0019221, GO:0048583, GO:00082 84, GO:0007275, GO:0023051, GO:0010646, GO:0048584, GO:0051239, GO:0032879, GO:0006954, GO:0007165, GO:0023033, GO:0043230, GO:0098771, GO:0055065, GO:0016021, GO:1903561, GO:0009966, GO:0035466, GO:0050801, GO:0010647, GO:0006811, GO:0065008, GO:0051240, GO:0098590, GO:0 055082, GO:0055080, GO:0023056, GO:0006875, GO:0070062, GO:0051716, GO:0048878, GO:0043269, GO:0065009, GO:0051050, GO:0050865, GO:0098 857, GO:0006873, GO:0048518, GO:0043119, GO:0030003, GO:0048731, GO:0042592, GO:0045121, GO:0006952, GO:0002217, GO:0042829, GO:0048522,GO:0051242、GO:0046903、GO:0005102、GO:0030154、GO:0019725、GO:0001775、GO:0009967、GO:0035468、GO:0002376、GO:0072503、GO:0045321、GO:0050863、GO:0050878、GO:0048869、GO:0002703、GO:0050670、GO:0022407、GO:0032944、GO:0016020、GO:1902533、GO:0010740、GO:0043270、GO:0045785、GO:0072507、GO:0009888、GO:0022409、GO:0042493、GO:0017035、GO:0002682、GO:0006874、GO:0032101、GO:0070663、GO:0007204、GO:1902531、GO:0010627、GO:1903039、GO:1903037、GO:0002694、GO:0031012、GO:0009605、GO:0044281、GO:2000021、GO:0055074、GO:0035296、GO:0097746、GO:0042312、GO:0044093、GO:0002685、GO:0098589、GO:0051480、GO:0003013、GO:0008015、GO:0070261、GO:1901700、GO:0007187、GO:0030155、GO:0003006、GO:0034220、GO:0050870、GO:0009611、GO:0002245、GO:0008217、GO:1903524、GO:0042129、GO:0033993、GO:0050880、GO:0007188、GO:0051704、GO:0051706、GO:0035150、GO:0030198、GO:0032103、GO:0043062、GO:0050867、GO:0040017、GO:0002687、GO:0022857、GO:0005386、GO:0015563、GO:0015646、GO:0022891、GO:0022892、GO:0048608、GO:0015267、GO:0015249、GO:0015268、GO:0002274、GO:0001890、GO:0048513、GO:0022803、GO:0022814、GO:0002684、GO:0050776、GO:0002819、GO:0045937、GO:0010562、GO:0002366、GO:0061458、GO:0051094、GO:0034762、GO:2000147、GO:0030141、GO:0002263、GO:0006955、GO:0015075、GO:0099503、GO:0000003、GO:0019952、GO:0050876、GO:0098772、GO:0002252、GO:0009653、GO:0050900、GO:1901701、GO:0042802、GO:0043085、GO:0048554、GO:0030335、GO:0005215、GO:0005478、GO:0022414、GO:0044702、GO:0051241、GO:0002696、GO:0046873、GO:0042060、GO:0003018、GO:0032940、GO:0031410、GO:0016023、GO:0002822、GO:0046394、GO:0051272、GO:0097708、GO:0009986、GO:0009928、GO:0009929、GO:0016053、GO:0051928、GO:0042327、GO:0031225、GO:0010469、GO:0009987、GO:0008151、GO:0044763、GO:0050875、GO:0006950、GO:0043207、GO:0002886、GO:0051249、GO:0098655、GO:0005575、GO:0008372、GO:0002697、GO:0019935、GO:0007267、GO:0032496、GO:0070160、GO:0005216、GO:0034765、GO:0006820、GO:0006822、GO:0005911、GO:0019933、GO:0004252、GO:0048545、GO:0051924、GO:0006812、GO:0006819、GO:0015674、GO:0019932、GO:0051707、GO:0009613、GO:0042828、GO:0001934、GO:0022838、GO:1902105、GO:0006636、GO:0071624、GO:0055085、GO:0010959、GO:0005923、GO:0030001、GO:0002237、GO:0009607、GO:0002699、GO:0005261、GO:0015281、GO:0015338、GO:1903522、GO:0043408、GO:0008324、GO:0015711、GO:0071622、GO:0070665、GO:0002683、GO:0010543、GO:0050730、GO:0007189、GO:0010579、GO:0010580、GO:0016338、GO:0050671、GO:0015318、GO:0050777、GO:0050793、GO:0030054、GO:0022610、GO:0032946、GO:0043300、GO:0042102、GO:0001817、GO:0002275、GO:0032844、GO:0060429、GO:0001653、GO:0031347、GO:0048646、GO:0042981、GO:0051345、GO:0002690、GO:0043302、GO:0098660、GO:0009719、GO:0048018、GO:0071884、GO:0009116、GO:0043168、GO:0002444、GO:0043296、GO:0065007、GO:0098662、GO:0043299、GO:0030193、GO:0042119、GO:0050921、GO:0002688、GO:0043410、GO:0022836、GO:0090022、GO:0002888、GO:0002821、GO:1900046、GO:0042509、GO:0042510、GO:0042513、GO:0042516、GO:0042519、GO:0042522、GO:0042525、GO:0042528、GO:0035295、GO:0043235、GO:0022839、GO:0090023、GO:0043065、GO:0046718、GO:0019063、GO:0043067、GO:0043070、GO:0030545、GO:0001816、GO:0003382、GO:0044409、GO:0051806、GO:0030260、GO:0051828、GO:0036230、GO:0010941、GO:0009725、GO:0002476、GO:0002526、GO:0051384、GO:0050790、GO:0048552、GO:0051247、GO:0008285、GO:0097755、GO:0045909、GO:0031960、GO:0070374、GO:0002824、GO:0030728、GO:0007155、GO:0098602、GO:0035556、GO:0007242、GO:0007243、GO:0023013、GO:0023034、GO:0010942、GO:0070372、GO:0051046、GO:0043068、GO:0043071、GO:1902107、GO:0002283、GO:0005509、GO:0050818、GO:0051336、GO:0009119、GO:0003073、GO:0036018、GO:0046635、GO:2000026、GO:0006082、GO:0001819、GO:0004175、GO:0016809、GO:0050764、GO:0043436、GO:0005201、GO:0097028、GO:0008528、GO:0045055、GO:0016477、GO:0030168、GO:0035239、GO:0070820、GO:0031349、GO:0001932、GO:0098797、GO:0045137、GO:0043312、GO:0002446、GO:0052547、GO:0048585、GO:0009070、GO:0009113、GO:0034764、GO:0022600、GO:0016323、GO:0045597、GO:0042803、GO:0016324、GO:0045177、GO:0008406、GO:0006887、GO:0016194、GO:0016195、GO:0008236、GO:0072358、GO:0001944、GO:0002521、GO:1902624、GO:0044283、GO:0048519、GO:0043118、GO:0045684、GO:0006690、GO:0010522、GO:0022890、GO:0015082、GO:0019752、GO:0071396、GO:0001525、GO:0050731、GO:0036017、GO:0042609、GO:0050817、GO:0070252、GO:0060670、GO:0019369、GO:0019229、GO:0009164、GO:0017171、GO:0045907、GO:0008289、GO:1902622、GO:0050920、GO:0051047、GO:0046649、GO:0032270、GO:0009991、GO:0033628、 GO:0004715、GO:0045776、GO:0042454、GO:0005515、GO:0001948、GO:0045308、GO:0002706、GO:1903530、GO:1901657、GO:0030322、GO:0042270、GO:0045088、GO:0046717、GO:0016661、GO:0008584、GO:0002428、GO:1901568、GO:0042325、GO:0044433、GO:0044057、GO:0031638、GO:0006953、GO:0050729、GO:0046546、GO:0042531、GO:0042511、GO:0042515、GO:0042517、GO:0042520、GO:0042523、GO:0042526、GO:0042529、GO:0046850、GO:0005178、GO:0048514、GO:0045682、GO:0003674、GO:0005554、GO:0046634、GO:0061041、GO:0008016、GO:0043407、GO:0046456、GO:0007596、GO:0045606、GO:0014070、GO:0048870、GO:0051674、GO:0002704、GO:0007584、GO:0070228、GO:0002675、GO:0052548、GO:0001664、GO:0090330、GO:0045117、GO:0034340、GO:0044853、GO:0032587、GO:0007586、GO:0097529、GO:0045595、GO:0040012、GO:0050866、GO:0010035、GO:0034767、GO:0098801、GO:0015079、GO:0015388、GO:0022817、GO:0044706、GO:1901605、GO:0009636、GO:0007599、GO:0002705、GO:2000145、GO:0034103、GO:0032642、GO:0098805、GO:0051209、GO:1901137、GO:0090066、GO:0098641、GO:0032409、GO:0007589、GO:0046128、GO:0061134、GO:0015893、GO:0001726、GO:0001893、GO:0030334、GO:0042398, and one or more second gene ontologs selected from the group consisting of any combination thereof, containing at least one reduced gene within the ontology.

[0220] In the embodiment, the second gene set contains approximately 1 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 2 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 3 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 4 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 5 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 6 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 7 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 8 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 9 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 10 to 1000 reduced genes within one or more of the second gene ontologies.

[0221] In the embodiment, the second gene set contains approximately 15 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 20 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 25 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 30 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 35 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 40 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 45 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 50 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 55 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 60 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 65 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 70 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 75 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 80 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 85 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 90 to 1000 reduced genes within one or more of the second gene ontologies.In the embodiment, the second gene set contains approximately 95 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 100 to 1000 reduced genes within one or more of the second gene ontologies.

[0222] In the embodiment, the second gene set contains approximately 105 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 115 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 120 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 125 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 130 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 135 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 140 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 145 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 150 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 155 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 160 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 165 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 170 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 175 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 180 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 185 to 1000 reduced genes within one or more of the second gene ontologies.In the embodiment, the second gene set contains approximately 190 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 195 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 200 to 1000 reduced genes within one or more of the second gene ontologies.

[0223] In the embodiment, the second gene set contains approximately 205 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 215 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 220 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 225 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 230 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 235 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 240 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 245 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 250 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 255 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 260 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 265 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 270 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 275 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 280 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 285 to 1000 reduced genes within one or more of the second gene ontologies.In the embodiment, the second gene set contains approximately 290 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 295 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 300 to 1000 reduced genes within one or more of the second gene ontologies.

[0224] In the embodiment, the second gene set contains approximately 305 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 315 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 320 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 325 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 330 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 335 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 340 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 345 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 350 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 355 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 360 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 365 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 370 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 375 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 380 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 385 to 1000 reduced genes within one or more of the second gene ontologies.In the embodiment, the second gene set contains approximately 390 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 395 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 400 to 1000 reduced genes within one or more of the second gene ontologies.

[0225] In the embodiment, the second gene set contains approximately 405 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 415 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 420 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 425 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 430 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 435 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 440 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 445 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 450 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 455 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 460 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 465 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 470 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 475 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 480 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 485 to 1000 reduced genes within one or more of the second gene ontologies.In the embodiment, the second gene set contains approximately 490 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 495 to 1000 reduced genes within one or more of the second gene ontologies.

[0226] In the embodiment, the second gene set contains approximately 500 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 505 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 510 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 515 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 520 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 525 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 530 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 535 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 540 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 545 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 550 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 555 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 565 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 570 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 575 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 580 to 1000 reduced genes within one or more of the second gene ontologies.In the embodiment, the second gene set contains approximately 585 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 590 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 595 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 600 to 1000 reduced genes within one or more of the second gene ontologies.

[0227] In the embodiment, the second gene set contains approximately 605 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 615 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 620 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 625 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 630 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 635 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 640 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 645 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 650 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 655 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 660 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 665 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 670 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 675 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 680 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 685 to 1000 reduced genes within one or more of the second gene ontologies.In the embodiment, the second gene set contains approximately 690 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 695 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 700 to 1000 reduced genes within one or more of the second gene ontologies.

[0228] In the embodiment, the second gene set contains approximately 705 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 715 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 720 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 725 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 730 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 735 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 740 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 745 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 750 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 755 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 760 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 765 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 770 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 775 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 780 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 785 to 1000 reduced genes within one or more of the second gene ontologies.In the embodiment, the second gene set contains approximately 790 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 795 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 800 to 1000 reduced genes within one or more of the second gene ontologies.

[0229] In the embodiment, the second gene set contains approximately 805 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 815 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 820 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 825 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 830 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 835 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 840 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 845 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 850 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 855 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 860 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 865 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 870 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 875 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 880 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 885 to 1000 reduced genes within one or more of the second gene ontologies.In the embodiment, the second gene set contains approximately 890 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 895 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 900 to 1000 reduced genes within one or more of the second gene ontologies.

[0230] In the embodiment, the second gene set contains approximately 905 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 915 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 920 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 925 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 930 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 935 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 940 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 945 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 950 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 955 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 960 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 965 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 970 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 975 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 980 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 985 to 1000 reduced genes within one or more of the second gene ontologies.In the embodiment, the second gene set contains approximately 990 to 1000 reduced genes within one or more of the second gene ontologies. In the embodiment, the second gene set contains approximately 995 to 1000 reduced genes within one or more of the second gene ontologies.

[0231] In this embodiment, the second gene set is located within one or more of the second gene ontologies, with the following elements: 1, 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, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111 ,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 1 74, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 20 5, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236 ,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268、269、270、271、272、273、274、275、276、277、278、279、280、281、282、283、284、285、286、287、288、289、290、291、292、293、294、295、296、297、298、299、300、301、302、303、304、305、306、307、308、309、310、311、312、313、314、315、316、317、318、319、320、321、322、323、324、325、326、327、328、329、330、331、332、333、334、335、336、337、338、339、340、341、342、343、344、345、346、347、348、349、350、351、352、353、354、355、356、357、358、359、360、361、362、363、364、365、366、367、368、369、370、371、372、373、374、375、376、377、378、379、380、381、382、383、384、385、386、387、388、389、390、391、392、393、394、395、396、397、398、399、400、401、402、403、404、405、406、407、408、409、410、411、412、413、414、415、416、417、418、419、420、421、422、423、424、425、426、427、428、429、430、431、432、433、434、435、436、437、438、439、440、441、442、443、444、445、446、447、448、449、450、451、452、453、454、455、456、457、458、459、460、461、462、463、464、465、466、467、468、469、470、471、472、473、474、475、476、477、478、479、480、481、482、483、484、485、486、487、488、489、490、491、492、493、494、495、496、497、498、499、500、501、502、503、504、505、506、507、508、509、510、511、512、513、514、515、516、517、518、519、520、521、522、523、524、525、526、527、528、529、530、231、532、533、534、535、536、537、538、539、540、541、542、543、544、545、546、547、548、549、550、551、552、553、554、555、556、557、558、559、560、561、562、563、564、565、566、567、568、569、570、571、572、573、574、575、576、577、578、579、580、581、582、583、584、585、586、587、588、589、590、591、592、593、594、595、596、597、598、599、600、601、605、603、604、605、606、607、608、609、610、611、612、613、614、615、616、617、618、619、620、621、622、623、624、625、626、627、628、629、630、631、632、633、634、635、636、637、638、639、640、641、642、643、644、645、646、647、648、649、650、651、652、653、654、655、656、657、658、659、660、661、662、663、664、665、666、667、668、669、670、671、672、673、674、675、676、677、678、679、680、681、682、683、684、685、686、687、688、689、690、691、692、693、694、695、696、697、698、699、700、701、702、703、704、705、706、707、708、709、710、711、712、713、717、715、716、714、718、719、720、721、722、723、724、725、726、727、728、729、730、731、732、733、734、735、736、737、738、739、740、741、742、743、744、745、746、747、748、749、750、751、752、753、757、755、756、754、758、759、760、761、762、763、764、765、766、767、768, 769, 770, 771, 772, 773, 774, 775, 776, 777, 778, 779, 780, 781, 782, 783, 784, 785, 786, 787, 788, 789, 790, 791, 792, 793, 794, 795, 796, 797, 798, 799, 800, 801, 802, 803, 804, 805, 806, 807, 808, 809, 810, 811, 812, 813, 817, 815, 816, 814, 818, 819, 820, 821, 822, 823, 824, 825, 82 6, 827, 828, 829, 830, 831, 832, 833, 834, 835, 836, 837, 838, 839, 840, 841, 842, 843, 845, 846, 847, 848, 849, 850, 851, 852, 853, 854, 855, 856, 854, 858, 859, 860, 861, 862, 863, 864, 865, 866, 867, 868, 869, 870, 871, 872, 873, 874, 875, 876, 877, 878, 879, 880, 881, 882, 883, 884, 885, 8 86, 887, 888, 889, 890, 891, 892, 893, 894, 895, 896, 897, 898, 899, 900, 901, 902, 903, 904, 905, 906, 907, 908, 909, 910, 911, 912, 913, 917, 915, 916, 914, 918, 919, 920, 921, 922, 923, 924, 925, 926, 927, 928, 929, 930, 931, 932, 933, 934, 935, 936, 937, 938, 939, 940, 941, 942, 943, 945, Contains 946, 947, 948, 949, 950, 951, 952, 953, 954, 955, 956, 954, 958, 959, 960, 961, 962, 963, 964, 965, 966, 967, 968, 969, 970, 971, 972, 973, 974, 975, 976, 977, 978, 979, 980, 981, 982, 983, 984, 985, 986, 987, 988, 989, 990, 991, 992, 993, 994, 995, 996, 997, 998, 999, or 1000 reduced genes.

[0232] The desired determined gene expression profile information for dopaminergic progenitor cells includes reduced gene expression levels for pluripotent stem cells for a second gene set, the second gene set may include at least one reduced gene in one or more second gene ontologies in Table 8. In the context of a second gene ontology, “one or more” as used herein means at least one of the second gene ontologies, e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, etc.

[0233] In the embodiment, the second gene set contains approximately 1 to 500 reduced genes out of 1 to 1000 genes in the second gene ontology. In the embodiment, the second gene set contains approximately 1 to 500 reduced genes out of 50 to 1000 genes in the second gene ontology. In the embodiment, the second gene set contains approximately 1 to 500 reduced genes out of 100 to 1000 genes in the second gene ontology. In the embodiment, the second gene set contains approximately 1 to 500 reduced genes out of 150 to 1000 genes in the second gene ontology. In the embodiment, the second gene set contains approximately 1 to 500 reduced genes out of 200 to 1000 genes in the second gene ontology. In the embodiment, the second gene set contains approximately 250 to 500 reduced genes out of 50 to 1000 genes in the second gene ontology. In the embodiment, the second gene set contains approximately 1 to 500 reduced genes out of 300 to 1000 genes in the second gene ontology. In the embodiment, the second gene set contains approximately 1 to 500 reduced genes out of 350 to 1000 genes in the second gene ontology. In the embodiment, the second gene set contains approximately 1 to 500 reduced genes out of 400 to 1000 genes in the second gene ontology. In the embodiment, the second gene set contains approximately 1 to 500 reduced genes out of 450 to 1000 genes in the second gene ontology. In the embodiment, the second gene set contains approximately 1 to 500 reduced genes out of 500 to 1000 genes in the second gene ontology. In the embodiment, the second gene set contains approximately 1 to 500 reduced genes out of 550 to 1000 genes in the second gene ontology. In the embodiment, the second gene set contains approximately 1 to 500 reduced genes out of 600 to 1000 genes in the second gene ontology. In the embodiment, the second gene set contains approximately 1 to 500 reduced genes out of 650 to 1000 genes in the second gene ontology. In the embodiment, the second gene set contains approximately 1 to 500 reduced genes out of 700 to 1000 genes in the second gene ontology. In the embodiment, the second gene set contains approximately 1 to 500 reduced genes out of 750 to 1000 genes in the second gene ontology.In the embodiment, the second gene set contains approximately 1 to 500 reduced genes out of 800 to 1000 genes in the second gene ontology. In the embodiment, the second gene set contains approximately 1 to 500 reduced genes out of 850 to 1000 genes in the second gene ontology. In the embodiment, the second gene set contains approximately 1 to 500 reduced genes out of 900 to 1000 genes in the second gene ontology. In the embodiment, the second gene set contains approximately 1 to 500 reduced genes out of 950 to 1000 genes in the second gene ontology.

[0234] In the embodiment, the second gene set contains approximately 1 to 500 reduced genes within 1 to 300 genes in the second gene ontology. In the embodiment, the second gene set contains approximately 1 to 500 reduced genes within 10 to 300 genes in the second gene ontology. In the embodiment, the second gene set contains approximately 1 to 500 reduced genes within 20 to 300 genes in the second gene ontology. In the embodiment, the second gene set contains approximately 1 to 500 reduced genes within 30 to 300 genes in the second gene ontology. In the embodiment, the second gene set contains approximately 1 to 500 reduced genes within 40 to 300 genes in the second gene ontology. In the embodiment, the second gene set contains approximately 1 to 500 reduced genes within 50 to 300 genes in the second gene ontology. In the embodiment, the second gene set contains approximately 1 to 500 reduced genes within 60 to 300 genes in the second gene ontology. In the embodiment, the second gene set contains approximately 1 to 500 reduced genes within 70 to 300 genes in the second gene ontology. In the embodiment, the second gene set contains approximately 1 to 500 reduced genes within 80 to 300 genes in the second gene ontology. In the embodiment, the second gene set contains approximately 1 to 500 reduced genes within 90 to 300 genes in the second gene ontology. In the embodiment, the second gene set contains approximately 1 to 500 reduced genes within 100 to 300 genes in the second gene ontology. In the embodiment, the second gene set contains approximately 1 to 500 reduced genes within 110 to 300 genes in the second gene ontology. In the embodiment, the second gene set contains approximately 1 to 500 reduced genes within 120 to 300 genes in the second gene ontology. In the embodiment, the second gene set contains approximately 1 to 500 reduced genes within 130 to 300 genes in the second gene ontology. In the embodiment, the second gene set contains approximately 1 to 500 reduced genes within 140 to 300 genes in the second gene ontology. In the embodiment, the second gene set contains approximately 1 to 500 reduced genes within 150 to 300 genes in the second gene ontology.In the embodiment, the second gene set contains approximately 1 to 500 reduced genes within 160 to 300 genes in the second gene ontology. In the embodiment, the second gene set contains approximately 1 to 500 reduced genes within 170 to 300 genes in the second gene ontology. In the embodiment, the second gene set contains approximately 1 to 500 reduced genes within 180 to 300 genes in the second gene ontology. In the embodiment, the second gene set contains approximately 1 to 500 reduced genes within 190 to 300 genes in the second gene ontology. In the embodiment, the second gene set contains approximately 1 to 500 reduced genes within 200 to 300 genes in the second gene ontology. In the embodiment, the second gene set contains approximately 1 to 500 reduced genes within 210 to 300 genes in the second gene ontology. In the embodiment, the second gene set contains approximately 1 to 500 reduced genes within 220 to 300 genes in the second gene ontology. In the embodiment, the second gene set contains approximately 1 to 500 reduced genes within 230 to 300 genes in the second gene ontology. In the embodiment, the second gene set contains approximately 1 to 500 reduced genes within 240 to 300 genes in the second gene ontology. In the embodiment, the second gene set contains approximately 1 to 500 reduced genes within 250 to 300 genes in the second gene ontology. In the embodiment, the second gene set contains approximately 1 to 500 reduced genes within 260 to 300 genes in the second gene ontology. In the embodiment, the second gene set contains approximately 1 to 500 reduced genes within 270 to 300 genes in the second gene ontology. In one embodiment, the second gene set contains approximately 1 to 500 reduced genes within 280 to 300 genes in the second gene ontology. In another embodiment, the second gene set contains approximately 1 to 500 reduced genes within 290 to 300 genes in the second gene ontology.

[0235] In the embodiment, the second gene set includes approximately 1 to 500 reduced genes within 1 to 290 genes in the second gene ontology. In the embodiment, the second gene set includes approximately 1 to 500 reduced genes within 1 to 280 genes in the second gene ontology. In the embodiment, the second gene set includes approximately 1 to 500 reduced genes within 1 to 270 genes in the second gene ontology. In the embodiment, the second gene set includes approximately 1 to 500 reduced genes within 1 to 260 genes in the second gene ontology. In the embodiment, the second gene set includes approximately 1 to 500 reduced genes within 1 to 250 genes in the second gene ontology. In the embodiment, the second gene set includes approximately 1 to 500 reduced genes within 1 to 240 genes in the second gene ontology. In the embodiment, the second gene set includes approximately 1 to 500 reduced genes within 1 to 230 genes in the second gene ontology. In the embodiment, the second gene set includes approximately 1 to 500 reduced genes within 1 to 220 genes in the second gene ontology. In the embodiment, the second gene set includes approximately 1 to 500 reduced genes within 1 to 210 genes in the second gene ontology. In the embodiment, the second gene set includes approximately 1 to 500 reduced genes within 1 to 200 genes in the second gene ontology. In the embodiment, the second gene set includes approximately 1 to 500 reduced genes within 1 to 190 genes in the second gene ontology. In the embodiment, the second gene set includes approximately 1 to 500 reduced genes within 1 to 180 genes in the second gene ontology. In the embodiment, the second gene set contains approximately 1 to 500 reduced genes within 1 to 170 genes in the second gene ontology. In the embodiment, the second gene set contains approximately 1 to 500 reduced genes within 1 to 160 genes in the second gene ontology. In the embodiment, the second gene set contains approximately 1 to 500 reduced genes within 1 to 150 genes in the second gene ontology. In the embodiment, the second gene set contains approximately 1 to 500 reduced genes within 1 to 140 genes in the second gene ontology.In the embodiment, the second gene set contains approximately 1 to 500 reduced genes within 1 to 130 of the second gene ontology. In the embodiment, the second gene set contains approximately 1 to 500 reduced genes within 1 to 120 of the second gene ontology. In the embodiment, the second gene set contains approximately 1 to 500 reduced genes within 1 to 110 of the second gene ontology. In the embodiment, the second gene set contains approximately 1 to 500 reduced genes within 1 to 100 of the second gene ontology. In the embodiment, the second gene set contains approximately 1 to 500 reduced genes within 1 to 90 of the second gene ontology. In the embodiment, the second gene set contains approximately 1 to 500 reduced genes within 1 to 80 of the second gene ontology. In the embodiment, the second gene set contains approximately 1 to 500 reduced genes within 1 to 70 of the second gene ontology. In the embodiment, the second gene set contains approximately 1 to 500 reduced genes within 1 to 60 of the second gene ontology. In the embodiment, the second gene set contains approximately 1 to 500 reduced genes within 1 to 50 of the second gene ontology. In the embodiment, the second gene set contains approximately 1 to 500 reduced genes within 1 to 40 of the second gene ontology. In the embodiment, the second gene set contains approximately 1 to 500 reduced genes within 1 to 30 of the second gene ontology. In the embodiment, the second gene set contains approximately 1 to 500 reduced genes within 1 to 20 of the second gene ontology. In one embodiment, the second gene set contains approximately 1 to 500 reduced genes within 1 to 10 of the second gene ontology. In another embodiment, the second gene set contains approximately 1 to 500 reduced genes within 1 to 5 of the second gene ontology.

[0236] In this embodiment, the second gene set is as follows: 1, 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, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79 ,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 1 47, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 1 78, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 20 9, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240 ,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 3 22, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 37 2, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, ​​383, 384, 385, 386, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422 , containing at least one reduced gene within the second gene ontology of 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, or 463.

[0237] In this embodiment, the second gene set is as follows: 1, 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, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79 ,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 1 47, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 1 78, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 20 9, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240 ,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 3 34, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 36 5, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, ​​383, 384, 385, 386, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396 ,397,398,399,400,401,402,403,404,405,406,407,408,409,410,411,412,413,414,415,416,417,418,419,420,421,422,423,424,425,426,427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 4 Within 59, 460, 461, 462, or 463 second gene ontologies, 1, 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, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74,75、76、77、78、79、80、81、82、83、84、85、86、87、88、89、90、91、92、93、94、95、96、97、98、99、100、101、102、103、104、105、106、107、108、109、110、111、112、113、114、115、116、117、118、119、120、121、122、123、124、125、126、127、128、129、130、131、132、133、134、135、136、137、138、139、140、141、142、143、144、145、146、147、148、149、150、151、152、153、154、155、156、157、158、159、160、161、162、163、164、165、166、167、168、169、170、171、172、173、174、175、176、177、178、179、180、181、182、183、184、185、186、187、188、189、190、191、192、193、194、195、196、197、198、199、200、201、202、203、204、205、206、207、208、209、210、211、212、213、214、215、216、217、218、219、220、221、222、223、224、225、226、227、228、229、230、231、232、233、234、235、236、237、238、239、240、241、242、243、244、245、246、247、248、249、250、251、252、253、254、255、256、257、258、259、260、261、262、263、264、265、266、267、268、269、270、271、272、273、274、275、276、277、278、279、280、281、282、283、284、285、286、287、288、289、290、291、292、293、294、295、296、297、298、299、300、301、302、303、304、305、306、307、308、309、310、311、312、313、314、315、316、317、318、319、320、321、322、323、324、325、326、327、328、329、330、331、332、333、334、335、336、337、338、339、340、341、342、343、344、345、346、347、348、349、350、351、352、353、354、355、356、357、358、359、360、361、362、363、364、365、366、367、368、369、370、371、372、373、374、375、376、377、378、379、380、381、382、383、384、385、386、387、388、389、390、391、392、393、394、395、396、397、398、399、400、401、402、403、404、405、406、407、408、409、410、411、412、413、414、415、416、417、418、419、420、421、422、423、424、425、426、427、428、429、430、431、432、433、434、435、436、437、438、439、440、441、442、443、444、445、446、447、448、449、450、451、452、453、454、455、456、457、458、459、460、461、462、463、464、465、466、467、468、469、470、471、472、473、474、475、476、477、478、479、480、481、482、483、484、485、486、487、488、489、490、491、492、493、494、495、496、497、498、499、500、501、502、503、504、505、506、507、508、509、510、511、512、513、514、515、516、517、518、519、520、521、522、523、524、525、526、527、528、529、530、231、532、533、534、535、536、537、538、539、540、541、542、543、544、545、546、547、548、549、550、551、552、553、554、555、556、557、558、559、560、561、562、563、564、565、566、567、568、569、570、571、572、573、574、575、576、577、578、579、580、581、582、583、584、585、586、587、588、589、590、591、592、593、594、595、596、597、598、599、600、601、605、603、604、605、606、607、608、609、610、611、612、613、614、615、616、617、618、619、620、621、622、623、624、625、626、627、628、629、630、631、632、633、634、635、636、637、638、639、640、641、642、643、644、645、646、647、648、649、650、651、652、653、654、655、656、657、658、659、660、661、662、663、664、665、666、667、668、669、670、671、672、673、674、675、676、677、678、679、680、681、682、683、684、685、686、687、688、689、690、691、692、693、694、695、696、697、698、699、700、701、702、703、704、705、706、707、708、709、710、711、712、713、717、715、716、714、718、719、720、721、722、723、724、725、726、727、728、729、730、731、732、733、734、735、736、737、738、739、740、741、742、743、744、745、746、747、748、749、750、751、752、753、757、755、756、754、758、759、760、761、762、763、764、765、766、767、768、769、770、771、772、773、774、775、776、777、778、779、780、781、782、783、784、785、786、787、788、789、790、791、792、793、794、795、796、797、798、799、800、801、802、803、804、805、806、807、808、809、810、811、812、813、817、815、816、814、818、819、820、821、822、823、824、825、826、827、828、829、830、831, 832, 833, 834, 835, 836, 837, 838, 839, 840, 841, 842, 843, 845, 846, 847, 848, 849, 850, 851, 852, 853, 854, 855, 856, 854, 858, 859, 860, 861, 862, 863, 864, 865, 866, 867, 868, 869, 870, 871, 872, 873, 874, 875, 876, 877, 878, 879, 880, 881, 882, 883, 884, 885, 886, 887, 888, 889, 890, 891, 892, 893, 894, 895, 896, 897, 898, 899, 900, 901, 902, 903, 904, 905, 906, 907, 908, 909, 910, 911, 912, 913, 917, 915, 916, 914, 918, 919, 920, 921, 922, 923, 924, 925, 926, 927, 928, 929, 930, 931, 932, 9 33, 934, 935, 936, 937, 938, 939, 940, 941, 942, 943, 945, 946, 947, 948, 949, 950, 951, 952, 953, 954, 955, 956, 954, 958, 959, 960, 961, 962, 963, 964, 965, 966, 967, 968, 96 Contains 9,970,971,972,973,974,975,976,977,978,979,980,981,982,983,984,985,986,987,988,989,990,991,992,993,994,995,996,997,998,999, or 1000 reduced genes.

[0238] In this embodiment, the second gene ontology is one of the gene ontologies listed in Table 8. In this embodiment, the second gene ontology is GO:0044459, GO:0071944, GO:0005886, GO:0005904, GO:0031226, GO:0005887, GO:0042127, GO:0005576, GO:0044421, GO:0070887, GO:0034097, GO:0050896, GO:0051869, GO:0071345, GO:0048856, GO:0010033, GO:0044425, GO:0007166, GO:0032501, GO:004 4707, GO:0050874, GO:0023052, GO:0023046, GO:0044700, GO:0031982, GO:0031988, GO:0032502, GO:0044767, GO:0007154, GO:0071310, GO: 0005615, GO:0042221, GO:0031224, GO:0051049, GO:0019221, GO:0048583, GO:0008284, GO:0007275, GO:0023051, GO:0010646, GO:0048584, GO:0051239, GO:0032879, GO:0006954, GO:0007165, GO:0023033, GO:0043230, GO:0098771, GO:0055065, GO:0016021, GO:1903561, GO:00099 66, GO:0035466, GO:0050801, GO:0010647, GO:0006811, GO:0065008, GO:0051240, GO:0098590, GO:0055082, GO:0055080, GO:0023056, GO:00 06875, GO:0070062, GO:0051716, GO:0048878, GO:0043269, GO:0065009, GO:0051050, GO:0050865, GO:0098857, GO:0006873, GO:0048518, GO :0043119, GO:0030003, GO:0048731, GO:0042592, GO:0045121, GO:0006952, GO:0002217, GO:0042829, GO:0048522, GO:0051242, GO:0046903,GO:0005102、GO:0030154、GO:0019725、GO:0001775、GO:0009967、GO:0035468、GO:0002376、GO:0072503、GO:0045321、GO:0050863、GO:0050878、GO:0048869、GO:0002703、GO:0050670、GO:0022407、GO:0032944、GO:0016020、GO:1902533、GO:0010740、GO:0043270、GO:0045785、GO:0072507、GO:0009888、GO:0022409、GO:0042493、GO:0017035、GO:0002682、GO:0006874、GO:0032101、GO:0070663、GO:0007204、GO:1902531、GO:0010627、GO:1903039、GO:1903037、GO:0002694、GO:0031012、GO:0009605、GO:0044281、GO:2000021、GO:0055074、GO:0035296、GO:0097746、GO:0042312、GO:0044093、GO:0002685、GO:0098589、GO:0051480、GO:0003013、GO:0008015、GO:0070261、GO:1901700、GO:0007187、GO:0030155、GO:0003006、GO:0034220、GO:0050870、GO:0009611、GO:0002245、GO:0008217、GO:1903524、GO:0042129、GO:0033993、GO:0050880、GO:0007188、GO:0051704、GO:0051706、GO:0035150、GO:0030198、GO:0032103、GO:0043062、GO:0050867、GO:0040017、GO:0002687、GO:0022857、GO:0005386、GO:0015563、GO:0015646、GO:0022891、GO:0022892、GO:0048608、GO:0015267、GO:0015249、GO:0015268、GO:0002274、GO:0001890、GO:0048513、GO:0022803、GO:0022814、GO:0002684、GO:0050776、GO:0002819、GO:0045937、GO:0010562、GO:0002366、GO:0061458、GO:0051094、GO:0034762、GO:2000147、GO:0030141、GO:0002263、GO:0006955、GO:0015075、GO:0099503、GO:0000003、GO:0019952、GO:0050876、GO:0098772、GO:0002252、GO:0009653、GO:0050900、GO:1901701、GO:0042802、GO:0043085、GO:0048554、GO:0030335、GO:0005215、GO:0005478、GO:0022414、GO:0044702、GO:0051241、GO:0002696、GO:0046873、GO:0042060、GO:0003018、GO:0032940、GO:0031410、GO:0016023、GO:0002822、GO:0046394、GO:0051272、GO:0097708、GO:0009986、GO:0009928、GO:0009929、GO:0016053、GO:0051928、GO:0042327、GO:0031225、GO:0010469、GO:0009987、GO:0008151、GO:0044763、GO:0050875、GO:0006950、GO:0043207、GO:0002886、GO:0051249、GO:0098655、GO:0005575、GO:0008372、GO:0002697、GO:0019935、GO:0007267、GO:0032496、GO:0070160、GO:0005216、GO:0034765、GO:0006820、GO:0006822、GO:0005911、GO:0019933、GO:0004252、GO:0048545、GO:0051924、GO:0006812、GO:0006819、GO:0015674、GO:0019932、GO:0051707、GO:0009613、GO:0042828、GO:0001934、GO:0022838、GO:1902105、GO:0006636、GO:0071624、GO:0055085、GO:0010959、GO:0005923、GO:0030001、GO:0002237、GO:0009607、GO:0002699、GO:0005261、GO:0015281、GO:0015338、GO:1903522、GO:0043408、GO:0008324、GO:0015711、GO:0071622、GO:0070665、GO:0002683、GO:0010543、GO:0050730、GO:0007189、GO:0010579、GO:0010580、GO:0016338、GO:0050671、GO:0015318、GO:0050777、GO:0050793、GO:0030054、GO:0022610、GO:0032946、GO:0043300、GO:0042102、GO:0001817、GO:0002275、GO:0032844、GO:0060429、GO:0001653、GO:0031347、GO:0048646、GO:0042981、GO:0051345、GO:0002690、GO:0043302、GO:0098660、GO:0009719、GO:0048018、GO:0071884、GO:0009116、GO:0043168、GO:0002444、GO:0043296、GO:0065007、GO:0098662、GO:0043299、GO:0030193、GO:0042119、GO:0050921、GO:0002688、GO:0043410、GO:0022836、GO:0090022、GO:0002888、GO:0002821、GO:1900046、GO:0042509、GO:0042510、GO:0042513、GO:0042516、GO:0042519、GO:0042522、GO:0042525、GO:0042528、GO:0035295、GO:0043235、GO:0022839、GO:0090023、GO:0043065、GO:0046718、GO:0019063、GO:0043067、GO:0043070、GO:0030545、GO:0001816、GO:0003382、GO:0044409、GO:0051806、GO:0030260、GO:0051828、GO:0036230、GO:0010941、GO:0009725、GO:0002476、GO:0002526、GO:0051384、GO:0050790、GO:0048552、GO:0051247、GO:0008285、GO:0097755、GO:0045909、GO:0031960、GO:0070374、GO:0002824、GO:0030728、GO:0007155、GO:0098602、GO:0035556、GO:0007242、GO:0007243、GO:0023013、GO:0023034、GO:0010942、GO:0070372、GO:0051046、GO:0043068、GO:0043071、GO:1902107、GO:0002283、GO:0005509、GO:0050818、GO:0051336、GO:0009119、GO:0003073、GO:0036018、GO:0046635、GO:2000026、GO:0006082、GO:0001819、GO:0004175、GO:0016809、GO:0050764、GO:0043436、GO:0005201、GO:0097028、GO:0008528、GO:0045055、GO:0016477、GO:0030168、GO:0035239、GO:0070820、GO:0031349、GO:0001932、GO:0098797、GO:0045137、GO:0043312、GO:0002446、GO:0052547、GO:0048585、GO:0009070、GO:0009113、GO:0034764、GO:0022600、GO:0016323、GO:0045597、GO:0042803、GO:0016324、GO:0045177、GO:0008406、GO:0006887、GO:0016194、GO:0016195、GO:0008236、GO:0072358、GO:0001944、GO:0002521、GO:1902624、GO:0044283、GO:0048519、GO:0043118、GO:0045684、GO:0006690、GO:0010522、GO:0022890、GO:0015082、GO:0019752、GO:0071396、GO:0001525、GO:0050731、GO:0036017、GO:0042609、GO:0050817、GO:0070252、GO:0060670、GO:0019369、GO:0019229、GO:0009164、GO:0017171、GO:0045907、GO:0008289、GO:1902622、GO:0050920、GO:0051047、GO:0046649、GO:0032270、GO:0009991、GO:0033628、GO:0004715、GO:004577、 6、GO:0042454、GO:0005515、GO:0001948、GO:0045308、GO:0002706、GO:1903530、GO:1901657、GO:0030322、GO:0042270、GO:0045088、GO:0046717、GO:0016661、GO:0008584、GO:0002428、GO:1901568、GO:0042325、GO:0044433、GO:0044057、GO:0031638、GO:0006953、GO:0050729、GO:0046546、GO:0042531、GO:0042511、GO:0042515、GO:0042517、GO:0042520、GO:0042523、GO:0042526、GO:0042529、GO:0046850、GO:0005178、GO:0048514、GO:0045682、GO:0003674、GO:0005554、GO:0046634、GO:0061041、GO:0008016、GO:0043407、GO:0046456、GO:0007596、GO:0045606、GO:0014070、GO:0048870、GO:0051674、GO:0002704、GO:0007584、GO:0070228、GO:0002675、GO:0052548、GO:0001664、GO:0090330、GO:0045117、GO:0034340、GO:0044853、GO:0032587、GO:0007586、GO:0097529、GO:0045595、GO:0040012、GO:0050866、GO:0010035、GO:0034767、GO:0098801、GO:0015079、GO:0015388、GO:0022817、GO:0044706、GO:1901605、GO:0009636、GO:0007599、GO:0002705、GO:2000145、GO:0034103、GO:0032642、GO:0098805、GO:0051209、GO:1901137、GO:0090066、GO:0098641、GO:0032409、GO:0007589、GO:0046128、GO:0061134、GO:0015893、GO:0001726、GO:0001893、GO:0030334、GO:0042398、Or any one of these combinations.

[0239] In the embodiment, the desired determined gene expression profile information for dopaminergic progenitor cells includes a reduction in gene expression levels for pluripotent stem cells for a second gene set, the second gene set includes at least one reduced gene in one or more second gene ontologies selected from the group consisting of GO0070887, GO0044459, and GO0044281. In the embodiment, the desired determined gene expression profile information for dopaminergic progenitor cells includes a reduction in gene expression levels for pluripotent stem cells for a second gene set, the second gene set comprising at least one reduced gene in a second gene ontology selected from the group consisting of GO0042127, GO006954, and GO0032502, and any combination thereof.

[0240] In the embodiment, the second gene set includes at least one (e.g., 1, 2, 3, 4, 5, 6, etc.) reduced gene from Table 9, Table 10, Table 11, or any combination thereof.

[0241] In the embodiment, the second gene set includes at least one (e.g., 1, 2, 3, 4, 5, 6, etc.) of the reduced genes listed in Table 9. In the embodiment, the at least one (e.g., 1, 2, 3, 4, 5, 6, etc.) of the reduced genes are DYSF, RASAL3, AKR1C3, CGREF1, SULT2B1, CAV2, IL12A, HMGA1, HHLA2, HMX2, CARD11, TSPO, IRF6, CEBPB, BCL11B, CASR, INPP5D, FGF21, NODAL, TNFRSF1B, HPSE, GRPR, TNMD, SPINT2, IER5, CAV1, JAML, SOX10, SFN, NPY5R, MYB, HMOX1, CDH 5, HEY2, CLDN7, CXCR2, FGF2, APELA, FLT3LG, CD22, CDCA7L, NPM1, STYK1, SKOR2, LRRC32, HRG, CDH3, IL4R, TERT, ANG, RAB25, NRK, ADM, MARVELD 3, DPP4, CD4, LTF, FGF4, ERBB3, IFITM1, P3H2, BAX, WNT11, CEBPA, AVPR1A, PTPRZ1, EIF5A, EPO, NPR1, NQO2, FGF16, EPHA1, CCL26, NR1D1, SYK, PT GES, TCIRG1, HCLS1, RAC2, NME2, TESC, HCK, FZD5, ETS1, APLN, TRIM71, ADA, MYC, GCNT2, SFRP1, FGFR4, EMX1, KDR, RARG, CD74, DRD3, PDPN, TRNP 1, HPN, PLAU, TNFSF12, GAS6, SRPX, FGF19, PROK2, TSLP, SHMT2, PIM2, GHRHR, EBB, ADORA1, NOS3, LIF, PINX1, TNFRSF8, FA2H, LECT1, CHRM1, NME1 , SOX15, S100A11, NCCRP1, CD40, SERPINB3, RARRES3, LIN28A, TCL1A, ICOSLG, HYAL1, AIF1, LEP, EEF1E1, PRKCH, VIPR1, IL34, SH2B3, SPINT1, ES RP2, PYCARD, CLEC4G, MATK, EAF2, TACR1, EGFL7, CCNI2, GAL, FERMT1, SFRP5, PPP1R16B, MLXIPL, OVOL1, CD9, TNFSF9, KDF1, MST1R, IL23A, FLT1,FLT3, HLA-G, ADAMTS8, GUCY2C, MMP9, ALOX15B, VDR, SIX4, LGALS3, LAMC2, CCNE1, NPPC, CLC, APOE, MAP3K5, CCND1, XCLND1 PTPN6, GLI1, TCL1B, PIM1, ARG2, LYN, NRARP, ELL3, TDGF1, FOSL1, CDCA7, NANOG, CCKBR, BNC1, PNP, TRIBBD1, PRGN3 KIAA1462, HTR1A, BTK, FZD7, IFNLR1, JAK3, CD55, TFAP4, SLA, FBX02, RBPMS2, OSMR, IL12RB2, EPCAM, IPO1, IOP2, CH PTAFR, CXCL1, SFRP2, PF4, CCDC88B, PRKCQ, CXCL5, TGFA, GJ A1, FZD9, RPA3, TACSTD2, TNFRSF11A, CNN1, and PTGER2.

[0242] In the embodiment, at least one (e.g., 1, 2, 3, 4, 5, 6, etc.) of the reduced genes are DYSF, RASAL3, AKR1C3, CGREF1, SULT2B1, CAV2, IL12A, HMGA1, HHLA2, HMX2, CARD11, TSPO, IRF6, CEBPB, BCL11B, CASR, INPP5D, FGF21, NODAL, TNFRSF1B, HPSE, GRPR, TNMD, SPINT2, IER5, CAV1, JAML, SOX10, SFN, NPY5R, MYB, HMOX1, CDH5, HEY2, CLDN7, C XCR2, FGF2, APELA, FLT3LG, CD22, CDCA7L, NPM1, STYK1, SKOR2, LRRC32, HRG, CDH3, IL4R, TERT, ANG, RAB25, NRK, ADM, MARVELD3, DPP4, CD4, LTF, FGF4, E RBB3, IFITM1, P3H2, BAX, WNT11, CEBPA, AVPR1A, PTPRZ1, EIF5A, EPO, NPR1, NQO2, FGF16, EPHA1, CCL26, NR1D1, SYK, PTGES, TCIRG1, HCLS1, RAC2, NME2, T ESC, HCK, FZD5, ETS1, APLN, TRIM71, ADA, MYC, GCNT2, SFRP1, FGFR4, EMX1, KDR, RARG, CD74, DRD3, PDPN, TRNP1, HPN, PLAU, TNFSF12, GAS6, SRPX, FGF19, PROK2, TSLP, SHMT2, PIM2, GHRHR, EBB, ADORA1, NOS3, LIF, PINX1, TNFRSF8, FA2H, LECT1, CHRM1, NME1, SOX15, S100A11, NCCRP1, CD40, SERPINB3, RARRES 3, LIN28A, TCL1A, ICOSLG, HYAL1, AIF1, LEP, EEF1E1, PRKCH, VIPR1, IL34, SH2B3, SPINT1, ESRP2, PYCARD, CLEC4G, MATK, EAF2, TACR1, EGFL7, CCNI2, GA L, FERMT1, SFRP5, PPP1R16B, MLXIPL, OVOL1, CD9, TNFSF9, KDF1, MST1R, IL23A, FLT1, FLT3, HLA-G, ADAMTS8, GUCY2C, MMP9, ALOX15B, VDR, SIX4, LGALS3,The group is selected from LAMC2, CCNE1, NPPC, CLC, APOE, MAP3K5, CCND1, XCL1, PTPN6, GLI1, TCL1B, PIM1, ARG2, LYN, NRARP, ELL3, TDGF1, FOSL1, CDCA7, NANOG, CCKBR, BNC1, PNP, TRIB1, HPGD, PRTN3, KIAA1462, HTR1A, BTK, FZD7, IFNLR1, JAK3, CD55, TFAP4, SLA, FBX02, RBPMS2, OSMR, IL12RB2, EPCAM, IL6, IDO1, CHP2, PTAFR, CXCL1, SFRP2, PF4, CCDC88B, PRKCQ, CXCL5, TGFA, GJA1, FZD9, RPA3, TACSTD2, TNFRSF11A, CNN1, and PTGER2. ,

[0243] In the embodiment, the second gene set includes at least one (e.g., 1, 2, 3, 4, 5, 6, etc.) of the reduced genes in Table 10. In the embodiment, the at least one (e.g., 1, 2, 3, 4, 5, 6, etc.) of the reduced genes are C3, AFAP1L2, PTGDR, CMKLR1, CEBPB, NFKBID, TNFRSF1B, SMPDL3B, F2RL1, HMOX1, CXCR2, FPR2, IL17RE, CHST4, IL4R, NFKBIZ, RELB, ADM, ALOX5, SPP1, SIGIRR, EPO, CCL26, SYK, PTGES, TFR2, AHCY, TCIRG1, CHI3L1, UGT1A1, NLRP10, HCK, RARRES2, KLKB1, CXCL2, F 12, ALOX15, PROK2, ELF3, ADORA1, CXCL6, CD40, HYAL1, AIF1, ADGRE2, IL34, AHSG, THEMIS2, MMP25, PLSCR1, NMI, PYCARD, TACR1, LBP, GAL, F11R, LY75, IL23A, NRROS, XCL1, ASS1, LYN, BTK, ...

Claims

1. A computer-based method for classifying in vitro populations of neural progenitor cells, The method involves receiving a test dataset containing gene expression levels and the expression levels of one or more metagenes in cells or multiple cells included in an in vitro population of neural progenitor cells, wherein the one or more metagenes are determined based on correlated gene expression levels of reference cells in a reference database, and the reference cells are one or more neurons at different stages of differentiation. Applying the expression level of one or more metagenes as input to a process configured to determine the probability of cells or multiple cells having the determined metagene expression level of dopaminergic progenitor cells, Determining the deviation score of the cell or the plurality of cells, wherein the deviation score indicates the degree to which the gene expression level in the test dataset deviates from the gene expression level in one or more reference cells in the reference database, and the one or more reference cells are in a differentiation stage that indicates the determined dopaminergic progenitor cell. A method comprising outputting a computer-calculated label classification, which includes an indication of whether the cells or the plurality of cells derived from an in vitro population of neural progenitor cells are determined to be dopaminergic progenitor cells, based on the probability and the deviation score.

2. A computer-based method according to claim 1, wherein the process includes a supervised classification model trained using (i) the expression levels of one or more metagenes of the reference cells in the reference database, and (ii) classification labels indicating each of the one or more different differentiation stages of the reference cells in the reference database, in order to determine the probability of a cell or a plurality of cells having a determined metagene expression level of dopaminergic progenitor cells.

3. A method performed by a computer for training a process to determine the probability of a cell or a group of cells having a determined metagene expression level of a dopaminergic progenitor cell, the method comprising training a supervised classification model to determine the probability of a cell or a group of cells having a determined metagene expression level of a dopaminergic progenitor cell, the expression levels of one or more metagenes, wherein the one or more metagenes are determined based on correlated gene expression levels of reference cells in a reference database, and the reference cells are neurons at one or more different stages of differentiation, and (ii) classification labels indicating each of the one or more different stages of differentiation of the reference cells in the reference database.

4. A computer-based method for classifying in vitro populations of neural progenitor cells, The method involves receiving a test dataset containing gene expression levels and the expression levels of one or more metagenes in cells or multiple cells included in an in vitro population of neural progenitor cells, wherein the one or more metagenes are determined based on correlated gene expression levels of reference cells in a reference database, and the reference cells are one or more neurons at different stages of differentiation. The process involves applying the expression levels of one or more metagenes as input to a process, the process including a supervised classification model trained using (i) the expression levels of one or more metagenes of reference cells in the reference database, and (ii) classification labels indicating each of the one or more different differentiation stages of the reference cells in the reference database, in order to determine the probability of a cell or a group of cells having the determined metagene expression levels of dopaminergic progenitor cells. Determining the deviation score of the cell or the plurality of cells, wherein the deviation score indicates the degree to which the gene expression level in the test dataset deviates from the gene expression level in one or more reference cells in the reference database, and the one or more reference cells are in a differentiation stage that indicates the determined dopaminergic progenitor cell. A method comprising outputting a computer-calculated label classification, which includes an indication of whether the cells or a plurality of cells from an in vitro population of neural progenitor cells are determined to be dopaminergic progenitor cells, based on the aforementioned probability and the aforementioned deviation score.

5. The method according to any one of claims 1, 2, and 4, further comprising identifying an in vitro population of neural progenitor cells as a population containing dopaminergic progenitor cells determined based on the computer-calculated label classification.

6. The computer-based method according to any one of claims 2 to 5, wherein the supervised classification model is a logistic regression model.

7. The computer-based method according to any one of claims 1 to 6, wherein the reference cells are an in vitro population of neural progenitor cells.

8. The computer-based method according to any one of claims 1, 2, and 4-7, wherein the in vitro population of neural progenitor cells is formed by culturing one or more induced pluripotent stem cells (iPSCs) in vitro for a certain period of time under conditions that enable differentiation of one or more iPSCs into neural progenitor cells, and optionally the neural progenitor cells are one or more of floorplate midbrain progenitor cells, determined dopaminergic progenitor cells, or dopamine (DA) neurons.

9. The computer-based method according to claim 8, wherein the iPSC is a human iPSC.

10. The method performed by a computer according to claim 9, wherein the human being is a healthy subject.

11. The computer-based method according to claim 9, wherein the human subject is a subject having Parkinson's disease.

12. The method, performed by computer according to any one of claims 8 to 11, wherein the culture is performed for a period of 2 to 25 days, or approximately 2 to approximately 25 days.

13. The computer-based method according to any one of claims 8 to 11, wherein the iPSC is cultured for two days, or about two days, or at least two days.

14. The computer-based method according to any one of claims 8 to 11, wherein the iPSC is cultured for 5 days, or about 5 days, or at least 5 days.

15. The computer-based method according to any one of claims 8 to 11, wherein the iPSC is cultured for 10 days, or about 10 days, or at least 10 days.

16. The computer-based method according to any one of claims 8 to 11, wherein the iPSC is cultured for 13 days, or about 13 days, or at least 13 days.

17. The computer-based method according to any one of claims 8 to 11, wherein the iPSC is cultured for 15 days, or about 15 days, or at least 15 days.

18. The computer-based method according to any one of claims 8 to 11, wherein the iPSC is cultured for 18 days, or about 18 days, or at least 18 days.

19. The computer-based method according to any one of claims 8 to 11, wherein the iPSC is cultured for 25 days, or about 25 days, or at least 25 days.

20. The computer-based method according to any one of claims 1 to 19, wherein the reference database includes gene expression levels determined from one or more reference cell populations, each of the one or more reference cell populations is formed by culturing one or more iPSCs in vitro for different periods under conditions that allow one or more iPSCs to differentiate into neural progenitor cells, and optionally the neural progenitor cells are one or more of floorplate midbrain progenitor cells, determined dopaminergic progenitor cells, or dopamine (DA) neurons.

21. The computer-based method according to claim 20, wherein the aforementioned different period is 2 to 30 days.

22. The computer-based method according to claim 20, wherein the aforementioned different period is 11 to 25 days.

23. The computer-based method according to any one of claims 1 to 28, wherein the one or more reference cells of differentiation stages in the reference database are formed by culturing the one or more iPSCs in vitro for one or more different periods under conditions that enable the differentiation of the one or more iPSCs into neural progenitor cells, wherein the neural progenitor cells are one or more of floorplate midbrain progenitor cells, determined dopaminergic progenitor cells, or dopamine (DA) neurons, and the different periods are about 11 to about 25 days, optionally 13 days or about 13 days, 18 days or about 18 days, or 25 days or about 25 days.

24. The computer-based method according to any one of claims 20 to 23, wherein at least one of the one or more reference cell populations in the reference database includes gene expression levels determined by culturing the iPSC for 13, 18, or 25 days, or about 13, about 18, or about 25 days.

25. The conditions under which one or more iPSCs can be differentiated into neural progenitor cells are: (a) A first incubation comprising (i) an inhibitor of TGF-β / activin-Nodal signaling, (ii) at least one activator of sonic hedgehog (SHH) signaling, (iii) an inhibitor of bone morphogenetic protein (BMP) signaling, and (iv) an inhibitor of glycogen synthase kinase 3β (GSK3β) signaling, optionally under conditions that differentiate the cells into floorplate midbrain progenitor cells, wherein the first incubation is optionally initiated on day 0 of the culture, and, (b) A computer-based method according to any one of claims 8 to 24, comprising culturing the iPSCs by a second incubation of the cells after the first incubation, the second incubation comprising culturing the cells under conditions that induce neural differentiation of the cells, optionally, the second incubation is started 11 days or about 11 days after the first incubation, and further optionally, the second incubation is 11 to 25 days or about 11 to about 25 days.

26. The computer-based method according to claim 25, wherein the conditions for differentiating the cells into neurons include exposing the cells 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 β3 (TGFβ3) (collectively "BAGCT"), and (vi) an inhibitor of Notch signaling.

27. The computer-based method according to any one of claims 20 to 26, wherein at least one of the one or more reference cell populations in the reference database includes a gene expression level determined by culturing the iPSC for 13 days or about 13 days.

28. The computer-based method according to any one of claims 20 to 27, wherein at least one of the one or more reference cell populations includes a gene expression level determined by culturing the iPSC for 18 days or about 18 days.

29. The computer-based method according to any one of claims 20 to 28, wherein at least one of the one or more reference cell populations includes a gene expression level determined by culturing the iPSC for 25 days or about 25 days.

30. The computer-based method according to any one of claims 1 to 29, wherein the expression levels of the one or more metagenes and the one or more metagenes are determined by using a dimensionality reduction technique on one or more reference cells in the one or more reference databases.

31. The computer-based method according to claim 30, wherein the dimensionality reduction method is used in a reference cell population that includes gene expression levels determined on day 13 or approximately day 13 of in vitro culture under conditions that differentiate iPSCs into neural progenitor cells.

32. The computer-based method according to claim 30 or 31, wherein the dimensionality reduction method is used in a reference cell population that includes gene expression levels determined on day 18 or approximately day 18 of in vitro culture under conditions that differentiate iPSCs into neural progenitor cells.

33. The computer-based method according to any one of claims 30 to 32, wherein the dimensionality reduction method is used in a reference cell population that includes gene expression levels determined on day 25 or approximately day 25 of in vitro culture under conditions that differentiate iPSCs into neural progenitor cells.

34. The aforementioned dimensionality reduction method, A reference cell population including gene expression levels determined on day 13 or approximately day 13 of in vitro culture under conditions that differentiate iPSCs into neural progenitor cells. A reference cell population including gene expression levels determined on day 18 or approximately day 18 of in vitro culture under conditions that differentiate iPSCs into neural progenitor cells. A computer-based method according to any one of claims 30 to 33, used in each of a reference cell population, including gene expression levels determined on day 25 or approximately day 25 of in vitro culture under conditions for differentiating iPSCs into neural progenitor cells.

35. The computer-based method according to any one of claims 2 to 34, wherein the supervised classification model is trained using the expression levels of the one or more metagenes determined from the one or more reference cells.

36. The computer-based method according to any one of claims 2 to 35, wherein the supervised classification model is trained using the expression levels of one or more metagenes determined from one or more reference cells, including gene expression levels on days 11 to 25 of in vitro culture under conditions for differentiation of iPSCs into neural progenitor cells, and optionally, one or more gene expression levels on days 13, 18, and 25 of in vitro culture under conditions for differentiation of iPSCs into neural progenitor cells.

37. The computer-based method according to any one of claims 2 to 36, wherein the supervised classification model is trained using the expression levels of one or more metagenes determined from one or more reference cells, including gene expression levels determined on day 13 or about day 13 of in vitro culture under conditions for differentiating iPSCs into neural progenitor cells.

38. The computer-based method according to any one of claims 2 to 37, wherein the supervised classification model is trained using the expression levels of one or more metagenes determined from one or more reference cells, including the gene expression levels determined on day 18 or about day 18 of in vitro culture under conditions for differentiating iPSCs into neural progenitor cells.

39. The computer-based method according to any one of claims 2 to 38, wherein the supervised classification model is trained using the expression levels of one or more metagenes determined from one or more reference cells, including the gene expression levels determined on day 25 or about day 25 of in vitro culture under conditions for differentiating iPSCs into neural progenitor cells.

40. The aforementioned supervised classification model, A reference cell population including gene expression levels determined on day 13 or approximately day 13 of in vitro culture under conditions that differentiate iPSCs into neural progenitor cells. A reference cell population including gene expression levels determined on day 18 or approximately day 18 of in vitro culture under conditions that differentiate iPSCs into neural progenitor cells. A computer-based method according to any one of claims 2 to 39, which is trained using the expression levels of one or more metagenes determined from each of a reference cell population, including the gene expression levels determined on day 25 or approximately day 25 of in vitro culture under conditions for differentiating iPSCs into neural progenitor cells.

41. The computer-based method according to any one of claims 2 to 40, wherein the classification label indicating each of the one or more different differentiation stages of the reference cell is either a determined dopaminergic progenitor cell or not a determined dopaminergic progenitor cell.

42. A computer-based method according to any one of claims 2 to 41, wherein the classification label indicating each of the one or more different differentiation stages of the reference cell is determined using an in vivo method.

43. The aforementioned in vivo method, The in vitro population of neural progenitor cells, including a reference cell population, is transplanted into a brain region of an animal model of Parkinson's disease. The evaluation of the occurrence of an outcome associated with the therapeutic effect of the transplantation in the animal model, wherein the outcome is optionally selected from nerve innervation or engraftment of host cells, reduction of brain lesions in the animal model, or recovery of brain lesions in the animal model. If the transplantation results in the occurrence of the outcome accompanied by a therapeutic effect, the classification label is designated as the determined dopaminergic progenitor cell, or The computer-based method according to claim 42, comprising: if the transplant does not result in the occurrence of the outcome with a therapeutic effect, designating the cells as not being the determined dopaminergic progenitor cells and assigning them the classification label.

44. The computer-based method according to claim 43, wherein the brain region is the substantia nigra.

45. The computer-based method according to claim 43 or claim 44, wherein the in vivo method includes a behavioral test.

46. A computer-based method according to any one of claims 2 to 41, wherein the classification label indicating each of the one or more different differentiation stages of the reference cell is determined using an in vitro method.

47. The in vitro method includes evaluating the dopamine production level of a reference cell population, The computer-based method according to claim 46, wherein if the dopamine production level is increased compared to that of pluripotent stem cells, the classification label is designated as that of a determined dopaminergic progenitor cell.

48. The computer-based method according to claim 46 or 47, wherein the evaluation of dopamine production is performed by high-performance liquid chromatography.

49. The in vitro method includes evaluating the level of tyrosine hydroxylase expression in a reference cell population, A computer-based method according to any one of claims 46 to 48, wherein if the reference cell population expresses high levels of tyrosine hydroxylase, it is designated as not being a determined dopaminergic progenitor cell.

50. The computer-based method according to claim 49, wherein the level of tyrosine hydroxylase expression is evaluated using flow cytometry.

51. The computer-based method according to any one of claims 2 to 50, wherein the reference database further comprises the classification labels of one or more reference cells.

52. A computer-based method according to any one of claims 1, 2, and 4 to 51, wherein the expression level of one or more metagenes in the test dataset is determined based on (i) one or more metagenes determined from one or more reference cells in the reference database, and (ii) the gene expression level in the test dataset.

53. The computer-based method according to claim 52, wherein the expression level of one or more metagenes in the test dataset is determined by a regression analysis based on (i) one or more metagenes determined from one or more reference cells in the reference database, and (ii) the gene expression level in the test dataset.

54. A computer-based method according to any one of claims 1, 2, and 4 to 51, wherein the expression levels of one or more metagenes in the test dataset are determined by merging the gene expression levels in the test dataset with the reference database to create an updated reference database, and applying the dimensionality reduction method to the updated reference database.

55. A computer-based method according to any one of claims 30 to 54, wherein the dimensionality reduction method is a conventional non-negative matrix factorization, discriminant non-negative matrix factorization, graph normalization non-negative matrix factorization, bootstrap sparse non-negative matrix factorization, or normalization non-negative matrix factorization.

56. The method performed by a computer according to any one of claims 30 to 55, wherein the dimensionality reduction method is a conventional non-negative matrix factorization.

57. The computer-based method according to any one of claims 2 to 56, wherein the number of the one or more metagenes is selected based on the performance of the supervised classification model in determining the probability of cells or multiple cells having a determined metagene expression level of dopaminergic progenitor cells.

58. The computer-based method according to any one of claims 30 to 57, wherein the number of the one or more metagenes is selected based on evaluating one or more metrics determined by performing the dimensionality reduction method using a plurality of candidate numbers of metagenes.

59. The computer-based method according to claim 58, wherein one or more metrics include Cofen distance, variance, residual, sum of squared residuals (RSS), silhouette, and / or sparseness value.

60. A computer-based method according to any one of claims 1, 2, and 4 to 59, wherein the computer-calculated label classification indicates that the cells or the plurality of cells having the determined metagene expression level of dopaminergic progenitor cells are determined dopaminergic progenitor cells if the probability of such cells or the plurality of cells having the determined metagene expression level of dopaminergic progenitor cells is greater than a probability threshold.

61. The probability threshold is set such that the determined dopaminergic progenitor cells are identified with a sensitivity of more than 75%, 80%, 85%, 90%, or 95%, or with a sensitivity of more than approximately 75%, approximately 80%, approximately 85%, approximately 90%, or approximately 95%, and / or The computer-based method according to claim 60, wherein the probability threshold is set such that the determined dopaminergic progenitor cells are identified with a specificity greater than 75%, 80%, 85%, 90%, or 95%, or greater than approximately 75%, approximately 80%, approximately 85%, approximately 90%, or approximately 95%.

62. The computer-based method according to claim 60, wherein the probability threshold is set such that the determined dopaminergic progenitor cells are identified with a sensitivity of more than 98% or about 98% and a specificity of 100%.

63. The computer-based method according to any one of claims 60 to 62, wherein the probability threshold is determined by using the area under the receiver operational characteristic (ROC) curve based on the supervised classification model.

64. The computer-based method according to any one of claims 60 to 63, wherein the probability threshold is 0.4 to 0.8 or about 0.4 to about 0.

8.

65. The computer-based method according to any one of claims 60 to 63, wherein the probability threshold is 0.4, 0.45, 0.5, 0.55, 0.6, 0.65, 0.7, 0.75, or 0.8, or about 0.4, about 0.45, about 0.5, about 0.55, about 0.6, about 0.65, about 0.7, about 0.75, or about 0.

8.

66. The computer-based method according to any one of claims 1, 2, and 4 to 65, wherein the deviation score of the cell or the plurality of cells is determined using a single-gene deviation score for each of one or more genes in the test dataset.

67. The computer-based method according to claim 66, wherein the single-gene deviation score is determined using the difference between the gene expression level in the test dataset and the gene expression level in one or more reference cells in the reference database.

68. The computer-based method according to claim 67, wherein the difference is an absolute difference.

69. The computer-based method according to any one of claims 66 to 68, wherein the single-gene deviation score is determined using the standard deviation of gene expression levels in one or more of the one or more reference cells.

70. The aforementioned single-gene deviation score is The difference between the gene expression level in the test dataset and the gene expression level in one or more reference cells in the reference database, and A computer-based method according to any one of claims 66 to 69, wherein the z-value is determined using the standard deviation of gene expression levels in one or more of the one or more reference cells of the reference database.

71. A computer-based method according to any one of claims 1, 2, and 4 to 70, wherein the gene expression level in one or more reference cells in the reference database is determined based on the average gene expression level in one or more reference cells in the reference database.

72. A computer-based method according to any one of claims 1, 2, and 4 to 70, wherein the gene expression level in one or more reference cells in the reference database is determined based on the expression level of one or more metagenes in the test dataset.

73. The computer-based method according to claim 72, wherein the gene expression level in one or more reference cells in the reference database is determined by a regression analysis based on (i) the expression level of one or more metagenes in the test dataset, and (ii) the gene expression level in the test dataset.

74. The computer-based method according to any one of claims 66 to 73, wherein the deviation score is a summary statistic based on all single-gene deviation scores.

75. The computer-based method according to any one of claims 66 to 73, wherein the deviation score is a summary statistic based on the single-gene deviation score of one or more marker genes.

76. The computer-based method according to claim 74 or 75, wherein the summary statistic is a sum.

77. The computer-based method according to claim 74 or 75, wherein the summary statistics are a weighted sum.

78. The computer-based method according to claim 77, wherein the single-gene deviation score of one or more marker genes has a higher weight.

79. The computer-based method according to claim 74 or 75, wherein the summary statistic is a percentile value.

80. The aforementioned percentile value is between the 50th percentile and the 100th percentile or approximately between the 50th percentile and approximately the 100th percentile, and / or The computer-based method according to claim 79, wherein the percentile value is the 50th, 60th, 70th, 80%, 90%, or 95th percentile, or approximately the 50th, approximately 60%, approximately 70%, approximately 80%, approximately 90%, or approximately 95th percentile.

81. The computer-based method according to any one of claims 75 to 80, wherein the marker gene includes a radial glial cell marker, an early neuronal development gene, a pluripotency-specific marker, a mid-to-late neuronal cell marker, a neurofilament polypeptide light chain marker, a neurofilament polypeptide medium chain marker, a nestin filament marker, an early patterning marker, a neural progenitor cell marker, an early migration marker, a stage-specific transcription factor, a gene necessary for the normal development of neurons, a gene that controls the development of dopaminergic neurons, a gene that regulates the discriminability and fate of neural progenitor cells, a dopaminergic neuron marker, an astrocyte marker, a forebrain marker, a hindbrain marker, a subthalamic nucleus marker, a radial glial cell marker, a cell cycle marker, or any combination thereof.

82. The computer-based method according to any one of claims 75 to 81, wherein the marker gene includes WNT1, VIM, TOP2A, TH, SOX2A, SLIT2, RFX4, POU5F1, PITX2, PAX6, OTX2, NR4A2, NHLH2, NEUROD4, NEUROD1, NES, NEFM, NEFL, NASP, MAP2, LMX1A, LIN28A, HOXA2, HMGB2, HES1, FOXG1, FOXA2, FABP7, DDC, DCX, BARHL2, BARJL1, ASPM, ALDH1A1, or any combination thereof.

83. The computer-calculated label classification indicates that the cells or a plurality of cells from the in vitro population of neural progenitor cells are determined dopaminergic progenitor cells, according to any one of claims 1, 2, and 4 to 82, if the deviation score indicates that at least 50%, 50%, 70%, 80%, 90%, or 95% of the gene expression levels in the test dataset, or at least about 50%, about 50%, about 70%, about 80%, about 90%, or about 95%, are within 5 × standard deviations of the gene expression levels of one or more reference cells in the reference database.

84. The computer-calculated label classification indicates that the cells or a plurality of cells from the in vitro population of neural progenitor cells are determined dopaminergic progenitor cells, if the deviation score indicates that at least 95% or at least about 95% of the gene expression levels in the test dataset are within 10, 9, 8, 7, 6, or 5 × standard deviations from the gene expression levels of one or more reference cells in the reference database. The computer-calculated label classification indicates that the cells or a plurality of cells from the in vitro population of neural progenitor cells are determined dopaminergic progenitor cells, according to any one of claims 1, 2, and 4 to 82.

85. The computer-calculated label classification indicates that the cells or a plurality of cells from the in vitro population of neural progenitor cells are determined dopaminergic progenitor cells, according to any one of claims 1, 2, and 4 to 82, if the deviation score indicates that at least 50%, 50%, 70%, 80%, 90%, or 95% of the marker gene expression levels in the test dataset, or at least about 50%, about 50%, about 70%, about 80%, about 90%, or about 95%, are within 5 × standard deviations of the gene expression levels of one or more reference cells in the reference database.

86. The computer-calculated label classification indicates that the cells or a plurality of cells from the in vitro population of neural progenitor cells are determined dopaminergic progenitor cells, if the deviation score indicates that at least 95% or at least about 95% of the marker gene expression levels in the test dataset are within 10, 9, 8, 7, 6, or 5 × standard deviations from the gene expression levels of one or more reference cells in the reference database. The computer-calculated label classification indicates that the cells or a plurality of cells from the in vitro population of neural progenitor cells are determined dopaminergic progenitor cells, according to any one of claims 1, 2, and 4 to 82.

87. The aforementioned computer-calculated label classification is The probability of having the determined metagene expression level of dopaminergic progenitor cells for the cell or the plurality of cells is greater than the probability threshold, A computer-based method according to any one of claims 60 to 82, which indicates that the cells or a plurality of cells from the in vitro population of neural progenitor cells are determined dopaminergic progenitor cells, if the deviation score indicates that at least 50%, 50%, 70%, 80%, 90%, or 95% of the gene expression levels in the test dataset, or at least about 50%, about 50%, about 70%, about 80%, about 90%, or about 95%, are within 5 × standard deviations of the gene expression levels of one or more reference cells in the reference database.

88. The aforementioned computer-calculated label classification is The probability of having the determined metagene expression level of dopaminergic progenitor cells in the cell or the plurality of cells is greater than the probability threshold, A computer-based method according to any one of claims 60 to 82, which indicates that the cells or a plurality of cells from the in vitro population of neural progenitor cells are determined dopaminergic progenitor cells, if the deviation score indicates that at least 50%, 50%, 70%, 80%, 90%, or 95% of the marker gene expression levels in the test dataset, or at least about 50%, about 50%, about 70%, about 80%, about 90%, or about 95%, are within 5 × standard deviations of the gene expression levels of one or more reference cells in the reference database.

89. The aforementioned computer-calculated label classification is The probability of having the determined metagene expression level of dopaminergic progenitor cells, or of the plurality of cells, is greater than the probability threshold. The deviation score is such that at least 50%, 50%, 70%, 80%, 90%, or 95% of the gene expression levels in the test dataset, or at least about 50%, about 50%, about 70%, about 80%, about 90%, or about 95%, is within 5 × standard deviations of the gene expression levels of one or more reference cells in the reference database. A computer-based method according to any one of claims 60 to 82, which indicates that the cells or a plurality of cells from the in vitro population of neural progenitor cells are determined dopaminergic progenitor cells, if the deviation score indicates that at least 50%, 50%, 70%, 80%, 90%, or 95% of the marker gene expression levels in the test dataset, or at least about 50%, about 50%, about 70%, about 80%, about 90%, or about 95%, are within 5 × standard deviations of the gene expression levels of one or more reference cells in the reference database.

90. The aforementioned computer-calculated label classification is A computer-based method according to any one of claims 75 to 89, wherein if the difference in the expression of the marker gene between the test dataset and the reference cells in the reference database is not statistically significant based on the multiple comparison adjusted significance level, it indicates that the cells or a plurality of cells from the in vitro population of neural progenitor cells are determined dopaminergic progenitor cells.

91. The computer-based method according to claim 90, wherein the multiple comparison-corrected significance level is the Bonferroni-corrected significance level or the false detection rate-corrected significance level.

92. The computer-based method according to claim 90 or 91, wherein the multiple comparison correction significance level is 0.01, 0.05, or 0.

1.

93. The method, performed by computer according to any one of claims 1 to 92, wherein the gene expression level is obtained from microarray analysis of intracellular RNA, RNA sequencing, or both.

94. The method, performed by a computer according to any one of claims 1 to 93, wherein the gene expression level is obtained from RNA sequencing.

95. The computer-based method according to claim 93 or 94, wherein the RNA sequencing is performed on bulk RNA derived from the plurality of cells or the plurality of reference cells.

96. The computer-based method according to claim 93 or 94, wherein the RNA sequencing is performed on RNA derived from a single cell or a single reference cell.

97. The computer-based method according to claim 93 or 94, wherein the gene expression levels of the reference cells in the reference database include expression levels determined by RNA sequencing performed on bulk RNA from multiple reference cells and RNA from a single reference cell.

98. A method performed by a computer according to any one of claims 1, 2, and 4 to 97, wherein receiving the test dataset includes receiving input from an array analysis system.

99. A computer-based method according to any one of claims 1, 2, and 4 to 98, wherein receiving the test dataset includes receiving input via a computer network.

100. A method carried out by a computer according to any one of claims 1, 2, and 4 to 99, wherein the one or more reference databases form part of a storage medium.

101. A computer-based method according to any one of claims 1, 2, and 4-100, comprising repeating the receiving, applying, determining, and outputting steps if the computer-calculated label classification indicates that the cell or group of cells are not determined dopaminergic neurons, and optionally, the steps are repeated in an in vitro population of the same or different neural progenitor cells.

102. The computer-based method according to claim 101, wherein the receiving, applying, determining, and outputting steps are repeated one, two, three, four, five, six, seven, eight, nine, or ten days after a prior iteration of the method, or approximately one, approximately two, approximately three, approximately four, approximately five, approximately six, approximately seven, approximately eight, approximately nine, or approximately ten days after the method.

103. If the computer-calculated label classification indicates that the cells or a group of cells are not determined dopaminergic neurons, the computer-based method of performing the receiving, applying, determining, and outputting steps is repeated, the steps being repeated using a different in vitro population of neural progenitor cells formed by culturing another iPSC clone under conditions that allow the one or more iPSCs to differentiate into neural progenitor cells, and optionally the neural progenitor cells are one or more of floorplate midbrain progenitor cells, determined dopaminergic progenitor cells, or dopamine (DA) neurons.

104. The computer-driven method according to claim 103, wherein the in vitro population of the different neural progenitor cells is formed from the same human subjects as in the prior replicates of the method.

105. A computer-based method according to any one of claims 101 to 104, wherein the receiving, applying, determining, and outputting steps are repeated until an output indicates that the cells or the plurality of cells are determined to be dopaminergic neurons in an in vitro population of neural progenitor cells formed by culturing the iPSCs for different periods and / or under different conditions that allow one or more iPSCs to differentiate into neural progenitor cells.

106. A population of determined dopaminergic progenitor cells identified by the method described in any one of claims 5 to 105.

107. A treatment method comprising administering the population of determined dopaminergic progenitor cells described in claim 106 to a subject having Parkinson's disease.

108. The method according to claim 107, wherein the administration is performed by transplanting the determined population of dopaminergic progenitor cells into one or more brain regions of the subject.

109. The method according to claim 108, wherein one or more brain regions include the substantia nigra.

110. The method according to any one of claims 107 to 109, wherein the determined population of dopaminergic progenitor cells is autologous to the subject.

111. The method according to any one of claims 107 to 109, wherein the determined population of dopaminergic progenitor cells is allogeneic with respect to the subject.

112. A method for treating a subject with Parkinson's disease, A method comprising transplanting a determined population of dopaminergic progenitor cells into a brain region of a subject having Parkinson's disease, wherein the determined population of dopaminergic progenitor cells is identified using a computer-based method as described in any one of claims 5 to 105.

113. The method according to claim 112, wherein the determined population of dopaminergic progenitor cells is autologous to the subject.

114. The method according to claim 112 or 113, wherein the determined population of dopaminergic progenitor cells is allogeneic with respect to the subject.

115. (Not mentioned in the original text)

116. Approximately 1×10 ６ pieces, or at least 1 x 10 ６ pieces, or 1 x 10 ６ The method according to any one of claims 107 to 114, wherein a single cell is injected into the substantia nigra.

117. The method according to any one of claims 107 to 116, wherein the cells are injected into both the left and right hemispheres.

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