Methods for identifying dopaminergic neurons and progenitor cells

Abstract

Provided herein are, among other things, methods for assaying iPSC-derived neural progenitor cell populations, thereby providing user-friendly molecular diagnostic tools for neural cell types, including dopaminergic neurons, which are useful for efficient and accurate characterization of the identity and functionality of iPSC-derived dopaminergic neurons prior to clinical applications, such as the treatment of Parkinson's disease or multiple sclerosis.

Description

[Technical Field]

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

[0002] The present invention involves the establishment of key statistical models and data processing steps that allow for the evaluation of expression data derived from cultured neurons derived from induced pluripotent stem cells, by comparing test data with reference data sets derived, for example, from pre-characterized neurons, neural progenitor cells, or pluripotent stem cells with known biological characteristics. Summary of the Invention

[0003] In one aspect, a computer-implemented method for identifying determined dopaminergic progenitor cells within an in vitro population of neural progenitor cells is provided, the method including: receiving a test dataset including data such as gene expression profile information for the in vitro population of neural progenitor cells, querying a gene expression reference database to compare the test dataset with the gene expression reference database, the gene expression reference database including gene expression profile information for desired determined dopaminergic progenitor cells, and outputting a computer-calculated label classification including an indication of whether the in vitro population of neural progenitor cells includes the determined dopaminergic progenitor cells.

[0004] Provided herein is a computer-implemented method for classifying an in vitro population of neural progenitor cells, the method comprising: receiving a test dataset comprising gene expression levels and expression levels of one or more meta-genes in a cell or plurality of cells comprised in the in vitro population of neural progenitor cells, wherein the one or more meta-genes are determined based on correlating gene expression levels of reference cells in a reference database, the reference cells being neural cells at one or more different stages of differentiation; applying the expression levels of the one or more meta-genes as input to a process configured to determine a probability of a cell or plurality of cells having the determined meta-gene expression levels of dopaminergic progenitor cells; determining a deviation score for the cell or plurality of 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 the reference database, the one or more reference cells being at a determined stage of differentiation indicative of dopaminergic progenitor cells; and outputting a computer-calculated label classification comprising an indication of whether the cell or plurality of cells from the in vitro population of neural progenitor cells is the determined dopaminergic progenitor cell 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 meta-genes of reference cells in the reference database and (ii) classification labels indicative of each of one or more different differentiation stages of the reference cells in the reference database to determine the probability of a cell or cells having a determined meta-gene expression level of a dopaminergic progenitor cell.

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

[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 dataset comprising gene expression levels and expression levels of one or more meta-genes in a cell or plurality of cells comprised in the in vitro population of neural progenitor cells, wherein the one or more meta-genes are determined based on correlating gene expression levels of reference cells in a reference database, the reference cells being neural cells at one or more different stages of differentiation; and applying the expression levels of the one or more meta-genes as inputs to a process, which processes (i) the expression levels of the reference cells in the reference database to determine a probability of the cell or plurality of cells having the determined meta-gene expression levels of dopaminergic progenitor cells. and (ii) a supervised classification model trained using expression levels of one or more meta-genes of a reference cell and (iii) classification labels indicative of each of one or more different differentiation stages of a reference cell in a reference database; determining a deviation score for the cell or plurality of cells, the deviation score indicating the degree to which gene expression levels in the test dataset deviate from gene expression levels in one or more reference cells in the reference database, the one or more reference cells being at a differentiation stage indicative of a determined dopaminergic progenitor cell; and outputting a computerized label classification including an indication of whether the cell or plurality of cells from the in vitro population of neural progenitor cells is the determined dopaminergic progenitor cell based on the probability and deviation score.

[0008] In some embodiments of any of the foregoing, the method includes identifying the in vitro population of neural progenitor cells as a population comprising the determined dopaminergic progenitor cells based on the computational label classification.

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

[0010] In some embodiments of any of the foregoing, the reference cells are an in vitro population of neural progenitor cells. In some embodiments of any of the foregoing, 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 allow the one or more iPSCs to differentiate into neural progenitor cells, and optionally the neural progenitor cells are one or more of floor plate midbrain progenitor cells, committed 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 embodiments of any of the foregoing, the culturing is for a period of about 2 days to about 25 days. In some embodiments of any of the foregoing, the iPSCs are cultured for 2 days, about 2 days, or at least 2 days. In some embodiments of any of the foregoing, the iPSCs are cultured for 5 days, about 5 days, or at least 5 days. In some embodiments of any of the foregoing, the iPSCs are cultured for 10 days, about 10 days, or at least 10 days. In some embodiments of any of the foregoing, the iPSCs are cultured for 13 days, about 13 days, or at least 13 days. In some embodiments of any of the foregoing, the iPSCs are cultured for 15 days, about 15 days, or at least 15 days. In some embodiments of any of the foregoing, the iPSCs are cultured for 18 days, about 18 days, or at least 18 days. In some embodiments of any of the foregoing, the iPSCs are cultured for 25 days, about 25 days, or at least 25 days.

[0012] In some embodiments of any of the foregoing, the reference database comprises 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 a different period of time under conditions capable of differentiating the one or more iPSCs into neural progenitor cells, optionally, the neural progenitor cells being one or more of floor plate midbrain progenitor cells, committed dopaminergic progenitor cells, or dopamine (DA) neurons. In some embodiments, the different period of time is between 2 and 30 days. In some embodiments, the different period of time is between 11 and 25 days.

[0013] In some embodiments of any of the foregoing, the one or more differentiation-stage reference cells in the reference database are formed by culturing one or more iPSCs in vitro for one or more different time periods under conditions capable of differentiating the one or more iPSCs into neural progenitor cells, where optionally the neural progenitor cells are one or more of floor plate midbrain progenitor cells, committed dopaminergic progenitor cells, or dopamine (DA) neurons, and the different time periods are from about 11 days to about 25 days, optionally at or about 13 days, 18 days, or about 18 days, or 25 days. In some embodiments of any of the foregoing, at least one of the one or more reference cell populations in the reference database comprises gene expression levels determined by culturing iPSCs for about 13, 18, or 25 days.

[0014] In some embodiments of any of the foregoing, the conditions capable of differentiating one or more iPSCs into neural progenitor cells include (a) exposing the cells to (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 allow the cells to differentiate into floor plate mesencephalic progenitor cells. and (b) a second incubation after the first incubation of the cells, the second incubation comprising culturing the cells under conditions that cause the cells to undergo neural differentiation, optionally beginning about 11 days after the first incubation, and further optionally, the second incubation being for about 11 days to about 25 days. In some embodiments, the conditions that cause the cells to undergo neural differentiation include exposing the cells to (i) brain-derived neurotrophic factor (BDNF), (ii) ascorbic acid, (iii) glial cell line-derived neurotrophic factor (GDNF), (iv) dibutyryl cyclic AMP (dbcAMP), (v) transforming growth factor beta 3 (TGFβ3) (collectively "BAGCT"), and (vi) an inhibitor of Notch signaling.

[0015] In some embodiments of any of the foregoing, at least one of the one or more reference cell populations in the reference database comprises gene expression levels determined by culturing iPSCs for about 13 days. In some embodiments of any of the foregoing, at least one of the one or more reference cell populations comprises gene expression levels determined by culturing iPSCs for about 18 days. In some embodiments of any of the foregoing, at least one of the one or more reference cell populations comprises gene expression levels determined by culturing iPSCs for about 25 days.

[0016] In some embodiments of any of the foregoing, 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 of one or more reference databases. In some embodiments, the dimensionality reduction technique is used on a reference cell population comprising gene expression levels determined at about day 13 of in vitro culture under conditions that differentiate iPSCs into neural progenitor cells. In some embodiments of any of the foregoing, the dimensionality reduction technique is used on a reference cell population comprising gene expression levels determined at about day 18 of in vitro culture under conditions that differentiate iPSCs into neural progenitor cells. In some embodiments of any of the foregoing, the dimensionality reduction technique is used on a reference cell population comprising gene expression levels determined at about day 25 of in vitro culture under conditions that differentiate iPSCs into neural progenitor cells. In some embodiments of any of the foregoing, the dimensionality reduction technique is used on each of the reference cell populations comprising gene expression levels determined at about day 13 of in vitro culture under conditions that differentiate iPSCs into neural progenitor cells, the reference cell populations comprising gene expression levels determined at about day 18 of in vitro culture under conditions that differentiate iPSCs into neural progenitor cells, and the reference cell populations comprising gene expression levels determined at about day 25 of in vitro culture under conditions that differentiate iPSCs into neural progenitor cells.

[0017] In some embodiments of any of the foregoing, the supervised classification model is trained using expression levels of one or more meta-genes determined from one or more reference cells. In some embodiments of any of the foregoing, the supervised classification model is trained using expression levels of one or more meta-genes determined from the one or more reference cells, including gene expression levels determined at one or more of days 11-25 of in vitro culture under conditions that differentiate iPSCs into neural progenitor cells, optionally at days 13, 18, and 25 of in vitro culture under conditions that differentiate iPSCs into neural progenitor cells. In some embodiments of any of the foregoing, the supervised classification model is trained using expression levels of one or more meta-genes determined from the one or more reference cells, including gene expression levels determined at about day 13 of in vitro culture under conditions that differentiate iPSCs into neural progenitor cells. In some embodiments of any of the foregoing, the supervised classification model is trained using expression levels of one or more meta-genes determined from the one or more reference cells, including gene expression levels determined at about day 18 of in vitro culture under conditions that differentiate iPSCs into neural progenitor cells. In some embodiments of any of the foregoing, the supervised classification model is trained using expression levels of one or more meta-genes determined from one or more reference cells, the expression levels comprising gene expression levels determined at about day 25 of in vitro culture under conditions that differentiate iPSCs into neural progenitor cells. In some embodiments of any of the foregoing, the supervised classification model is trained using expression levels of one or more meta-genes determined from each of the following reference cell populations: a reference cell population comprising gene expression levels determined at about day 13 of in vitro culture under conditions that differentiate iPSCs into neural progenitor cells; a reference cell population comprising gene expression levels determined at about day 18 of in vitro culture under conditions that differentiate iPSCs into neural progenitor cells; and a reference cell population comprising gene expression levels determined at about day 25 of in vitro culture under conditions that differentiate iPSCs into neural progenitor cells.

[0018] In some embodiments of any of the foregoing, 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 a not determined dopaminergic progenitor cell.

[0019] In some embodiments of any of the foregoing, the classification labels indicative of each of one or more distinct differentiation stages of the 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 comprising the reference cell population into a brain region of an animal model of Parkinson's disease; and evaluating the occurrence of an outcome associated with a therapeutic effect of the transplant in the animal model, optionally the outcome being selected from innervation or host cell engraftment, reduction of brain lesions in the animal model, or recovery of brain lesions in the animal model; and assigning the classification label as being the determined dopaminergic progenitor cells if the transplant results in the occurrence of the outcome associated with a therapeutic effect, or assigning the classification label as not being the determined dopaminergic progenitor cells if the transplant does not result in the occurrence of the outcome associated with a therapeutic effect. In some embodiments, the brain region is the substantia nigra. In some embodiments of any of the foregoing, the in vivo method includes behavioral testing.

[0020] In some embodiments of any of the foregoing, the classification labels indicative of each of one or more different differentiation stages of the reference cells are determined using an in vitro method. In some embodiments, the in vitro method includes assessing the dopamine production level of the reference cell population, and assigning the classification label as being a determined dopaminergic progenitor cell if the dopamine production level is increased compared to pluripotent stem cells. In some embodiments of any of the foregoing, assessing dopamine production is by high performance liquid chromatography.

[0021] In some embodiments of any of the foregoing, the in vitro method includes assessing the level of tyrosine hydroxylase expression in a reference cell population, and assigning a classification label as not being a determined dopaminergic progenitor cell if the reference cell population expresses high tyrosine hydroxylase. In some embodiments, the level of tyrosine hydroxylase expression is assessed using flow cytometry.

[0022] In some embodiments of any of the foregoing, the reference database further comprises classification labels of one or more reference cells.

[0023] In some embodiments of any of the foregoing, the expression levels of the one or more metagenes in the test dataset are determined based on (i) the one or more metagenes determined from one or more reference cells in the reference database, and (ii) the gene expression levels in the test dataset. In some embodiments, the expression levels of the one or more metagenes in the test dataset are determined using a regression analysis based on (i) the one or more metagenes determined from one or more reference cells in the reference database, and (ii) the gene expression levels in the test dataset. In some embodiments of any of the foregoing, the expression levels of the 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 a dimensionality reduction technique to the updated reference database.

[0024] In some embodiments of any of the foregoing, the dimensionality reduction technique is conventional non-negative matrix factorization, discriminant non-negative matrix factorization, graph regularized non-negative matrix factorization, bootstrapping sparse non-negative matrix factorization, or regularized non-negative matrix factorization. In some embodiments of any of the foregoing, the dimensionality reduction technique is conventional non-negative matrix factorization.

[0025] In some embodiments of any of the foregoing, the number of one or more metagenes is selected based on the performance of the supervised classification model in determining the probability of a cell or cells having a determined dopaminergic progenitor metagene expression level. In some embodiments of any of the foregoing, the number of one or more metagenes is selected based on evaluating one or more metrics determined from performing a dimensionality reduction technique using a plurality of candidate numbers of metagenes. In some embodiments, the one or more metrics include cophenetic distance, variance, residual, residual sum of squares (RSS), silhouette, and / or sparseness value.

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

[0027] In some embodiments of any of the foregoing, the deviation score for the cell or plurality 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 of 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 embodiments of any of the foregoing, the single-gene deviation score is determined using the standard deviation of the gene expression levels in one or more of the one or more reference cells. In some embodiments of any of the foregoing, the single-gene deviation score is a z-score determined using the difference between the gene expression level of the test dataset and the gene expression level in one or more reference cells in the reference database and the standard deviation of the gene expression levels in one or more of the one or more reference cells in the reference database.

[0028] In some embodiments of any of the foregoing, the gene expression levels in the one or more reference cells in the reference database are determined based on the average gene expression levels in the one or more reference cells in the reference database. In some embodiments of any of the foregoing, the gene expression levels in the one or more reference cells in the reference database are determined based on the expression levels of one or more meta-genes in the test dataset. In some embodiments, the gene expression levels in the one or more reference cells in the reference database are determined using a regression analysis based on (i) the expression levels of one or more meta-genes in the test dataset, and (ii) the gene expression levels in the test dataset.

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

[0030] In some embodiments of any of the foregoing, the summary statistic is a percentile value. In some embodiments, the percentile value is between about the 50% percentile and about the 100% percentile, and / or the percentile value is about the 50%, 60%, 70%, 80%, 90%, or 95% percentile.

[0031] In some embodiments of any of the foregoing, the marker genes comprise radial glia cell markers, early neuronal development genes, pluripotency-specific markers, mid- to late-stage neuronal 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 required for normal neuronal development, genes controlling dopaminergic neuron development, genes regulating neural progenitor cell identity and fate, dopaminergic neuron markers, astrocyte markers, forebrain markers, hindbrain markers, subthalamic nucleus markers, radial glia markers, cell cycle markers, or any combination of any of these. In some embodiments of any of the foregoing, 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 of any of these.

[0032] In some embodiments of any of the foregoing, the computerized label classification indicates that the cell or cells from the in vitro population of neural progenitor cells are determined dopaminergic progenitor cells if 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 embodiments of any of the foregoing, the computerized label classification indicates that the cell or cells from the in vitro population of neural progenitor cells are determined dopaminergic progenitor cells if the deviation score indicates that at least about 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 embodiments of any of the foregoing, the computerized label classification indicates that the cell or cells from the in vitro population of neural progenitor cells are determined dopaminergic progenitor cells 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 embodiments of any of the foregoing, the computerized label classification indicates that the cell or cells from the in vitro population of neural progenitor cells are determined dopaminergic progenitor cells 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 embodiments of any of the foregoing, the computerized label classification indicates that the cell or cells from the in vitro population of neural progenitor cells are the determined dopaminergic progenitor cells if the probability of the cell or cells having the determined meta-gene 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 embodiments of any of the foregoing, the computerized label classification indicates that the cell or cells from the in vitro population of neural progenitor cells are the determined dopaminergic progenitor cells if the probability of the cell or cells having the determined meta-gene 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 embodiments of any of the foregoing, the computerized label classification indicates that the cell or cells from the in vitro population of neural progenitor cells are the determined dopaminergic progenitor cells if the probability of the cell or cells having the determined meta-gene expression levels 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 embodiments of any of the foregoing, the computed label classification indicates that the cell or cells from the in vitro population of neural progenitor cells are determined dopaminergic progenitor cells if the difference in expression of the marker genes between the test dataset and the reference cells in the reference database is not statistically significant based on a multiple-comparison corrected significance level. In some embodiments, the multiple-comparison corrected significance level is a Bonferroni corrected significance level or a false discover rate corrected significance level. In some embodiments of any of the foregoing, the multiple comparison corrected significance level is 0.01, 0.05, or 0.1.

[0035] In some embodiments of any of the foregoing, the gene expression levels are obtained from microarray analysis of intracellular RNA, RNA sequencing, or both. In some embodiments of any of the foregoing, the gene expression levels are obtained from RNA sequencing. In some embodiments of any of the foregoing, RNA sequencing is performed on bulk RNA from a plurality of cells or a plurality of reference cells. In some embodiments of any of the foregoing, RNA sequencing is performed on RNA from a single cell or a single reference cell. In some embodiments of any of the foregoing, the gene expression levels of the reference cells in the reference database include expression levels determined by RNA sequencing performed on bulk RNA from a plurality of reference cells and RNA from a single reference cell.

[0036] In some embodiments of any of the foregoing, receiving the test dataset comprises receiving input from an array analysis system. In some embodiments of any of the foregoing, receiving the test dataset comprises receiving input via a computer network. In some embodiments of any of the foregoing, 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 receiving, applying, determining, and outputting steps if the computerized label classification indicates that the cell or cells are not the determined dopaminergic neuronal cell, optionally with the same or a different in vitro population of neural progenitor cells. In some embodiments, the receiving, applying, determining, and outputting steps are repeated about 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 days after the prior iteration of the method.

[0038] In some embodiments of any of the foregoing, the method includes, if the computerized label classification indicates that the cell or cells are not committed dopaminergic neurons, repeating the receiving, applying, determining, and outputting steps, which steps are repeated with a different in vitro population of neural progenitor cells formed by culturing another iPSC clone under conditions capable of differentiating the one or more iPSCs into neural progenitor cells, optionally the neural progenitor cells being one or more of floor plate midbrain progenitor cells, committed 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 subject as the prior iteration of this method.

[0039] In some embodiments of any of the foregoing, the receiving, applying, determining, and outputting steps are repeated until an indication is output that a cell or cells in an in vitro population of neural progenitor cells formed by culturing iPSCs for different time periods and / or under different conditions capable of differentiating one or more iPSCs into neural progenitor cells is a determined dopaminergic neuron.

[0040] Also provided herein is a determined population of dopaminergic progenitor cells identified by the method of some embodiments of any of the foregoing.

[0041] Also provided herein are methods of treatment, comprising administering to a subject having Parkinson's disease the determined population of dopaminergic progenitor cells of some embodiments of any of the foregoing. 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 embodiments of any of the foregoing, the determined population of dopaminergic progenitor cells is autologous to the subject. In some embodiments of any of the foregoing, the determined population of dopaminergic progenitor cells is allogeneic to the subject.

[0043] Also provided herein is a method of treating a subject with Parkinson's disease, comprising transplanting a determined population of dopaminergic progenitor cells into a brain region of the subject with Parkinson's disease, wherein the determined population of dopaminergic progenitor cells has been identified using the computer-implemented method of any of the preceding embodiments.

[0044] In some embodiments, the determined population of dopaminergic progenitor cells is autologous to the subject. In some embodiments of any of the foregoing, the determined population of dopaminergic progenitor cells is allogeneic to the subject. In some embodiments of any of the foregoing, the determined population of dopaminergic progenitor cells is about 1 x 10 6 pieces, or at least 1 x 10 6 In some embodiments of any of the foregoing, the cells are injected into both the left and right hemispheres. Further aspects of the present invention are described below: [Section 1] 1. A computer-implemented method for classifying an in vitro population of neural progenitor cells, comprising: receiving a test dataset comprising gene expression levels and expression levels of one or more meta-genes in a cell or cells comprised in an 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, the reference cells being neural cells at one or more different stages of differentiation; applying the expression levels of the one or more meta-genes as inputs to a process configured to determine the probability of a cell or a plurality of cells having the determined dopaminergic progenitor meta-gene expression levels; determining a deviation score for the cell or plurality of cells, the deviation score indicating the degree to which the gene expression levels in the test dataset deviate from gene expression levels in one or more reference cells in the reference database, the one or more reference cells being at a differentiation stage indicative of a determined dopaminergic progenitor cell; and outputting a computed label classification based on the probability and the deviation score, the label classification including an indication of whether the cell or cells from the in vitro population of neural progenitor cells is a determined dopaminergic progenitor cell. [Section 2] 2. The computer-implemented method of claim 1, wherein the process comprises a supervised classification model trained using (i) the expression levels of the one or more metagenes of the reference cells in the reference database and (ii) classification labels indicative of each of the one or more different differentiation stages of the reference cells in the reference database to determine the probability of a cell or cells having a determined metagene expression level of a dopaminergic progenitor cell. [Section 3] 1. A computer-implemented method for training a process for determining the probability of a cell or a plurality of cells having a determined metagene expression level of a dopaminergic progenitor cell, the method comprising: training a supervised classification model using (i) expression levels of one or more metagenes, the one or more metagenes being determined based on correlated gene expression levels of reference cells in a reference database, the reference cells being neuronal cells at one or more different differentiation stages; and (ii) classification labels indicative of each of the one or more different differentiation stages of the reference cells in the reference database, to determine the probability of the cell or a plurality of cells having the determined metagene expression level of a dopaminergic progenitor cell. [Section 4] 1. A computer-implemented method for classifying an in vitro population of neural progenitor cells, comprising: receiving a test dataset comprising gene expression levels and expression levels of one or more meta-genes in a cell or cells comprised in an 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, the reference cells being neural cells at one or more different stages of differentiation; applying the expression levels of the one or more metagenes as input to a process, the process including a supervised classification model trained using (i) the expression levels of the one or more metagenes of reference cells in the reference database and (ii) classification labels indicative of each of the one or more different differentiation stages of reference cells in the reference database, to determine a probability of a cell or cells having a determined metagene expression level of a dopaminergic progenitor cell; determining a deviation score for the cell or plurality of cells, the deviation score indicating the degree to which the gene expression levels in the test dataset deviate from gene expression levels in one or more reference cells in the reference database, the one or more reference cells being at a differentiation stage indicative of a determined dopaminergic progenitor cell; an indication of whether the cell or cells from the in vitro population of neural progenitor cells are determined dopaminergic progenitor cells based on the probability and the deviation score. and outputting the computed label classification. [Section 5] 5. The method of any one of paragraphs 1, 2, and 4, further comprising identifying the in vitro population of neural progenitor cells as a population comprising the determined dopaminergic progenitor cells based on the computerized label classification. [Section 6] 6. The computer-implemented method according to any one of items 2 to 5, wherein the supervised classification model is a logistic regression model. [Section 7] Item 7. The computer-implemented method of any one of Items 1 to 6, wherein the reference cells are an in vitro population of neural progenitor cells. [Section 8] 8. The computer-implemented method of any one of paragraphs 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 period of time under conditions that allow the iPSCs to differentiate into neural progenitor cells, and optionally the neural progenitor cells are one or more of floor plate midbrain progenitor cells, committed dopaminergic progenitor cells, or dopamine (DA) neurons. [Section 9] Item 9. The computer-implemented method of Item 8, wherein the iPSCs are human iPSCs. [Section 10] 10. The computer-implemented method of paragraph 9, wherein the human is a healthy subject. [Section 11] 10. The computer-implemented method of paragraph 9, wherein the human is a subject with Parkinson's disease. [Section 12] Item 12. The computer-implemented method according to any one of Items 8 to 11, wherein the culture is for a period of 2 to 25 days, or for a period of about 2 to about 25 days. [Section 13] 12. The computer-implemented method of any one of items 8 to 11, wherein the iPSCs are cultured for 2 days, or about 2 days, or at least 2 days. [Section 14] 12. The computer-implemented method of any one of items 8 to 11, wherein the iPSCs are cultured for 5 days, or about 5 days, or at least 5 days. [Section 15] 12. The computer-implemented method of any one of items 8 to 11, wherein the iPSCs are cultured for 10 days, or about 10 days, or at least 10 days. [Section 16] 12. The computer-implemented method of any one of items 8 to 11, wherein the iPSCs are cultured for 13 days, or about 13 days, or at least 13 days. [Section 17] 12. The computer-implemented method of any one of items 8 to 11, wherein the iPSCs are cultured for 15 days, or about 15 days, or at least 15 days. [Section 18] 12. The computer-implemented method of any one of items 8 to 11, wherein the iPSCs are cultured for 18 days, or about 18 days, or at least 18 days. [Section 19] 12. The computer-implemented method of any one of items 8 to 11, wherein the iPSCs are cultured for 25 days, or about 25 days, or at least 25 days. [Section 20] 20. The computer-implemented method of any one of paragraphs 1 to 19, wherein the reference database comprises gene expression levels determined from one or more reference cell populations, each of the one or more reference cell populations being formed by culturing one or more iPSCs in vitro for different periods of time under conditions capable of differentiating the one or more iPSCs into neural progenitor cells, and optionally the neural progenitor cells are one or more of floor plate midbrain progenitor cells, committed dopaminergic progenitor cells, or dopamine (DA) neurons. [Section 21] Item 21. The computer-implemented method of item 20, wherein the different periods are 2 to 30 days. [Section 22] Item 21. The computer-implemented method of item 20, wherein the different periods are 11 to 25 days. [Section 23] 29. The computer-implemented method of any one of paragraphs 1 to 28, wherein the reference cells of one or more differentiation stages in the reference database are formed by culturing one or more iPSCs in vitro for one or more different time periods under conditions capable of differentiating the one or more iPSCs into neural progenitor cells, optionally wherein the neural progenitor cells are one or more of floor plate midbrain progenitor cells, committed dopaminergic progenitor cells, or dopamine (DA) neurons, and the different time periods are from about 11 days to about 25 days, optionally 13 days or about 13 days, 18 days or about 18 days, or 25 days or about 25 days. [Section 24] 24. The computer-implemented method of any one of paragraphs 20 to 23, wherein at least one of the one or more reference cell populations in the reference database comprises gene expression levels determined by culturing the iPSCs for 13, 18, or 25 days, or for about 13, about 18, or about 25 days. [Section 25] The conditions for differentiating the one or more iPSCs into neural progenitor cells are (a) a first incubation comprising exposing the cells to (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 floor plate midbrain progenitor cells, optionally wherein the first incubation begins on day 0 of the culture; and 25. The computer-implemented method of any one of items 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 conducive to neural differentiation of the cells, optionally the second incubation beginning 11 days or about 11 days after the first incubation, and further optionally the second incubation being for 11 to 25 days or about 11 to about 25 days. [Section 26] 26. The computer-implemented method of claim 25, wherein the conditions that cause the cells to undergo neural differentiation include exposing the cells to (i) brain-derived neurotrophic factor (BDNF), (ii) ascorbic acid, (iii) glial cell line-derived neurotrophic factor (GDNF), (iv) dibutyryl cyclic AMP (dbcAMP), (v) transforming growth factor beta 3 (TGFβ3) (collectively "BAGCT"), and (vi) an inhibitor of Notch signaling. [Section 27] 27. The computer-implemented method of any one of items 20 to 26, wherein at least one of the one or more reference cell populations in the reference database comprises gene expression levels determined by culturing the iPSCs for 13 days or about 13 days. [Section 28] 28. The computer-implemented method of any one of paragraphs 20 to 27, wherein at least one of the one or more reference cell populations comprises a gene expression level determined by culturing the iPSCs for 18 days or about 18 days. [Section 29] 29. The computer-implemented method of any one of paragraphs 20 to 28, wherein at least one of the one or more reference cell populations comprises a gene expression level determined by culturing the iPSCs for 25 days or about 25 days. [Section 30] 30. The computer-implemented method of any one of paragraphs 1 to 29, wherein the one or more metagenes and expression levels of the one or more metagenes are determined by using a dimensionality reduction technique on one or more reference cells of the one or more reference databases. [Section 31] 31. The computer-implemented method of claim 30, wherein the dimensionality reduction technique is used on a reference cell population comprising gene expression levels determined at or about day 13 of in vitro culture under conditions that differentiate iPSCs into neural progenitor cells. [Section 32] 32. The computer-implemented method of claim 30 or 31, wherein the dimensionality reduction technique is used on a reference cell population comprising gene expression levels determined at or about day 18 of in vitro culture under conditions that differentiate iPSCs into neural progenitor cells. [Section 33] 33. The computer-implemented method of any one of paragraphs 30 to 32, wherein the dimensionality reduction technique is used on a reference cell population comprising gene expression levels determined at or about day 25 of in vitro culture under conditions that differentiate iPSCs into neural progenitor cells. [Section 34] The dimensionality reduction method is a reference cell population, comprising gene expression levels determined at or about day 13 of in vitro culture under conditions that differentiate iPSCs into neural progenitor cells; a reference cell population, comprising gene expression levels determined at or about day 18 of in vitro culture under conditions that differentiate iPSCs into neural progenitor cells; 34. The computer-implemented method of any one of paragraphs 30 to 33, wherein the reference cell population comprises a gene expression level determined at or about day 25 of in vitro culture under conditions that differentiate iPSCs into neural progenitor cells. [Section 35] 35. The computer-implemented method of any one of paragraphs 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. [Section 36] 36. The computer-implemented method of any one of paragraphs 2 to 35, wherein the supervised classification model is trained using the expression levels of the one or more meta-genes determined from one or more reference cells, comprising gene expression levels at one or more of days 11 to 25 of in vitro culture under conditions that differentiate iPSCs into neural progenitor cells, and optionally days 13, 18, and 25 of in vitro culture under conditions that differentiate iPSCs into neural progenitor cells. [Section 37] 37. The computer-implemented method of any one of paragraphs 2 to 36, wherein the supervised classification model is trained using the expression levels of the one or more meta-genes determined from the one or more reference cells, wherein the expression levels of the genes are determined at or about day 13 of in vitro culture under conditions that differentiate iPSCs into neural progenitor cells. [Section 38] 38. The computer-implemented method of any one of paragraphs 2 to 37, wherein the supervised classification model is trained using the expression levels of the one or more meta-genes determined from the one or more reference cells, wherein the expression levels of the genes are determined at or about day 18 of in vitro culture under conditions that differentiate iPSCs into neural progenitor cells. [Section 39] 39. The computer-implemented method of any one of paragraphs 2 to 38, wherein the supervised classification model is trained using the expression levels of the one or more meta-genes determined from the one or more reference cells, wherein the expression levels of the genes are determined at or about day 25 of in vitro culture under conditions that differentiate iPSCs into neural progenitor cells. [Section 40] The supervised classification model is a reference cell population, comprising gene expression levels determined at or about day 13 of in vitro culture under conditions that differentiate iPSCs into neural progenitor cells; a reference cell population, comprising gene expression levels determined at or about day 18 of in vitro culture under conditions that differentiate iPSCs into neural progenitor cells; 40. The computer-implemented method of any one of paragraphs 2 to 39, wherein the method is trained using the expression levels of the one or more meta-genes determined from each of a reference cell population, the reference cell population comprising gene expression levels determined at or about day 25 of in vitro culture under conditions that differentiate iPSCs into neural progenitor cells. [Section 41] Item 41. The computer-implemented method according to any one of Items 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 a determined dopaminergic progenitor cell. [Section 42] Item 42. The computer-implemented method of any one of items 2 to 41, wherein the classification labels indicative of each of the one or more different differentiation stages of the reference cell are determined using an in vivo method. [Section 43] The in vivo method comprises: Transplanting the in vitro population of neural progenitor cells, including the reference cell population, into a brain region of an animal model of Parkinson's disease; assessing the occurrence of an outcome associated with a therapeutic effect of the transplant in the animal model, optionally the outcome being selected from innervation or host cell engraftment, reduction of brain lesions in the animal model, or recovery of brain lesions in the animal model; assigning the classification label as being a determined dopaminergic progenitor cell if the transplantation results in the occurrence of the outcome with a therapeutic effect; or and designating the classification label as not being a determined dopaminergic progenitor cell if the transplantation does not result in the occurrence of the outcome with a therapeutic effect. [Section 44] 44. The computer-implemented method of paragraph 43, wherein the brain region is the substantia nigra. [Section 45] 45. The computer-implemented method of paragraph 43 or paragraph 44, wherein the in vivo method comprises a behavioral test. [Section 46] Item 42. The computer-implemented method of any one of items 2 to 41, wherein the classification labels indicative of each of the one or more different differentiation stages of the reference cell are determined using an in vitro method. [Section 47] the in vitro method comprising assessing the level of dopamine production in a reference cell population; 47. The computer-implemented method of claim 46, wherein the classification label is assigned as being a determined dopaminergic progenitor cell if the dopamine production level is increased compared to pluripotent stem cells. [Section 48] Item 48. The computer-implemented method of item 46 or 47, wherein the assessment of dopamine production is by high performance liquid chromatography. [Section 49] the in vitro method comprising assessing the level of tyrosine hydroxylase expression in a reference cell population; 49. The computer-implemented method of any one of paragraphs 46 to 48, wherein if the reference cell population expresses high tyrosine hydroxylase, a classification label is assigned as not being a determined dopaminergic progenitor cell. [Section 50] 50. The computer-implemented method of paragraph 49, wherein said level of tyrosine hydroxylase expression is assessed using flow cytometry. [Section 51] Item 51. The computer-implemented method of any one of Items 2 to 50, wherein the reference database further comprises the classification labels of the one or more reference cells. [Section 52] 52. The computer-implemented method of any one of paragraphs 1, 2, and 4-51, wherein the expression levels of the one or more metagenes in the test dataset are determined based on (i) the one or more metagenes determined from the one or more reference cells in the reference database, and (ii) the gene expression levels in the test dataset. [Section 53] 53. The computer-implemented method of claim 52, wherein the expression levels of the one or more metagenes in the test dataset are determined using a regression analysis based on (i) the one or more metagenes determined from the one or more reference cells in the reference database, and (ii) the gene expression levels in the test dataset. [Section 54] 52. The computer-implemented method of any one of paragraphs 1, 2, and 4-51, wherein the expression levels of the one or more meta-genes 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 technique to the updated reference database. [Section 55] 55. The computer-implemented method of any one of paragraphs 30 to 54, wherein the dimensionality reduction technique is conventional nonnegative matrix factorization, discriminant nonnegative matrix factorization, graph normalized nonnegative matrix factorization, bootstrap sparse nonnegative matrix factorization, or normalized nonnegative matrix factorization. [Section 56] 56. The computer-implemented method of any one of paragraphs 30 to 55, wherein the dimensionality reduction technique is conventional non-negative matrix factorization. [Section 57] 57. The computer-implemented method of any one of paragraphs 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 a cell or cells having a determined metagene expression level of a dopaminergic progenitor cell. [Section 58] 58. The computer-implemented method of any one of paragraphs 30 to 57, wherein the number of one or more metagenes is selected based on evaluating one or more metrics determined from performing the dimensionality reduction method using a plurality of candidate numbers of metagenes. [Section 59] 59. The computer-implemented method of clause 58, wherein the one or more metrics include cophenetic distance, variance, residual, residual sum of squares (RSS), silhouette, and / or sparseness value. [Section 60] 60. The computer-implemented method of any one of paragraphs 1, 2, and 4-59, wherein the computer-calculated label classification indicates that the cell or cells from the in vitro population of neural progenitor cells are determined dopaminergic progenitor cells if the probability of the cell or cells having the determined dopaminergic progenitor meta-gene expression level is greater than a probability threshold. [Section 61] the probability threshold is set such that the determined dopaminergic progenitor cells are identified with a sensitivity of greater than or greater than about 75%, 80%, 85%, 90%, or 95%; and / or 61. The computer-implemented method of claim 60, wherein the probability threshold is set such that the determined dopaminergic progenitor cells are identified with a specificity of greater than 75%, 80%, 85%, 90%, or 95%, or greater than about 75%, about 80%, about 85%, about 90%, or about 95%. [Section 62] 61. The computer-implemented method of claim 60, wherein the probability threshold is set such that the determined dopaminergic progenitor cells are identified with a sensitivity of greater than or about greater than 98% and a specificity of 100%. [Section 63] 63. The computer-implemented method of any one of paragraphs 60 to 62, wherein the probability threshold is determined by using an area under a receiver operating characteristic (ROC) curve based on the supervised classification model. [Section 64] 64. The computer-implemented method of any one of paragraphs 60 to 63, wherein the probability threshold is 0.4 to 0.8 or about 0.4 to about 0.8. [Section 65] 64. The computer-implemented method of any one of paragraphs 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. [Section 66] 66. The computer-implemented method of any one of paragraphs 1, 2, and 4-65, wherein the deviation score of the cell or plurality of cells is determined using a single-gene deviation score for each of one or more genes in the test dataset. [Section 67] 67. The computer-implemented method of paragraph 66, wherein the single gene deviation score is determined using the difference between the gene expression level of the test dataset and the gene expression level in one or more reference cells in the reference database. [Section 68] 68. The computer-implemented method of claim 67, wherein the difference is an absolute difference. [Section 69] 69. The computer-implemented method of any one of paragraphs 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. [Section 70] The single gene deviation score is: a difference between the gene expression levels of the test dataset and the gene expression levels in the one or more reference cells in the reference database; and 70. The computer-implemented method of any one of paragraphs 66 to 69, wherein the z-score 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. [Section 71] Item 71. The computer-implemented method of any one of items 1, 2, and 4 to 70, wherein the gene expression levels in one or more reference cells in the reference database are determined based on an average gene expression level in one or more reference cells in the reference database. [Section 72] 71. The computer-implemented method of any one of paragraphs 1, 2, and 4-70, wherein the gene expression levels in the one or more reference cells in the reference database are determined based on the expression levels of the one or more meta-genes in the test dataset. [Section 73] 73. The computer-implemented method of claim 72, wherein the gene expression levels in the one or more reference cells in the reference database are determined using a regression analysis based on (i) the expression levels of the one or more meta-genes in the test dataset, and (ii) the gene expression levels in the test dataset. [Section 74] 74. The computer-implemented method of any one of paragraphs 66 to 73, wherein the deviation score is a summary statistic based on all single-gene deviation scores. [Section 75] 74. The computer-implemented method of any one of paragraphs 66 to 73, wherein the deviation score is a summary statistic based on single-gene deviation scores of one or more marker genes. [Section 76] 76. The computer-implemented method of clause 74 or clause 75, wherein the summary statistic is a sum. [Section 77] 76. The computer-implemented method of clause 74 or clause 75, wherein the summary statistic is a weighted sum. [Section 78] 78. The computer-implemented method of Paragraph 77, wherein the single gene deviation score of the one or more marker genes has a higher weight. [Section 79] 76. The computer-implemented method of claim 74 or 75, wherein the summary statistic is a percentile value. [Section 80] the percentile value is between the 50th percentile and the 100th percentile or between about the 50th percentile and about the 100th percentile, and / or 80. The computer-implemented method of claim 79, wherein the percentile value is the 50%, 60%, 70%, 80%, 90%, or 95% percentile, or about the 50%, about 60%, about 70%, about 80%, about 90%, or about the 95% percentile. [Section 81] 81. The computer-implemented method of any one of paragraphs 75 to 80, wherein the marker gene comprises a radial glial cell marker, an early neuronal development gene, a pluripotency-specific marker, a mid- to late-stage neuronal 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 required for normal development of neurons, a gene controlling dopaminergic neuron development, a gene regulating the identity 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 glia marker, a cell cycle marker, or any combination of any of these. [Section 82] 82. The computer-implemented method of any one of paragraphs 75 to 81, wherein the marker genes comprise 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 of any of these. [Section 83] 83. The computer-implemented method of any one of paragraphs 1, 2, and 4-82, wherein the computerized label classification indicates that the cell or 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, or at least about 50%, about 50%, about 70%, about 80%, about 90%, or about 95% of, the gene expression levels in the test dataset are within 5× standard deviations of the gene expression levels of the one or more reference cells in the reference database. [Section 84] 83. The computer-implemented method of any one of paragraphs 1, 2, and 4-82, wherein the computerized label classification indicates that the cell or 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 x standard deviations of the gene expression levels of the one or more reference cells in the reference database. [Section 85] 83. The computer-implemented method of any one of paragraphs 1, 2, and 4-82, wherein the computerized label classification indicates that the cell or 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%, or at least about 50%, about 50%, about 70%, about 80%, about 90%, or about 95% of the marker gene expression levels in the test dataset are within 5× standard deviations of the gene expression levels of the one or more reference cells in the reference database. [Section 86] 83. The computer-implemented method of any one of paragraphs 1, 2, and 4-82, wherein the computerized label classification indicates that the cell or cells from the in vitro population of neural progenitor cells are determined dopaminergic progenitor cells if the deviation score indicates that at least or at least about 95% of the marker gene expression levels in the test dataset are within 10, 9, 8, 7, 6, or 5 x standard deviations of the gene expression levels of the one or more reference cells in the reference database. [Section 87] The computed label classification comprises: the probability of the cell or cells having the determined dopaminergic progenitor meta-gene expression level is greater than the probability threshold; and 83. The computer-implemented method of any one of paragraphs 60 to 82, wherein the deviation score indicates that at least 50%, 50%, 70%, 80%, 90%, or 95% of, or at least about 50%, about 50%, about 70%, about 80%, about 90%, or about 95% of, the gene expression levels in the test dataset are within 5× standard deviations of the gene expression levels of the one or more reference cells in the reference database, indicating that the cell or cells from the in vitro population of neural progenitor cells are determined dopaminergic progenitor cells. [Section 88] The computed label classification comprises: the probability of the cell or cells having the determined dopaminergic progenitor meta-gene expression level is greater than the probability threshold; and 83. The computer-implemented method of any one of paragraphs 60 to 82, wherein the deviation score indicates that at least 50%, 50%, 70%, 80%, 90%, or 95% of, or at least about 50%, about 50%, about 70%, about 80%, about 90%, or about 95% of, the marker gene expression levels in the test dataset are within 5× standard deviations of the gene expression levels of the one or more reference cells in the reference database, indicating that the cell or cells from the in vitro population of neural progenitor cells are determined dopaminergic progenitor cells. [Section 89] The computed label classification comprises: the probability of the cell or cells having the determined dopaminergic progenitor meta-gene expression level is greater than the probability threshold; the deviation score is such that at least 50%, 50%, 70%, 80%, 90%, or 95% of, or at least about 50%, about 50%, about 70%, about 80%, about 90%, or about 95% of, the gene expression levels in the test dataset are within 5× standard deviations of the gene expression levels of the one or more reference cells in the reference database; 83. The computer-implemented method of any one of paragraphs 60 to 82, wherein the deviation score indicates that at least 50%, 50%, 70%, 80%, 90%, or 95% of, or at least about 50%, about 50%, about 70%, about 80%, about 90%, or about 95% of, the marker gene expression levels in the test dataset are within 5× standard deviations of the gene expression levels of the one or more reference cells in the reference database, indicating that the cell or cells from the in vitro population of neural progenitor cells are determined dopaminergic progenitor cells. [Section 90] The computed label classification comprises: 90. The computer-implemented method of any one of paragraphs 75 to 89, wherein if the difference in expression of the marker gene between the test data set and reference cells in the reference database is not statistically significant based on a multiple comparisons corrected significance level, it indicates that the cell or cells from the in vitro population of neural progenitor cells are determined dopaminergic progenitor cells. [Section 91] 91. The computer-implemented method of paragraph 90, wherein the multiple comparison corrected significance level is a Bonferroni corrected significance level or a false discovery rate corrected significance level. [Section 92] 92. The computer-implemented method of claim 90 or 91, wherein the multiple comparison corrected significance level is 0.01, 0.05, or 0.1. [Section 93] Item 93. The computer-implemented method of any one of items 1 to 92, wherein the gene expression levels are obtained from microarray analysis of intracellular RNA, RNA sequencing, or both. [Section 94] Item 94. The computer-implemented method of any one of items 1 to 93, wherein the gene expression levels are obtained from RNA sequencing. [Section 95] 95. The computer-implemented method of paragraph 93 or paragraph 94, wherein said RNA sequencing is performed on bulk RNA from said plurality of cells or a plurality of reference cells. [Section 96] 95. The computer-implemented method of paragraph 93 or paragraph 94, wherein said RNA sequencing is performed on RNA from said single cell or a single reference cell. [Section 97] 95. The computer-implemented method of claim 93 or 94, wherein the gene expression levels of reference cells in the reference database comprise expression levels determined by RNA sequencing performed on bulk RNA from a plurality of reference cells and RNA from a single reference cell. [Section 98] Item 1, 2, and 4-97, wherein receiving the test dataset comprises receiving input from an array analysis system. [Section 99] Item 1, 2, and 4-98, wherein receiving the test dataset comprises receiving input via a computer network. [Section 100] 99. The computer-implemented method of any one of paragraphs 1, 2, and 4-99, wherein the one or more reference databases form part of a storage medium. [Section 101] 101. The computer-implemented method of any one of paragraphs 1, 2, and 4-100, further comprising repeating the receiving, applying, determining, and outputting steps if the computer-calculated label classification indicates that the cell or cells are not determined dopaminergic neurons, and optionally, the steps are repeated on the same or a different in vitro population of neural progenitor cells. [Section 102] 102. The computer-implemented method of claim 101, wherein the receiving, applying, determining, and outputting steps are repeated 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 days after a prior iteration of the method, or repeated about 1, about 2, about 3, about 4, about 5, about 6, about 7, about 8, about 9, or about 10 days after a prior iteration of the method. [Section 103] If the computer-calculated label classification indicates that the cell or cells are not committed dopaminergic neurons, repeating the receiving, applying, determining, and outputting steps, wherein the steps are repeated with a different in vitro population of neural progenitor cells formed by culturing another iPSC clone under conditions capable of differentiating the one or more iPSCs into neural progenitor cells, and optionally the neural progenitor cells are one or more of floor plate midbrain progenitor cells, committed dopaminergic progenitor cells, or dopamine (DA) neurons. [Section 104] 104. The computer-implemented method of paragraph 103, wherein the distinct in vitro populations of neural progenitor cells are generated from the same human subject as in the previous iteration of the method. [Section 105] 105. The computer-implemented method of any one of paragraphs 101 to 104, wherein the receiving, applying, determining, and outputting steps are repeated until an indication is output that the cell or cells in an in vitro population of neural progenitor cells formed by culturing iPSCs for different time periods and / or under different conditions that enable the one or more iPSCs to differentiate into neural progenitor cells is a determined dopaminergic neuron. [Section 106] A determined population of dopaminergic progenitor cells identified by the method according to any one of paragraphs 5 to 105. [Section 107] 107. A method of treatment, comprising administering the population of determined dopaminergic progenitor cells of paragraph 106 to a subject with Parkinson's disease. [Section 108] 108. The method of paragraph 107, wherein said administering is by transplanting said population of determined dopaminergic progenitor cells into one or more brain regions of said subject. [Section 109] 109. The method of paragraph 108, wherein said one or more brain regions includes the substantia nigra. [Section 110] 109. The method of any one of paragraphs 107 to 109, wherein said population of determined dopaminergic progenitor cells is autologous to said subject. [Section 111] 110. The method of any one of paragraphs 107 to 109, wherein said population of determined dopaminergic progenitor cells is allogeneic to said subject. [Section 112] 1. A method of treating a subject having Parkinson's disease, comprising: 106. 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 has been identified using the computer-implemented method of any one of paragraphs 5 to 105. [Section 113] 113. The method of paragraph 112, wherein said population of determined dopaminergic progenitor cells is autologous to said subject. [Section 114] 114. The method of paragraph 112 or 113, wherein said population of determined dopaminergic progenitor cells is allogeneic to said subject. [Section 116] Approximately 1×10 6 pieces, or at least 1 x 10 6 pcs or 1 x 10 6 Item 115. The method according to any one of items 107 to 114, wherein the cells are injected into the substantia nigra. [Section 117] 117. The method of any one of paragraphs 107 to 116, wherein the cells are injected into both the left and right hemisphere. [Brief explanation of the drawings]

[0045] [Figure 1] 1 shows developmental stages when conventional biomarkers cannot be used for stage discrimination.

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

[0047] [Figure 3A] Here is an example of NeuroTest output: a table of statistical scores. [Figure 3B] Figure 3B shows an example of NeuroTest output: a histogram showing 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 NeuroTest, respectively. Figure 3B shows NeuroScore on the y-axis converted to a percentage value. [Figure 3C] Figure 3C shows an example of NeuroTest output: a scatter plot showing 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 NeuroTest, respectively. Figure 3C shows NeuroScore on the y-axis converted to a percentage value. In Figure 3C, NeuroScore is labeled "neuri" and Novelty Score is labeled "deviation."

[0048] [Figure 4]A scatter plot showing the NeuroScore (y-axis) and Novelty Score (x-axis) for the validation dataset is shown. Validation of the NeuroTest model was first trained on identified genes from the microarray data and supplemented with RNA-seq-based gene expression data. Here, the model was trained using Illumina bead array data (using 5-fold cross-validation), so RNA-seq data was used as validation. Validation RNA-seq data was generated or downloaded from public data repositories. Samples in the upper left quadrant pass for both high NeuroScore and low Novelty. "Undiff" samples (predominantly undifferentiated IPSCs, diamonds) fail due to their low NeuroScore and elevated levels of Novelty compared to the reference data model. In Figure 4, NeuroScore is denoted as "N-score."

[0049] [Figure 5] NeuroTest results from the analysis of 86 publicly available neuronal RNAseq datasets are shown. Data points highlighted with black circles are specifically from the challenge dataset. Solid 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 to neurons, reflects the input data. Tabular output reveals that NeuroTest demonstrated a "pass" score for the DA neuronal preparation. In Figure 5, NeuroScore is denoted as "N-score."

[0050] [Figure 6] NeuroTest demonstrates the use of gene expression as a phenotype to identify neural progenitor cells.

[0051] [Figure 7]Metagene expression levels (metagene contributions) for cell samples on day 18 of the dopaminergic neuron differentiation protocol are shown. Metagenes and their expression levels were obtained by applying conventional nonnegative matrix factorization (NMF) to single-cell RNA-seq (scRNA-seq) data, which were then integrated to approximate bulk RNA-seq data (bulk from single cells). The bulk RNA-seq data were collected from each of the four cell lines. Both scRNA-seq and bulk RNA-seq data were collected for each sample collected from the cell lines.

[0052] [Figure 8] Receiver operating characteristic (ROC) curves are shown illustrating the classification performance of a logistic regression model trained to identify determined dopaminergic progenitors within an in vitro population of neural progenitor cells.

[0053] [Figure 9]Another exemplary workflow for constructing and using NeuroTest is shown. In this exemplary workflow, gene expression data from publicly available databases, an scRNAseq dataset, and a matched bulk RNAseq dataset are collected for an in vitro population of neural progenitors, including a determined dopaminergic progenitor. These datasets are fed into a process that calculates 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 metagene expression levels (circle 5). This model can be validated using additional data, such as bulk RNAseq data not used to train the 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). A novelty score is also calculated for each training sample, and these scores and the trained model are used to identify a NeuroScore and a novelty score threshold to be used in evaluating future test samples (circle 8). For prospective test samples, RNAseq data are subjected to sequence alignment using Salmon pseudoaligner (circle 1). The test RNAseq data are then fed into the trained model (circle 2), which outputs a NeuroScore (circle 10) and Novelty Score (circle 11) for the 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] 1 shows gene expression deviations for an exemplary sample 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 that indicate how much individual gene expression deviates from expected values, where the expected values ​​are determined from cells of known identity (e.g., reference cells).

[0055] [Figure 11] 1 shows NeuroTest output (NeuroScore and Novelty Score) for cell samples at various stages (days) of the dopaminergic neuron differentiation protocol. The horizontal dashed line indicates where NeuroScore=0. The vertical dashed line indicates where Novelty Score=5. In this exemplary embodiment, samples with a NeuroScore>0 and a Novelty Score<5 are identified as containing determined dopaminergic progenitor cells. DETAILED DESCRIPTION OF THE INVENTION

[0056] Provided herein are methods for classifying whether an in vitro population of neural progenitor cells contains a specific differentiated neural cell type. In some embodiments, the provided methods classify whether an in vitro population of differentiated neural cells contains determined dopaminergic progenitor cells. In some embodiments, the provided methods identify whether an in vitro population of neural cells 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. Cell populations sorted according to the provided methods can be used, for example, to identify cells of interest for therapeutic applications. Accordingly, populations of determined dopaminergic progenitor cells identified by the provided methods, and pharmaceutical compositions comprising the same, are also provided. In some embodiments, the determined dopaminergic progenitor cells have therapeutic applications in the treatment of neurodegenerative diseases, such as Parkinson's disease.

[0057] In provided methods, the method includes receiving a test dataset including (1) gene expression levels and (2) expression levels of one or more metagenes for a cell or cells included in an in vitro population of neural progenitor cells, where the 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 that have been subjected to a process for differentiating pluripotent stem cells, such as induced pluripotent stem cells (iPSCs), into neural cells, such as dopaminergic neurons or determined precursors of dopaminergic neurons. In some embodiments, the method includes applying the expression levels of the one or more metagenes as input to a process configured to determine the probability of a cell or cells in the in vitro population of neural progenitor cells having the determined metagene expression levels of dopaminergic progenitor cells. In some embodiments, the method also includes determining a deviation score for a cell or plurality of cells in the in vitro population of neural progenitor cells, the deviation score indicating the degree to which gene expression levels in the test dataset deviate from gene expression levels in one or more reference cells in the reference database, the one or more reference cells being at a differentiation stage indicative of a determined dopaminergic progenitor cell. 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 computed label classification that provides an indication of whether the cell or plurality of cells from the in vitro population of neural progenitor cells is a determined dopaminergic progenitor cell based on the probability and the deviation score, thereby classifying the in vitro population of neural progenitor cells as a population that is or comprises a determined dopaminergic progenitor cell. In some embodiments, the present disclosure thus allows for identification of whether an in vitro population of neural progenitor cells is a population containing a determined dopaminergic progenitor cell based on the classification.

[0058] In some embodiments, a specific differentiated neural cell population differentiated from pluripotent stem cells containing determined dopaminergic progenitor cells may be cells at a stage of differentiation where the cells are not identifiable by one or a few characteristics or properties. The methods provided herein enable the determination of cell identity when one or a few characteristics or properties, such as gene expression markers or functional properties, are unavailable (e.g., unknown) or cannot be practically used to determine cell identity. For example, as shown in FIG. 1, cells at a stage of differentiation where no clear biomarkers are available can be used to determine cell identity. While pluripotent stem cells can be identified as positive by clear biomarkers, such as the expression levels of specific genes, and differentiated cells can be identified as positive based on functional markers, the 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 cell phenotype. In some aspects, the methods provided herein overcome the lack of a single or a few characteristics or properties (e.g., biomarkers) by examining a group of related 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 committed dopaminergic progenitor cells.

[0059] Induced pluripotent stem cells (iPSCs) are considered useful as cell therapies due to their ability to differentiate into at least specialized cell types. For example, like pluripotent stem cells, iPSCs can differentiate into specific cell types that can be used to replace diseased or damaged tissue. In some cases, iPSCs differentiated into specific neural cell types or precursors can be used to treat neurodegenerative diseases, for example, by differentiating the iPSCs and transplanting the differentiated neural cells into the brain of a subject with the neurodegenerative disease. The inability to determine the identity 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. Therefore, without the ability to determine whether differentiating cells progress through transitional stages, as needed, the differentiation process can be time-consuming and inefficient, potentially preventing treatment of the subject if the differentiation process fails. Furthermore, in some cases, therapeutic treatment may involve administering (e.g., injecting) differentiated cells that have not yet entered the final differentiation stage to a subject.

[0060] In some embodiments, cells at intermediate stages of differentiation cannot be identified or easily identified by clear biomarkers. The methods provided herein allow for the identification of cells at stages of differentiation when clear features or characteristics are not available or can actually be used to determine cell identity. In some embodiments, the methods provided herein can be used to improve the differentiation process, for example, by allowing for the determination of cell identity throughout the stages of differentiation, and to determine whether cells undergoing the differentiation process are differentiated appropriately and / or according to defined criteria. If it is determined that the cells are not properly differentiated, in some embodiments, the process can be terminated or, optionally, restarted using a different iPSC clone derived from the patient.

[0061] In some embodiments, the methods provided herein can be used in combination with processes that involve generating neural cells useful for treating neurodegenerative diseases, such as Parkinson's disease, by differentiation from iPSCs. In some embodiments, the methods provided herein can be used to identify neural cells generated by a differentiation process, e.g., a process described in Section II, that are useful for treating Parkinson's disease.

[0062] The methods provided herein can be used to determine whether an in vitro population of cells contains a predetermined dopaminergic progenitor cell. In some embodiments, the methods provided herein include determining metagenes and their expression levels for a test cell contained in the in vitro population. In some embodiments, the methods provided herein include determining a probability of the test cell having the determined metagene expression level for a dopaminergic progenitor cell. 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 extent to which the gene expression level of the test cell deviates from an expected gene expression level. In some embodiments, the expected gene expression level is based on gene expression levels of reference cells known to be the determined dopaminergic progenitor cell. In some embodiments, the methods provided herein include outputting a computed label classification based on one or both of (i) the probability of the test cell having the determined metagene expression level for a dopaminergic progenitor cell and (ii) the deviation score. In some embodiments, the deviation score is based on a subset of marker genes. In some embodiments, determining the probability of a test cell having a determined dopaminergic progenitor meta-gene expression level allows for the identification of cells having a desired phenotype that lacks individual marker genes. In some embodiments, determining a deviation score allows for the identification of cells that may contain abnormalities, for example, in the expression of specific marker genes. Thus, the methods provided herein provide a multifaceted approach to determining cells suitable for therapy.

[0063] The following subsections describe exemplary features of the provided methods for classifying in vitro populations of neural progenitor cells as containing or not containing specific differentiated neural cell types, as well as methods for identifying specific differentiated neural cell types. Related compositions and methods for their manufacture and use are also described.

[0064] I. Methods for determining committed dopaminergic cells Provided herein, among other things, are methods for identifying dopaminergic precursors in in vitro cell populations of neural progenitor cells using gene expression as a phenotype. The methods provided herein provide, among other things, information regarding whether a cell preparation (e.g., a population of neural progenitor cells) contains cells committed to differentiate into a specific functional cell type (e.g., committed dopaminergic precursor cells), or whether the cell preparation contains cells from earlier stages (e.g., pluripotent stem cells, specified cells), other differentiating neuronal types, and other differentiated cell types.

[0065] Thus, in one aspect, a computer-implemented method for identifying determined dopaminergic progenitor cells within an in vitro population of neural progenitor cells is provided, the method including: receiving a test dataset including data such as gene expression profile information for the in vitro population of neural progenitor cells, querying a gene expression reference database and comparing the test dataset to the gene expression reference database, the gene expression reference database including gene expression profile information for desired determined dopaminergic progenitor cells, and outputting a computer-calculated label classification including an indication of whether the in vitro population of neural progenitor cells includes the determined dopaminergic progenitor cells.

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

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

[0068] In some embodiments, the computer-implemented method includes a machine learning model trained to determine the probability of a cell or plurality of cells in an in vitro population of neural progenitor cells as having the determined dopaminergic progenitor meta-gene expression level. In embodiments, the machine learning model outputs a probability of the cell or plurality of cells having the determined dopaminergic progenitor meta-gene expression level (also referred to herein as a NeuroScore). In embodiments, the computer-implemented method further includes determining a deviation score (also referred to herein as a Novelty Score) for the cell or plurality of cells, the deviation score indicating the extent to which the gene expression level of the cell or plurality of cells deviates from an expected gene expression level. In some embodiments, the expected gene expression level is based on the gene expression levels of a reference cell, e.g., a reference cell known to be the determined dopaminergic progenitor cell. In some embodiments, the computer-implemented 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 new way to define determined dopaminergic progenitor cells or dopaminergic cells. This new method is referred to as a computational definition, while the previous types of definitions are referred to as biological definitions (functional, structural, and origin). Computational definitions are related to biological definitions, but as discussed herein, computational definitions provide a more robust and accurate way to compare two different cells and determine whether they are the same type of cell or different cell types. In some embodiments, computational definitions provide a more robust and accurate way to identify cells of unknown identity.

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

[0071] A. Reference cell In some aspects, the methods provided herein include the use of reference cells and / or a reference database to identify (e.g., determine) the presence of determined dopaminergic progenitor cells within an in vitro population of neural progenitor cells. Reference cell types contemplated for use in accordance with the methods provided herein include cells with a known identity (e.g., labeled cells) and a known characteristic, e.g., 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 a cell of unknown identity (e.g., unlabeled) with a particular characteristic, e.g., gene expression pattern, has a particular cell identity.

[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 a healthy human subject. In some embodiments, the iPSCs are generated from fibroblasts collected from a human subject with Parkinson's disease. In some embodiments, the iPSCs are generated from fibroblasts collected from a human subject prone to developing Parkinson's disease. Exemplary methods of 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 floor plate midbrain progenitor cells, committed 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 committed dopaminergic progenitor cells. In some embodiments, the reference cells are dopaminergic neurons. In some embodiments, the differentiated cells, committed dopaminergic cells, and / or dopaminergic cells are derived from iPSCs, e.g., iPSCs described above, cultured under conditions that promote differentiation into dopaminergic cells.

[0074] In some embodiments, the reference cells are cells that have been described, e.g., labeled and characterized, in a publicly available database.

[0075] In some embodiments, the reference cell has a known identity. Thus, in some cases, the identity of the cell can be used as a label for the reference cell. In some embodiments, the reference cell label indicates a cell phenotype. In some embodiments, the reference cell label indicates a cell characteristic, e.g., a gene expression level. In some embodiments, the reference cell label indicates whether the reference cell is a pluripotent stem cell. In some embodiments, the reference cell label indicates whether the reference cell is a determined dopaminergic progenitor cell. In some embodiments, the reference cell label indicates whether 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, e.g., any of the periods described in Section II.

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

[0078] In some embodiments, a reference cell is characterized as a dopaminergic neuron if it expresses a marker of a midbrain dopaminergic neuron, such as expression of FOXA2 or tyrosine hydroxylase (TH). In some embodiments, the reference cell expresses TH (TH+). In some embodiments, the reference cell expresses FOXA2 (FOXA2+). In some embodiments, the reference cell expresses TH and FOXA2 (TH+FOXA2+).

[0079] In some embodiments, the reference cells are determined to be dopaminergic neurons or capable of becoming dopaminergic neurons, i.e., determined dopaminergic progenitor cells, as confirmed based on one or more characteristics indicating that the reference cells may have functional activity of dopaminergic neurons but may not yet express, or may not express at high levels, markers of dopaminergic neurons. For example, the reference cells may exhibit lower levels of TH than dopaminergic neurons but still exhibit one or more characteristics of determined dopaminergic progenitor cells, indicating that the differentiated cells may have functional activity of dopaminergic neurons. In some embodiments, the one or more characteristics of the reference cells include the ability to survive, engraft, and / or innervate other cells in vivo, e.g., when administered to an animal model. In some embodiments, the reference cells are capable of innervating 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 a neurodegenerative disease. In some embodiments, the reference cells, when transplanted, improve or ameliorate symptoms of the neurodegenerative disease. In some embodiments, the neurodegenerative disease is Parkinson's disease. In some embodiments, the reference cells, when transplanted into a subject, e.g., a patient in need of transplantation, improve Parkinson's symptoms.

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

[0082] As described above, in some embodiments, any of several reference cell characteristics of a particular reference cell can be determined, including any one or more characteristics, traits, features, or attributes of the reference cell. In some embodiments, the reference cell characteristics can be used as data to characterize or describe a particular reference cell population. For example, the reference cell characteristics can 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 cellular characteristic, or any combination thereof. Any one or more of the reference cell characteristics can be used as data to be entered into a reference cell database or added to the reference cell database.

[0083] In some embodiments, the reference cell characteristic includes a protein expression level. In some embodiments, the reference cell characteristic includes a post-translational protein modification level. In some embodiments, the reference cell characteristic includes a non-coding RNA expression profile. In some embodiments, the reference cell characteristic includes an epigenetic profile. In some embodiments, the reference cell characteristic includes a transcriptional profile. In some embodiments, the reference cell characteristic includes a gene expression level. In some embodiments, the reference cell database can include information regarding any one or more of the above reference cell characteristics.

[0084] In some embodiments, the gene expression levels are obtained using microarray analysis. In some embodiments, the gene expression levels are obtained using RNA sequencing. In some embodiments, the gene expression levels are obtained using both microarray analysis and RNA sequencing. In some embodiments, RNA sequencing is performed on bulk RNA from a plurality of cells. In some embodiments, RNA sequencing is performed on a single cell. In some embodiments, RNA sequencing is performed on bulk RNA from a plurality of cells and on a single cell.

[0085] In some aspects, a plurality of reference cells having a known identity, e.g., a label, and a known characteristic, e.g., a gene expression level, are used to populate the reference database. In some embodiments, the plurality of 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 a different label from other reference cells in the reference database. Thus, in some embodiments, the reference database can include a plurality of reference cells, some of which have the same label as other cells in the reference database and some of which have a different label from other cells in the reference database.

[0086] In some embodiments, the reference cell characteristics of a particular reference cell are included in a reference database. In some embodiments, the reference database includes a reference cell label. In some embodiments, the reference database includes protein expression levels of the reference cell. In some embodiments, the reference database includes an epigenetic profile of the reference cell. In some embodiments, the reference database includes a transcriptional profile 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 a publicly available database. In some embodiments, the reference database includes microarray data. In some embodiments, the reference database includes RNA-seq data. In some embodiments, the reference database includes microarray data and RNA-seq data.

[0087] In some embodiments, the reference database comprises bulk RNA-seq data. In some embodiments, the bulk RNA-seq data is obtained from a plurality of reference cells. In some embodiments, the bulk RNA-seq data is obtained from pooled RNA from a plurality of reference cells.

[0088] Any known and available method can be used to obtain bulk RNA sequence data (see, e.g., Chao et al., 2019, BMC Genomics 20:571, incorporated herein by reference in its entirety). For example, total RNA from a sample, e.g., multiple reference cells 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 total RNA or mRNA library preparation. For library preparation, total RNA or mRNA is fragmented and converted to cDNA using reverse transcription. After construction, amplification, and optional barcoding of 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 comprises single-cell RNA-seq data. In some embodiments, the use of single-cell RNA-seq data provides certain advantages. In some embodiments, the use of single-cell RNA-seq data allows for 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-seq data reduces the number of reference cells required for use in the methods provided herein. In some embodiments, the use of single-cell RNA-seq data improves characterization of biological variation across reference cells. In some embodiments, the use of single-cell RNA-seq data allows for easier validation and interpretation of gene expression levels.

[0090] Any known, 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 by reference in their entireties). For single-cell RNA sequencing, a sample, e.g., a single cell 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 cell is 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 primed mRNA molecules are converted to cDNA using reverse transcription. In some cases, a unique molecular Identifiers can be used to label single mRNA molecules based on their cell origin. The cDNA pool is then amplified, optionally barcoded, and sequenced, for example, using next-generation sequencing (NGS) with 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 can approach saturation when sequenced at a read depth of 1,000,000.

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

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

[0093] The reference database is comprised of cellular datasets, each of which is comprised of feature data, such as 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, output from DNA sequence analysis, or any other type of cellular feature.

[0094] B. Test Cells In some aspects, the methods provided herein allow for the determination of whether a cell or cells of unknown identity are determined dopaminergic progenitor cells. In some embodiments, the cell or cells of unknown identity are test cells. In some embodiments, the test cells are an in vitro population of cells. In some embodiments, the test cells are included in an in vitro population of neural progenitor cells. In some embodiments, the test cells comprise cells differentiated under conditions to become dopaminergic neurons. In some embodiments, the test cells comprise cells differentiated according to any of the methods described in Section II. In some embodiments, the test cells comprise cells differentiated under conditions to become dopaminergic neurons for any of the time periods described in Section II. In some embodiments, the differentiating 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 a healthy human subject. In some embodiments, the iPSCs are generated from fibroblasts collected from a human subject with Parkinson's disease. Exemplary methods of iPSC generation are described in Section II.

[0095] In some embodiments, the identity of the test cells, e.g., determining whether the test cells are determined dopaminergic progenitor cells, indicates whether an in vitro population of cells comprises a population of determined dopaminergic progenitor cells.

[0096] In some embodiments, a test dataset is determined from a test cell. In some embodiments, the test dataset is used to determine whether the test cell is a determined dopaminergic progenitor cell. In some embodiments, the test dataset is used to determine whether the test cell comprises a determined dopaminergic progenitor cell.

[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. Unknown in this context means that a calculated definition is desired. Typically, a test dataset is composed of a comprehensive profile as discussed herein when related to a comprehensive profile in a reference database. The test dataset can be merged with the reference database to form an updated reference database. In certain embodiments, this can be as convenient as adding data to an existing spreadsheet. Thus, a test dataset containing gene expression profile information of an in vitro population of neural progenitor cells can be included (merged) with the reference database after it has been determined that the in vitro population of neural progenitor cells contains the 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 as those determined for the reference cells. In some embodiments, the test dataset can 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 type of cellular characteristic.

[0099] In some embodiments, the test dataset comprises protein expression levels. In some embodiments, the test dataset comprises post-translational protein modification levels. In some embodiments, the test dataset comprises a non-coding RNA expression profile. In some embodiments, the test dataset comprises an epigenetic profile. In some embodiments, the test dataset comprises a transcriptional profile. In some embodiments, the test dataset comprises 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 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. Exemplary methods for extracting, preparing, and analyzing bulk RNA and single-cell RNA are described above in Section I.A.

[0101] In some embodiments, the test cell characteristic is included in a test dataset. In some embodiments, the test dataset includes protein expression levels of the test cells. In some embodiments, the test dataset includes an epigenetic profile of the test cells. In some embodiments, the test dataset includes a transcriptional profile of the test cells. In some embodiments, the test dataset includes gene expression levels of the test cells. In some embodiments, the test dataset includes microarray data. In some embodiments, the test dataset includes RNA-seq data. In some embodiments, the test dataset includes microarray data and RNA-seq data. In some embodiments, the test dataset includes bulk RNA-seq data. In some embodiments, the test dataset includes single-cell RNA-seq data. In some embodiments, the test dataset includes bulk RNA-seq data and single-cell RNA-seq data. In some embodiments, the test dataset includes expression levels of one or more meta-genes. Determining meta-genes and their expression levels is discussed in Section IC.

[0102] C. Metagene In some aspects, the methods provided herein use metagenes and metagene expression levels to determine the identity of a test cell. Metagene refers to a pattern of gene expression. For example, a metagene can be a group of genes with correlated gene expression. In some embodiments, a metagene combines information from multiple individual genes, and the metagene 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 expression level is based on the combined individual gene expression levels, and determining the metagene includes determining the extent to which the expression levels of the individual genes contribute to the metagene expression level. For example, the metagene expression level can be a weighted combination of individual gene expression levels, and determining the metagene includes determining the weight of the individual genes for each metagene. In some embodiments, the metagenes and their expression levels reflect the correlated expression levels of the individual genes overall. In some embodiments, the metagenes and their expression levels reflect individual genes co-expressed by cells of the same phenotype (e.g., determined dopaminergic progenitor cells). Exemplary co-expressed genes of determined dopaminergic progenitor cells are discussed in Section III.

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

[0104] 1.Determining metagenes In some embodiments, the metagene is determined based on the gene expression levels of a reference cell. In some embodiments, the gene expression levels of the reference cell are included in a reference database. Exemplary reference cells and reference databases are described in Section I.A. In some embodiments, the metagene is determined using a reference database that includes microarray data. In some embodiments, the metagene is determined using a reference database that includes RNA-seq data. In some embodiments, the metagene is determined using a reference database that includes microarray data and a reference database that includes RNA-seq data. In some embodiments, the metagene is determined using a reference database that includes bulk RNA-seq data. In some embodiments, the metagene is determined using a reference database that includes single-cell RNA-seq data. In some embodiments, the metagene is determined using a reference database that includes bulk RNA-seq data and a reference database that includes single-cell RNA-seq data.

[0105] In some embodiments, the metagenes are determined computationally. In some embodiments, the metagenes are determined using a dimensionality reduction technique. A dimensionality reduction technique converts data from a high-dimensional space (e.g., individual genes) to a low-dimensional space (e.g., a metagene) such that the low-dimensional representation of the data still retains meaningful or informative properties of the original data. In some embodiments, the metagenes are determined by applying a dimensionality reduction technique to a database.

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

[0107] In some embodiments, the dimensionality reduction technique is nonnegative matrix factorization (NMF). NMF can be used to decompose a matrix into two matrices such that all three matrices have no negative elements. This nonnegativity may make the resulting matrix easier to inspect, for example, if the original matrix itself contains only nonnegative values. In some embodiments, the dimensionality reduction technique is conventional NMF. In some embodiments, the dimensionality reduction technique is discriminant NMF. In some embodiments, the dimensionality reduction technique is regularized NMF. In some embodiments, the dimensionality reduction technique is graph regularized NMF. In some embodiments, the dimensionality reduction technique is bootstrap sparse NMF.

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

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

[0110] 2. Metagene Expression Levels In some embodiments, the determined metagene expression level 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 of the test datasets described in Section IB). Determination of the metagene expression level is possible if expression levels of the same or similar sets of genes are included in the reference database used to determine the metagene and the reference database and / or test dataset used to determine the metagene expression level.

[0111] In some embodiments, the metagene gene expression levels are determined using a reference database comprising microarray data. In some embodiments, the metagene gene expression levels are determined using a reference database comprising RNA-seq data. In some embodiments, the metagene gene expression levels are determined using a reference database comprising microarray data and a reference database comprising RNA-seq data. In some embodiments, the metagene gene expression levels are determined using a reference database comprising bulk RNA-seq data. In some embodiments, the metagene gene expression levels are determined using a reference database comprising single-cell RNA-seq data. In some embodiments, the metagene gene expression levels are determined using a reference database comprising bulk RNA-seq data and a reference database comprising single-cell RNA-seq data.

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

[0113] In some embodiments, the metagene gene expression levels are determined using a test dataset comprising microarray data. In some embodiments, the metagene gene expression levels are determined using a test dataset comprising RNA-seq data. In some embodiments, the metagene gene expression levels are determined using a test dataset comprising microarray data and RNA-seq data. In some embodiments, the metagene gene expression levels are determined using a test dataset comprising bulk RNA-seq data. In some embodiments, the metagene gene expression levels are determined using a test dataset comprising single-cell RNA-seq data. In some embodiments, the metagene gene expression levels are determined using a test dataset comprising bulk RNA-seq data and single-cell RNA-seq data.

[0114] In some embodiments, the metagenes are determined using a reference database comprising bulk RNA-seq data, and the metagene expression levels are determined using a test dataset comprising bulk RNA-seq data. In some embodiments, the metagenes are determined using a reference database comprising bulk RNA-seq data, and the metagene expression levels are determined using a test dataset comprising single-cell RNA-seq data. In some embodiments, the metagenes are determined using a reference database comprising single-cell RNA-seq data, and the metagene expression levels are determined using a test dataset comprising bulk RNA-seq data. In some embodiments, the metagenes are determined in a reference database comprising bulk RNA-seq data and a reference database comprising single-cell RNA-seq data, and the metagene expression levels are determined using a test dataset comprising bulk RNA-seq data. In some embodiments, the metagenes are determined in a reference database comprising bulk RNA-seq data and a reference database comprising single-cell RNA-seq data, and the metagene expression levels are determined using a test dataset comprising bulk RNA-seq data. In some embodiments, the metagenes are determined in a reference database comprising bulk RNA-seq data and a reference database comprising single-cell RNA-seq data, and the metagene expression levels are determined using a test dataset comprising single-cell RNA-seq 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 metagene expression levels.

[0116] In some embodiments, one or more outputs of the dimensionality reduction procedure and a reference database are used to determine metagene expression levels based on the reference database. In some embodiments, one or more outputs of the dimensionality reduction procedure 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 multiple individual genes are combined to form the meta-gene. 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 meta-gene. In some embodiments, one or more outputs of the dimensionality reduction method include weights of individual genes, for example, if the meta-gene expression level is a weighted combination of individual gene expression levels.

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

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

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

[0121] In some embodiments, the machine learning model is trained on 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 (e.g., determined using any of the reference databases described in Section I.A. and any of the methods described in Section I.C.).

[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 labels indicate whether the corresponding reference cells are determined dopaminergic progenitor cells. In some embodiments, the reference cell labels indicate a period during which the corresponding reference cells differentiate under conditions to become dopaminergic neurons, e.g., any of the periods described in Section II. In some embodiments, the reference cell labels indicate whether the period is at least about 18 days. In some embodiments, the reference cell labels indicate whether the period is about 18-25 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 including any of these models.

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

[0125] At some point after the reference database is received, the method can include performing unsupervised classification. This means that a new filtering of the data is performed, with no prior idea of ​​the results of the filtering. The filtering is typically performed multiple times, for example, at least 5, 10, 20, 50, 100, 200, 300, or 500 times. The filtering results are analyzed for stable results, meaning that the results of the filtering provide the same results or results that are at least 80%, 85%, 90%, 95%, 97%, 99%, or 100% similar to the previous results. Re-filtering the data can be done entirely de novo, or can start with certain assumptions.

[0126] In some embodiments, a metagene expression level for a test cell is determined based on a test dataset (e.g., using any of the test datasets described in Section I.B. and any of the methods described in Section I.C.), 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 the test cell having the determined dopaminergic progenitor metagene expression level. In some embodiments, the machine learning model outputs a likelihood of the test cell having the determined dopaminergic progenitor metagene expression level. In some embodiments, the machine learning model outputs a probability of the test cell having the determined dopaminergic progenitor metagene expression level. The output (e.g., binary prediction, likelihood, probability) is also referred to herein as a "NeuroScore."

[0127] In some embodiments, the NeuroScore output of the test cell, e.g., the probability of the test cell having a determined dopaminergic progenitor meta-gene expression level, is compared to a predetermined threshold. In some embodiments, the methods provided herein output a computed label classification that, if above the predetermined threshold, indicates that the test cell contains the determined dopaminergic progenitor cell.

[0128] Various methods and criteria can be used to set the predetermined threshold for the NeuroScore. For example, the predetermined threshold can be set to optimize specificity and / or sensitivity in predicting whether a test cell has a determined dopaminergic progenitor metagene expression level. In some embodiments, the predetermined threshold is set to identify test cells having a determined dopaminergic progenitor metagene expression level with greater than about 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, or 99% sensitivity. In some embodiments, the predetermined threshold is set to identify test cells having a determined dopaminergic progenitor metagene expression level with greater than about 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, or 99% specificity. In some embodiments, the predetermined threshold is set such that test cells having the determined dopaminergic progenitor meta-gene expression level are identified with greater than 98% sensitivity and 100% specificity.

[0129] In some embodiments, the predetermined threshold is set based on a NeuroScore 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. 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 18-25 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 amelioration of symptoms of Parkinson's disease, have a NeuroScore above the predetermined threshold.

[0130] In some embodiments, if the NeuroScore of the test cell indicates a probability of greater than about 0.4 that the test cell has the determined dopaminergic progenitor cell metagene expression level, the computed label classification indicates that the test cell is or contains the determined dopaminergic progenitor cell. In some embodiments, if the NeuroScore of the test cell indicates a probability of greater than about 0.45 that the test cell has the determined dopaminergic progenitor cell metagene expression level, the computed label classification indicates that the test cell is or contains the determined dopaminergic progenitor cell. In some embodiments, if the NeuroScore of the test cell indicates a probability of greater than about 0.5 that the test cell has the determined dopaminergic progenitor cell metagene expression level, the computed label classification indicates that the test cell is or contains the determined dopaminergic progenitor cell. In some embodiments, if the NeuroScore of the test cell indicates a probability of greater than about 0.55 that the test cell has the determined dopaminergic progenitor cell metagene expression level, the computed label classification indicates that the test cell is or contains the determined dopaminergic progenitor cell. In some embodiments, if the NeuroScore of the test cell indicates a probability of greater than about 0.6 that the test cell has the determined dopaminergic progenitor cell metagene expression level, the computed label classification indicates that the test cell is or contains the determined dopaminergic progenitor cell. In some embodiments, if the NeuroScore of the test cell indicates a probability of greater than about 0.65 that the test cell has the determined dopaminergic progenitor cell metagene expression level, the computed label classification indicates that the test cell is or contains the determined dopaminergic progenitor cell.In some embodiments, if the NeuroScore of the test cell indicates a probability of greater than about 0.7 that the test cell has the determined dopaminergic progenitor cell metagene expression level, the computed label classification indicates that the test cell is or contains the determined dopaminergic progenitor cell. In some embodiments, if the NeuroScore of the test cell indicates a probability of greater than about 0.75 that the test cell has the determined dopaminergic progenitor cell metagene expression level, the computed label classification indicates that the test cell is or contains the determined dopaminergic progenitor cell. In some embodiments, if the NeuroScore of the test cell indicates a probability of greater than about 0.8 that the test cell has the determined dopaminergic progenitor cell metagene expression level, the computed label classification indicates that the test cell is or contains the determined dopaminergic progenitor cell. In some embodiments, if the NeuroScore of the test cell indicates a probability of greater than about 0.85 that the test cell has the determined dopaminergic progenitor cell metagene expression level, the computed label classification indicates that the test cell is or contains the determined dopaminergic progenitor cell. In some embodiments, if the NeuroScore of the test cell indicates a probability of greater than about 0.9 that the test cell has the determined dopaminergic progenitor cell metagene expression level, the computed label classification indicates that the test cell is or contains the determined dopaminergic progenitor cell. In some embodiments, if the NeuroScore of the test cell indicates a probability of greater than about 0.95 that the test cell has the determined dopaminergic progenitor cell metagene expression level, the computed 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 cell exceeds an approximate probability threshold, the computed label classification indicates that the test cell is or contains a determined dopaminergic progenitor cell. In some embodiments, the probability threshold is about 0.4 to 1. In some embodiments, the probability threshold is about 0.4 to 0.9. In some embodiments, the probability threshold is about 0.4 to 0.8. In some embodiments, the probability threshold is about 0.4 to 0.7. In some embodiments, the probability threshold is about 0.4 to 0.6. In some embodiments, the probability threshold is about 0.5 to 0.8. In some embodiments, the probability threshold is about 0.5 to 0.7. In some embodiments, the probability threshold is about 0.5 to 0.6.

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

[0133] E. Deviation Score (e.g., Novelty Score) In some aspects, the methods provided herein include calculating a deviation score. The deviation score, also referred to herein as a Novelty Score, indicates the degree to which gene expression levels contained in a test dataset (e.g., any of those described in Section IB) differ from expected gene expression levels. The expected gene expression values ​​can be determined using a variety of methods. In some embodiments, the expected gene expression levels are based on gene expression levels contained 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 levels are based on the expression levels of one or more meta-genes determined for the test dataset, e.g., determined using any of the exemplary methods described in Section I-C herein. In some embodiments, the expected gene expression levels are calculated based on the gene expression levels in the test dataset and the meta-genes and their expression levels determined for the test dataset. Any method can be used that can be used to calculate a predicted value (e.g., expected gene expression level) based on the relationship between one or more predictors (e.g., meta-gene expression levels in the test dataset) and a dependent value (e.g., gene expression levels in the test dataset). In some embodiments, regression analysis is used to calculate the expected gene expression levels for the test dataset.

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

[0136] In some embodiments, the deviation score is based on a preselected set of marker genes. In some embodiments, the marker genes are selected based on their diagnostic ability, for example, when their expression levels can be used to distinguish cell types (e.g., between determined dopaminergic progenitor cells and other cell types). In some embodiments, the marker genes include a radial glial cell marker, an early neuronal development gene, a pluripotency-specific marker, an intermediate-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 required for normal neuronal development, a gene controlling dopaminergic neuron development, a gene regulating neural progenitor cell identity and fate, a dopaminergic neuron marker, an astrocyte marker, a forebrain marker, a hindbrain marker, a subthalamic nucleus marker, a radial glia marker, a cell cycle marker, or any combination of any of these. In some embodiments, the marker genes include genes not expected to be expressed by determined dopaminergic progenitor cells. In some embodiments, the marker genes include one or more of any of the genes listed in Table E1.

[0137] In some embodiments, preliminary deviation scores are calculated, and the maximum preliminary deviation score is output as the deviation score. In some embodiments, the first deviation score is calculated based on all genes whose expression levels are 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 all genes whose expression levels are included in the test dataset, and the second deviation score is calculated based on a set of preselected marker genes. In some embodiments, the deviation score is the maximum value 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 contained in the test dataset and the gene expression level of one or more reference cells. In some embodiments, the one or more reference cells are at a stage of differentiation that indicates a determined dopaminergic progenitor cell. In some embodiments, the residual is normalized. In some embodiments, the residual is normalized by dividing by the variance of the gene expression levels in a reference database, such as any of those described in Section IA. In some embodiments, the residual is normalized by dividing by the standard deviation of the gene expression levels 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 an average single-gene deviation score. In some embodiments, the deviation score is a 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 than the single-gene deviation scores for other genes. In some embodiments, the deviation score is a single-gene deviation score that corresponds to a percentile of one or more single-gene deviation scores. In some embodiments, the percentile is between about the 50% percentile and about the 100% percentile. In some embodiments, the percentile is between about the 60% percentile and about the 100% percentile. In some embodiments, the percentile is between about the 70% percentile and about the 100% percentile. In some embodiments, the percentile is between about the 80% percentile and about the 100% percentile. In some embodiments, the percentile is about the 90th percentile to about the 100th percentile, hi some embodiments, the percentile is about the 95th percentile.

[0140] In some embodiments, the Novelty Score output of the test cell is compared to a predetermined threshold. In some embodiments, the methods provided herein output a computed label classification, where the computed label classification does not exceed the predetermined threshold, indicating that the test cell is or comprises a determined dopaminergic progenitor cell.

[0141] A variety of methods and criteria can be used to set the predetermined threshold for the Novelty Score. 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 comprises gene expression levels of reference cells differentiated according to any of the methods described in Section II.

[0142] In some embodiments, the computed label classification indicates that the test cell is or contains a determined dopaminergic progenitor cell if the Novelty Score of the test cell indicates that at least about 50% of the gene expression levels in the test dataset are within 5× standard deviations from the expected gene expression levels. In some embodiments, the computed label classification indicates that the test cell is or contains a determined dopaminergic progenitor cell if the Novelty Score of the test cell indicates that at least about 60% of the gene expression levels in the test dataset are within 5× standard deviations from the expected gene expression levels. In some embodiments, the computed label classification indicates that the test cell is or contains a determined dopaminergic progenitor cell if the Novelty Score of the test cell indicates that at least about 70% of the gene expression levels in the test dataset are within 5× standard deviations from the expected gene expression levels. In some embodiments, the computed label classification indicates that the test cell is or contains a determined dopaminergic progenitor cell if the Novelty Score of the test cell indicates that at least about 80% of the gene expression levels in the test dataset are within 5× standard deviations from the expected gene expression levels. In some embodiments, the computed label classification indicates that the test cell is or contains a determined dopaminergic progenitor cell if the Novelty Score of the test cell indicates that at least about 90% of the gene expression levels in the test dataset are within 5× standard deviations from the expected gene expression levels. In some embodiments, the computed label classification indicates that the test cell is or contains a determined dopaminergic progenitor cell if the Novelty Score of the test cell indicates that at least about 95% of the gene expression levels in the test dataset are within 5× standard deviations from the expected gene expression levels.

[0143] In some embodiments, the computed label classification indicates that the test cell is or contains a determined dopaminergic progenitor cell if the Novelty Score of the test cell indicates that at least about 95% of the gene expression levels in the test dataset are within 10× standard deviations from the expected gene expression levels. In some embodiments, the computed label classification indicates that the test cell is or contains a determined dopaminergic progenitor cell if the Novelty Score of the test cell indicates that at least about 95% of the gene expression levels in the test dataset are within 9× standard deviations from the expected gene expression levels. In some embodiments, the computed label classification indicates that the test cell is or contains a determined dopaminergic progenitor cell if the Novelty Score of the test cell indicates that at least about 95% of the gene expression levels in the test dataset are within 8× standard deviations from the expected gene expression levels. In some embodiments, the computed label classification indicates that the test cell is or contains a determined dopaminergic progenitor cell if the Novelty Score of the test cell indicates that at least about 95% of the gene expression levels in the test dataset are within 7× standard deviations from the expected gene expression levels. In some embodiments, the computed label classification indicates that the test cell is or contains a determined dopaminergic progenitor cell if the Novelty Score of the test cell indicates that at least about 95% of the gene expression levels in the test dataset are within 6× standard deviations from the expected gene expression levels. In some embodiments, the computed label classification indicates that the test cell is or contains a determined dopaminergic progenitor cell if the Novelty Score of the test cell indicates that at least about 95% of the gene expression levels in the test dataset are within 5× standard deviations from the expected gene expression levels.

[0144] In some embodiments, the computed label classification indicates that the test cell is or contains a determined dopaminergic progenitor cell if the Novelty Score of the test cell indicates that at least about 50% of the marker gene expression levels in the test dataset are within 5× standard deviations from the expected gene expression levels. In some embodiments, the computed label classification indicates that the test cell is or contains a determined dopaminergic progenitor cell if the Novelty Score of the test cell indicates that at least about 60% of the marker gene expression levels in the test dataset are within 5× standard deviations from the expected gene expression levels. In some embodiments, the computed label classification indicates that the test cell is or contains a determined dopaminergic progenitor cell if the Novelty Score of the test cell indicates that at least about 70% of the marker gene expression levels in the test dataset are within 5× standard deviations from the expected gene expression levels. In some embodiments, the computed label classification indicates that the test cell is or contains a determined dopaminergic progenitor cell if the Novelty Score of the test cell indicates that at least about 80% of the marker gene expression levels in the test dataset are within 5× standard deviations from the expected gene expression levels. In some embodiments, the computed label classification indicates that the test cell is or contains a determined dopaminergic progenitor cell if the Novelty Score of the test cell indicates that at least about 90% of the marker gene expression levels in the test dataset are within 5× standard deviations from the expected gene expression levels. In some embodiments, the computed label classification indicates that the test cell is or contains a determined dopaminergic progenitor cell if the Novelty Score of the test cell indicates that at least about 95% of the gene expression levels in the test dataset are within 5× standard deviations from the expected gene expression levels.

[0145] In some embodiments, the computed label classification indicates that the test cell is or contains a determined dopaminergic progenitor cell if the Novelty Score of the test cell indicates that at least about 95% of the marker gene expression levels in the test dataset are within 10× standard deviations from the expected gene expression levels. In some embodiments, the computed label classification indicates that the test cell is or contains a determined dopaminergic progenitor cell if the Novelty Score of the test cell indicates that at least about 95% of the marker gene expression levels in the test dataset are within 9× standard deviations from the expected gene expression levels. In some embodiments, the computed label classification indicates that the test cell is or contains a determined dopaminergic progenitor cell if the Novelty Score of the test cell indicates that at least about 95% of the marker gene expression levels in the test dataset are within 8× standard deviations from the expected gene expression levels. In some embodiments, the computed label classification indicates that the test cell is or contains a determined dopaminergic progenitor cell if the Novelty Score of the test cell indicates that at least about 95% of the marker gene expression levels in the test dataset are within 7× standard deviations from the expected gene expression levels. In some embodiments, the computed label classification indicates that the test cell is or contains a determined dopaminergic progenitor cell if the Novelty Score of the test cell indicates that at least about 95% of the marker gene expression levels in the test dataset are within 6× standard deviations from the expected gene expression levels. In some embodiments, the computed label classification indicates that the test cell is or contains a determined dopaminergic progenitor cell if the Novelty Score of the test cell indicates that at least about 95% of the marker gene expression levels in the test dataset are within 5× standard deviations from the expected gene expression levels.

[0146] In some embodiments, the computerized label classification indicates that the test cell is or contains determined dopaminergic progenitor cells if the test cell has a Novelty Score of less than about 10. In some embodiments, the computerized label classification indicates that the test cell is or contains determined dopaminergic progenitor cells if the test cell has a Novelty Score of less than about 9. In some embodiments, the computerized label classification indicates that the test cell is or contains determined dopaminergic progenitor cells if the test cell has a Novelty Score of less than about 8. In some embodiments, the computerized label classification indicates that the test cell is or contains determined dopaminergic progenitor cells if the test cell has a Novelty Score of less than about 7. In some embodiments, the computerized label classification indicates that the test cell is or contains determined dopaminergic progenitor cells if the test cell has a Novelty Score of less than about 6. In some embodiments, the computed label classification indicates that the test cell is or contains a determined dopaminergic progenitor cell if the test cell has a Novelty Score of less than about 5.

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

[0148] An exemplary process according to the provided methods is shown in FIG. 9. In some embodiments, a reference database containing gene expression levels from a publicly available database is used. In some embodiments, a reference database containing gene expression levels obtained from single-cell RNA-seq is used. In some embodiments, a reference database containing gene expression levels obtained from bulk RNA-seq is used. In some embodiments, the 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 a test cell having the determined dopaminergic progenitor metagene expression levels (circle 5). In some embodiments, additional data, for example, bulk RNA-seq 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 methods provided herein (circle 7) to classify test cells. In some embodiments, a Novelty Score is calculated based on a reference database. In some embodiments, the Novelty Score based on the reference database is used to identify a NeuroScore and a Novelty Score threshold (circle 8).

[0150] In some embodiments, the test cells are used to generate a test dataset comprising 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, the 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, a NeuroScore (circle 10) and a Novelty Score (circle 11) are output 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, e.g., if both thresholds are met. In some embodiments, the test cells are discarded, e.g., if the thresholds are not met.

[0151] In some embodiments, the reference cells and reference database are generated according to, for example, any of the methods described in Sections IA and II. In some embodiments, the reference cells are generated using iPSCs generated from a subject with Parkinson's disease. In some embodiments, the reference database comprises gene expression levels of reference cells that can be differentiated from iPSCs over various periods of time in culture, e.g., for 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 the iPSCs into neurons. In some embodiments, the reference database comprises bulk RNA-seq data. In some embodiments, the reference database comprises single-cell RNA-seq data. In some embodiments, the reference database comprises reference cell labels that indicate whether the reference cells exhibit determined characteristics of dopaminergic progenitor cells, as determined by functional assays, e.g., using animal models of neurodegenerative disease. In some embodiments, the reference database comprises reference cell labels of cell populations differentiated from iPSCs into neurons for 18 days, about 18 days, or at least 18 days. Methods of differentiation may include any of those described in Section II.

[0152] In some embodiments, a reference database comprising single-cell RNA-seq data is used to determine metagenes, e.g., using any 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 comprising bulk RNA-seq data, e.g., using any of the methods described in Section I.C2.

[0153] In some embodiments, the metagene expression levels are used to train a machine learning model, such as any 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, the test cells and test dataset are generated using, for example, any of the methods described in Sections I.B. and II. In some embodiments, the test cells are generated using iPSCs generated from patients with Parkinson's disease. In some embodiments, the test dataset is used to determine meta-gene expression levels in the test cells using, for example, any of the methods described in Section I.C2. In some embodiments, the test cells are comprised in an in vitro population of cells. In some embodiments, the test cells are comprised in an in vitro population of neural progenitor cells.

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

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

[0157] In some embodiments, the methods provided herein include outputting a computed label classification including an indication of whether the test cell contains a determined dopaminergic progenitor cell. In some embodiments, the computed label classification is based on the NeuroScore and a comparison thereof to its corresponding predetermined threshold. In some embodiments, the computed label classification is based on the Novelty Score and a comparison thereof to its corresponding predetermined threshold. In some embodiments, the computed label classification is based on both the NeuroScore and a comparison thereof to its corresponding predetermined threshold and the Novelty Score and a comparison thereof to its corresponding predetermined threshold.

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

[0159] In some embodiments, the computerized 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 comprising the test cells identified as determined dopaminergic progenitor cells is selected for use. In some embodiments, an in vitro population of cells comprising the test cells identified as determined dopaminergic progenitor cells is selected for transplantation, for example, according to any of the methods described in Section V.

[0160] In some embodiments, the computed 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 about 95% of the gene expression levels in the test dataset were within 5× standard deviations from the expected gene expression level. In some embodiments, an in vitro population of cells comprising test cells that are not identified as determined dopaminergic progenitor cells is not further differentiated. In some embodiments, an in vitro population of cells comprising test cells that are not identified as determined dopaminergic progenitor cells is discarded. In some embodiments, the methods provided herein are 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 with 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, a computed label classification is output for the additional set of test cells.

[0161] In some embodiments, the computed 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 about 0.5 that the test cells have the determined meta-gene expression levels of dopaminergic progenitor cells. In some embodiments, the Novelty Score of the test cells indicates that more than about 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 including the 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 computed label classification is output for the additional set of test cells.

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

[0163] In some embodiments, the methods provided herein are repeated until a computerized label classification is provided that indicates that a test cell generated from a subject is or contains a determined dopaminergic progenitor cell.

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

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

[0166] II. Methods for Differentiating Cells In some aspects, the methods provided herein involve the use of reference and / or test cells that are the product of a method for differentiating cells. In some embodiments, the reference and / or test cells described in Sections IA and IB are the product 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 derived from non-pluripotent cells. iPSCs can be generated by a process known as reprogramming, in which non-pluripotent cells are effectively "dedifferentiated" to an embryonic stem cell-like state by engineering 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 derived from non-pluripotent cells of a subject. In some embodiments, the non-pluripotent cells are fibroblasts. In some embodiments, the subject is a human. In some embodiments, the subject is a human with Parkinson's disease. In some embodiments, the pluripotent stem cells are iPSCs.

[0168] Standard, art-accepted tests, such as the ability to form teratomas in 8-12 week-old SCID mice, can be used to establish the pluripotency of a cell population. However, distinguishing 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 can express at least some, or any, or all, markers from the 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, the pluripotent stem cell characteristic is a cell morphology 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 characteristics of pluripotent stem cells, including the expression of stem cell markers, the formation of tumors containing cells from all three germ layers, and the ability to contribute to many different tissues when injected into mouse embryos at very early stages 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 neural cells differentiated from pluripotent stem cells. In some embodiments, the cells are differentiated using any available or known method for inducing cell differentiation, using a method for differentiating cells, e.g., iPSCs, into any neural cell type. As will be understood, the specific differentiation protocol and timing of culture may result in different states of differentiated neural cells. In some embodiments, differentiation is carried out by culturing pluripotent stem cells, e.g., iPSCs, under conditions that produce neural progenitor cells that are or include committed neural cells. In some embodiments, iPSCs are differentiated under conditions that result in floor plate midbrain progenitor cells, committed dopaminergic progenitor cells, and / or dopamine (DA) neurons. In some embodiments, iPSCs are cultured under conditions for differentiation into committed dopaminergic progenitor cells. In some embodiments, iPSCs are cultured under conditions that differentiate into dopaminergic neurons. Any available and known method can be used to induce differentiation of cells, e.g., pluripotent stem cells, into floor plate midbrain progenitor cells, committed dopaminergic progenitor cells, and / or dopamine (DA) neurons. Exemplary methods for differentiation into neural cells can be found, for example, in International Publication Nos. 2013104752, 2010096496, 2013067362, 2014176606, 2016196661, 2015143342, and U.S. Patent Application Publication No. 20160348070, the contents of which are incorporated herein by reference in their entireties. In some embodiments, iPSCs can be differentiated in culture as part of their differentiation into neural cells. In some embodiments, cells are cultured or incubated in the presence of one or more factors capable of inducing or promoting differentiation of iPSCs into neural cells.In some embodiments, iPSCs are cultured in the presence of one or more of: (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, iPSCs are cultured in the presence of: (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 inhibitor of TGF-β / activin-Nodal signaling is SB431542 (e.g., about 1 μM to about 20 μM, 10 μM, etc.). In some embodiments, the 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 palmorphamin (e.g., about 0.1 μM to about 10 μM, 2 μM, etc.). In some embodiments, the at least one activator of SHH signaling includes an SHH protein (e.g., about 10 ng / mL to about 500 ng / mL, 100 ng / mL, etc.) and palmorphamin (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 beginning of culture or incubation (day 0). In some embodiments, the presence of one or more factors or agents, each independently, can be maintained in the culture for the entire culture period or for a portion of the culture period. In some embodiments, one or more factors or agents, each independently, are present in the culture for a period that allows differentiation of the iPSCs into midbrain floor plate precursors, or until the iPSCs exhibit characteristics of midbrain floor plate precursors as determined by a classification label according to the methods provided. In some embodiments, one or more factors or agents, each independently, are 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 culture. For example, in an exemplary protocol, culturing under conditions that differentiate iPSCs into neural cells includes starting a first incubation on about day 0, the first incubation including culturing the pluripotent stem cells and exposing the cells to (i) an inhibitor of TGF-β / Activin-Nodal signaling daily from day 0 to day 10, (ii) at least one activator of Sonic hedgehog (SHH) signaling daily from day 1 to day 6, (iii) an inhibitor of bone morphogenetic protein (BMP) signaling daily from day 0 to day 10, and (iv) an inhibitor of glycogen synthase kinase 3β (GSK3β) signaling daily from day 0 to day 12.

[0172] In some embodiments, a second culture or incubation can be performed on cells differentiated in the first culture, where the second culture or incubation is performed in the presence of one or more additional agents or factors under conditions that further neuronally differentiate the cells. In some embodiments, the second culture or initiation can be initiated when or around the time that the cells in the first culture differentiate into midbrain floor plate precursors, or until such cells exhibit characteristics of midbrain floor plate precursors as determined by a classification label according to the methods provided. In some embodiments, the one or more additional agents or factors can include any one or more of the one or more factors present in the first culture. In some embodiments, the one or more additional agents or factors may include one or more of: (i) brain-derived neurotrophic factor (BDNF), (ii) ascorbic acid, (iii) glial cell line-derived neurotrophic factor (GDNF), (iv) cyclic AMP (cAMP), e.g., dibutyryl cyclic AMP (dbcAMP), (v) transforming growth factor beta 3 (TGFβ3) (collectively "BAGCT"), and (vi) an inhibitor of Notch. In some embodiments, the additional agents or factors include: (i) brain-derived neurotrophic factor (BDNF), (ii) ascorbic acid, (iii) glial cell line-derived neurotrophic factor (GDNF), (iv) dibutyryl cyclic AMP (dbcAMP), (v) transforming growth factor beta 3 (TGFβ3) (collectively "BAGCT"), and (vi) an inhibitor of Notch. In some embodiments, cells are exposed to BDNF at a concentration of about 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 about 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 about 0.05 mM to 5 mM, e.g., about 0.5 mM.In some embodiments, the cells are exposed to transforming growth factor β3 (TGFβ3) at a concentration of about 0.1 ng / mL to 10 ng / mL, for example, 1 ng / mL.

[0173] In some embodiments, the second culture or incubation can be carried out for a period during which the cells differentiate into determined dopaminergic progenitor cells or until such cells exhibit characteristics of dopaminergic neurons as determined by a classification label according to the methods provided. In some embodiments, the second culture or incubation can be carried out for a period during which the cells differentiate into dopaminergic neurons or until such cells exhibit characteristics of dopaminergic neurons as determined by a classification label according to the methods provided. In some embodiments, the second culture or incubation is carried out until about 30 days after the initiation of the first culture or incubation. In some embodiments, the second culture or incubation is carried out from about 11 to 25 days after the initiation of the first culture or incubation, e.g., from day 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, or 25. In some embodiments, the second culturing or incubation is carried out until about day 18 after initiation of the first culture. In some embodiments, the second culturing is carried out until about day 25 after initiation of the first culture.

[0174] In some embodiments, the cells of the culture are exposed to one or more additional factors or agents during the culture period or for a period of time. In some embodiments, the presence of one or more additional factors or agents can each independently be maintained in the culture for the entire culture period or for a portion of the culture period. In some embodiments, the one or more additional factors or agents are each independently present in the culture for a period during which the cells differentiate into determined dopaminergic progenitor cells or until such cells exhibit characteristics of dopaminergic neurons as determined by a classification label according to the provided methods. In some embodiments, the one or more additional factors or agents are each independently present in the culture for a period during which the cells differentiate into dopaminergic neurons or until such cells exhibit characteristics of dopaminergic neurons as determined by a classification label according to the provided methods. In some embodiments, the second culture or incubation is performed up to about 30 days after the initiation of the first culture or incubation. In some embodiments, one or more additional agents or factors are each independently present in the culture from the initiation of the second culture until about day 11 to about day 25 after the initiation of the first culture or incubation, e.g., 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 each independently present in the culture from the initiation of the second culture until about day 18 after the initiation of the first culture. In some embodiments, one or more additional agents or factors are each independently present in the culture from the initiation of the second culture until about day 25 after the initiation of the first culture.For example, in an exemplary protocol, culturing under conditions that differentiate iPSCs into neural cells further comprises a second incubation, in which the cells from the first incubation are further cultured by exposing them to (i) brain-derived neurotrophic factor (BDNF), (ii) ascorbic acid, (iii) glial cell line-derived neurotrophic factor (GDNF), (iv) dibutyryl cyclic AMP (dbcAMP), (v) transforming growth factor β3 (TGFβ3) (collectively "BAGCT"), and (vi) an inhibitor of Notch, beginning on day 11. In some embodiments, the cells are exposed to BAGCT until the neurally differentiated cells are harvested, e.g., until day 18 or until day 25. In some embodiments, the second incubation may further comprise culturing the cells by exposing them to an inhibitor of GSK3β signaling daily from day 11 to day 12.

[0175] In some embodiments, incubation can include exposing the cells to an inhibitor of Rho-associated protein kinase (ROCK) signaling one or more times during the culture, e.g., at about day 0, day 7, day 16, and / or day 20 from the initiation 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, culturing iPSCs under conditions for differentiation into neural cells can span a period of 10 to 30 days from the initiation of culture until harvesting of the differentiated cells. It is understood that the specific timing can be selected based on the desired differentiation state of the cells, e.g., as determined empirically by functional or other phenotypic assays, or based on the taxonomic label of the differentiated cells determined according to the provided methods. In some embodiments, the reference cells are differentiated by culturing for a specific or defined period of time. In some embodiments, the reference cells are differentiated by culturing for a period determined such that the cells exhibit a desired functional or phenotypic attribute or characteristic, e.g., as described in Section IA. In some embodiments, the test cells are differentiated by culturing for a period of time. In some embodiments, the test cells are differentiated by culturing for a period of time, at which time it is determined that the test cells exhibit a desired taxonomic label according to the provided methods. In some embodiments, the provided methods can be used to assess whether the test cells have been cultured under conditions for differentiation into a desired neural cell, e.g., a determined dopaminergic progenitor cell, according to its taxonomic label, as determined according to any of the provided methods.

[0177] In embodiments, iPSCs are cultured for at least 10 days to differentiate into neurons. In embodiments, iPSCs are cultured for at least 11 days to differentiate into neurons. In embodiments, iPSCs are cultured for at least 12 days to differentiate into neurons. In embodiments, iPSCs are cultured for at least 13 days to differentiate into neurons. In embodiments, iPSCs are cultured for at least 14 days to differentiate into neurons. In embodiments, iPSCs are cultured for at least 15 days to differentiate into neurons. In embodiments, iPSCs are cultured for at least 16 days to differentiate into neurons. In embodiments, iPSCs are cultured for at least 17 days to differentiate into neurons. In embodiments, iPSCs are cultured for at least 18 days to differentiate into neurons. In embodiments, iPSCs are cultured for at least 19 days to differentiate into neurons. In embodiments, iPSCs are cultured for at least 20 days to differentiate into neurons.

[0178] In embodiments, iPSCs are cultured for about 10 days to differentiate into neurons. In embodiments, iPSCs are cultured for about 11 days to differentiate into neurons. In embodiments, iPSCs are cultured for about 12 days to differentiate into neurons. In embodiments, iPSCs are cultured for about 13 days to differentiate into neurons. In embodiments, iPSCs are cultured for about 14 days to differentiate into neurons. In embodiments, iPSCs are cultured for about 15 days to differentiate into neurons. In embodiments, iPSCs are cultured for about 16 days to differentiate into neurons. In embodiments, iPSCs are cultured for about 17 days to differentiate into neurons. In embodiments, iPSCs are cultured for about 18 days to differentiate into neurons. In embodiments, iPSCs are cultured for about 19 days to differentiate into neurons. In embodiments, iPSCs are cultured for about 20 days to differentiate into neurons. In embodiments, iPSCs are cultured for about 21 days for differentiation into neurons. In embodiments, iPSCs are cultured for about 22 days for differentiation into neurons. In embodiments, iPSCs are cultured for about 23 days for differentiation into neurons. In embodiments, iPSCs are cultured for about 24 days for differentiation into neurons. In embodiments, iPSCs are cultured for about 25 days for differentiation into neurons.

[0179] In some embodiments, reference cells, e.g., as described in Section IA, are subjected to a differentiation method as described herein. In some embodiments, test cells, e.g., as described in Section IB, are subjected to a differentiation method as described herein. In some embodiments, both the reference cells and the test cells are subjected to the same differentiation method provided herein.

[0180] III. Exemplary Characteristics of Determined Dopaminergic Neurons In some embodiments, the committed dopaminergic progenitor cells identified by the methods provided herein have some degree of increased and / or decreased gene expression levels relative to pluripotent stem cells. In some embodiments, in vitro populations of neural progenitor cells having some degree of increased and / or decreased gene expression levels relative to pluripotent stem cells include in vitro populations that contain desired committed dopaminergic progenitor cells.

[0181] In embodiments, the desired determined dopaminergic progenitor cell gene expression profile information comprises increased gene expression levels relative to pluripotent stem cells in a first set of genes, the first set of genes comprising at least one increased gene within one or more first gene ontologies of Table 1.

[0182] In embodiments, the desired determined dopaminergic progenitor cell gene expression profile information comprises increased gene expression levels relative to pluripotent stem cells in a first set of genes, the first set of genes comprising at least one increased gene within one or more first gene ontologies selected from the group consisting of the gene ontologies in Table 1.

[0183] In embodiments, the desired determined dopaminergic progenitor cell gene expression profile information includes increased gene expression levels for pluripotent stem cells in a first set of genes, the first set of genes 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, GO:0050874, GO:0050875 ... 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, At least one increased gene within one or more first gene ontologies is 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 embodiments, the desired determined dopaminergic progenitor cell gene expression profile information includes increased gene expression levels for pluripotent stem cells in a first set of genes, the first set of genes 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, GO:0050874, GO:0050875 ... 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 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 embodiments, the first gene set comprises between about 1 and 500 increased genes within one or more of the first gene ontologies. In embodiments, the first gene set comprises between about 2 and 500 increased genes within one or more of the first gene ontologies. In embodiments, the first gene set comprises between about 3 and 500 increased genes within one or more of the first gene ontologies. In embodiments, the first gene set comprises between about 4 and 500 increased genes within one or more of the first gene ontologies. In embodiments, the first gene set comprises between about 5 and 500 increased genes within one or more of the first gene ontologies. In embodiments, the first gene set comprises between about 6 and 500 increased genes within one or more of the first gene ontologies. In embodiments, the first gene set comprises between about 7 and 500 increased genes within one or more of the first gene ontologies. In embodiments, the first gene set comprises between about 8 and 500 increased genes within one or more of the first gene ontologies. In embodiments, the first gene set comprises between about 9 and 500 increased genes within one or more of the first gene ontologies. In embodiments, the first gene set comprises between about 10 and 500 increased genes within one or more of the first gene ontologies.

[0186] In embodiments, the first gene set comprises between about 15 and 500 increased genes within one or more of the first gene ontologies. In embodiments, the first gene set comprises between about 20 and 500 increased genes within one or more of the first gene ontologies. In embodiments, the first gene set comprises between about 25 and 500 increased genes within one or more of the first gene ontologies. In embodiments, the first gene set comprises between about 30 and 500 increased genes within one or more of the first gene ontologies. In embodiments, the first gene set comprises between about 35 and 500 increased genes within one or more of the first gene ontologies. In embodiments, the first gene set comprises between about 40 and 500 increased genes within one or more of the first gene ontologies. In embodiments, the first gene set comprises between about 45 and 500 increased genes within one or more of the first gene ontologies. In embodiments, the first gene set comprises between about 50 and 500 increased genes within one or more of the first gene ontologies. In embodiments, the first gene set comprises between about 55 and 500 increased genes within one or more of the first gene ontologies. In embodiments, the first gene set comprises between about 60 and 500 increased genes within one or more of the first gene ontologies. In embodiments, the first gene set comprises between about 65 and 500 increased genes within one or more of the first gene ontologies. In embodiments, the first gene set comprises between about 70 and 500 increased genes within one or more of the first gene ontologies. In embodiments, the first gene set comprises between about 75 and 500 increased genes within one or more of the first gene ontologies. In embodiments, the first gene set comprises between about 80 and 500 increased genes within one or more of the first gene ontologies. In embodiments, the first gene set comprises between about 85 and 500 increased genes within one or more of the first gene ontologies. In embodiments, the first gene set comprises between about 90 and 500 increased genes within one or more of the first gene ontologies.In embodiments, the first gene set comprises between about 95 and 500 increased genes within one or more of the first gene ontologies. In embodiments, the first gene set comprises between about 100 and 500 increased genes within one or more of the first gene ontologies.

[0187] In embodiments, the first gene set comprises between about 105 and 500 increased genes within one or more of the first gene ontologies. In embodiments, the first gene set comprises between about 115 and 500 increased genes within one or more of the first gene ontologies. In embodiments, the first gene set comprises between about 120 and 500 increased genes within one or more of the first gene ontologies. In embodiments, the first gene set comprises between about 125 and 500 increased genes within one or more of the first gene ontologies. In embodiments, the first gene set comprises between about 130 and 500 increased genes within one or more of the first gene ontologies. In embodiments, the first gene set comprises between about 135 and 500 increased genes within one or more of the first gene ontologies. In embodiments, the first gene set comprises between about 140 and 500 increased genes within one or more of the first gene ontologies. In embodiments, the first gene set comprises between about 145 and 500 increased genes within one or more of the first gene ontologies. In embodiments, the first gene set comprises between about 150 and 500 increased genes within one or more of the first gene ontologies. In embodiments, the first gene set comprises between about 155 and 500 increased genes within one or more of the first gene ontologies. In embodiments, the first gene set comprises between about 160 and 500 increased genes within one or more of the first gene ontologies. In embodiments, the first gene set comprises between about 165 and 500 increased genes within one or more of the first gene ontologies. In embodiments, the first gene set comprises between about 170 and 500 increased genes within one or more of the first gene ontologies. In embodiments, the first gene set comprises between about 175 and 500 increased genes within one or more of the first gene ontologies. In embodiments, the first gene set comprises between about 180 and 500 increased genes within one or more of the first gene ontologies. In embodiments, the first gene set comprises between about 185 and 500 increased genes within one or more of the first gene ontologies.In embodiments, the first gene set comprises between about 190 and 500 increased genes within one or more of the first gene ontologies. In embodiments, the first gene set comprises between about 195 and 500 increased genes within one or more of the first gene ontologies. In embodiments, the first gene set comprises between about 200 and 500 increased genes within one or more of the first gene ontologies.

[0188] In embodiments, the first gene set comprises between about 205 and 500 increased genes within one or more of the first gene ontologies. In embodiments, the first gene set comprises between about 215 and 500 increased genes within one or more of the first gene ontologies. In embodiments, the first gene set comprises between about 220 and 500 increased genes within one or more of the first gene ontologies. In embodiments, the first gene set comprises between about 225 and 500 increased genes within one or more of the first gene ontologies. In embodiments, the first gene set comprises between about 230 and 500 increased genes within one or more of the first gene ontologies. In embodiments, the first gene set comprises between about 235 and 500 increased genes within one or more of the first gene ontologies. In embodiments, the first gene set comprises between about 240 and 500 increased genes within one or more of the first gene ontologies. In embodiments, the first gene set comprises between about 245 and 500 increased genes within one or more of the first gene ontologies. In embodiments, the first gene set comprises between about 250 and 500 increased genes within one or more of the first gene ontologies. In embodiments, the first gene set comprises between about 255 and 500 increased genes within one or more of the first gene ontologies. In embodiments, the first gene set comprises between about 260 and 500 increased genes within one or more of the first gene ontologies. In embodiments, the first gene set comprises between about 265 and 500 increased genes within one or more of the first gene ontologies. In embodiments, the first gene set comprises between about 270 and 500 increased genes within one or more of the first gene ontologies. In embodiments, the first gene set comprises between about 275 and 500 increased genes within one or more of the first gene ontologies. In embodiments, the first gene set comprises between about 280 and 500 increased genes within one or more of the first gene ontologies. In embodiments, the first gene set comprises between about 285 and 500 increased genes within one or more of the first gene ontologies.In embodiments, the first gene set comprises between about 290 and 500 increased genes within one or more of the first gene ontologies. In embodiments, the first gene set comprises between about 295 and 500 increased genes within one or more of the first gene ontologies. In embodiments, the first gene set comprises between about 300 and 500 increased genes within one or more of the first gene ontologies.

[0189] In embodiments, the first gene set comprises between about 305 and 500 increased genes within one or more of the first gene ontologies. In embodiments, the first gene set comprises between about 315 and 500 increased genes within one or more of the first gene ontologies. In embodiments, the first gene set comprises between about 320 and 500 increased genes within one or more of the first gene ontologies. In embodiments, the first gene set comprises between about 325 and 500 increased genes within one or more of the first gene ontologies. In embodiments, the first gene set comprises between about 330 and 500 increased genes within one or more of the first gene ontologies. In embodiments, the first gene set comprises between about 335 and 500 increased genes within one or more of the first gene ontologies. In embodiments, the first gene set comprises between about 340 and 500 increased genes within one or more of the first gene ontologies. In embodiments, the first gene set comprises between about 345 and 500 increased genes within one or more of the first gene ontologies. In embodiments, the first gene set comprises between about 350 and 500 increased genes within one or more of the first gene ontologies. In embodiments, the first gene set comprises between about 355 and 500 increased genes within one or more of the first gene ontologies. In embodiments, the first gene set comprises between about 360 and 500 increased genes within one or more of the first gene ontologies. In embodiments, the first gene set comprises between about 365 and 500 increased genes within one or more of the first gene ontologies. In embodiments, the first gene set comprises between about 370 and 500 increased genes within one or more of the first gene ontologies. In embodiments, the first gene set comprises between about 375 and 500 increased genes within one or more of the first gene ontologies. In embodiments, the first gene set comprises between about 380 and 500 increased genes within one or more of the first gene ontologies. In embodiments, the first gene set comprises between about 385 and 500 increased genes within one or more of the first gene ontologies.In embodiments, the first gene set comprises about 390-500 increased genes within one or more of the first gene ontologies. In embodiments, the first gene set comprises about 395-500 increased genes within one or more of the first gene ontologies. In embodiments, the first gene set comprises about 400-500 increased genes within one or more of the first gene ontologies.

[0190] In embodiments, the first gene set comprises between about 405 and 500 increased genes within one or more of the first gene ontologies. In embodiments, the first gene set comprises between about 415 and 500 increased genes within one or more of the first gene ontologies. In embodiments, the first gene set comprises between about 420 and 500 increased genes within one or more of the first gene ontologies. In embodiments, the first gene set comprises between about 425 and 500 increased genes within one or more of the first gene ontologies. In embodiments, the first gene set comprises between about 430 and 500 increased genes within one or more of the first gene ontologies. In embodiments, the first gene set comprises between about 435 and 500 increased genes within one or more of the first gene ontologies. In embodiments, the first gene set comprises between about 440 and 500 increased genes within one or more of the first gene ontologies. In embodiments, the first gene set comprises between about 445 and 500 increased genes within one or more of the first gene ontologies. In embodiments, the first gene set comprises between about 450 and 500 increased genes within one or more of the first gene ontologies. In embodiments, the first gene set comprises between about 455 and 500 increased genes within one or more of the first gene ontologies. In embodiments, the first gene set comprises between about 460 and 500 increased genes within one or more of the first gene ontologies. In embodiments, the first gene set comprises between about 465 and 500 increased genes within one or more of the first gene ontologies. In embodiments, the first gene set comprises between about 470 and 500 increased genes within one or more of the first gene ontologies. In embodiments, the first gene set comprises between about 475 and 500 increased genes within one or more of the first gene ontologies. In embodiments, the first gene set comprises about 480-500 increased genes within one or more of the first gene ontologies. In embodiments, the first gene set comprises about 485-500 increased genes within one or more of the first gene ontologies.In embodiments, the first gene set comprises about 490-500 increased genes within one or more of the first gene ontologies. In embodiments, the first gene set comprises about 495-500 increased genes within one or more of the first gene ontologies.

[0191] In embodiments, the first set of genes includes 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, 20, 21, 22, 23 , 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 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, 236 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, 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 increased genes.

[0192] The desired determined dopaminergic progenitor cell gene expression profile information may include increased gene expression levels relative to pluripotent stem cells in a first gene set, where the first gene set may include at least one increased gene within one or more first gene ontologies of Table 1. "One or more," as used herein in the context of a first gene ontology, refers to 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, the first gene set includes approximately 1-500 increased genes within 1-300 of the first gene ontologies. In embodiments, the first gene set includes approximately 1-500 increased genes within 10-300 of the first gene ontologies. In an embodiment, the first gene set comprises approximately 1 to 500 increased genes among 20 to 300 of the first gene ontology. In an embodiment, the first gene set comprises approximately 1 to 500 increased genes among 30 to 300 of the first gene ontology. In an embodiment, the first gene set comprises approximately 1 to 500 increased genes among 40 to 300 of the first gene ontology. In an embodiment, the first gene set comprises approximately 1 to 500 increased genes among 50 to 300 of the first gene ontology. In an embodiment, the first gene set comprises approximately 1 to 500 increased genes among 60 to 300 of the first gene ontology. In an embodiment, the first gene set comprises approximately 1 to 500 increased genes among 70 to 300 of the first gene ontology. In an embodiment, the first gene set comprises about 1 to 500 increased genes among 80 to 300 of the first gene ontology. In an embodiment, the first gene set comprises about 1 to 500 increased genes among 90 to 300 of the first gene ontology. In an embodiment, the first gene set comprises about 1 to 500 increased genes among 100 to 300 of the first gene ontology. In an embodiment, the first gene set comprises about 1 to 500 increased genes among 110 to 300 of the first gene ontology.In an embodiment, the first gene set comprises approximately 1 to 500 increased genes among 120 to 300 of the first gene ontology. In an embodiment, the first gene set comprises approximately 1 to 500 increased genes among 130 to 300 of the first gene ontology. In an embodiment, the first gene set comprises approximately 1 to 500 increased genes among 140 to 300 of the first gene ontology. In an embodiment, the first gene set comprises approximately 1 to 500 increased genes among 150 to 300 of the first gene ontology. In an embodiment, the first gene set comprises approximately 1 to 500 increased genes among 160 to 300 of the first gene ontology. In an embodiment, the first gene set comprises approximately 1 to 500 increased genes among 170 to 300 of the first gene ontology. In one embodiment, the first gene set comprises approximately 1 to 500 increased genes among 180 to 300 of the first gene ontology. In one embodiment, the first gene set comprises approximately 1 to 500 increased genes among 190 to 300 of the first gene ontology. In one embodiment, the first gene set comprises approximately 1 to 500 increased genes among 200 to 300 of the first gene ontology. In one embodiment, the first gene set comprises approximately 1 to 500 increased genes among 210 to 300 of the first gene ontology. In one embodiment, the first gene set comprises approximately 1 to 500 increased genes among 220 to 300 of the first gene ontology. In one embodiment, the first gene set comprises approximately 1 to 500 increased genes among 230 to 300 of the first gene ontology. In an embodiment, the first gene set comprises approximately 1 to 500 increased genes among 240 to 300 of the first gene ontology. In an embodiment, the first gene set comprises approximately 1 to 500 increased genes among 250 to 300 of the first gene ontology. In an embodiment, the first gene set comprises approximately 1 to 500 increased genes among 260 to 300 of the first gene ontology. In an embodiment, the first gene set comprises approximately 1 to 500 increased genes among 270 to 300 of the first gene ontology.In one embodiment, the first gene set comprises approximately 1 to 500 increased genes within 280 to 300 of the first gene ontology. In one embodiment, the first gene set comprises approximately 1 to 500 increased genes within 290 to 300 of the first gene ontology.

[0193] In embodiments, the first gene set comprises about 1 to 500 increased genes among 1 to 290 of the first gene ontology. In embodiments, the first gene set comprises about 1 to 500 increased genes among 1 to 280 of the first gene ontology. In embodiments, the first gene set comprises about 1 to 500 increased genes among 1 to 270 of the first gene ontology. In embodiments, the first gene set comprises about 1 to 500 increased genes among 1 to 260 of the first gene ontology. In embodiments, the first gene set comprises about 1 to 500 increased genes among 1 to 250 of the first gene ontology. In embodiments, the first gene set comprises about 1 to 500 increased genes among 1 to 240 of the first gene ontology. In embodiments, the first gene set comprises about 1 to 500 increased genes among 1 to 230 of the first gene ontology. In embodiments, the first gene set comprises about 1 to 500 increased genes among 1 to 220 of the first gene ontology. In embodiments, the first gene set comprises about 1 to 500 increased genes among 1 to 210 of the first gene ontology. In embodiments, the first gene set comprises about 1 to 500 increased genes among 1 to 200 of the first gene ontology. In embodiments, the first gene set comprises about 1 to 500 increased genes among 1 to 190 of the first gene ontology. In embodiments, the first gene set comprises about 1 to 500 increased genes among 1 to 180 of the first gene ontology. In embodiments, the first gene set comprises about 1 to 500 increased genes within 1 to 170 of the first gene ontology. In embodiments, the first gene set comprises about 1 to 500 increased genes within 1 to 160 of the first gene ontology. In embodiments, the first gene set comprises about 1 to 500 increased genes within 1 to 150 of the first gene ontology. In embodiments, the first gene set comprises about 1 to 500 increased genes within 1 to 140 of the first gene ontology.In embodiments, the first gene set comprises about 1 to 500 increased genes within 1 to 130 of the first gene ontology. In embodiments, the first gene set comprises about 1 to 500 increased genes within 1 to 120 of the first gene ontology. In embodiments, the first gene set comprises about 1 to 500 increased genes within 1 to 110 of the first gene ontology. In embodiments, the first gene set comprises about 1 to 500 increased genes within 1 to 100 of the first gene ontology. In embodiments, the first gene set comprises about 1 to 500 increased genes within 1 to 90 of the first gene ontology. In embodiments, the first gene set comprises about 1 to 500 increased genes within 1 to 80 of the first gene ontology. In embodiments, the first gene set comprises about 1 to 500 increased genes among 1 to 70 of the first gene ontology. In embodiments, the first gene set comprises about 1 to 500 increased genes among 1 to 60 of the first gene ontology. In embodiments, the first gene set comprises about 1 to 500 increased genes among 1 to 50 of the first gene ontology. In embodiments, the first gene set comprises about 1 to 500 increased genes among 1 to 40 of the first gene ontology. In embodiments, the first gene set comprises about 1 to 500 increased genes among 1 to 30 of the first gene ontology. In embodiments, the first gene set comprises about 1 to 500 increased genes among 1 to 20 of the first gene ontology. In embodiments, the first gene set comprises about 1 to 500 increased genes among 1 to 10 of the first gene ontology. In an embodiment, the first gene set comprises about 1-500 augmented genes within 1-5 of the first gene ontologies.

[0194] In an embodiment, the first set of genes is selected from the group consisting of 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, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 12 , 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, 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, 2 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.

[0195] In an embodiment, the first set of genes is selected from the group consisting of: 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, 1 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, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 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 in the first Gene Ontology. 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 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, 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, 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, 53 490, 491, 492, 493, 494, 495, 496, 497, 498, 499 or 500 increased genes.

[0196] In embodiments, the first gene ontology is any one of the gene ontologies listed in Table 1. In embodiments, the first gene ontology is any one of 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 combination thereof.

[0197] In embodiments, the gene expression profile information of the desired determined dopaminergic progenitor cells comprises increased gene expression levels relative to pluripotent stem cells in a first set of genes, the first set of genes comprising at least one increased gene within one or more first gene ontologies selected from the group consisting of GO0005509, GO0016339, GO0007416, and GO0048731. In embodiments, the gene expression profile information of the desired determined dopaminergic progenitor cells comprises increased gene expression levels relative to pluripotent stem cells in a first set of genes, the first set of genes comprising at least one increased gene within one or more first gene ontologies that are GO0005509, GO0016339, GO0007416, or GO0048731. In embodiments, the gene expression profile information of the desired determined dopaminergic progenitor cells comprises an increase in gene expression levels relative to pluripotent stem cells in a first set of genes, the first set of genes comprising at least one increased gene within one or more first gene ontologies selected from the group consisting of GO0048699, GO0050767, GO0060160, GO0097458, GO0010975, GO0022008, and any combination thereof. In embodiments, the gene expression profile information of the desired determined dopaminergic progenitor cells comprises an increase in gene expression levels relative to pluripotent stem cells in a first set of genes, the first set of genes comprising at least one increased gene within one or more first gene ontologies that are GO0048699, GO0050767, GO0060160, GO0097458, GO0010975, GO0022008, or any combination thereof.

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

[0199] In embodiments, the first set of genes includes at least one (e.g., 1, 2, 3, 4, 5, 6, etc.) increased gene from Table 2. In embodiments, the at least one (e.g., 1, 2, 3, 4, 5, 6, etc.) increased gene is selected from the group consisting of 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, 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 embodiments, the at least one (e.g., 1, 2, 3, 4, 5, 6, etc.) increased gene is selected from the group consisting of 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, Selected from the group consisting of 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 embodiments, the first set of genes includes at least one (e.g., 1, 2, 3, 4, 5, 6, etc.) increased gene from Table 3. In embodiments, the at least one (e.g., 1, 2, 3, 4, 5, 6, etc.) increased gene is selected from the group consisting of 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 , 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 embodiments, at least one (e.g., 1, 2, 3, 4, 5, 6, etc.) increased gene is selected from the group consisting of 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, 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 embodiments, the first set of genes includes at least one (e.g., 1, 2, 3, 4, 5, 6, etc.) increased gene in Table 4. In embodiments, the at least one (e.g., 1, 2, 3, 4, 5, 6, etc.) increased gene is DRD2, RGS4, or PALM.

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

[0205] In embodiments, the first set of genes includes at least one (e.g., 1, 2, 3, 4, 5, 6, etc.) increased gene from Table 5. In embodiments, the at least one (e.g., 1, 2, 3, 4, 5, 6, etc.) increased gene is selected from the group consisting of 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, 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 embodiments, the first set of genes includes at least one (e.g., 1, 2, 3, 4, 5, 6, etc.) increased gene from Table 5. In embodiments, the at least one (e.g., 1, 2, 3, 4, 5, 6, etc.) increased gene is selected from the group consisting of 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 Selected from the group consisting of 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 embodiments, the first set of genes includes at least one (e.g., 1, 2, 3, 4, 5, 6, etc.) increased gene from Table 6. In embodiments, at least one (e.g., 1, 2, 3, 4, 5, 6, etc.) increased gene 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 embodiments, at least one (e.g., 1, 2, 3, 4, 5, 6, etc.) increased gene 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 embodiments, the first set of genes includes at least one (e.g., 1, 2, 3, 4, 5, 6, etc.) increased gene from Table 7. In embodiments, the at least one (e.g., 1, 2, 3, 4, 5, 6, etc.) increased gene is selected from the group consisting of 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 embodiments, the at least one (e.g., 1, 2, 3, 4, 5, 6, etc.) increased gene is selected from the group consisting of 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 , 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 embodiments, the at least one increased 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 embodiments, the at least one increased 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 embodiments, the increased expression level is at least 4-fold higher relative to pluripotent stem cells. In embodiments, the increased expression level is about 4-fold higher relative to pluripotent stem cells. In embodiments, the increased expression level is at least 5-fold higher relative to pluripotent stem cells. In embodiments, the increased expression level is about 5-fold higher relative to pluripotent stem cells. In embodiments, the increased expression level is at least 6-fold higher relative to pluripotent stem cells. In embodiments, the increased expression level is about 6-fold higher relative to pluripotent stem cells. In embodiments, the increased expression level is at least 7-fold higher relative to pluripotent stem cells. In embodiments, the increased expression level is about 7-fold higher relative to pluripotent stem cells. In embodiments, the increased expression level is at least 8-fold higher relative to pluripotent stem cells. In embodiments, the increased expression level is about 8-fold higher relative to pluripotent stem cells. In embodiments, the increased expression level is at least 9-fold higher relative to pluripotent stem cells. In embodiments, the increased expression level is about 9-fold higher relative to pluripotent stem cells. In embodiments, the increased expression level is at least 10-fold higher relative to pluripotent stem cells. In embodiments, the increased expression level is about 10-fold higher relative to pluripotent stem cells.

[0213] In embodiments, the increased expression level is at least 11-fold higher relative to pluripotent stem cells. In embodiments, the increased expression level is about 11-fold higher relative to pluripotent stem cells. In embodiments, the increased expression level is at least 12-fold higher relative to pluripotent stem cells. In embodiments, the increased expression level is about 12-fold higher relative to pluripotent stem cells. In embodiments, the increased expression level is at least 13-fold higher relative to pluripotent stem cells. In embodiments, the increased expression level is about 13-fold higher relative to pluripotent stem cells. In embodiments, the increased expression level is at least 14-fold higher relative to pluripotent stem cells. In embodiments, the increased expression level is about 14-fold higher relative to pluripotent stem cells. In embodiments, the increased expression level is at least 15-fold higher relative to pluripotent stem cells. In embodiments, the increased expression level is about 15-fold higher relative to pluripotent stem cells. In embodiments, the increased expression level is at least 16-fold higher relative to pluripotent stem cells. In embodiments, the increased expression level is about 16-fold higher relative to pluripotent stem cells. In embodiments, the increased expression level is at least 17-fold higher relative to pluripotent stem cells. In embodiments, the increased expression level is about 17-fold higher relative to pluripotent stem cells. In embodiments, the increased expression level is at least 18-fold higher relative to pluripotent stem cells. In embodiments, the increased expression level is about 18-fold higher relative to pluripotent stem cells. In embodiments, the increased expression level is at least 19-fold higher relative to pluripotent stem cells. In embodiments, the increased expression level is about 19-fold higher relative to pluripotent stem cells. In embodiments, the increased expression level is at least 20-fold higher relative to pluripotent stem cells. In embodiments, the increased expression level is about 20-fold higher relative to pluripotent stem cells.

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

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

[0216] In an embodiment, the desired determined dopaminergic progenitor cell gene expression profile information comprises reduced gene expression levels relative to pluripotent stem cells for a second set of genes, the second set of genes comprising at least one reduced gene within one or more second gene ontologies of Table 8.

[0217] In embodiments, the desired determined dopaminergic progenitor cell gene expression profile information comprises reduced gene expression levels relative to pluripotent stem cells for a second set of genes, the second set of genes comprising at least one reduced gene within one or more second gene ontologies selected from the group consisting of the gene ontologies in Table 8.

[0218] In embodiments, the desired determined gene expression profile information for dopaminergic progenitor cells includes decreased gene expression levels relative to pluripotent stem cells for a second set of genes, the second set of genes 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 ... 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、At least one reduced gene in a second gene ontology is included, including one or more of GO:0030334, GO:0042398, or any combination thereof.

[0219] In embodiments, the desired determined gene expression profile information for dopaminergic progenitor cells includes decreased gene expression levels relative to pluripotent stem cells for a second set of genes, the second set of genes 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 ... 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、At least one reduced gene within one or more second gene ontologies selected from the group consisting of GO:0030334, GO:0042398, and any combination thereof.

[0220] In embodiments, the second gene set comprises between about 1 and 1,000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 2 and 1,000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 3 and 1,000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 4 and 1,000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 5 and 1,000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 6 and 1,000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 7 and 1,000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 8 and 1000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 9 and 1000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 10 and 1000 down-regulated genes within one or more of the second gene ontologies.

[0221] In embodiments, the second gene set comprises between about 15 and 1000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 20 and 1000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 25 and 1000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 30 and 1000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 35 and 1000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 40 and 1000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 45 and 1,000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 50 and 1,000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 55 and 1,000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 60 and 1,000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 65 and 1,000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 70 and 1,000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises about 75-1000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises about 80-1000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises about 85-1000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises about 90-1000 down-regulated genes within one or more of the second gene ontologies.In embodiments, the second gene set comprises about 95-1000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises about 100-1000 down-regulated genes within one or more of the second gene ontologies.

[0222] In embodiments, the second gene set comprises between about 105 and 1000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 115 and 1000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 120 and 1000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 125 and 1000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 130 and 1000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 135 and 1000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 140 and 1000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 145 and 1000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 150 and 1000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 155 and 1000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 160 and 1000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 165 and 1000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 170 and 1000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 175 and 1000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 180 and 1000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 185 and 1000 down-regulated genes within one or more of the second gene ontologies.In embodiments, the second gene set comprises between about 190 and 1000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 195 and 1000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 200 and 1000 down-regulated genes within one or more of the second gene ontologies.

[0223] In embodiments, the second gene set comprises between about 205 and 1000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 215 and 1000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 220 and 1000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 225 and 1000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 230 and 1000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 235 and 1000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 240 and 1000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 245 and 1000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 250 and 1000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 255 and 1000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 260 and 1000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 265 and 1000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 270 and 1000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 275 and 1000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 280 and 1000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 285 and 1000 down-regulated genes within one or more of the second gene ontologies.In embodiments, the second gene set comprises between about 290 and 1000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 295 and 1000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 300 and 1000 down-regulated genes within one or more of the second gene ontologies.

[0224] In embodiments, the second gene set comprises between about 305 and 1000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 315 and 1000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 320 and 1000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 325 and 1000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 330 and 1000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 335 and 1000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 340 and 1000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 345 and 1000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 350 and 1000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 355 and 1000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 360 and 1000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 365 and 1000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 370 and 1000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 375 and 1000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 380 and 1000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 385 and 1000 down-regulated genes within one or more of the second gene ontologies.In embodiments, the second gene set comprises between about 390 and 1000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 395 and 1000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 400 and 1000 down-regulated genes within one or more of the second gene ontologies.

[0225] In embodiments, the second gene set comprises between about 405 and 1000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 415 and 1000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 420 and 1000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 425 and 1000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 430 and 1000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 435 and 1000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 440 and 1000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 445 and 1000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 450 and 1000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 455 and 1000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 460 and 1000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 465 and 1000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 470 and 1000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 475 and 1000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 480 and 1000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 485 and 1000 down-regulated genes within one or more of the second gene ontologies.In embodiments, the second gene set comprises between about 490 and 1000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 495 and 1000 down-regulated genes within one or more of the second gene ontologies.

[0226] In embodiments, the second gene set comprises between about 500 and 1,000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 505 and 1,000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 510 and 1,000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 515 and 1,000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 520 and 1,000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 525 and 1,000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 530 and 1000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 535 and 1000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 540 and 1000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 545 and 1000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 550 and 1000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 555 and 1000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 565 and 1000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 570 and 1000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 575 and 1000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 580 and 1000 down-regulated genes within one or more of the second gene ontologies.In embodiments, the second gene set comprises between about 585 and 1000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 590 and 1000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 595 and 1000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 600 and 1000 down-regulated genes within one or more of the second gene ontologies.

[0227] In embodiments, the second gene set comprises between about 605 and 1000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 615 and 1000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 620 and 1000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 625 and 1000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 630 and 1000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 635 and 1000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 640 and 1000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 645 and 1000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 650 and 1000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 655 and 1000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 660 and 1000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 665 and 1000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 670 and 1000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 675 and 1000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 680 and 1000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 685 and 1000 down-regulated genes within one or more of the second gene ontologies.In embodiments, the second gene set comprises about 690-1000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises about 695-1000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises about 700-1000 down-regulated genes within one or more of the second gene ontologies.

[0228] In embodiments, the second gene set comprises between about 705 and 1000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 715 and 1000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 720 and 1000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 725 and 1000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 730 and 1000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 735 and 1000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 740 and 1000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 745 and 1000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 750 and 1000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 755 and 1000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 760 and 1000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 765 and 1000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 770 and 1000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 775 and 1000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 780 and 1000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set comprises between about 785 and 1000 down-regulated genes within one or more of the second gene ontologies.In embodiments, the second gene set comprises about 790-1000 down-regulated genes within one or more of the second gene ontologies. In embodiments, the second gene set com...

Claims

1. 1. A computer-implemented method for classifying an in vitro population of neural progenitor cells, comprising: receiving a test dataset comprising (a) gene expression levels and (b) expression levels of one or more meta-genes in a cell or cells comprised in an 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, the reference cells being neural cells at one or more different stages of differentiation; applying the expression levels of the one or more meta-genes as inputs to a process configured to determine the probability of a cell or a plurality of cells having the determined dopaminergic progenitor meta-gene expression levels; determining a deviation score for the cell or plurality of cells, the deviation score indicating the degree to which the gene expression levels in the test dataset deviate from gene expression levels in one or more reference cells in the reference database, the one or more reference cells being at a differentiation stage indicative of a determined dopaminergic progenitor cell; and outputting a computer-calculated classification label comprising an indication of whether the cell or cells from the in vitro population of neural progenitor cells are or are not determined dopaminergic progenitor cells based on the probability and the deviation score.

2. 1. A computer-implemented method for training a process for determining the probability of a cell or a plurality 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 plurality of cells having a determined metagene expression level of a dopaminergic progenitor cell, the method comprising: training a supervised classification model using: (i) expression levels of one or more metagenes, the one or more metagenes determined based on correlated gene expression levels of reference cells in a reference database, the reference cells being neural cells at one or more different differentiation stages, one of the differentiation stages being indicative of a determined dopaminergic progenitor cell; and (ii) classification labels indicative of each of the one or more different differentiation stages of the reference cells in the reference database.

3. 3. The computer-implemented method of claim 1 or 2, wherein the reference cells are an in vitro population of neural progenitor cells.

4. 4. The computer-implemented method of claim 1, 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 period of time under conditions that allow the iPSCs to differentiate into neural progenitor cells, and optionally the neural progenitor cells are one or more of floor plate midbrain progenitor cells, committed dopaminergic progenitor cells, or dopamine (DA) neurons.

5. 5. The computer-implemented method of claim 4, wherein the culturing is for a period of 2 days to 25 days, or from about 2 days to about 25 days.

6. 6. The computer-implemented method of claim 1, wherein the reference database comprises gene expression levels determined from one or more reference cell populations, each of the one or more reference cell populations formed by culturing one or more iPSCs in vitro for different periods of time under conditions capable of differentiating the one or more iPSCs into neural progenitor cells, and optionally the neural progenitor cells are one or more of floor plate midbrain progenitor cells, committed dopaminergic progenitor cells, or dopamine (DA) neurons.

7. 7. The computer-implemented method of any one of claims 1 to 6, wherein the one or more metagenes and expression levels of the one or more metagenes are determined by using a dimension reduction technique on one or more reference cells of the one or more reference databases.

8. 8. The computer-implemented method of claim 1, wherein the classification labels indicative of each of the one or more different differentiation stages of the reference cell are determined using an in vivo method.

9. The in vivo method comprises: Transplanting the in vitro population of neural progenitor cells, including the reference cell population, into a brain region of an animal model of Parkinson's disease; assessing the occurrence of an outcome associated with a therapeutic effect of the transplant in the animal model, optionally the outcome being selected from innervation or host cell engraftment, reduction of brain lesions in the animal model, or recovery of brain lesions in the animal model; assigning the classification label as being a determined dopaminergic progenitor cell if the transplantation results in the occurrence of the outcome with a therapeutic effect; or and designating the classification label as not being a determined dopaminergic progenitor cell if the transplantation does not result in the occurrence of the outcome with a therapeutic effect.

10. 8. The computer-implemented method of any one of claims 1 and 3 to 7, wherein the classification labels indicative of each of the one or more different differentiation stages of the reference cell are determined using an in vitro method.

11. the in vitro method comprising assessing the level of dopamine production in a reference cell population; 11. The computer-implemented method of claim 10, wherein the classification label is assigned as being a determined dopaminergic progenitor cell if the dopamine production level is increased compared to pluripotent stem cells.

12. 10. The computer-implemented method of any one of claims 1 and 3-9, wherein the expression levels of the one or more metagenes in the test dataset are determined based on (i) the one or more metagenes determined from the one or more reference cells in the reference database, and (ii) the gene expression levels in the test dataset.

13. 13. The computer-implemented method of claim 12, wherein the expression levels of the one or more metagenes in the test dataset are determined using a regression analysis based on (i) the one or more metagenes determined from the one or more reference cells in the reference database, and (ii) the gene expression levels in the test dataset.

14. 14. The computer-implemented method of any one of claims 1 and 3-13, wherein the expression levels of the one or more meta-genes 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 technique to the updated reference database.

15. 15. The computer-implemented method of any one of claims 1 to 14, wherein the number of one or more metagenes is selected based on evaluating one or more metrics determined from performing the dimensionality reduction technique using multiple candidate numbers of metagenes.

16. 16. The computer-implemented method of any one of claims 1 and 3-15, wherein the computer-calculated classification label indicates that the cell or cells from the in vitro population of neural progenitor cells are determined dopaminergic progenitor cells if the probability of the cell or cells having the determined dopaminergic progenitor meta-gene expression level is greater than a probability threshold.

17. the probability threshold is set such that the determined dopaminergic progenitor cells are identified with a sensitivity of greater than or greater than about 75%, 80%, 85%, 90%, or 95%; and / or 17. The computer-implemented method of claim 16, wherein the probability threshold is set such that the determined dopaminergic progenitor cells are identified with a specificity of greater than or greater than about 75%, 80%, 85%, 90%, or 95%.

18. 18. The computer-implemented method of any one of claims 1 and 3-17, wherein the deviation score of the cell or plurality of cells is determined using a single-gene deviation score for each of one or more genes in the test dataset.

19. 20. The computer-implemented method of Claim 18, wherein the single gene deviation score is determined using the difference between the gene expression level of the test dataset and the gene expression level in one or more reference cells in the reference database.

20. 20. The computer-implemented method of claim 18 or 19, 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.

21. The single gene deviation score is: a difference between the gene expression levels of the test dataset and the gene expression levels in the one or more reference cells in the reference database; and 21. The computer-implemented method of claim 20, wherein the z-score 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.

22. 22. The computer-implemented method of Claim 21, wherein the gene expression levels in the one or more reference cells in the reference database are determined using a regression analysis based on (i) the expression levels of the one or more meta-genes in the test dataset, and (ii) the gene expression levels in the test dataset.

23. 23. The computer-implemented method of any one of claims 18 to 22, wherein the deviation score is a summary statistic based on all single-gene deviation scores.

24. 23. The computer-implemented method of any one of claims 18 to 22, wherein the deviation score is a summary statistic based on single-gene deviation scores of one or more marker genes.

25. 25. The computer-implemented method of claim 23 or claim 24, wherein the summary statistic is a sum or a percentile value.

26. the percentile value is between the 50% percentile and the 100% percentile or between about the 50% percentile and about the 100% percentile; and / or 26. The computer-implemented method of claim 25, wherein the percentile value is at or about the 50%, 60%, 70%, 80%, 90%, or 95% percentile.

27. 25. The computer-implemented method of claim 24, wherein the marker genes comprise a radial glia cell marker, an early neuronal development gene, a pluripotency-specific marker, an intermediate to late neuronal 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 required for normal development of neurons, a gene controlling dopaminergic neuron development, a gene regulating neural progenitor cell identity and fate, a dopaminergic neuron marker, an astrocyte marker, a forebrain marker, a hindbrain marker, a subthalamic nucleus marker, a radial glia marker, a cell cycle marker, or any combination thereof.

28. 25. The computer-implemented method of claim 24, wherein the marker genes comprise 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 of any of these.

29. 29. The computer-implemented method of any one of claims 1 and 3-28, wherein the computer-calculated classification label indicates that the cell or 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, or at least about 50%, 50%, 70%, 80%, 90%, or 95% of, the gene expression levels in the test dataset are within 5x standard deviations from the gene expression levels of the one or more reference cells in the reference database.

30. 29. The computer-implemented method of any one of claims 1 and 3-28, wherein the computer-calculated classification label indicates that the cell or cells from the in vitro population of neural progenitor cells are determined dopaminergic progenitor cells if the deviation score indicates that at least or at least about 95% of the gene expression levels in the test dataset are within 10, 9, 8, 7, 6, or 5 x standard deviations of the gene expression levels of the one or more reference cells in the reference database.

31. The computed classification label may include: the probability of the cell or cells having the determined dopaminergic progenitor meta-gene expression level is greater than the probability threshold; and 29. The computer-implemented method of any one of claims 16 to 28, wherein the deviation score indicates that at least, or at least about, 50%, 60%, 70%, 80%, 90%, or 95% of the gene expression levels in the test dataset are within 5x standard deviations of the gene expression levels of the one or more reference cells in the reference database, indicating that the cell or cells from the in vitro population of neural progenitor cells are determined dopaminergic progenitor cells.

32. The computed classification label may include:

25. The computer-implemented method of claim 24, wherein if the difference in expression of the marker gene between the test dataset and reference cells in the reference database is not statistically significant based on a multiple comparisons corrected significance level, it indicates that the cell or cells from the in vitro population of neural progenitor cells are determined dopaminergic progenitor cells.

33. 33. The computer-implemented method of claim 32, wherein the multiple comparisons-corrected significance level is a Bonferroni-corrected significance level or a false discovery rate-corrected significance level.

34. 34. The computer-implemented method of any one of claims 1 to 33, wherein the gene expression levels are obtained from microarray analysis of intracellular RNA, RNA sequencing, or both.

35. 35. The computer-implemented method of claim 34, wherein the RNA sequencing is performed on bulk RNA from the plurality of cells or a plurality of reference cells.

36. 35. The computer-implemented method of claim 34, wherein the RNA sequencing is performed on RNA from a single cell or a single reference cell.

37. 37. The computer-implemented method of any one of claims 1 and 3-36, wherein receiving the test dataset comprises receiving input from an array analysis system.

38. A computer-implemented method according to any preceding claim, wherein the one or more reference databases form part of a storage medium.

39. 39. The computer-implemented method of any one of claims 1 and 3-38, further comprising repeating the receiving, applying, determining, and outputting steps if the computer-calculated classification label indicates that the cell or cells are not determined dopaminergic neuronal cells, optionally wherein the steps are repeated using the same or a different in vitro population of neural progenitor cells.

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