Methods of identifying dopaminergic neurons and progenitor cells

Abstract

Provided herein are, inter alia, methods of assaying neuronal progenitor cell populations derived from iPSCs, thereby providing for a user friendly molecular diagnostic tool for neuronal cell types, including dopaminergic neurons. The methods provided are valuable for the efficient and precise characterization of identity and functionality of iPSC-derived dopaminergic neurons prior to their clinical application such as the treatment of Parkinson's disease or Multiple Sclerosis.

Description

METHODS OF IDENTIFYING DOPAMINERGIC NEURONS AND PROGENITOR CELLSCROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims priority to U.S. provisional applications: 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 its entirety for all purposes.BACKGROUND

[0002] This invention includes the establishment of key statistical models and data processing steps that will enable the evaluation of expression data derived from cultured neurons derived from induced pluripotent stem cells. It compares test data to a reference set of data from, for example, previously characterized neurons, neuronal progenitor cells, pluripotent stem cells with known biological characteristics.BRIEF SUMMARY

[0003] In one aspect, a computer implemented method of identifying a determined dopaminergic precursor cell within an in vitro population of neuronal progenitor cells is provided. The method includes, receiving a test dataset including data including gene expression profile information for an in vitro population of neuronal 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 a desirable determined dopaminergic precursor cell; and outputting a computed label classification including an indication of whether the in vitro population of neuronal progenitor cells includes a determined dopaminergic precursor cell.

[0004] Provided herein are computer implemented methods of classifying an in vitro population of neuronal progenitor cells, the methods comprising receiving a test dataset comprising gene expression levels and expression levels of one or more metagenes for a cell or a plurality of cells comprised in an in vitro population of neuronal progenitor cells, wherein the one or more metagenes are determined based on correlated gene expression levels of reference cells in a reference database, wherein the reference cells are neuronal cells at one or more different stages of differentiation; applying the expression levels of the one or more metagenes as input to a process configured to determine a probability of the cell or the plurality of cells having metagene expression levels of a determined dopaminergic precursor cell;determining a deviation score for the cell or the plurality of cells, wherein the deviation score indicates 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, wherein the one or more reference cells are at a stage of differentiation indicating a determined dopaminergic precursor cell; and outputting, based on the probability and the deviation score, a computed label classification comprising an indication of whethersaid cell or said plurality of cells from the in vitro population of neuronal progenitor cells is a determined dopaminergic precursor cell.

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

[0006] Also provided herein are computer implemented methods of training a process to determine a probability of a cell or a plurality of cells having metagene expression levels of a determineddopaminergic precursor cell, the methods comprising training a supervised classification model using (i) expression levels of one or more metagenes, wherein the one or more metagenes are determined based on correlated gene expression levels of reference cells in a reference database, wherein the reference cells are neuronal cells at one or more different stages of differentiation; and (ii) class labels indicating each of the one or more different stages of differentiation for reference cells in the reference database, to determine a probability of a cell or a plurality of cells having metagene expression levels of a determined dopaminergic precursor cell.

[0007] Also provided herein are computer implemented methods of classifying an in vitro population of neuronal progenitor cells, the methods comprising receiving a test dataset comprising gene expression levels and expression levels of one or more metagenes for a cell or a plurality of cells comprised in an in vitro population of neuronal progenitor cells, wherein the one or more metagenes are determined based on correlated gene expression levels of reference cells in a reference database, wherein the reference cells are neuronal 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 comprising a supervised classification model trained using (i) expression levels of the one or more metagenes of reference cells in the reference database; and (ii) class labels indicating each of the one or more different stages of differentiation of reference cells in the reference database, to determine a probability of a cell or a plurality of cells having metagene expression levels of a determined dopaminergic precursor cell;determining a deviation score for the cell or the plurality of cells, wherein the deviation score indicates 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, wherein the one or more reference cells are at a stage of differentiation indicating a determined dopaminergic precursor cell; and outputting, based on the probability and the deviation score, a computed label classification comprising an indication of whether said cell or plurality of cells from the in vitro population of neuronal progenitor cells is a determined dopaminergic precursor cell.

[0008] In some of any of the preceding embodiments, the method comprises, based on the computed label classification, identifying the in vitro population of neuronal progenitor cells as a population comprising determined dopaminergic precursor cells.

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

[0010] In some of any of the preceding embodiments, the reference cells are an in vitro population of neuronal progenitor cells. In some of any of the preceding embodiments, said in vitro population of neuronal progenitor cells is formed by culturing one or more induced pluripotent stem cells (iPSC) in vitro for a period of time under conditions capable of differentiating the one or more iPSCs to a neuronal progenitor cell, optionally wherein the neuronal progenitor cell is one or more of a floor plate midbrain progenitor cells, determined dopaminergic precursor cells, or dopamine (DA) neurons. In some embodiments, said iPSC is a human iPSC. In some embodiments, said human is a healthy subject. In some embodiments, said human is a subject with Parkinson’s disease.

[0011] In some of any of the preceding embodiments, the culturing is for period of time that is between at or about 2 and at or about 25 days. In some of any of the preceding embodiments, said iPSC is cultured for, for about, or for at least 2 days. In some of any of the preceding embodiments, said iPSC is cultured for, for about, or for at least 5 days. In some of any of the preceding embodiments, said iPSC is cultured for, for about, or for at least 10 days. In some of any of the preceding embodiments, said iPSC is cultured for, for about, or for at least 13 days. In some of any of the preceding embodiments, said iPSC is cultured for, for about, or for at least 15 days. In some of any of the preceding embodiments, said iPSC is cultured for, for about, or for at least 18 days. In some of any of the preceding embodiments, said iPSC is cultured for, for about, or for at least 25 days.

[0012] In some of any of the preceding embodiments, the reference database comprises gene expression levels determined from one or more reference cell populations, wherein each of the one or more reference cell populations are formed by culturing one or more iPSC in vitro for a different period of time each under conditions capable of differentiating the one or more iPSCs to a neuronal progenitor cell, optionally wherein the neuronal progenitor cell is one or more of a floor plate midbrain progenitor cells, determined dopaminergic precursor cells, or dopamine (DA) neuron. 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 of any of the preceding embodiments, the one or more stages of differentiation of reference cells in the reference database are formed by culturing one or more iPSC in vitro for one or more different period of time under conditions capable of differentiating the one or more iPSCs to a neuronal progenitor cell, optionally wherein the neuronal progenitor cell is one or more of a floor plate midbrain progenitor cells, determined dopaminergic precursor cells, or dopamine (DA) neuron, wherein the different period of time is between about 11 days and about 25 days, optionally a period of time of ator about 13 days; a period of time of at or about 18 days; or a period of time of at or about 25 days. In some of any of the preceding embodiments, at least one of the one or more reference cell populations in the reference database comprises gene expression levels determined by culturing the iPSC for at or about day 13, 18, or 25 days.

[0014] In some of any of the preceding embodiments, the conditions capable of differentiating the one or more iPSCs to a neuronal progenitor cell comprises culturing the iPSCs by (a) a first incubation comprising exposing the cells to (i) an inhibitor of TGF^ / activing- 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 3b (05K3b) signaling, optionally under conditions to differentiate the cells to floor plate midbrain progenitor cells, optionally wherein the first incubation is initiated on day 0 of the culturing; and (b) a second incubation of cells after the first incubation, wherein the second incubation comprises culturing the cells under conditions to neurally differentiate the cells, optionally wherein the second incubation is initiated at or about day 11 after the first incubation, and further optionally wherein the second incubation is for between at or about 11 and at or about 25 days. In some embodiments, the conditions to neurally differentiate the cells comprises exposing the cells to (i) brain-derived neurotrophic factor (BDNF); (ii) ascorbic acid; (iii) glial cell- derived neurotrophic factor (GDNF); (iv) dibutyryl cyclic AMP (dbcAMP); (v) transforming growth factor beta-3 (TϋRb3) (collectively,“BAGCT”); and (vi) an inhibitor of Notch signaling.

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

[0016] In some of any of the preceding embodiments, the one or more metagenes and the 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 database. In some embodiments, the dimensionality reduction technique is used on a reference cell population comprising gene expression levels determined at or about 13 days of culturing iPSC in vitro under conditions to differentiate neuronal progenitor cells. In some of any of the preceding embodiments, the dimensionality reduction technique is used on a reference cell population comprising gene expression levels determined at or about 18 days of culturing iPSC in vitro under conditions to differentiate neuronal progenitor cells. In some of any of the preceding embodiments, the dimensionality reduction technique is used on a reference cell population comprising gene expression levels determined at or about 25 days of culturing iPSC in vitro under conditions to differentiate neuronal progenitor cells. In some of any of the preceding embodiments, the dimensionalityreduction technique is used on each of a reference cell population comprising gene expression levels determined at or about 13 days of culturing iPSC in vitro under conditions to differentiate neuronal progenitor cells; a reference cell population comprising gene expression levels determined at or about 18 days of culturing iPSC in vitro under conditions to differentiate neuronal progenitor cells; and a reference cell population comprising gene expression levels determined at or about 25 days of culturing iPSC in vitro under conditions to differentiate neuronal progenitor cells.

[0017] In some of any of the preceding embodiments, the supervised classification model is trained using the expression levels of the one or more metagenes determined from the one or more reference cells. In some of any of the preceding embodiments, the supervised classification model is trained using the expression levels of the one or more metagenes determined from one or more reference cells comprising gene expression levels between 11 and 25 days of culturing iPSC in vitro under conditions to differentiate neuronal progenitor cells, optionally one or more of 13, 18, and 25 days of culturing iPSC in vitro under conditions to differentiate neuronal progenitor cells. In some of any of the preceding embodiments, the supervised classification model is trained using the expression levels of the one or more metagenes determined from the one or more reference cells comprising gene expression levels determined at or about 13 days of culturing iPSC in vitro under conditions to differentiate neuronal progenitor cells. In some of any of the preceding embodiments, the supervised classification model is trained using the expression levels of the one or more metagenes determined from the one or more reference cells comprising gene expression levels determined at or about 18 days of culturing iPSC in vitro under conditions to differentiate neuronal progenitor cells. In some of any of the preceding embodiments, the supervised classification model is trained using the expression levels of the one or more metagenes determined from the one or more reference cells comprising gene expression levels determined at or about 25 days of culturing iPSC in vitro under conditions to differentiate neuronal progenitor cells. In some of any of the preceding embodiments, the supervised classification model is trained using the expression levels of the one or more metagenes determined from each of a reference cell population comprising gene expression levels determined at or about 13 days of culturing iPSC in vitro under conditions to differentiate neuronal progenitor cells; a reference cell population comprising gene expression levels determined at or about 18 days of culturing iPSC in vitro under conditions to differentiate neuronal progenitor cells; and a reference cell population comprising gene expression levels determined at or about 25 days of culturing iPSC in vitro under conditions to differentiate neuronal progenitor cells.

[0018] In some of any of the preceding embodiments, the class label indicating each of the one or more different stages of differentiation of the reference cells is either a determined dopaminergic precursor cell or a not a determined dopaminergic precursor cell.

[0019] In some of any of the preceding embodiments, the class label indicating each of the one or more different stages of differentiation of the reference cells is determined using an in vivo method. Insome embodiments, the in vivo method comprises transplanting the in vitro population of neuronal progenitor cells comprising a 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 transplantation on the animal model, optionally wherein the outcome is selected from innervation or engrafting with host cells, reduction of a brain lesion in the animal model, or reversal of a brain lesion in the animal model; and designating the class label as a determined dopaminergic precursor cell if the transplantation results in the occurrence of the outcome associated with a therapeutic effect; or designating the class label as not a determined dopaminergic precursor cell if the transplantation 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 of any of the preceding embodiments, the in vivo method comprises a behavioral assay.

[0020] In some of any of the preceding embodiments, the class label indicating each of the one or more different stages of differentiation of the reference cells is determined using an in vitro method. In some embodiments, the in vitro method comprises assessing dopamine production levels of a reference cell population; and the class label is designated as a determined dopaminergic precursor cell if the dopamine production levels are increased relative to a pluripotent stem cell. In some of any of the preceding embodiments, assessment of dopamine production is by high performance liquidchromatography.

[0021] In some of any of the preceding embodiments, the in vitro method comprises assessing levels of Tyrosine Hydroxylase expression for a reference cell population; and the class label is designated as a not a determined dopaminergic precursor cell if the reference cell population expresses high Tyrosine Hydroxylase. In some embodiments, the levels of Tyrosine Hydroxylase expression are assessed using flow cytometry.

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

[0023] In some of any of the preceding embodiments, the expression levels of the one or more metagenes in the test dataset is 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. In some embodiments, the expression levels of the one or more metagenes in the test dataset is determined using 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. In some of any of the preceding embodiments, the expression levels of the one or more metagenes in the test dataset is 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 on the updated reference database.

[0024] In some of any of the preceding embodiments, 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 of any of the preceding embodiments, the dimensionality reduction technique is conventional non-negative matrix factorization.

[0025] In some of any of the preceding embodiments, the number of the one or more metagenes is chosen based on the performance of the supervised classification model in determining a probability of a cell or a plurality of cells having metagene expression levels of a determined dopaminergic precursor cell. In some of any of the preceding embodiments, the number of the one or more metagenes is chosen based on evaluating one or more metrics determined from performing the dimensionality reduction technique using multiple candidate numbers of metagenes. In some embodiments, the one or more metrics comprise cophenetic distance, dispersion, residuals, residual sum of squares (RSS), silhouette, and / or sparseness values.

[0026] In some of any of the preceding embodiments, the computed label classification indicates that said cell or plurality of cells from the in vitro population of neuronal progenitor cells is a determined dopaminergic precursor cell if the probability of the cell or the plurality of cells having metagene expression levels of the determined dopaminergic precursor cell is greater than a threshold probability value. In some embodiments, the threshold probability value is set such that a determined dopaminergic precursor cell is identified with greater than or greater than about 75%, 80%, 85%, 90%, or 95% sensitivity; and / or the threshold probability value is set such that a determined dopaminergic precursor cell is identified with greater than or greater than about 75%, 80%, 85%, 90%, or 95% specificity. In some embodiments, the threshold probability value is set such that a determined dopaminergic precursor cell is identified with greater than or greater than about 98% sensitivity and 100% specificity. In some of any of the preceding embodiments, the threshold probability value is determined by using the area under a receiver operator characteristic (ROC) curve based on the supervised classification model. In some of any of the preceding embodiments, the threshold probability value is between or between about 0.4 and 0.8 inclusive. In some of any of the preceding embodiments, the threshold probability value is or is about 0.4, 0.45, 0.5, 0.55, 0.6, 0.65, 0.7, 0.75, or 0.8.

[0027] In some of any of the preceding embodiments, the deviation score for the cell or the plurality of cells is determined using a single-gene deviation score for each of one or more genes in the test dataset. In some embodiments, the single-gene deviation scores are determined using differences between the gene expression levels of the test dataset and the gene expression levels in one or more reference cells in the reference database. In some embodiments, the differences are absolute differences. In some of any of the preceding embodiments, the single -gene deviation scores are determined using standard deviations of gene expression levels in one or more of the one or more reference cells. In some of any of the preceding embodiments, the single-gene deviation scores are z-scores determined using the differencesbetween 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 the standard deviations of gene expression levels in one or more of the one or more reference cells of the reference database.

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

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

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

[0031] In some of any of the preceding embodiments, the marker genes comprise radial glial cell markers, early neuronal development genes, pluripotency specific markers, intermediate to late 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 development of neurons, genes controlling dopaminergic neuron development, genes regulating identity and fate of neuronal progenitor cells, dopaminergic neuron markers, astrocyte markers, forebrain markers, hindbrain markers, subthalamic nucleus markers, radial glial markers, cell cycle markers, or any combination of any of the foregoing. In some of any of the preceding embodiments, the marker genes are or 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 the foregoing.

[0032] In some of any of the preceding embodiments, the computed label classification indicates that said cell or plurality of cells from the in vitro population of neuronal progenitor cells is a determined dopaminergic precursor cell if the deviation score indicates that at least or at least about 50%, 50%, 70%,80%, 90%, or 95% of gene expression levels in the test dataset are no more than five standard deviations away from gene expression levels of the one or more reference cells in the reference database. In some of any of the preceding embodiments, the computed label classification indicates that said cell or plurality of cells from the in vitro population of neuronal progenitor cells is a determined dopaminergic precursor cell if the deviation score indicates that at least or at least about 95% of gene expression levels in the test dataset are no more than 10, 9, 8, 7, 6, or 5 standard deviations away from the gene expression levels of the one or more reference cells in the reference database. In some of any of the preceding embodiments, the computed label classification indicates that said cell or plurality of cells from the in vitro population of neuronal progenitor cells is a determined dopaminergic precursor cell if the deviation score indicates that at least or at least about 50%, 50%, 70%, 80%, 90%, or 95% of marker gene expression levels in the test dataset are no more than five standard deviations away from the gene expression levels of the one or more reference cells in the reference database. In some of any of the preceding embodiments, the computed label classification indicates that said cell or plurality of cells from the in vitro population of neuronal progenitor cells is a determined dopaminergic precursor cell if the deviation score indicates that at least or at least about 95% of marker gene expression levels in the test dataset are no more than 10, 9,8, 7, 6, or 5 standard deviations away from the gene expression levels of the one or more reference cells in the reference database.

[0033] In some of any of the preceding embodiments, the computed label classification indicates that said cell or plurality of cells from the in vitro population of neuronal progenitor cells is a determined dopaminergic precursor cell if the probability of the cell or the plurality of cells having metagene expression levels of the determined dopaminergic precursor cell is greater than the threshold probability value; and the deviation score indicates that at least or at least about 50%, 50%, 70%, 80%, 90%, or 95% of gene expression levels in the test dataset are no more than five standard deviations away from the gene expression levels of the one or more reference cells in the reference database. In some of any of the preceding embodiments, the computed label classification indicates that said cell or plurality of cells from the in vitro population of neuronal progenitor cells is a determined dopaminergic precursor cell if the probability of the cell or the plurality of cells having metagene expression levels of the determined dopaminergic precursor cell is greater than the threshold probability value; and the deviation score indicates that at least or at least about 50%, 50%, 70%, 80%, 90%, or 95% of marker gene expression levels in the test dataset are no more than five standard deviations away from the gene expression levels of the one or more reference cells in the reference database. In some of any of the precedingembodiments, the computed label classification indicates that said cell or plurality of cells from the in vitro population of neuronal progenitor cells is a determined dopaminergic precursor cell if the probability of the cell or the plurality of cells having metagene expression levels of the determined dopaminergic precursor cell is greater than the threshold probability value; the deviation score indicates that at least or at least about 50%, 50%, 70%, 80%, 90%, or 95% of gene expression levels in the testdataset are no more than five standard deviations away from the gene expression levels of the one or more reference cells in the reference database; the deviation score indicates that at least or at least about 50%, 50%, 70%, 80%, 90%, or 95% of marker gene expression levels in the test dataset are no more than five standard deviations away from the gene expression levels of the one or more reference cells in the reference database.

[0034] In some of any of the preceding embodiments, the computed label classification indicates that said cell or plurality of cells from the in vitro population of neuronal progenitor cells is a determined dopaminergic precursor cell if the differences in expression of the marker genes between the test dataset and reference cells of the reference database is statistically insignificant 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 of any of the preceding embodiments, the multiple -comparison corrected significance level is 0.01, 0.05, or 0.1.

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

[0036] In some of any of the preceding embodiments, receiving said test dataset comprises receiving input from an array analysis system. In some of any of the preceding embodiments, receiving the test dataset comprises receiving input via a computer network. In some of any of the preceding embodiments, said one or more reference databases forms part of a storage medium.

[0037] In some of any of the preceding embodiments, the method comprises repeating the receiving, applying, determining, and outputting steps if the computed label classification indicates that said cell or plurality of cells is not a determined dopaminergic neuronal cell, optionally wherein the steps are repeated the same or a different in vitro population of neuronal progenitor cells. In some embodiments, the receiving, applying, determining, and outputting steps are repeated or repeated about one, two, three, four, five, six, seven, eight, nine, or 10 days after the previous iteration of the method.

[0038] In some of any of the preceding embodiments, the method comprises repeating the receiving, applying, determining, and outputting steps if the computed label classification indicates that said cell or plurality of cells is not a determined dopaminergic neuronal cell, wherein the steps are repeated using different in vitro population of neuronal progenitor cells formed by culturing another iPSC clone underconditions capable of differentiating the one or more iPSCs to a neuronal progenitor cell, optionally wherein the neuronal progenitor cell is one or more of a floor plate midbrain progenitor cells, determined dopaminergic precursor cells, or dopamine (DA) neurons. In some embodiments, said different in vitro population of neuronal progenitor cells is formed from the same human subject as the previous iteration of the method.

[0039] In some of any of the preceding embodiments, the receiving, applying, determining, and outputting steps are repeated on in vitro population of neuronal progenitor cells formed by culture of iPSC for different periods of time and / or under different conditions capable of differentiating the one or more iPSCs to a neuronal progenitor cell, until an indication that said cell or said plurality of cells is a determined dopaminergic neuronal cell is output.

[0040] Also provided herein are populations of determined dopaminergic precursor cells identified by the method of some of any of the preceding embodiments.

[0041] Also provided herein are methods of treatment, the methods comprising administering to a subject having Parkinson’s disease the population of determined dopaminergic precursor cells of some of any of the preceding embodiments. In some embodiments, the administering is by implanting the population of determined dopaminergic precursor cells into one or more brain regions of the subject. In some embodiments, the one or more brain regions comprise the substantia nigra.

[0042] In some of any of the preceding embodiments, the population of determined dopaminergic precursor cells is autologous to the subject. In some of any of the preceding embodiments, the population of determined dopaminergic precursor cells is allogeneic to the subject.

[0043] Also provided herein are methods of treating a subject having Parkinson’s disease, the methods comprising implanting a population of determined dopaminergic precursor cells into a brain region of a subject having Parkinson’s disease, wherein the population of determined dopaminergic precursor cells has been identified using the computer implemented method of some of any of the preceding embodiments.

[0044] In some embodiments, the population of determined dopaminergic precursor cells is autologous to the subject. In some of any of the preceding embodiments, the population of determined dopaminergic precursor cells is allogeneic to the subject. In some of any of the preceding embodiments, about or at least or lx106cells are injected into the substantia nigra. In some of any of the preceding embodiments, the cells are injected into both the left and right hemispheres.BRIEF DESCRIPTION OF FIGURES

[0045] FIG. 1 shows the stages of development and when conventional biomarkers cannot be used for stage identification.

[0046] FIG. 2 shows an outline of NeuroTest showing key components and data flow. NeuroTest is a computer implemented method of identifying a determined dopaminergic precursor cell within an invitro population of neuronal progenitor cells. The outline shown in FIG. 2 is an outline of exemplary components and data flow in NeuroTest. In this exemplary embodiment, RNA sequencing (RNAseq) data from an in vitro population of neuronal progenitor cells (test sample) is provided to NeuroTest. For each test sample, NeuroTest provides two parameters as output: a NeuroScore and a Novelty Score. Together, these parameters are used to determine if the test sample contains a determined dopaminergic precursor cell.

[0047] FIG. 3A-3C show example output of NeuroTest: (FIG. 3A) a table of the statistical scores, (FIG. 3B) as a histogram or (FIG. 3C) a scatter plot showing NeuroScore on the y-axis and Novelty on the x-axis. FIG. 3B and FIG. 3C show induced pluripotent stem cells (iPSC) and dopaminergic (DA) neurons failing and passing NeuroTest, respectively. FIG. 3B and FIG. 3C are displaying a NeuroScore on the y-axis which is rescaled to a percentage value. In FIG. 3C, the NeuroScore is referred to as “neuri,” and the Novelty Score is referred to as“deviation.”

[0048] FIG. 4 shows a scatter plot showing NeuroScores (y-axis) and novelty scores (x-axis) for the validation data set. Validating the NeuroTest model, initially trained on discriminating genes from the microarray data and supplemented with RNAseq based gene expression data. Here RNAseq data was used as validation since the model training was done with Illumina beadarray data (by using 5 fold cross- validation). The validation RNAseq data was generated or downloaded from public data repositories. The samples in the upper left quadrant pass for both high NeuroScore and low novelty. The“Undiff’ samples (mostly undifferentiated IPSC, diamonds) fail NeuroTest due to getting a low NeuroScore and having elevated levels of novelty compared to the reference data model. In FIG. 4, the NeuroScore is referred to as“N-score.”

[0049] FIG. 5 shows the NeuroTest result from the analysis of 86 publicly available neuronal RNAseq datasets. The datapoints highlighted with the black circles are specifically the data points from the challenge datasets. The solid background datapoints are from the Neurotest validation analysis of the 695 samples of validation data. These results provide context for the Neurotest challenge data. The spread of the challenge data, spanning the range from iPSC to cancer cells to neuronal reflects the input data. The tabular output reveals that NeuroTest gave a“pass” score to DA neuron cellular preparations. In FIG. 5, the NeuroScore is referred to as“N-score.”

[0050] FIG. 6 shows how NeuroTest uses gene expression as a phenotype to identify neuronal precursor cells.

[0051] FIG. 7 shows metagene expression levels (metagene contribution) for cell samples at day 18 of a dopaminergic neuron differentiation protocol. Metagenes and expression levels thereof were derived by applying conventional non-negative matrix factorization (NMF) on single -cell RNAseq (scRNAseq) data, scRNAseq data aggregated to approximate bulk RNAseq data (bulk from single cell), and bulk RNAseq data collected from each of four cell lines. For each sample collected from the cell lines, both scRNAseq and bulk RNAseq data were collected.

[0052] FIG. 8 shows a receiver operating characteristic (ROC) curve showing classification performance of a logistic regression model trained to identify a determined dopaminergic precursor cell within an in vitro population of neuronal progenitor cells.

[0053] FIG. 9 shows another exemplary workflow for building and using NeuroTest. In this exemplary workflow, gene expression data from publically available databases, scRNAseq datasets, and matched bulk RNAseq datasets are collected for in vitro populations of neuronal progenitor cells containing determined dopaminergic precursor cells. These datasets are supplied (circles 3 and 4) to a process that calculates metagenes and expression levels thereof. Metagene expression levels are supplied (circle 5) as training data to a classification model configured to determine the probability of a sample having metagene expression levels of a determined dopaminergic precursor cell. This model can be validated (circle 6) using additional data, for instance bulk RNAseq data not used in training the model. The trained model is then used as part of NeuroTest (circle 7) in order to test future test samples from other in vitro populations. Novelty Scores are also calculated per training sample, and these scores and the trained model are used to identify NeuroScore and Novelty Score thresholds (circle 8) that will be used to evaluate the future test samples. For future test samples, RNAseq data is subjected to sequence alignment using the Salmon pseudoaligner (circle 1). Next, the test RNAseq data is supplied to the trained model (circle 2), and a NeuroScore (circle 10) and Novelty Score (circle 11) are output for the test sample. These scores are compared to the previously determined thresholds in order to determine if the test sample should be transplanted, additionally screened, or discarded.

[0054] FIG. 10 shows gene expression deviation of an exemplary sample from an in vitro population of neural progenitor cells. Gene expression deviation is shown for several individual marker genes and is calculated as normalized residuals showing how far individual gene expression deviates from expected values, where the expected values are determined from cells with known identity (e.g., reference cells).

[0055] FIG. 11 shows the output of NeuroTest (NeuroScores and Novelty Scores) for cell samples at various stages (days) of a dopaminergic neuron differentiation protocol. The horizontal dashed line is at NeuroScore = 0. The vertical dashed line is at Novelty Score = 5. In this exemplary embodiment, samples with a Neuroscore > 0 and a Novelty Score < 5 are identified as containing determined dopaminergic precursor cells.DETAILED DESCRIPTION

[0056] Provided herein is a method of classifying whether an in vitro population of neuronal progenitor cells contains a particular differentiated neuronal cell type. In some embodiments, the provided methods classify whether an in vitro population of differentiated neuronal cells contains determined dopamingeric precursor cells. In some embodiments, the methods provided herein identify whether an in vitro population of neuronal cells contain determined dopaminergic precursor cells. In some embodiments, determined dopaminergic precursor cells are cells that differentiate intodopaminergic neurons and cannot differentiate into non-dopaminergic cells. A cell population that is classified according to the provided method can be used to identify cells of interest, for example, for therapeutic application. Thus, also provided are populations of determined dopaminergic precursor cells identified by the provide methods, and pharmaceutical compositions containing the same. In some embodiments, the determined dopaminergic precursor cells have therapeutic application in the treatment of neurodegenerative diseases, such as Parkinson’s disease.

[0057] In provided methods, the methods include receiving a test dataset that includes (1) gene expression levels and (2) expression levels of one or more metagenes for a cell or a plurality of cells contained in an in vitro population of neuronal progenitor cells in which the one or more metagenes are determined based on correlated gene expression levels of reference cells in a reference database. In some embodiments, the in vitro population of neuronal progenitor cells is a population of cells that has been subjected to a process to differentiate pluripotent stem cells, such as induced pluripotent stem cells (iPSCs), into neuronal cells, such as dopaminergic neurons or a determined precursor of dopaminergic neurons. In some embodiments, the methods include applying the expression levels of the one or more metagenes as input to a process configured to determine a probability of the cell or the plurality of cells in the in vitro population of neuronal progenitor cells having metagene expression levels of a determined dopaminergic precursor cell. In some embodiments, the methods include also determining a deviation score for the cell or the plurality of cells in the in vitro population of neuronal progenitor cells in which the deviation score indicates 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, wherein the one or more reference cells are at a stage of differentiation indicating a determined dopaminergic precursor cell. In some embodiments, the deviation score is determined using the gene expression levels in the test dataset and the gene expression levels in a reference database. In some embodiments, the methods include outputting, based on the probability and the deviation score, a computed label classification that provides an indication of whether said cell or said plurality of cells from the in vitro population of neuronal progenitor cells is a determined dopaminergic precursor cell, thereby classifying whether the in vitro population of neuronal progenitor cells is a population that is or contains determined dopaminergic precursor cell. In some embodiments, the methods thus can identify based on the classification whether the in vitro population of neuronal progenitor cells is a population that contains determined dopaminergic precursor cells.

[0058] In some embodiments, certain differentiated neuronal cell populations differentiated from pluripotent stem cells, including determined dopaminergic precursor cells, may be cells in a stage of differentiation where the cells are not identifiable by one or a small number of features or characteristics. The methods provided herein allow for the determination of cell identity when a single or small number of features or characteristics, such as gene expression markers or functional properties, are unavailable (e.g., unknown) or cannot be practically used to determine cellular identity. For example, as shown inFIG. 1, cells undergoing differentiation enter stages where no definitive biomarker can be used to determine the identity of the cell. While pluripotent stem cells can be positively identified with definitive biomarkers, for instance the expression levels of specific genes, and differentiated cells can be positively identified based on functional markers, individual markers for the identification of cells at various transient stages throughout differentiation are unknown. Without such markers, there has been previous difficulty in characterizing, defining, and / or identifying pre-differentiated cells with particular cell phenotypes. In some aspects, the methods provided herein overcome the lack of a single or small number of features or characteristics (e.g., biomarkers) by examining groups of related genes and expression levels thereof. Such an approach does not rely on knowledge of individual marker genes and instead uses a whole transcriptome approach in characterizing and identifying determined dopaminergic precursor cells.

[0059] Induced pluripotent stem cells (iPSCs) are considered useful as a cell therapy for at least their ability to be differentiated into specialized cell types. For example, iPSCs, like pluripotent stem cells, can be differentiated into specific cell types that can be used to replace diseased or damaged tissue. In some cases, iPSCs that have been differentiated into a particular neuronal cell type or precursor may be used to treat neurodegenerative diseases, for example by differentiating iPSCs and implanting the differentiated neuronal cells into the brain of a subject having a neurodegenerative disease. The inability to determine the identity of the differentiated cells throughout the differentiation process can lead to uncertainty about the success of the process. For example, the differentiation process may need to be run to completion in order to determine if the differentiation process was successful. Thus, without the ability to determine whether differentiating cells are progressing through the transient stages as needed, the differentiation process becomes time consuming and inefficient, and can hinder treatment of the subject, for example when a differentiation process fails. Furthermore, in some cases, the therapeutic treatment can include administering (e.g., injecting) to the subject differentiated cells that have not entered a final differentiation stage.

[0060] In some embodiments, cells at an intermediate stage of differentiation cannot be, or cannot easily be, identified by definitive biomarkers. The methods provided herein allow for the identification of cells at stages of differentiation where no definitive features or characteristics are available or can be pratically used to determine cell identity. In some embodiments, the methods provided herein improve the differentiation process, for example, by allowing a determination of cell identity throughout the stages of differentiation, which can be used to determine whether cells undergoing a differentiation process are differentiating appropriately and / or according to defined standards. If it is determined that the cells are not differentiating appropriately, in some embodiments, the process can be terminated and optionally reinitiated with different iPSC clones from the patient.

[0061] In some embodiments, the methods provided herein may be used in combination with a process that includes generating neuronal cells useful for the treatment of a neurodegenerative disease,such as Parkinson’s disease, by differentiation from iPSCs. In some embodiments, the methods provided herein can be used to identify neuronal cells generated by a differentiation process, for example a process described in Section II, that are useful for the treatment of Parkinson’s disease.

[0062] The methods provided herein can be used to determine if an in vitro population of cells comprises predetermined dopaminergic precursor cells. In some embodiments, the methods provided herein comprise determining metagenes and expression levels thereof of test cells comprised in the in vitro population. In some embodiments, the methods provided herein comprise determining the probability of the test cells having metagene expression levels of a determined dopaminergic precursor cell. In some embodiments, the probability is determined using a machine learning model. In some embodiments, the methods provided herein comprise determining a deviation score indicating the degree to which the gene expression levels of the test cells deviate from expected gene expression levels. In some embodiments, the expected gene expression levels are based on gene expression levels of reference cells that are known to be determined dopaminergic precursor cells. In some embodiments, the methods provided herein comprise outputting a computed label classification based on one or both of (i) the probability of the test cells having metagene expression levels of a determined dopaminergic precursor 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 the test cells having metagene expression levels of a determined dopaminergic precursor cell allows for the identification of cells with the desired phenotype, said phenotypes lacking individual marker genes. In some embodiments, determining the deviation score allows for the identification of cells that may contain abnormalities, for instance in the expression of certain marker genes. Thus, the methods provided herein provide a multifaceted approach for determining suitable cells for treatment.

[0063] In the subsections below, exemplary features of provided methods of classifying whether an in vitro population of neuronal progenitor cells contains a particular differentiated neuronal cell type, and methods for identifying a particular differentiated neuronal cell type, are described. Related compositions and methods of production and uses thereof also are described.I. METHODS OF DETERMINING A DETERMINED DOPAMINERGIC CELL

[0064] Provided herein are, inter alia, methods that use gene expression as a phenotype to identify dopaminergic precursors in an in vitro cell population of neuronal progenitor cells. The methods provided herein provide, inter alia, information whether a cell preparation (e.g., a population of neuronal progenitor cells) includes cells that are determined to differentiate into a specific functional cell type (e.g., a determined dopaminergic precursor cell) or whether the cell preparation includes cells from earlier stages (e.g. pluripotent stem cells, specified cells), other differentiating neuron types, and other differentiated cell types.

[0065] Thus, in one aspect, a computer implemented method of identifying a determined dopaminergic precursor cell within an in vitro population of neuronal progenitor cells is provided. Themethod includes, receiving a test dataset including data including gene expression profile information for an in vitro population of neuronal 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 a desirable determined dopaminergic precursor cell; and outputting a computed label classification including an indication of whether the in vitro population of neuronal progenitor cells includes a determined dopaminergic precursor cell.

[0066] The methods provided herein may define a determined state of a cell and predict whether a cell preparation will differentiate into a specific cell type. The reference database provided herein may include gene expression profile information of two cell types. In embodiments, the cells identified with the methods provided herein are determined to differentiate into a specific functional cell type. Whether a cell is determined to differentiate into a specific functional cell type (e.g., a determined dopaminergic precursor cell) may further be demonstrated in vitro or in vivo by allowing the cells to fully differentiate. In embodiments, the cells identified with the methods provided herein are pluripotent stem cells, specified cells, differentiating neuron types other than dopaminergic precursors 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 neuronal progenitor cells includes the determined dopaminergic precursor cell, the machine learning model outputting the computed label classification. In embodiments, the in vitro population of neuronal progenitor cells are formed by allowing an induced pluripotent stem cell (iPSC) to differentiate in vitro. In embodiments, the iPSC is a human iPSC. In embodiments, the iPSC is cultured for at least 15 days under conditions fordifferentiation into a neuronal progenitor cell. In embodiments, the iPSC is cultured for about 18 days under conditions for differentiation into a neuronal progenitor cell. The in vitro cell population of neuronal progenitor cells provided herein may be formed by methods commonly known and used in the art to differentiate dopaminergic neurons from iPSCs. Exemplary methods of differentiation processes are described in Section II. Different timepoints of the process for differentiating dopaminergic neurons from iPCSs may result in cells that are at different stages of differention. Therefore, the term“dl8” or “day 18” as provided herein refers to the 18thday of the process of differentiating an iPSC to form a dopaminergic neuron. Likewise, the term“d0” or“day 0” refers to the day of the process ofdifferentiating an iPSC to form a dopaminergic neuron is initiated. The provided methods can be used to classify, and thus identify, a differentiated population of neuronal cells that, based on classification labels in accord with the provided methods, is determined to contain a particular neuronal progenitor cell, such as a determined dopaminergic precursor cell.

[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 comprised in the in vitro population of neuronal progenitor cells as having metagene expression levels of a determineddopaminergic precursor cell. In embodiments, the machine learning model outputs the probability (also referred to herein as a Neuroscore) of the cell or plurality of cells having metagene expression levels of a determined dopaminergic precursor cell. In embodiments, the computer implemented method further includes determining a deviation score (also referred to herein as Novelty score) for the cell or plurality of cells, wherein the deviation score is indicative of the degree to which gene expression levels of the cell or plurality of cells deviates from expected gene expression levels. In some embodiments, the expected gene expression levels are based on gene expression levels of reference cells, e.g., reference cells that are known to be determined dopaminergic precursor cells. In some embodiments, the computer implemented method includes outputting based on the probability and the deviation score the computed label classification.

[0069] The methods, algorithms, and systems described herein are designed to produce a new way of defining a determined dopaminergic precursor cell or dopaminergic cell. This new way is called a computed definition and the previous types of definitions are referred to as biological definitions (functional, structural, genesis). The computed definition is related to a biological definition, but as discussed herein, the computed definition provides a more robust and accurate way of comparing two different cells and determining whether they are the same type of cell or different cell types. In some embodiments, the computed definition provides a more robust and accurate way of identifying a cell of unknown identity.

[0070] The computed definition refers to the use of computational analysis of information to arrive at the definition. Disclosed are databases of information about one or more cells. For example, some of the databases are reference databases. A reference database can comprise cell datasets that are produced from cell data for at least two known cell lines, tissues, or primary cells. By known cell line, tissue, or primary cell is meant a cell line for which some characteristic, such as phenotype, such as dopaminergic cell, a determined dopaminergic precursor cell, and has been identified by conventional biological assays, e.g. derivation method, source material, biochemical assays (e.g. enzyme activity, e.g. alkaline phosphatase activity) or markers like specific, identified proteins which are thought to be able to identify a specific cell type. In some embodiments, the cells for which some characteristics are known are referred to as reference cells. A computed phenotype can be defined by the global profiling methods, such as gene expression (or other molecular profiling method) which is then utilized in the methods disclosed herein. Biological phenotypes, such as whether a cell is a stem cell or differentiated cell, which have been determined using subsets of profiling data, such as a subset of markers or gene expression, can be used and incorporated into the methods in the form of labeled associated biological classes.A. Reference Cells

[0071] The methods provided herein, in some aspects, include the use of reference cells and / or reference databases to identify (e.g., determine) the presence of determined dopaminergic precursor cells within an in vitro population of neuronal progenitor cells. The types of reference cells contemplated foruse according to the methods provided herein include cells with known identity (e.g., labeled cell) and known characteristics, e.g., have characterized gene expression profiles. In some embodiments, the reference databases comprise reference cell labels and the corresponding reference cell characteristics from a plurality of reference cells. In some embodiments, the reference database can be used, e.g., according to the methods provided herein, to determine whether a cell of unknown identity (e.g., unlabeled) having certain characteristics, e.g., gene expression patterns, has a certain cellular identity.

[0072] In some embodiments, the reference cell is a pluripotent stem cell. In some embodiments, the pluripotent stem cell is an induced pluripotent stem cell (iPSC). In some embodiments, the iPSC is generated from fibroblasts collected from a healthy human subject. In some embodiments, the iPSC is generated from fibroblasts collected from a human subject having Parkinson’s disease. In some embodiments, the iPSC is generated from fibroblasts collected from a human subject predisposed to developing Parkinson’s disease. Exemplary methods for iPSC generation are described in Section II.

[0073] In some embodiments, the reference cell is a cell differentiated under conditions to become a neuronal progenitor cell, such as a floor plate midbrain progenitor cells, determined dopaminergic precursor cells, or a dopaminergic neuron. In some embodiments, the reference cell is a cell differentiated according to any of the methods described in Section II. In some embodiments, the reference cell is a determined dopaminergic precursor cell. In some embodiments, the reference cell is a dopaminergic neuron. In some embodiments, the differentiated cell, the determined dopaminergic cell, and / or the dopaminergic cell is derived from an iPSC, for example an iPSC as described above, that has been cultured under conditions to promote differentiateion into a dopaminergic cell.

[0074] In some embodiments, the reference cell is a cell that is described, e.g., labelled, characterized, in a publically available database.

[0075] In some embodiments, the reference cell is of known identity. Thus, in some instances, the identity of the cell can be used as a label for the reference cell. In some embodiments, the reference cell label is indicative of a cellular phenotype. In some embodiments, the reference cell label is indicative of cellular characteristics, e.g., gene expression levels. In some embodiments, the reference cell label indicates if the reference cell is a pluripotent stem cell. In some embodiments, the reference cell label indicates if the reference cell is a determined dopaminergic precursor cell. In some embodiments, the reference cell label indicates if the reference cell is a dopaminergic neurons.

[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 that the reference cell has been cultured under differentiation conditions. In some embodiments, the reference cell label indicates the period of time that the reference cell has been cultured under differentiation conditions to become a dopaminergic neuron, e.g., any of the periods of time described in Section II.

[0077] In some embodiments, the reference cell label is based on publically available annotations for the reference cell. In some embodiments, the reference cell label is based on the assessment ofdopamine production levels of the reference cell. In some embodiments, dopamine production levels are assessed using high performance liquid chromatography (HPLC). In some embodiments, the reference cell label is based on the assessment of tyrosine hydroxylase (TH) expression in the reference cell. In some embodiments, TH expression is assessed using cell staining methods. In some embodiments, the reference cell label is based on the assessment of FOXA2 expression in the reference cell. In some embodiments, FOXA2 expression is assessed using cell staining methods. In some embodiments, TH expression is assessed using flow cytometry.[0078J 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, a 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 cell is determined to or capable of becoming dopaminergic neuron, i.e. is a determined dopaminergic precursor cell, as ascertained based on one or more characteristics that indicate the reference cell is capable of having functional activity of a dopaminergic neuron but may not yet express a marker of a dopaminergic neuron or may not express it at a high level. For example, a reference cell may exhibit lower levels of TH than a dopaminergic neuron, yet still exhibits one or more characteristics of a determined dopaminergic precursor cell indicating the differentiated cell is capable of having functional activity of a dopaminergic neuron. In some embodiments, the one or more characteristics of the reference cell include activity to survive, engraft, and / or innervate other cells when administered in vivo, e.g. to an animal model. In some embodimetns, the reference cells are capable of innervating host tissue upon transplantation into an animal or human subject.

[0080] In some embodiments, the reference cell is a cell with therapeutic effect to treat a neurodegenerative disease. In some embodiments, the reference cell when implanted ameliorates or reverses symptoms of a neurodegenerative disease. In some embodiments, the neurodegenerative disease is Parkinson’s disease. In some embodiments, the reference cells when implanted in the substantia nigra of a subject, e.g., patient, in need thereof improves Parkinsonian symptoms.

[0081] In some embodiments, the reference cell is screened for its therapeutic effect to treat a neurodegenerative disease, such as determined in an animal model of a 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 and available animal model of Parkinson’s disease can be used for screening. In some embodiments, the animal model is a lesion model wherein animals received unilateral stereotaxic injection of 6-hydroxydopamine (6- OHDA) into the substantia nigra. In some embodiments, the animal model is a lesion model wherein animals received unilateral stereotaxic injection of 6-OHDA into the medial forebrain bundle. In someembodiments, the reference cells are implanted into the substantia nigra of the animal model. In some embodiments, a behavioral assay is performed to screen for therapeutic effects of the implantation on the animal model. In some embodiments, the behavioral assay comprises monitoring amphetamine-induced circling behavior. In some embodiments, the reference cell is determined to reduce, decrease or reverse a Parkinsonian model brain lesion in this model. In some embodiments, the reference cell may be a cell that does not reduce, decrease or reverse a Parkinsonian model brain lesion in this model. The reference database may include data from various reference cell populations that exhibit varied or different therapeutic effects to treat a neurodegenerative disease, such as in an animal model.

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

[0083] In some embodiments, reference cell characteristics include protein expression levels. In some embodiments, reference cell characteristics include post-translational protein modification levels.In some embodiments, reference cell characteristics include non-coding RNA expression profiles. In some embodiments, reference cell characteristics include epigenetic profiles. In some embodiments, reference cell characteristics include transcriptional profiles. In some embodiments, reference cell characteristics include gene expression levels. In some embodiments, the reference cell database can include information about 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 someembodiments, the gene expression levels are obtained using both microarray analysis and RNA sequencing. In some embodiments, the RNA sequencing is performed on bulk RNA from a plurality of cells. In some embodiments, the RNA sequencing is performed on single cells. In some embodiments, the RNA sequencing is performed on bulk RNA from a plurality of cells and on single cells.

[0085] In some aspects, a plurality of reference cells with known identities, e.g., labels, and known characteristics, e.g., gene expression levels, are used to populate a reference database. In some embodiments, the plurality of reference cells used to populate the reference database have different labels from one another. 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 thereference database have labels different from the other reference cells of the reference database. Thus, in some embodiments, the reference database may include a plurality of reference cells, some having the same label as other cells of the reference database and some having labels different from other cells in the reference database.

[0086] In some embodiments, the reference cell characteristics for particular reference cells are included in a reference database. In some embodiments, the reference database contains reference cell labels. In some embodiments, the reference database contains protein expression levels of reference cells. In some embodiments, the reference database contains epigenetic profiles of reference cells. In some embodiments, the reference database contains transcriptional profiles of reference cells. In some embodiments, the reference database contains gene expression levels of reference cells. In some embodiments, the reference database contains gene expression data from publically available databases.In some embodiments, the reference database contains microarray data. In some embodiments, the reference database contains RNA sequencing data. In some embodiments, the reference database contains microarray data and RNA sequencing data.

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

[0088] Any known and available methods for obtaining bulk RNA sequencing data can be used (for example, see Chao et al., 2019, BMC Genomics 20: 571, incorporated by reference herein in its entirety). For instance, total RNA from a sample, e.g., a plurality of reference cells from an in vitro population of cells, can be isolated using TRIZOL, treated with DNase I, and purified. Concentration and quality of isolated RNA can be measured and checked prior to library preparation for total RNA or rnRNA. For library preparation, total RNA or rnRNA are fragmented and converted to cDNA using reverse transcription. After construction, amplification, and optional barcoding of double-stranded cDNA, libraries can be processed for next generation sequencing using any known and available library preparation techniques, sequencing platforms, and genomic-alignment tools.

[0089] In some embodiments, the reference database includes single-cell RNA sequencing data. In some embodiments, the use of single-cell RNA sequencing data affords certain advantages. In some embodiments, the use of single -cell RNA sequencing data allows for characterization of subpopulations of cells, for instance of determined dopaminergic precursor cells within a larger in vitro population of cells. In some embodiments, the use of single -cell RNA sequencing data reduces the number of reference cells required for use in the methods provided herein. In some embodiments, the use of single -cell RNA sequencing data improves characteriziation of biological variability across reference cells. In some embodiments, the use of single -cell RNA sequencing data allows for easier validation and interpretation of gene expression levels.

[0090] Any known and available methods for single-cell RNA sequencing can be used (for example, see Zheng et al., 2017 (Nature Communications 8: 14049), and Haque et al., 2017 (Genome Medicine 9: 75 , incorporated by reference herein in their entirety). For single-RNA sequencing, single cells from a sample, for instance an in vitro population of cells, can be isolated using flow cytometric cell-sorting, microfluidic platform, or droplet-based methods. Isolated cells are lysed to allow capture of RNA molecules. Poly [T] -primers can be used for the analysis of polyadenylated mRNA molecules specifically, and primed mRNA molecules are converted to cDNA using reverse transcription. In some instances, unique molecular identifiers can be used to mark single mRNA molecules based on cellular origin. The cDNA pool is then amplified, optionally barcoded, and sequenced, for instance using next-generation sequencing (NGS) and with library preparation techniques, sequencing platforms, and genomic- alignment tools similar to those used for bulk RNA samples. In some instances, unbiased cell-type classification witin a mixed population of distinct cell types can be achieved with as few as 10,000 to 50,000 reads per cell, and single -cell libraries from various common protocols can be close to saturation when sequenced to a depth of 1,000,000 reads.

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

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

[0093] The reference database is made up of cell datasets, and each cell dataset is made up of characteristic data. Characteristic data are output from, for example, mRNA expression analysis, microRNA expression analysis, protein expression analysis, post-translational protein modification analysis, non-coding RNA expression analysis, DNA methylation pattern analysis, histone modification analysis, transcription factor-DNA site binding analysis, DNA sequence analysis or any other type of cell characteristic.B. Test Cells

[0094] In some aspects, the methods provided herein allow for determining whether a cell or plurality of cells of unknown identity are determined dopaminergic precursor cells. In someembodiments, the cell or plurality 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 contained in an in vitro population of neural progenitor cells. In some embodiments, the test cells include cells differentiated under conditions to become dopaminergic neurons. In some embodiments, the test cells include cells differentiated according to any of the methods described in Section II. In some embodiments, the test cells include cells differentiated under conditions to become dopaminergic neurons for any of the periods of time described in Section II. In some embodiments, the cells being differentiated are pluripotent stem cells. In some embodiments, the pluripotent stem cells are induced pluripotent stem cells (iPSCs). In some embodiments, the iPSCs are generated from fibroblasts collected from healthy human subjects. In some embodiments, the iPSCs are generated from fibroblasts collected from human subjects with Parkinson’ s disease. Exemplary methods for iPSC generation are described in Section II.

[0095] In some embodiments, the determination of the identity of the test cells, e.g., whether the test cells are determined dopaminergic precursor cells or not, indicates whether the in vitro population of cells contains a population of determined dopaminergic precursor cells or not.

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

[0097] A "test dataset" is a dataset that is produced from a cell (e.g., a neuronal progenitor cell) for which a computed definition is desired. It is produced from characteristic data for an unknown cell line, tissue, or primary cell. Unknown in this context means that a computed definition is desired. Typically the test dataset will be comprised of a global profile as discussed herein as it relates to the global profile of the reference database. The test dataset can be merged with the reference database forming an updated reference database. In certain embodiments this can be as simple as adding the data to an existing spreadsheet. Therefore, the test dataset including gene expression profile information for an in vitro population of neuronal progenitor cells may be included (merged) in the reference database after determining that the in vitro population of neuronal progenitor cells includes a determined dopaminergic precursor cell.

[0098] In some embodiments, the test data set includes characteristics of test cells. For example, in some cases, the test data set includes the same types of characteristics as those determined for reference cells. In some embodiments, the test dataset may include 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 cell characteristic.

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

[0100] 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 someembodiments, the gene expression levels are obtained using both microarray analysis and RNA sequencing. In some embodiments, the RNA sequencing is performed on bulk RNA from a plurality of cells. In some embodiments, the RNA sequencing is performed on single cells. In some embodiments, the RNA sequencing is performed on bulk RNA from a plurality of cells and on single cells. Exemplary methods of extracting, preapring and analyzing bulk RNA and single -cell RNA are described in Section I.A above.[001011 In some embodiments, the test cell characteristics are included in a test dataset. In some embodiments, the test dataset includes protein expression levels of test cells. In some embodiments, the test dataset includes epigenetic profiles of test cells. In some embodiments, the test dataset includes transcriptional profiles of test cells. In some embodiments, the test dataset includes gene expression levels of test cells. In some embodiments, the test dataset includes microarray data. In someembodiments, the test dataset includes RNA sequencing data. In some embodiments, the test dataset includes microarray data and RNA sequencing data. In some embodiments, the test dataset includes bulk RNA sequencing data. In some embodiments, the test dataset includes single-cell RNA sequencing data. In some embodiments, the test dataset includes bulk RNA sequencing data and single-cell RNA sequencing data.In some embodiments, the test dataset includes expression levels of one or more metagenes. Determination of metagenes and expression levels thereof is discussed in Section I.C.C. Metagenes

[0102] In some aspects, the methods provided herein make use of metagenes and expression levels of metagenes for determining the identity of test cells. A metagene refers to a pattern of gene expression. For example, a metagene may be a group of genes with correlated gene expression. In someembodiments, a metagene combines information from multiple individual genes, and the expression level of the metagene is calculated based on the expression levels of the individual genes. Multiple metagenes and expression levels thereof can be determined based on individual gene expression levels. In some embodiments, metagene expression levels are based on combined individual gene expression levels, and the determination of said metagenes comprises determining the degree to which an individual gene’s expression level contributes to the expression level of a metagene. For instance, metagene expressionlevels can be a weighted combination of individual gene expression levels, and the determination of said metagenes comprises determining for each metagene the weights of individual genes. In some embodiments, metagenes and expression levels thereof reflect correlated expression levels across individual genes. In some embodiments, metagenes and expression levels thereof reflect individual genes coexpressed by cells of the same phenotype (e.g., determined dopaminergic precursor cells). Exemplary coexpressed genes of determined dopaminergic precursor cells are discussed in Section III.

[0103] In some aspects, the methods provided herein use the expression levels of metagenes to determine if a cell contained in a population of cells is a determined dopaminergic precursor cell. In some embodiments, the expression levels of metagenes are used to determine whether a population of cells contained determined dopaminergic precursor cells. In some aspects, the use of metagenes reduces the number of features used in determining if a cell is a determined dopaminergic precursor cell or if a population of cells contains determined dopaminergic precursor cells. In some aspects, reducing the number of features makes such determination more computationally tractable. In some aspects, reducing the number of features improves the accuracy of such determination. For instance, the performance of a machine learning model trained using metagene expression levels may be higher than one trained on gene expression levels, particularly since metagenes combine and / or retain information from individual genes.1. Metagene Determination

[0104] In some embodiments, metagenes are determined based on the gene expression levels of reference cells. In some embodiments, the gene expression levels of reference cells are contained in a reference database. Exemplary reference cells and reference databases are described in Section I. A. In some embodiments, a reference database containing microarray data is used to determine metagenes. In some embodiments, a reference database containing RNA sequencing data is used to determine metagenes. In some embodiments, a reference database containing microarray data and reference database containing RNA sequencing data are used to determine metagenes. In some embodiments, a reference database containing bulk RNA sequencing data is used to determine metagenes. In some embodiments, a reference database containing single-cell RNA sequencing data is used to determine metagenes. In some embodiments, a reference database containing bulk RNA sequencing data and a reference database containing single -cell RNA sequencing data are used to determine metagenes.

[0105] In some embodiments, metagenes are computationally determined. In some embodiments, metagenes are determined using a dimensionality reduction technique. A dimensionality reduction technique transforms data from a higher-dimensional space (e.g., individual genes) into a lower dimensional space (e.g., metagenes) such that the lower-dimensional representation of the data still retains meaningful or informative properties of the original data. In some embodiments, metagenes are determined by applying a dimensionality reduction technique on 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 component 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 (PC A). 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 non-negative matrix factorization (NMF). Using NMF, a matrix can be factorized into two matrices such that all three matrices have no negative elements. This non-negativity can makes the resulting matrices easier to inspect, for instance when the original matrix itself contains only non-negative values. In some embodiments, the dimensionality reduction technique is conventional NMF. In some embodiments, the dimensionality reduction technique is 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 bootstrapping 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 Neighbor 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 a dimensionality reduction technique. In some embodiments, the dimensionality reduction technique is a connectivity-based clustering method. In some embodiments, the dimensionality reduction technique is hierarchical clustering.In some embodiments, the dimensionality reduction technique is a centroid-based clustering method. In some embodiments, the dimensionality reduction technique is k- means clustering. In some embodiments, the dimensionality reduction technique is a distribution-based clustering method. In some embodiments, the dimensionality reduction technique is Gaussian mixturemodeling. In some embodiments, the dimensionality reduction technique is a density-based clustering method. 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 method. In some embodiments, the dimensionality reduction technique is STING. In some embodiments, the dimensionality reduction technique is CLIQUE.2. Metagene Expression Levels

[0110] In some embodiments, expression levels of the determined metagenes are calculated. In some embodiments, metagene expression levels are determined using the same reference database used to determine metagenes. In some embodiments, metagene expression levels are determined using a reference database not used to determine metagenes. In some embodiments, metagene expression levels are determined using test datasets (e.g., any test dataset described in Section I.B.). Determination of metagene expression levels is possible if expression levels of the same or similar sets of genes are included in the reference databases used to determine metagenes and the reference databases and / or test dataset used to determine metagene expression levels.[001111 In some embodiments, metagene gene expression levels are determined using reference databases containing microarray data. In some embodiments, metagene gene expression levels are determined using a reference database containing RNA sequencing data. In some embodiments, metagene gene expression levels are determined using a reference database containing microarray data and reference databases comprising RNA sequencing data. In some embodiments, metagene gene expression levels are determined using reference database containing bulk RNA sequencing data. In some embodiments, metagene gene expression levels are determined using a reference database containing single -cell RNA sequencing data. In some embodiments, metagene gene expression levels are determined using a reference database containing bulk RNA sequencing data and a reference database containing single -cell RNA sequencing data.

[0112] In some embodiments, metagenes are determined using a reference database containing bulk RNA sequencing data, and metagene expression levels are determined using a reference database containing bulk RNA sequencing data. In some embodiments, metagenes are determined using a reference database containing bulk RNA sequencing data, and metagene expression levels are determined using a reference database containing single-cell RNA sequencing data. In some embodiments, metagenes are determined a reference database containing single-cell RNA sequencing data, and metagene expression levels are determined using a reference database containing bulk RNA sequencing data. In some embodiments, metagenes are determined using a reference database containing single-cell RNA sequencing data, and metagene expression levels are determined using a reference database containing single -cell RNA sequencing data. In some embodiments, metagenes are determined using a reference database containing bulk RNA sequencing data and a reference database containing single -cellRNA sequencing data, and metagene expression levels are determined a reference database containing bulk RNA sequencing data. In some embodiments, metagenes are determined using a reference database containing bulk RNA sequencing data and a reference database containing single-cell RNA sequencing data, and metagene expression levels are determined using a reference database containing single-cell RNA sequencing data.

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

[0114] In some embodiments, metagenes are determined using a reference database containing bulk RNA sequencing data, and metagene expression levels are determined using a test dataset containing bulk RNA sequencing data. In some embodiments, metagenes are determined using a reference database containing bulk RNA sequencing data, and metagene expression levels are determined using a test dataset containing single-cell RNA sequencing data. In some embodiments, metagenes are determined using a reference database containing single-cell RNA sequencing data, and metagene expression levels are determined using a test dataset containing bulk RNA sequencing data. In some embodiments, metagenes are determined using a reference database containing single-cell RNA sequencing data, and metagene expression levels are determined using a test dataset containing single-cell RNA sequencing data. In some embodiments, metagenes are determined using a reference database containing bulk RNA sequencing data and reference databases containing single-cell RNA sequencing data, and metagene expression levels are determined using a test dataset containing bulk RNA sequencing data. In some embodiments, metagenes are determined using a reference database containing bulk RNA sequencing data and reference databases containing single -cell RNA sequencing data, and metagene expression levels are determined using a test dataset containing single-cell RNA sequencing data.

[0115] In some embodiments, metagenes are determined by applying a dimensionality reduction technique on 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 technique 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 technique and a test dataset are used to determine metagene expression levels based on the test dataset.

[0117] In some embodiments, the one or more outputs of the dimensionality reduction technique includes information on how multiple individual genes are combined to form a metagene. In some embodiments, the one or more outputs of the dimensionality reduction technique includes information on the degree to which an individual gene’s expression level contributes to the expression level of a metagene. In some embodiments, the one or more outputs of the dimensionality reduction technique includes the weights of individual genes, for instance when metagene expression levels are a weighted combination of individual gene expression levels.

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

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

[0120] In some aspects, the methods provided herein include the use of a machine learning model.In some embodiments, the machine learning model is trained to determine the prospect of a cell or a plurality of cells having metagene expression levels of a determined dopaminergic precursor cell. In some embodiments, the machine learning model is trained to determine the probability of a cell or a plurality of cells having metagene expression levels of a determined dopaminergic precursor cell. In some embodiments, the machine learning model is trained to classify a cell or a plurality of cells as having metagene expression levels of a determined dopaminergic precursor cell or not.

[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 expressionlevels determined based on reference databases (e.g., as 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 comprised in the reference databases. In some embodiments, the reference cell labels indicate if the corresponding reference cells are determined dopaminergic precursor cells. In some embodiments, the reference cell labels indicate the period of time that corresponding reference cells have differentiated under conditions to become dopaminergic neurons, e.g., any of the periods of time described in Section II. In some embodiments, the reference cell labels indicate if the period of time is at least or at least about 18 days. In some embodiments, the reference cell labels indicate if the period of time is between or between about 18 and 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 someembodiments, 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 comprising any of the foregoing models.

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

[0125] At some point after a reference database is received the methods can include performing unsupervised classification. This means that a new sorting of the data is performed, with nopreconceptions about the results of the sorting. The sorting is typically performed multiple times, at least 5, 10, 20, 50, 100, 200, 300, 500, for example. The sorting results are analyzed for a result that is stable,meaning that the result of the sorting is providing the same result, or a similar result (at least 80%, 85%, 90%, 95%, 97%, 99% or 100% of the previous result). The re-sorting of the data can be performed completely de novo or it can start with certain assumptions.

[0126] In some embodiments, metagene expression levels for test cells are determined based on a test dataset (e.g., any of the test datasets described in Section I.B. and using any of the methods described in Section I.C.), and the metagene expression levels are applied as input to the trained machine learning model. In some embodiments, the machine learning model outputs a binary prediction of the test cells having metagene expression levels of a determined dopaminergic precursor cell. In some embodiments, the machine learning model outputs the prospect of the test cells having metagene expression levels of a determined dopaminergic precursor cell. In some embodiments, the machine learning model outputs the probability of the test cells having metagene expression levels of a determined dopaminergic precursor cell. The output (e.g., binary prediction, prospect, probability) is also referred to as a“Neuroscore” herein.

[0127] In some embodiments, the Neuroscore output for test cells, e.g. probability of the test cells having metagene expression levels of a determined dopaminergic precursor cell, is compared to a predetermined threshold. In some embodiments, the methods provided herein output a computed label classification, and the computed label classification indicates that the test cells comprise a determined dopaminergic precursor cell if the predetermined threshold is exceeded.

[0128] A variety of methods and criteria can be used to set a predetermined threshold for the Neuroscore. For instance, the predetermined threshold can be set in order to optimize specificity and / or sensitivity in predicting if test cells have metagene expression levels of a determined dopaminergic precursor cell. In some embodiments, the predetermined threshold is set such that test cells having metagene expression levels of a determined dopaminergic precursor cell are identified with greater than or greater than about 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, or 99% sensitivity. In some embodiments, the predetermined threshold is set such that test cells having metagene expression levels of a determined dopaminergic precursor cell are identified with greater than or 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 metagene expression levels of a determined dopaminergic precursor cell are identified with greater than or greater than 98% sensitivity and 100% specificity.

[0129] In some embodiments, the predetermined threshold is set based on Neuroscores calculated based on reference databases. In some embodiments, the reference databases comprise 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 or at least about 18 days have Neuroscores exceeding the predetermined threshold. In some embodiments, the predetermined threshold is set such that reference cells differentiated for between or between about 18 and 25 days have Neuroscores exceeding the predetermined threshold. In some embodiments, thepredetermined threshold is set such that reference cells known to have a therapeutic effect, e.g., reduce or reverse symptoms of Parkinson’s disease, have Neuroscores exceeding the predetermined threshold.

[0130] In some embodiments, the computed label classification indicates that the test cells are or contain a determined dopaminergic precursor cell if the test cells’ Neuroscore indicates a probability greater than or greater than about 0.4 of the test cells’ having metagene expression levels of a determined dopaminergic precursor cell. In some embodiments, the computed label classification indicates that the test cells are or contain a determined dopaminergic precursor cell if the test cells’ Neuroscore indicates a probability greater than or greater than about 0.45 of the test cells’ having metagene expression levels of a determined dopaminergic precursor cell. In some embodiments, the computed label classification indicates that the test cells are or contain a determined dopaminergic precursor cell if the test cells’ Neuroscore indicates a probability greater than or greater than about 0.5 of the test cells’ having metagene expression levels of a determined dopaminergic precursor cell. In some embodiments, the computed label classification indicates that the test cells are or contain a determined dopaminergic precursor cell if the test cells’ Neuroscore indicates a probability greater than or greater than about 0.55 of the test cells’ having metagene expression levels of a determined dopaminergic precursor cell. In some embodiments, the computed label classification indicates that the test cells are or contain a determined dopaminergic precursor cell if the test cells’ Neuroscore indicates a probability greater than or greater than about 0.6 of the test cells’ having metagene expression levels of a determined dopaminergic precursor cell. In some embodiments, the computed label classification indicates that the test cells are or contain a determined dopaminergic precursor cell if the test cells’ Neuroscore indicates a probability greater than or greater than about 0.65 of the test cells’ having metagene expression levels of a determined dopaminergic precursor cell. In some embodiments, the computed label classification indicates that the test cells are or contain a determined dopaminergic precursor cell if the test cells’ Neuroscore indicates a probability greater than or greater than about 0.7 of the test cells’ having metagene expression levels of a determined dopaminergic precursor cell. In some embodiments, the computed label classification indicates that the test cells are or contain a determined dopaminergic precursor cell if the test cells’ Neuroscore indicates a probability greater than or greater than about 0.75 of the test cells’ having metagene expression levels of a determined dopaminergic precursor cell. In some embodiments, the computed label classification indicates that the test cells are or contain a determined dopaminergic precursor cell if the test cells’ Neuroscore indicates a probability greater than or greater than about 0.8 of the test cells’ having metagene expression levels of a determined dopaminergic precursor cell. In some embodiments, the computed label classification indicates that the test cells are or contain a determined dopaminergic precursor cell if the test cells’ Neuroscore indicates a probability greater than or greater than about 0.85 of the test cells’ having metagene expression levels of a determined dopaminergic precursor cell. In some embodiments, the computed label classification indicates that the test cells are or contain a determined dopaminergic precursor cell if the test cells’Neuroscore indicates a probability greater than or greater than about 0.9 of the test cells’ having metagene expression levels of a determined dopaminergic precursor cell. In some embodiments, the computed label classification indicates that the test cells are or contain a determined dopaminergic precursor cell if the test cells’ Neuroscore indicates a probability greater than or greater than about 0.95 of the test cells’ having metagene expression levels of a determined dopaminergic precursor cell.

[0131] In some embodiments, the computed label classification indicates that the test cells are or contain a determined dopaminergic precursor cell if the test cells’ Neuroscore is greater than or greater than about a threshold probability value. In some embodiments, the threshold probability value is between or between about 0.4 and 1, inclusive. In some embodiments, the threshold probability value is between or between about 0.4 and 0.9, inclusive. In some embodiments, the threshold probability value is between or between about 0.4 and 0.8, inclusive. In some embodiments, the threshold probability value is between or between about 0.4 and 0.7, inclusive. In some embodiments, the threshold probability value is between or between about 0.4 and 0.6, inclusive. In some embodiments, the threshold probability value is between or between about 0.5 and 0.8, inclusive. In some embodiments, the threshold probability value is between or between about 0.5 and 0.7, inclusive. In some embodiments, the threshold probability value is between or between about 0.5 and 0.6, inclusive.

[0132] In some embodiments, the threshold probability value is or is about 0.4. In someembodiments, the threshold probability value is or is about 0.45. In some embodiments, the threshold probability value is or is about 0.5. In some embodiments, the threshold probability value is or is about 0.55. In some embodiments, the threshold probability value is or is about 0.6. In some embodiments, the threshold probability value is or is about 0.65. In some embodiments, the threshold probability value is or is about 0.7. In some embodiments, the threshold probability value is or is about 0.75. In some embodiments, the threshold probability value is or is about 0.8. In some embodiments, the threshold probability value is or is about 0.85. In some embodiments, the threshold probability value is or is about 0.9. In some embodiments, the threshold probability value is or is about 0.95.E. Deviation Score (e.g. Novelty Score)

[0133] In some aspects, the methods provided herein comprise calculating a deviation score. The deviation score, also referred to herein as a Novelty Score, indicates the degree to which gene expression levels comprised in a test dataset (e.g., any described in Section I.B.) differ from expected gene expression levels. Expected gene expression values can be determined using a variety of methods. In some embodiments, expected gene expression levels are based on gene expression levels comprised in a reference database, for instance any exemplified in Section I.A. In some embodiments, expected gene expression levels are based on average gene expression levels in a reference database.

[0134] In some embodiments, expected gene expression levels are based on the expression levels of one or more metagenes determined for a test dataset, for instance determined using any of the exemplary methods described in Section I.C. herein. In some embodiments, expected gene expression levels arecalculated based on gene expression levels in the test dataset and metagenes and expression levels thereof determined for the test dataset. Any method that can be used to calculate an expected value (e.g., expected gene expression level) based on the relationship between one or more predictors (e.g., metagene expression levels for the test dataset) and a dependent value (e.g., gene expression levels in the test dataset) can be used. In some embodiments, regression analysis is used to calculate expected gene expression levels for the test dataset.

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

[0136] In some embodiments, the deviation score is based on a set of preselected marker genes. In some embodiments, the marker genes are chosen based on their diagnostic capability, for instance if their expression levels can be used to distinguish between cell types (e.g., determined dopaminergic precursor cells and other cell types). In some embodiments, the marker genes comprise radial glial cell markers, early neuronal development genes, pluripotency specific markers, intermediate to late 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 development of neurons, genes controlling dopaminergic neuron development, genes regulating identity and fate of neuronal progenitor cells, dopaminergic neuron markers, astrocyte markers, forebrain markers, hindbrain markers, subthalamic nucleus markers, radial glial markers, cell cycle markers, or any combination of any of the foregoing. In some embodiments, the marker genes include genes not expected to be expressed by determined dopaminergic precursor cells. In some embodiments, the marker genes include one or more of any of the genes described in Table El.

[0137] In some embodiments, preliminary deviation scores are calculated, and the maximum preliminary deviation score is output as the deviation score. In some embodiments, a first deviation score is calculated based on all genes whose expression levels are contained in the test dataset, and a second deviation score is calculated based on a subset of genes. In some embodiments, a first deviation score is calculated based on all genes whose expression levels are contained in the test dataset, and a 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 single genes is calculated as residuals (i.e., differences) between gene expression levels comprised in a test dataset and gene expression levels of one or more reference cells. In some embodiments, the one or more reference cells are at a stage of differentiation indicating a determined dopaminergic precursor cell. In some embodiments, the residuals are normalized. In some embodiments, the residuals are normalized by dividing by the variance of gene expression levels in a reference database, e.g., any of those described in Section I.A. In someembodiments, the residuals are normalized by dividing by the standard deviation of gene expression levels in the reference database.

[0139] In some embodiments, the deviation score is a summary statistic of the one or more single gene deviation scores. Any known summary statistic can be used. In some embodiments, the deviation score is the 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, single-gene deviation scores of particular genes (e.g., marker genes, for instance those described in Table El herein), are weighted more than single-gene deviation scores for other genes. In some embodiments, the deviation score is the single-gene deviation score corresponding to a percentile of one or more single-gene deviation scores. In some embodiments, the percentile is between or between about the 50% percentile and the 100% percentile. In some embodiments, the percentile is between or between about the 60% percentile and the 100% percentile. In some embodiments, the percentile is between or between about the 70% percentile and the 100% percentile. In some embodiments, the percentile is between or between about the 80% percentile and the 100% percentile. In some embodiments, the percentile is between or between about the 90% percentile and the 100% percentile. In some embodiments, the percentile is or is about the 95% percentile.

[0140] In some embodiments, the Novelty Score output for test cells is compared to a predetermined threshold. In some embodiments, the methods provided herein output a computed label classification, and the computed label classification indicates that the test cells are or contain a determined dopaminergic precursor cell if the predetermined threshold is not exceeded.

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

[0142] In some embodiments, the computed label classification indicates that the test cells are or contain a determined dopaminergic precursor cell if the test cells’ Novelty Score indicates that at least or at least about 50% of gene expression levels in the test dataset are no more than five standard deviations away from expected gene expression levels. In some embodiments, the computed label classification indicates that the test cells are or contain a determined dopaminergic precursor cell if the test cells’ Novelty Score indicates that at least or at least about 60% of gene expression levels in the test dataset are no more than five standard deviations away from expected gene expression levels. In some embodiments, the computed label classification indicates that the test cells are or contain a determined dopaminergic precursor cell if the test cells’ Novelty Score indicates that at least or at least about 70% of gene expression levels in the test dataset are no more than five standard deviations away from expected gene expression levels. In some embodiments, the computed label classification indicates that the test cells are or contain a determined dopaminergic precursor cell if the test cells’ Novelty Score indicates that at leastor at least about 80% of gene expression levels in the test dataset are no more than five standard deviations away from expected gene expression levels. In some embodiments, the computed label classification indicates that the test cells are or contain a determined dopaminergic precursor cell if the test cells’ Novelty Score indicates that at least or at least about 90% of gene expression levels in the test dataset are no more than five standard deviations away from expected gene expression levels. In some embodiments, the computed label classification indicates that the test cells are or contain a determined dopaminergic precursor cell if the test cells’ Novelty Score indicates that at least or at least about 95% of gene expression levels in the test dataset are no more than five standard deviations away from expected gene expression levels.

[0143] In some embodiments, the computed label classification indicates that the test cells are or contain a determined dopaminergic precursor cell if the test cells’ Novelty Score indicates that at least or at least about 95% of gene expression levels in the test dataset are no more than 10 standard deviations away from expected gene expression levels. In some embodiments, the computed label classification indicates that the test cells are or contain a determined dopaminergic precursor cell if the test cells’ Novelty Score indicates that at least or at least about 95% of gene expression levels in the test dataset are no more than 9 standard deviations away from expected gene expression levels. In some embodiments, the computed label classification indicates that the test cells are or contain a determined dopaminergic precursor cell if the test cells’ Novelty Score indicates that at least or at least about 95% of gene expression levels in the test dataset are no more than 8 standard deviations away from expected gene expression levels. In some embodiments, the computed label classification indicates that the test cells are or contain a determined dopaminergic precursor cell if the test cells’ Novelty Score indicates that at least or at least about 95% of gene expression levels in the test dataset are no more than 7 standard deviations away from expected gene expression levels. In some embodiments, the computed label classification indicates that the test cells are or contain a determined dopaminergic precursor cell if the test cells’ Novelty Score indicates that at least or at least about 95% of gene expression levels in the test dataset are no more than 6 standard deviations away from expected gene expression levels. In some embodiments, the computed label classification indicates that the test cells are or contain a determined dopaminergic precursor cell if the test cells’ Novelty Score indicates that at least or at least about 95% of gene expression levels in the test dataset are no more than five standard deviations away from expected gene expression levels.

[0144] In some embodiments, the computed label classification indicates that the test cells are or contain a determined dopaminergic precursor cell if the test cells’ Novelty Score indicates that at least or at least about 50% of marker gene expression levels in the test dataset are no more than five standard deviations away from expected gene expression levels. In some embodiments, the computed label classification indicates that the test cells are or contain a determined dopaminergic precursor cell if the test cells’ Novelty Score indicates that at least or at least about 60% of marker gene expression levels inthe test dataset are no more than five standard deviations away from expected gene expression levels. In some embodiments, the computed label classification indicates that the test cells are or contain a determined dopaminergic precursor cell if the test cells’ Novelty Score indicates that at least or at least about 70% of marker gene expression levels in the test dataset are no more than five standard deviations away from expected gene expression levels. In some embodiments, the computed label classification indicates that the test cells are or contain a determined dopaminergic precursor cell if the test cells’ Novelty Score indicates that at least or at least about 80% of marker gene expression levels in the test dataset are no more than five standard deviations away from expected gene expression levels. In some embodiments, the computed label classification indicates that the test cells are or contain a determined dopaminergic precursor cell if the test cells’ Novelty Score indicates that at least or at least about 90% of marker gene expression levels in the test dataset are no more than five standard deviations away from expected gene expression levels. In some embodiments, the computed label classification indicates that the test cells are or contain a determined dopaminergic precursor cell if the test cells’ Novelty Score indicates that at least or at least about 95% of gene expression levels in the test dataset are no more than five standard deviations away from expected gene expression levels.

[0145] In some embodiments, the computed label classification indicates that the test cells are or contain a determined dopaminergic precursor cell if the test cells’ Novelty Score indicates that at least or at least about 95% of marker gene expression levels in the test dataset are no more than 10 standard deviations away from expected gene expression levels. In some embodiments, the computed label classification indicates that the test cells are or contain a determined dopaminergic precursor cell if the test cells’ Novelty Score indicates that at least or at least about 95% of marker gene expression levels in the test dataset are no more than 9 standard deviations away from expected gene expression levels. In some embodiments, the computed label classification indicates that the test cells are or contain a determined dopaminergic precursor cell if the test cells’ Novelty Score indicates that at least or at least about 95% of marker gene expression levels in the test dataset are no more than 8 standard deviations away from expected gene expression levels. In some embodiments, the computed label classification indicates that the test cells are or contain a determined dopaminergic precursor cell if the test cells’ Novelty Score indicates that at least or at least about 95% of marker gene expression levels in the test dataset are no more than 7 standard deviations away from expected gene expression levels. In some embodiments, the computed label classification indicates that the test cells are or contain a determined dopaminergic precursor cell if the test cells’ Novelty Score indicates that at least or at least about 95% of marker gene expression levels in the test dataset are no more than 6 standard deviations away from expected gene expression levels. In some embodiments, the computed label classification indicates that the test cells are or contain a determined dopaminergic precursor cell if the test cells’ Novelty Score indicates that at least or at least about 95% of marker gene expression levels in the test dataset are no more than five standard deviations away from expected gene expression levels.

[0146] In some embodiments, the computed label classification indicates that the test cells are or contain a determined dopaminergic precursor cell if the test cells’ Novelty Score is less than less than about 10. In some embodiments, the computed label classification indicates that the test cells are or contain a determined dopaminergic precursor cell if the test cells’ Novelty Score is less than less than about 9. In some embodiments, the computed label classification indicates that the test cells are or contain a determined dopaminergic precursor cell if the test cells’ Novelty Score is less than less than about 8. In some embodiments, the computed label classification indicates that the test cells are or contain a determined dopaminergic precursor cell if the test cells’ Novelty Score is less than less than about 7. In some embodiments, the computed label classification indicates that the test cells are or contain a determined dopaminergic precursor cell if the test cells’ Novelty Score is less than less than about 6. In some embodiments, the computed label classification indicates that the test cells are or contain a determined dopaminergic precursor cell if the test cells’ Novelty Score is less than less than about 5.F. Exemplary Method

[0147] In some embodiments, the methods provided herein are used to determine if test cells, e.g. a population of neuronal progenitor cells produced by a differentiation process from iPSCs, are or contain determined dopaminergic precursor cells. In some embodiments, the ability to determine if a test cell population contains determined dopaminergic precursor cells according to any of the methods provided herein can validate release of the cells for use in subsequent applications. In some embodiments, subsequent applications can include therapeutic applications of the determined dopaminergic precuros cells, such as for use in treating a neurodegeneriative disease. In some embodiments, the therapeutic applications include the implantation 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 implanted in the substantia nigra for treating the neurodegenerative disease, e.g. Parkinson’s disease.

[0148] An exemplary process in accord with the provided methods is shown in FIG. 9. In some embodiments, a reference database containing gene expression levels from publically available databases are used. In some embodiments, a reference database containing gene expression levels obtained from single -cell RNA sequencing are used. In some embodiments, a reference database containing gene expression levels obtained from bulk RNA sequencing are used. In some embodiments, the reference database is used (circles 3 and 4) to determine metagenes. In some embodiments, metagene expression levels are calculated for the reference databases and used (circle 5) to train a machine learning model to determine the probability of test cells having metagene expression levels of a determined dopaminergic precursor cell. In some embodiments, the machine learning model can be validated (circle 6) using additional data, for instance bulk RNA sequencing data not used in training the model.

[0149] In some embodiments, the trained machine learning is used as part of the methods provided herein (circle 7) for classifying test cells. In some embodiments, Novelty Scores are calculated based on the reference databases. In some embodiments, the Novelty Scores based on the reference databases are used to identify NeuroScore and Novelty Score thresholds (circle 8).

[0150] In some embodiments, test cells are used to produce a test dataset including 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 supplied to the 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 the Novelty Score are compared to the previously determined NeuroScore and Novelty Score thresholds. In some embodiments, the test cells are transplanted and / or screened, for instance if both thresholds are met. In some embodiments, the test cells are discarded, for instance if neither threshold is met.

[0151] In some embodiments, reference cells and reference databases are produced, for instance according to any of the methods described in Sections I.A and II. In some embodiments, the reference cells are produced using iPSCs generated from subjects with Parkinson’s disease. In some embodiments, the reference databases include gene expression levels of reference cells allowed to differentiate from iPSCs for various times in culture,, such as for, for about, or for at least 13, 18, and 25 days under conditions to differentiate iPSCs into neuronal cells. In some embodiments, the reference database includes bulk RNA sequencing data. In some embodiments, the reference database includes single -cell RNA sequencing data. In some embodiments, the reference database includes reference cell labels indicating if reference cells exhibit features of determined dopaminergic precursor cells, for example, as determined by functional assays, such as using animal models of a neurodegenerative disease. In some embodiments, the reference database includes reference cell labels of a cell population differentiated into neuronal cells from iPSCs for, for about, or for at least 18 days. The methods of differentiation can include any as described in Section II.

[0152] In some embodiments, the reference database including single -cell RNA sequencing data is used to determine metagenes, for instance using any of the methods described in Section I.C.l. In some embodiments, and based on the determined metagenes, metagene expression levels are determined using a reference database including bulk RNA sequencing data, for instance using any of the methods described in Section I.C.2.

[0153] In some embodiments, the metagene expression levels are used to train a machine learning model, for instance any 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 logisticregression model. In some embodiments, the machine learning model is trained using reference cell labels comprised in the reference databases.

[0154] In some embodiments, test cells and test datasets are produced, for instance using any of the methods described in Sections I.B. and II. In some embodiments, the test cells are produced using iPSCs generated from a patient with Parkinson’s disease. In some embodiments, the test dataset is used to determine metagene expression levels for the test cells, for instance using any of the methods described in Section I.C.2. In some embodiments, the test cells are contained in an in vitro population of cells. In some embodiments, the test cells are contained in an in vitro population of neuronal progenitor cells

[0155] In some embodiments, the metagene expression levels determined from the test dataset are supplied as input to the machine learning model. In some embodiments, the machine learning model outputs a Neuroscore (e.g., any exemplified in Section I.D.). In some embodiments, a Novelty Score is determined using the test dataset, for instance 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 cells.

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

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

[0158] In some embodiments, the computed label classification indicates that the test cells are or contain a determined dopaminergic precursor cell if the test cells’ Neuroscore indicates a probability greater than or greater than about 0.5 of the test cells’ having metagene expression levels of a predetermined dopaminergic precursor cell. In some embodiments, the computed label classification indicates that the test cells are or contain a determined dopaminergic precursor cell if the test cells’ Novelty Score indicates that at least or at least about 95% of gene expression levels in the test dataset are no more than five standard deviations away from expected gene expression levels. In some embodiments, the computed label classification indicates that the test cells are or contain a determined dopaminergic precursor cell if (i) the test cells’ Neuroscore indicates a probability greater than or greater than about 0.5 of the test cells’ having metagene expression levels of a determined dopaminergic precursor cell and (ii)the test cells’ Novelty Score indicates that at least or at least about 95% of gene expression levels in the test dataset are no more than five standard deviations away from expected gene expression levels.

[0159] In some embodiments, the test cells’ computed label classification indicates that the test cells are or contain determined dopaminergic precursor cells. In some embodiments, the in vitro population of cells comprising the test cells identified as determined dopaminergic precursor cells is selected for use. In some embodiments, the in vitro population of cells containing the test cells identified as determined dopaminergic precursor cells is selected for transplant, for instance according to any of the methods described in Section V.

[0160] In some embodiments, the test cells’ computed label classification indicates that the test cells do not contain determined dopaminergic precursor cells. In some embodiments, the test cells’ Novelty Score indicates that less than or less than about 95% of gene expression levels in the test dataset were no more than five standard deviations away from expected gene expression levels. In some embodiments, the in vitro population of cells comprising the test cells not dentified as determined dopaminergic precursor cells is no longer allowed to differentiate. In some embodiments, the in vitro population of cells containing the test cells not dentified as determined dopaminergic precursor cells is discarded. In some embodiments, the methods provided herein are repeated by producing an additional set of test cells and another test dataset. In some embodiments, the additional set of test cells is produced from the same subject with Parkinson’s disease. In some embodiments, the additional set of test cells is produced from the same population of iPSCs with which the first set of test cells was produced. In some embodiments, a computed label classification is output for the additional set of test cells.

[0161] In some embodiments, the test cells’ computed label classification indicates that the test cells do not contain determined dopaminergic precursor cells. In some embodiments, the test cells’ Neuroscore indicates that a probability less than or less than about 0.5 of the test cells’ having metagene expression levels of a determined dopaminergic precursor cell. In some embodiments, the test cells’ Novelty Score indicates that greater than or greater than about 95% of gene expression levels in the test dataset were no more than five standard deviations away from expected gene expression levels. In some embodiments, the in vitro population of cells containing the test cells not dentified as determined dopaminergic precursor cells is allowed to continue differentiating. In some embodiments, an additional set of test cells and test dataset from the same in vitro population of cells is collected. In some embodiments, a 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 between or between about one and 30 days after testing of 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 between or between about one and 25 days after testing of 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 between or between about one and 20 days after testing of the first set of testcells. In some embodiments, the additional set of test cells is collected and tested according to the methods provided herein between or between about one and 15 days after testing of 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 between or between about one and 10 days after testing of 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 between or between about one and 5 days after testing of 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 between or between about one and 3 days after testing of the first set of test cells.

[0163] In some embodiments, the methods provided herein are repeated until a computed label classification is provided indicating that test cells produced from the subject are or contain determined dopaminergic precursor cells.

[0164] In embodiments, the computed label classification is an 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 cellular RNA. In embodiments, the gene expression profile information is obtained from microarray analysis of cellular RNA derived from a single cell. In embodiments, the computed label classification is an unsupervised machine classification including a bootstrapping 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 includes receiving input from an array analysis system. In embodiments, receiving the test dataset includes receiving input via a computer network. Inembodiments, the data in the reference database is associated with one or more labeled associated biological classes of the cells.II. METHODS FOR DIFFERENTIATING CELLS

[0166] In some aspects, the methods provided herein include the use of reference cells and / or test cells that are the product of a method to differentiate a cell. In some embodiments, the reference cells and / or test cells described in Sections I.A. and I.B. are the product of a method to differentiate a pluripotent stem cell. Various sources of pluripotent stem cells can be used, including embryonic stem (ES) cells and induced pluripotent stem cells (iPSCs). In some embodiments, the cell is an iPSC. In some embodiments, the pluripotent stem cell is an iPSC. In some embodiments, the pluripotent stem cell is an iPSC, artificially derived from a non-pluripotent cell. iPSCs may be generated by a process known as reprogramming, wherein 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 cell is a pluripotent stem cell. In some embodiments, the cell is a pluripotent stem cell that was artificially derived from a non-pluripotent cell of a subject. In someembodiments, the non-pluripotent cell is a fibroblast. 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 cell is an iPSC.

[0168] A standard art-accepted test, such as the ability to form a teratoma in 8-12 week old SCID mice, can be used to establish the pluripotency of a cell population. However, identification of various pluripotent stem cell characteristics can also be used to identify pluripotent cells. In some aspects, pluripotent stem cells can be distinguished from other cells by particular characteristics, including by expression or non-expression of certain combinations of molecular markers. More specifically, human pluripotent stem cells may express at least some, and optionally all, of the markers from the following non-limiting list: SSEA-3, SSEA-4, TRA-1-60, TRA-1-81, TRA-2-49 / 6E, ALP, Sox2, E-cadherin, UTF- 1, Oct4, Lin28, Rexl, and Nanog. In some aspects, a pluripotent stem cell characteristic is a cell morphologies associated with pluripotent stem cells.

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

[0170] In some embodiments, the reference cells and / or the test cells are neuronal cells that have been differentitated from a pluripotent stem cell. In some embodiments, the cells are differentiated using methods that differentiate cells, e.g., iPSCs, into any neural cell type using any available or known method for inducing the differentiation of cells. As is understood, the particular differentiation protocol and timing of the culture may result in different states of differentiated neuronal cells. In some embodiments, the differentiation is carried out by culture of pluripotent stem cells, e.g. iPSCs, under conditions to produce neuronal progenitor cells that are or include cells that are committed to being a neuronal cell. In some embodiments, the iPSCs are differentiated under conditions to result in floor plate midbrain progenitor cells, determined dopaminergic precursor cells, and / or dopamine (DA) neurons. In some embodiments, iPSCs are cultured under conditions to for differentiation into determined dopaminergic precursor cells. In some embodiments, the iPSCs are cultured under conditions to differentiate into dopaminergic neurons. Any available and known method for inducing differentiation of the cells, e.g., pluripotent stem cells, into floor plate midbrain progenitor cells, determined dopaminergic precursor cells, and / or dopamine (DA) neurons can be used. Exemplary methods of differentiating neural cells can be found, e.g., in WO2013104752, W02010096496, WO2013067362, WO2014176606, WO2016196661, WO2015143342, US20160348070, the contents of which are hereby incorporated by reference in their entirety.In some embodiments, iPSCs are allowed to differentiate in culture as part ofdifferentiation into neuronal cells. In some embodiments, the cells are cultured or incubated in the presence of one or more factors able to induce or promote the differentiation of iPSCs into neuronal cells. In some embodiments, the iPSCs are cultured in the presence of one or more of (i) an inhibitor of TGF- b / activing-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 3b (05K3b) signaling. In some embodiments, the iPSCs are cultured in the presence of (i) an inhibitor of TGF^ / activing-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 3b (GSK3b) signaling. In some embodiments, the inhibitor of TGF^ / activing- Nodal signaling is SB431542 (e.g. between about 1 mM and about 20 mM, such as 10 mM). In some embodiments, the at least one activator of SHH signaling is SHH (e.g. between about 10 ng / mL and about 500 ng / mL, such as 100 ng / mL) or purmorphamine (e.g. between about 0.1 mM and about 10 mM, such as 2 mM). In some embodiments, the at least one activator of SHH signaling includes SHH protein (e.g. between about 10 ng / mL and about 500 ng / mL, such as 100 ng / mL) and purmorphamine (e.g.between about 0.1 mM and about 10 mM, such as 2 mM). In some embodiments, the inhibitor of BMP signaling is LDN193189 (e.g. between about 0.01 mM and about 5 mM, such as 0.1 mM). In some embodiments, the inhibitor of GSK3b signaling is CHIR99021 (e.g. between about 0.1 mM and about 10 mM, such as 2 mM).

[0171] In some embodiments, the iPSCs are exposed to the one or more factors or agents at the initiation of the culturing or incubation (day 0). In some embodiments, the presence of the one or more of the factors or agents, each independently, may be maintained in the culture for the duration of the culture or for a portion of the culture. In some embodiments, the one or more factors or agents are, each independently, present in the culture for a time period to allow differentiation of the iPSCs 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 provided methods. In some embodiments, the one or more factors or agents are, each independently, present in the culture for up to day 5, up to day 6, up to day 7, up to day 8, up to day 9, up to day 10, up to day 11, upt to day 12 or up to day 13 of the culture. For example, in an exemplary protocol, the culturing under conditions for differentiating iPSCs into neuronal cells includes initiating a first incubation on about day 0, wherein the first incubation includes culturing the pluripotent stem cells and exposing the cells to (i) an inhibitor of TGF^ / activing-Nodal signaling from day 0 through day 10, each day inclusive; (ii) at least one activator of Sonic Hedgehog (SHH) signaling from day 1 through day 6, each day inclusive; (iii) an inhibitor of bone morphogenetic protein (BMP) signaling from day 0 through day 10, each day inclusive; and (iv) an inhibitor of glycogen synthase kinase 3b (GSK3b) signaling from day 0 through day 12, each day inclusive.

[0172] In some embodiments, a second culture or incubation can be carried out on cells differentiated in the first culture, in which the second culture or incubation is carried out the presence of one or more additional agents or factors under conditions to further neurally differentiate the cells. In some embodiments, the second culture or initiation may be initiated at or about the time that the cells in the first culture have differentiated 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 provided methods. In some embodiments, the one or more additional agents or factors can include any one or more the the one or more factors present in the first culture. In some embodiments, the one or more additional agents or factors can include one or more of (i) brain-derived neurotrophic factor (BDNF); (ii) ascorbic acid; (iii) glial cell-derived neurotrophic factor (GDNF); (iv) cyclic AMP (cAMP), e.g. dibutyryl cyclic AMP (dbcAMP); (v) transforming growth factor beta-3 (TϋRb3) (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-derived neurotrophic factor (GDNF); (iv) dibutyryl cyclic AMP (dbcAMP); (v) transforming growth factor beta-3 (TϋRb3) (collectively,“BAGCT”); and (vi) an inhibitor of Notch. In some embodiments, the cells are exposed to a concentration of BDNF between about 1 ng / mL and 100 ng / mL (e.g. 20 ng / mL). In some embodiments, the cells are exposed to ascorbic acid at a concentration of between about 0.05 mM and 5 mM, e.g. 0.2 rnM. In some embodiments, the cells are exposed to GDNF at a concentration of between 1 ng / mL and 100 ng / mL, e.g. 20 ng / mL. In some embodiments, the cells are exposed to cAMP, e.g.dibutyryl cyclic AMP (dbcAMP), at a concentration between about 0.05 mM and 5 mM, e.g. about 0.5 mM. In some embodiments, the cells are exposed to transforming growth factor beta 3 (TϋRb3) at a concentration of between about 0.1 ng / mL and 10 ng / mL, e.g. 1 ng / mL.

[0173] In some embodiments, the second culture or incubation can be carried out for a period of time to differentate the cells into determined dopaminergic precursor 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 second culture or incubation can be carried out for a period of time to differentatie the cells 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 carried out up until about day 30 after the initiation of the first culture or incubations. In some embodiments, the second culture or incubation is carried out up until about day 11 to day 25 after initiation of the first culture or incubations, such as from day 11, day 12, day 13, day 14, day 15, day 16, day 17, day 18, day 19, day 20, day 21, day 22, day 23, day 24 or day 25. In some embodiments, the second culture or incubation is carried out to at or about day 18 after initiation of the first culture. In some embodiments, the second culture is carried out to at or about day 25 after initiation of the first culture.

[0174] In some embodiments, cells of the culture are exposed to the one or more additional factors or agents for the duration of the culture or for a period of time. In some embodiments, the presence of the one or more of additional factors or agents, each independently, may be maintained in the culture for the duration of the culture or for a portion of the culture. In some embodiments, the one or more additional factors or agents are, each independently, present in the culture for a time period to differentate the cells into determined dopaminergice precursor 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 time period to differentiate the cells into dopaminergic neurons, or until such cells exhibit characteristics of dopaminergic neurons as determined by a classification label in accord with the provided methods. In some embodiments, the second culture or incubation is carried out up until about day 30 after the initiation of the first culture or incubations. In some embodiments, the one or more additional agent or factor are, each independently, present in the culture from the initiation of the second culture until about day 11 to day 25 after initiation of the first culture or incubation, such as up until day 11, day 12, day 13, day 14, day 15, day 16, day 17, day 18, day 19, day 20, day 21, day 22, day 23, day 24 or day 25. In some embodiments, the one or more additional agent or factor are, each independently, present in the culture from the initiation of the second culture to at or about day 18 after initiation of the first culture. In some embodiments, the one or more additional agent or factor are, each independently, present in the culture from the initiation of the second culture until to at or about day 25 after initiation of the first culture. For example, in an exemplary protocol, the culturing under conditions for differentiating iPSCs into neuronal cells further includes a second incubation in which cells from the first incubation are further cultured by exposing the cells to (i) brain -derived neurotrophic factor (BDNF); (ii) ascorbic acid; (iii) glial cell-derived neurotrophic factor (GDNF); (iv) dibutyryl cyclic AMP (dbcAMP); (v) transforming growth factor beta-3 (TGFb3) (collectively,“BAGCT”); and (vi) an inhibitor of Notch, beginning on day 11. In some embodiments, the cells are exposed to BAGCT until harvest of the neurally differentiated cells, such as until day 18 or until day 25. In some embodiments, the second incubation may further include culture by exposing the cells to an inhibitor of GSK3P signaling from day 11 through day 12, each day inclusive.

[0175] In some embodiments, the incubation may include culture by exposing the cells to an inhibitor of Rho-associated protein kinase (ROCK) signaling at one or more times during the culturing, such as on 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. between about 1 mM and about 20 mM, such as about 10 mM.

[0176] In some embodiments, the culturing of the iPSCs under conditions for differentiation into neuronal cells can be for a time period from the initiation of the culturing until harvest of differentiated cells that is between 10 days and 30 days. It is understood that the particular timing may be chosen basedon the desired differentiation state of the cells, for example as determined empirically by a functional or other phenotypic assay or as determined based on classification label of the differentiated cells as determined in accord with the provided methods. In some embodiments, a reference cell isdifferentiated by culture for a certain or defined period of time. In some embodiments a reference cell is differentiated by culture for a total period of time in which the cell is determined to exhibit a desired functional or phenotypic attribute or feature, e.g. as described in Section I. A. In some embodiments, a test cell is differentiated by culture for a total period of time. In some embodiments, a test cell is differentiated by culture for a total period of time at which it is determined the test cell exhibits a desired classification label in accord with the provided methods. In some embodiments, the provided methods can be used to assess if a test cell has been cultured under conditions for its differentiation into a desired neuronal cell, e.g. determined dopaminergic precurosor cell, by its classification label as determined in accord with any of the provided methods.

[0177] In embodiments, the iPSC is cultured for differentation into a neuronal cell for at least 10 days. In embodiments, the iPSC is cultured for differentiation into a neuronal cell for at least 11 days. In embodiments, the iPSC is cultured for differentiation into a neuronal cell for at least 12 days. In embodiments, the iPSC is cultured for differentiation into a neuronal cell for at least 13 days. In embodiments, the iPSC is cultured for differentation into a neuronal cell for at least 14 days. In embodiments, the iPSC is cultured for differentation into a neuronal cell for at least 15 days. In embodiments, the iPSC is cultured for differentation into a neuronal cell for at least 16 days. In embodiments, the iPSC is cultured for differentation into a neuronal cell for at least 17 days. In embodiments, the iPSC is cultured for differentation into a neuronal cell for at least 18 days. In embodiments, the iPSC is cultured for differentation into a neuronal cell for at least 19 days. In embodiments, the iPSC is cultured for differentation into a neuronal cell for at least 20 days.

[0178] In embodiments, the iPSC is cultured for differentation into a neuronal cell for about 10 days. In embodiments, the iPSC is cultured for differentation into a neuronal cell for about 11 days. In embodiments, the iPSC is cultured for differentation into a neuronal cell for about 12 days. In embodiments, the iPSC is cultured for differentation into a neuronal cell for about 13 days. In embodiments, the iPSC is cultured for differentation into a neuronal cell for about 14 days. In embodiments, the iPSC is cultured for differentation into a neuronal cell for about 15 days. In embodiments, the iPSC is cultured for differentation into a neuronal cell for about 16 days. In embodiments, the iPSC is cultured for differentation into a neuronal cell for about 17 days. In embodiments, the iPSC is cultured for differentation into a neuronal cell for about 18 days. In embodiments, the iPSC is cultured for differentation into a neuronal cell for about 19 days. In embodiments, the iPSC is cultured for differentation into a neuronal cell for about 20 days. In embodiments, the iPSC is cultured for differentation into a neuronal cell for about 21 days. In embodiments, the iPSC is cultured for differentation into a neuronal cell for about 22 days. Inembodiments, the iPSC is cultured for differentation into a neuronal cell for about 23 days. In embodiments, the iPSC is cultured for differentation into a neuronal cell for about 24 days. In embodiments, the iPSC is cultured for differentation into a neuronal cell for about 25 days.

[0179] In some embodiments, reference cells, for example as described in Section I. A., undergo methods of differentiation as desribed herein. In some embodiments, test cells, for example as described in Section I.B., undergo methods of differentiation as described herein. In some embodiments, both reference cells and test cells undergo the same methods of differentiation as provided herein.III. EXEMPLARY FEATURES OF A DETERMINED DOPAMINERGIC NEURON

[0180] In some embodiments, the determined dopaminergic precursor cells identified by the methods provided herein have certain increased and / or decreased gene expression levels relative to a pluripotent stem cell. In some embodiments, an in vitro population of neuronal progenitor cells having certain increased and / or decreased gene expression levels relative to a pluripotent stem cell is indicative of the in vitro population comprising desirable determined dopaminergic precursor cells.

[0181] In embodiments, the gene expression profile information for the desirable determined dopaminergic precursor cell includes increased gene expression levels relative to a pluripotent stem cell for a first gene set, wherein the first gene set includes at least one increased gene within one or more first gene ontologies of Table 1.

[0182] In embodiments, the gene expression profile information for the desirable determined dopaminergic precursor cell includes increased gene expression levels relative to a pluripotent stem cell for a first gene set, wherein the first gene set includes at least one increased gene within one or more first gene ontologies selected from the group consisting of gene ontologies of Table 1.

[0183] In embodiments, the gene expression profile information for the desirable determined dopaminergic precursor cell includes increased gene expression levels relative to a pluripotent stem cell for a first gene set, wherein the first gene set includes at least one increased gene within one or more first gene ontologies 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:0045202, 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:0098794, GO:0022610, GO:0030424, GO:0099240, GO:0032989, GO:0120035, GO:0000902, GO:0007148, GO:0045790, GO:0045791, GO:0048812, GO:0036477, 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, 00:0044447, 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, 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 gene expression profile information for the desirable determined dopaminergic precursor cell includes increased gene expression levels relative to a pluripotent stem cell for a first gene set, wherein the first gene set includes at least one increased gene within one or more first gene ontologies selected from the group consisiting 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, 00:0044463, GO:0097458, GO:0045202, 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:0098794, GO:0022610, GO:0030424, GO:0099240, GO:0032989, GO:0120035, GO:0000902, GO:0007148, GO:0045790, GO:0045791, GO:0048812, GO:0036477, 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: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, 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 and any combination thereof.

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

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

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

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

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

[0190] In embodiments, the first gene set includes about 405-500 increased genes within one or more of the first gene ontologies. In embodiments, the first gene set includes about 415-500 increased genes within one or more of the first gene ontologies. In embodiments, the first gene set includes about 420-500 increased genes within one or more of the first gene ontologies. In embodiments, the first gene set includes about 425-500 increased genes within one or more of the first gene ontologies. In embodiments, the first gene set includes about 430-500 increased genes within one or more of the first gene ontologies. In embodiments, the first gene set includes about 435-500 increased genes within one or more of the first gene ontologies. In embodiments, the first gene set includes about 440-500 increased genes within one or more of the first gene ontologies. In embodiments, the first gene set includes about 445-500 increased genes within one or more of the first gene ontologies. In embodiments, the first gene set includes about 450-500 increased genes within one or more of the first gene ontologies. In embodiments, the first gene set includes about 455-500 increased genes within one or more of the first gene ontologies. In embodiments, the first gene set includes about 460-500 increased genes within one or more of the first gene ontologies. In embodiments, the first gene set includes about 465-500 increased genes within one or more of the first gene ontologies. In embodiments, the first gene set includes about470-500 increased genes within one or more of the first gene ontologies. In embodiments, the first gene set includes about 475-500 increased genes within one or more of the first gene ontologies. In embodiments, the first gene set includes about 480-500 increased genes within one or more of the first gene ontologies. In embodiments, the first gene set includes about 485-500 increased genes within one or more of the first gene ontologies. In embodiments, the first gene set includes about 490-500 increased genes within one or more of the first gene ontologies. In embodiments, the first gene set includes about 495-500 increased genes within one or more of the first gene ontologies.

[0191] In embodiments, the first gene set 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, 120, 121 ,122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142,143, 144, 145, 146, 147 ,148 ,149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163,164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184,185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205,206, 207 ,208, 209, 210, 211 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247 ,248 ,249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289,290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307 ,308, 309, 310,311 312, 313, 314, 315 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347 ,348 ,349, 350, 351, 352,353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373,374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, 391, 392, 393, 394,395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407 ,408, 409, 410, 411 412, 413, 414, 415 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436,437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447 ,448 ,449, 450, 451, 452, 453, 454, 455, 456, 457,458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478,479, 480, 481, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 492, 493, 494, 495, 496, 497, 498, 499 or 500 increased genes within one or more of the first gene ontologies.

[0192] The gene expression profile information for the desirable determined dopaminergic precursor cell may include increased gene expression levels relative to a pluripotent stem cell for a first gene set, wherein the first gene set includes at least one increased gene within one or more first gene ontologies of Table 1. “One or more” as described herein in the context of first gene ontologies refers to at least one, for example, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, etc. of first gene ontologies. Inembodiments, the first gene set includes about 1-500 increased genes within 1-300 of the first gene ontologies. In embodiments, the first gene set includes about 1-500 increased genes within 10-300 of the first gene ontologies. In embodiments, the first gene set includes about 1-500 increased genes within 20- 300 of the first gene ontologies. In embodiments, the first gene set includes about 1-500 increased genes within 30-300 of the first gene ontologies. In embodiments, the first gene set includes about 1-500 increased genes within 40-300 of the first gene ontologies. In embodiments, the first gene set includes about 1-500 increased genes within 50-300 of the first gene ontologies. In embodiments, the first gene set includes about 1-500 increased genes within 60-300 of the first gene ontologies. In embodiments, the first gene set includes about 1-500 increased genes within 70-300 of the first gene ontologies. In embodiments, the first gene set includes about 1-500 increased genes within 80-300 of the first gene ontologies. In embodiments, the first gene set includes about 1-500 increased genes within 90-300 of the first gene ontologies. In embodiments, the first gene set includes about 1-500 increased genes within 100-300 of the first gene ontologies. In embodiments, the first gene set includes about 1-500 increased genes within 110-300 of the first gene ontologies. In embodiments, the first gene set includes about 1- 500 increased genes within 120-300 of the first gene ontologies. In embodiments, the first gene set includes about 1-500 increased genes within 130-300 of the first gene ontologies. In embodiments, the first gene set includes about 1-500 increased genes within 140-300 of the first gene ontologies. In embodiments, the first gene set includes about 1-500 increased genes within 150-300 of the first gene ontologies. In embodiments, the first gene set includes about 1-500 increased genes within 160-300 of the first gene ontologies. In embodiments, the first gene set includes about 1-500 increased genes within 170-300 of the first gene ontologies. In embodiments, the first gene set includes about 1-500 increased genes within 180-300 of the first gene ontologies. In embodiments, the first gene set includes about 1- 500 increased genes within 190-300 of the first gene ontologies. In embodiments, the first gene set includes about 1-500 increased genes within 200-300 of the first gene ontologies. In embodiments, the first gene set includes about 1-500 increased genes within 210-300 of the first gene ontologies. In embodiments, the first gene set includes about 1-500 increased genes within 220-300 of the first gene ontologies. In embodiments, the first gene set includes about 1-500 increased genes within 230-300 of the first gene ontologies. In embodiments, the first gene set includes about 1-500 increased genes within 240-300 of the first gene ontologies. In embodiments, the first gene set includes about 1-500 increased genes within 250-300 of the first gene ontologies. In embodiments, the first gene set includes about 1- 500 increased genes within 260-300 of the first gene ontologies. In embodiments, the first gene set includes about 1-500 increased genes within 270-300 of the first gene ontologies. In embodiments, the first gene set includes about 1-500 increased genes within 280-300 of the first gene ontologies. In embodiments, the first gene set includes about 1-500 increased genes within 290-300 of the first gene ontologies.

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

[0194] In embodiments, the first gene set includes at least one increased gene within 1, 2, 3, 4, 5, 6,7 ,8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47 ,48 ,49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61 , 62, 63,64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91,92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135,136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147 ,148, 149, 150, 151, 152, 153, 154, 155, 156,157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177,178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198,199, 200, 201, 202, 203, 204, 205, 206, 207, or 208 first gene ontologies of Table 1.

[0195] In embodiments, the first gene set 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, 120, 121 ,122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142,143, 144, 145, 146, 147 ,148 ,149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163,164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184,185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205,206, 207 ,208, 209, 210, 211 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247 ,248 ,249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289,290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307 ,308, 309, 310,311 312, 313, 314, 315 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347 ,348 ,349, 350, 351, 352,353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373,374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, 391, 392, 393, 394,395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407 ,408, 409, 410, 411 412, 413, 414, 415 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436,437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447 ,448 ,449, 450, 451, 452, 453, 454, 455, 456, 457,458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478,479, 480, 481, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 492, 493, 494, 495, 496, 497, 498, 499 or 500 increased genes within 1, 2, 3, 4, 5, 6, 7 ,8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23,24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48 ,49, 50, 51,52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79,80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105,106, 107, 108, 109, 110, 111 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126,127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147,148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168,169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189,190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207 or 208 first gene ontologies of Table 1.

[0196] In embodiments, the first gene ontologies are any one of the gene ontologies listed in Table 1. In embodiments, the first gene ontologies are 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, 00:0044463, GO:0097458, GO:0045202, 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:0098794, GO:0022610, GO:0030424, GO:0099240, GO:0032989, GO:0120035, GO:0000902, GO:0007148, GO:0045790, GO:0045791, GO:0048812, GO:0036477, 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: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, 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 for the desirable determined dopaminergic precursor cell includes increased gene expression levels relative to a pluripotent stem cell for a first gene set, wherein the first gene set includes at least one increased gene within one or more first gene ontologies selected from the group consisting of: G00005509, G00016339, G00007416 and G00048731. In embodiments, the gene expression profile information for the desirable determined dopaminergic precursor cell includes increased gene expression levels relative to a pluripotent stem cell for a first gene set, wherein the first gene set includes at least one increased gene within one or more first gene ontologies of: G00005509, G00016339, G00007416 or G00048731. In embodiments, the gene expression profile information for the desirable determined dopaminergic precursor cell includes increased gene expression levels relative to a pluripotent stem cell for a first gene set, wherein the first gene set includes at least one increased gene within one or more first gene ontologies selected from the group consisting of: G00048699, G00050767, G00060160, G00097458, G00010975, G00022008 and any combination thereof. In embodiments, the gene expression profile information for the desirable determined dopaminergic precursor cell includes increased gene expression levels relative to a pluripotent stem cell for a first gene set, wherein the first gene set includes at least one increased gene within one or more first gene ontologies of: G00048699, G00050767, G00060160, G00097458,GO0010975, G00022008 or any combination thereof.

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

[0199] In embodiments, the first gene set includes at least one (e.g., 1, 2, 3, 4, 5, 6 etc.) increased gene of Table 2. In embodiments, the at least one (e.g., 1, 2, 3, 4, 5, 6 etc.) increased gene is GPM6A, DRD2, BMP7, EFNB3, SEMA3C, FSCN2, LGI1, SRCINl, WNT4, SLIT2, NRG1, TTBK1, RNF165, CDH2, ELAVL4, ONECUT2, KREMEN1, SCRT1, KIAA1024, DSC AM, MAP2, FAT4, PAK3, 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, RUNX1, DPYSL4, NRCAM, ZNF521, MDGA2, PROX1, ZNF536, MAP1A, NEGRI, 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 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, SRCINl, WNT4,SLIT2, NRG1, TTBK1, RNF165, CDH2, ELAVL4, ONECUT2, KREMEN1, SCRT1, KIAA1024, DSCAM, MAP2, FAT4, PAK3, 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, RUNX1, DPYSL4, NRCAM, ZNF521, MDGA2, PROX1, ZNF536, MAPI A, NEGRI, 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.

[0201] In embodiments, the first gene set includes at least one (e.g., 1, 2, 3, 4, 5, 6 etc.) increased gene of Table 3. In embodiments, the at least one (e.g., 1, 2, 3, 4, 5, 6 etc.) increased gene is DRD2, BMP7, EFNB3, SEMA3C, SRCINl, 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, NEGRI, 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, 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, SRCINl, 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, NEGRI, 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 gene set includes at least one (e.g., 1, 2, 3, 4, 5, 6 etc.) increased gene of 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 (e.g., 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 gene set includes at least one (e.g., 1, 2, 3, 4, 5, 6 etc.) increased gene of Table 5. In embodiments, the at least one (e.g., 1, 2, 3, 4, 5, 6 etc.) increased gene is GPM6A, KIFAP3, DRD2, EFNB3, FSCN2, SFC8A1, SCGN, SRCINl, PACRG, TRIM9, NRG1, TTBK1, HTR2A, SFC18A1, CERKF, CDH2, PAEMD, KREMEN1, TANC2, MAPK10, SCN3A, ERRC4, DSCAM, TGFB3, MAP2, EEFN1, PAK3, NGF, CPEB2, DDN, STMN2, ERP2, CAMK2B, SVOP,SRR, SNAP25, PPFIA2, KCNA2, SYT5, BAIAP3, CADM2, CHRM2, DCX, MAGI2, KEHE1, NTRK3, PITX3, P2RX3, ADGRA1, AVIE, CADM3, CDK5R2, IL6ST, KIF5C, SYNJ1, TSPOAP1, DRP2, TMPRSS3, SYBU, HMP19, SNAP91, SCN11A, PALM, SLC1A4, NRCAM, CACNG4, CNIH2, DGKI, CLSTN2, MAPI A, GLRA2, CUBN, SCN7A, EPB41L3, BSN, GAP43, EPHA7, 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, ADC Y API, 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 gene set includes at least one (e.g., 1, 2, 3, 4, 5, 6 etc.) increased gene of 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, SRCINl, PACRG, TRIM9, NRG1, TTBK1, HTR2A, SLC18A1, CERKL, CDH2, PALMD, KREMEN1, TANC2, MAPK10, SCN3A, LRRC4, DSCAM, TGFB3, MAP2, ELFN1, 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, SNAP91, SCN11A, PALM, SLC1A4, NRCAM, CACNG4, CNIH2, DGKI, CLSTN2, MAP1A, GLRA2, CUBN, SCN7A, EPB41L3, BSN, GAP43, EPHA7, VSTM2L, SPOCK1, CX3CL1, MAPK8IP2, CAMK2N1, PDE1C, NCAM2, SLC17A6, SLC18A3, KCNC1, ADGRL3, ZNF804A, SARM1, GRIK4, ENC1 , ASCL1, DMTN, KNCN,TMEM163, CLDN5, KCND3, PCDHB 13, 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 gene set includes at least one (e.g., 1, 2, 3, 4, 5, 6 etc.) increased gene of Table 6. In embodiments, the at least one (e.g., 1, 2, 3, 4, 5, 6 etc.) increased gene is EFNB3, SEMA3C, SRCINl, SLIT2, CDH2, KREMEN1, KIAA1024, DSCAM, MAP2, PAK3, NGF, SEMA6D, STMN2, CAMK2B, ISLR2, SNAP25, MAGI2, NTRK3, AVIL, KALRN, PMP22, NRCAM, NEGRI, PLXNA4, EPHA7, SPOCK1, CX3CL1, ZNF804A, ULK2, SARM1, PLXNA3, ENC1, TRIM67, NTN1, ZNF365, GFI1, ADCYAP1, CNR1, ANKRD1, MAPT, RUFY3, PLXNA2, PLXNC1, MAP1B, PTPRO or FZDl.

[0208] In embodiments, the at least one (e.g., 1, 2, 3, 4, 5, 6 etc.) increased gene is selected from the group consisiting of EFNB3, SEMA3C, SRCINl, SLIT2, CDH2, KREMEN1, KIAA1024, DSCAM, MAP2, PAK3, NGF, SEMA6D, STMN2, CAMK2B, ISLR2, SNAP25, MAGI2, NTRK3, AVIL, KALRN, PMP22, NRCAM, NEGRI, 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 gene set includes at least one (e.g., 1, 2, 3, 4, 5, 6 etc.) increased gene of Table 7. In embodiments, the at least one (e.g., 1, 2, 3, 4, 5, 6 etc.) increased gene is GPM6A, DRD2, BMP7, EFNB3, SEMA3C, FSCN2, LGI1, SRCINl, 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, 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, MAPI A, DCHS1, NEGRI, 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, ADC Y API, 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 consisiting of GPM6A, DRD2, BMP7, EFNB3, SEMA3C, FSCN2, LGI1, SRCINl, 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, 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, NEGRI, 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, PCDHB 14, PCDHB 16, 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 levels are at least 4 times higher relative to a pluripotent stem cell. In embodiments, the increased expression levels are about 4 times higher relative to a pluripotent stem cell. In embodiments, the increased expression levels are at least 5 times higher relative to a pluripotent stem cell. In embodiments, the increased expression levels are about 5 times higher relative to a pluripotent stem cell. In embodiments, the increased expression levels are at least 6 times higher relative to a pluripotent stem cell. In embodiments, the increased expression levels are about 6 times higher relative to a pluripotent stem cell. In embodiments, the increased expression levels are at least 7 times higher relative to a pluripotent stem cell. In embodiments, the increased expression levels are about 7 times higher relative to a pluripotent stem cell. In embodiments, the increased expression levels are at least 8 times higher relative to a pluripotent stem cell. In embodiments, the increased expression levels are about 8 times higher relative to a pluripotent stem cell. In embodiments, the increased expression levels are at least 9 times higher relative to a pluripotent stem cell. In embodiments, the increased expression levels are about 9 times higher relative to a pluripotent stem cell. In embodiments, the increased expression levels are at least 10 times higher relative to a pluripotent stem cell. In embodiments, the increased expression levels are about 10 times higher relative to a pluripotent stem cell.

[0213] In embodiments, the increased expression levels are at least 11 times higher relative to a pluripotent stem cell. In embodiments, the increased expression levels are about 11 times higher relative to a pluripotent stem cell. In embodiments, the increased expression levels are at least 12 times higher relative to a pluripotent stem cell. In embodiments, the increased expression levels are about 12 times higher relative to a pluripotent stem cell. In embodiments, the increased expression levels are at least 13 times higher relative to a pluripotent stem cell. In embodiments, the increased expression levels are about 13 times higher relative to a pluripotent stem cell. In embodiments, the increased expression levels are at least 14 times higher relative to a pluripotent stem cell. In embodiments, the increased expression levels are about 14 times higher relative to a pluripotent stem cell. In embodiments, the increased expression levels are at least 15 times higher relative to a pluripotent stem cell. In embodiments, the increased expression levels are about 15 times higher relative to a pluripotent stem cell. In embodiments, the increased expression levels are at least 16 times higher relative to a pluripotent stem cell. In embodiments, the increased expression levels are about 16 times higher relative to a pluripotent stem cell. In embodiments, the increased expression levels are at least 17 times higher relative to a pluripotent stem cell. In embodiments, the increased expression levels are about 17 times higher relative to a pluripotent stem cell. In embodiments, the increased expression levels are at least 18 times higherrelative to a pluripotent stem cell. In embodiments, the increased expression levels are about 18 times higher relative to a pluripotent stem cell. In embodiments, the increased expression levels are at least 19 times higher relative to a pluripotent stem cell. In embodiments, the increased expression levels are about 19 times higher relative to a pluripotent stem cell. In embodiments, the increased expression levels are at least 20 times higher relative to a pluripotent stem cell. In embodiments, the increased expression levels are about 20 times higher relative to a pluripotent stem cell.

[0214] In embodiments, the increased expression levels are about 4-100 times higher relative to a pluripotent stem cell. In embodiments, the increased expression levels are 4-100 times higher relative to a pluripotent stem cell. In embodiments, the increased expression levels are about 6-100 times higher relative to a pluripotent stem cell. In embodiments, the increased expression levels are 6-100 times higher relative to a pluripotent stem cell. In embodiments, the increased expression levels are about 8- 100 times higher relative to a pluripotent stem cell. In embodiments, the increased expression levels are 8-100 times higher relative to a pluripotent stem cell. In embodiments, the increased expression levels are about 10-100 times higher relative to a pluripotent stem cell. In embodiments, the increased expression levels are 10-100 times higher relative to a pluripotent stem cell. In embodiments, the increased expression levels are about 20-100 times higher relative to a pluripotent stem cell. In embodiments, the increased expression levels are 20-100 times higher relative to a pluripotent stem cell. In embodiments, the increased expression levels are about 30-100 times higher relative to a pluripotent stem cell. In embodiments, the increased expression levels are 30-100 times higher relative to a pluripotent stem cell. In embodiments, the increased expression levels are about 40-100 times higher relative to a pluripotent stem cell. In embodiments, the increased expression levels are 40-100 times higher relative to a pluripotent stem cell. In embodiments, the increased expression levels are about 50- 100 times higher relative to a pluripotent stem cell. In embodiments, the increased expression levels are 50-100 times higher relative to a pluripotent stem cell. In embodiments, the increased expression levels are about 60-100 times higher relative to a pluripotent stem cell. In embodiments, the increased expression levels are 60-100 times higher relative to a pluripotent stem cell. In embodiments, the increased expression levels are about 70-100 times higher relative to a pluripotent stem cell. In embodiments, the increased expression levels are 70-100 times higher relative to a pluripotent stem cell. In embodiments, the increased expression levels are about 80-100 times higher relative to a pluripotent stem cell. In embodiments, the increased expression levels are 80-100 times higher relative to a pluripotent stem cell. In embodiments, the increased expression levels are about 90-100 times higher relative to a pluripotent stem cell. In embodiments, the increased expression levels are 90-100 times higher relative to a pluripotent stem cell.

[0215] In embodiments, the increased expression levels are about 4-90 times higher relative to a pluripotent stem cell. In embodiments, the increased expression levels are 4-90 times higher relative to a pluripotent stem cell. In embodiments, the increased expression levels are about 4-80 times higherrelative to a pluripotent stem cell. In embodiments, the increased expression levels are 4-80 times higher relative to a pluripotent stem cell. In embodiments, the increased expression levels are about 4-70 times higher relative to a pluripotent stem cell. In embodiments, the increased expression levels are 4-70 times higher relative to a pluripotent stem cell. In embodiments, the increased expression levels are about 4-60 times higher relative to a pluripotent stem cell. In embodiments, the increased expression levels are 4-60 times higher relative to a pluripotent stem cell. In embodiments, the increased expression levels are about 4-50 times higher relative to a pluripotent stem cell. In embodiments, the increased expression levels are 4-50 times higher relative to a pluripotent stem cell. In embodiments, the increased expression levels are about 4-40 times higher relative to a pluripotent stem cell. In embodiments, the increased expression levels are 4-40 times higher relative to a pluripotent stem cell. In embodiments, the increased expression levels are about 4-30 times higher relative to a pluripotent stem cell. In embodiments, the increased expression levels are 4-30 times higher relative to a pluripotent stem cell. In embodiments, the increased expression levels are about 4-20 times higher relative to a pluripotent stem cell. In embodiments, the increased expression levels are 4-20 times higher relative to a pluripotent stem cell. In embodiments, the increased expression levels are about 4-10 times higher relative to a pluripotent stem cell. In embodiments, the increased expression levels are 4-10 times higher relative to a pluripotent stem cell. In embodiments, the increased expression levels are about 4-8 times higher relative to a pluripotent stem cell. In embodiments, the increased expression levels are 4-8 times higher relative to a pluripotent stem cell. In embodiments, the increased expression levels are about 4-6 times higher relative to a pluripotent stem cell. In embodiments, the increased expression levels are 4-6 times higher relative to a pluripotent stem cell.

[0216] In embodiments, the gene expression profile information for the desirable determined dopaminergic precursor cell includes decreased gene expression levels relative to a pluripotent stem cell for a second gene set, wherein the second gene set includes at least one decreased gene within one or more second gene ontologies of Table 8.

[0217] In embodiments, the gene expression profile information for the desirable determined dopaminergic precursor cell includes decreased gene expression levels relative to a pluripotent stem cell for a second gene set, wherein the second gene set includes at least one decreased gene within one or more second gene ontologies selected from the group consisting of gene ontologies of Table 8.

[0218] In embodiments, the gene expression profile information for the desirable determined dopaminergic precursor cell includes decreased gene expression levels relative to a pluripotent stem cell for a second gene set, wherein the second gene set includes at least one decreased gene within one or more second gene ontologies of GO:0044459 ,GO:0071944, GO:0005886, GO:0005904, GO:0031226, GO:0005887, GO:0042127, GO:0005576, GO:0044421, GO:0070887, GO:0034097, GO:0050896, GO:0051869, GO:0071345, GO:0048856, GO:0010033, GO:0044425, GO:0007166, GO:0032501, 00:0044707, GO:0050874, GO:0023052, GO:0023046, GO:0044700, GO:0031982, GO:0031988,GO:0032502, GO:0044767, GO:0007154, GO:0071310, GO:0005615, GO:0042221, GO:0031224, GO: 0051049, GO:0019221, GO:0048583, GO:0008284, GO:0007275, GO:0023051, GO:0010646, GO:0048584, GO:0051239, GO:0032879, GO:0006954, GO:0007165, GO:0023033, GO:0043230, GO:0098771, GO:0055065, GO:0016021, GO:1903561, GO:0009966, GO:0035466, GO:0050801, GO: 0010647, GO:0006811, GO:0065008, GO:0051240, GO:0098590, GO:0055082, GO:0055080, GO:0023056, GO:0006875, GO:0070062, GO:0051716, GO:0048878, GO:0043269, GO:0065009, GO:0051050, GO:0050865, GO:0098857, GO:0006873, GO:0048518, GO:0043119, GO:0030003, GO:0048731, GO:0042592, GO:0045121, GO:0006952, GO:0002217, GO:0042829, GO:0048522, GO:0051242, GO:0046903, GO:0005102, GO:0030154, GO:0019725, GO:0001775, GO:0009967, GO:0035468, GO:0002376, GO:0072503, GO:0045321, GO:0050863, GO:0050878, GO:0048869, GO:0002703,GO:0050670, GO:0022407, GO:0032944, GO:0016020, GO: 1902533, GO:0010740, GO:0043270, GO:0045785, GO:0072507, GO:0009888, GO:0022409, GO:0042493, GO:0017035, GO:0002682, GO:0006874, GO:0032101, GO:0070663, GO:0007204, GO: 1902531, GO:0010627, GO: 1903039, GO: 1903037, GO:0002694, GO:0031012, GO:0009605, GO:0044281, GO:2000021, GO:0055074, GO:0035296, GO:0097746, GO:0042312, GO:0044093, GO:0002685, GO:0098589, GO:0051480, GO:0003013, GO:0008015, GO:0070261, GO:1901700, GO:0007187, GO:0030155, GO:0003006, GO:0034220, GO:0050870, GO:0009611, GO:0002245, GO:0008217, GO:1903524, GO: 0042129, GO:0033993, GO:0050880, GO:0007188, GO:0051704, GO:0051706, GO:0035150, GO:0030198, GO:0032103, GO:0043062, GO:0050867, GO:0040017, GO:0002687, GO:0022857, GO:0005386, GO:0015563, GO:0015646, GO:0022891, GO:0022892, GO:0048608, GO:0015267, GO:0015249, GO:0015268, GO:0002274, GO:0001890, GO:0048513, GO:0022803, GO:0022814, GO:0002684, GO:0050776, GO:0002819, GO:0045937, GO:0010562, GO:0002366, GO:0061458, GO:0051094, GO:0034762, GO:2000147, GO:0030141, GO:0002263, GO:0006955, GO:0015075, GO:0099503, GO:0000003, GO:0019952, GO:0050876, GO:0098772, GO:0002252, GO:0009653, GO:0050900, GO: 1901701, GO:0042802, GO:0043085, GO:0048554, GO:0030335, GO:0005215, GO:0005478, GO:0022414, GO:0044702, GO:0051241, GO:0002696, GO:0046873, GO:0042060, GO:0003018, GO:0032940, GO:0031410, GO:0016023, GO:0002822, GO:0046394, GO:0051272, GO:0097708, GO:0009986, GO:0009928, GO:0009929, GO:0016053, GO:0051928, GO:0042327, GO: 0031225, GO:0010469, GO:0009987, GO:0008151, GO:0044763, GO:0050875, GO:0006950, GO:0043207, GO:0002886, GO:0051249, GO:0098655, GO:0005575, GO:0008372, GO:0002697, GO:0019935, GO:0007267, GO:0032496, GO:0070160, GO:0005216, GO:0034765, GO:0006820, GO:0006822, GO:0005911, GO:0019933, GO:0004252, GO:0048545, GO:0051924, GO:0006812, GO:0006819, GO:0015674, GO:0019932, GO:0051707, GO:0009613, GO:0042828, GO:0001934, GO:0022838, GO: 1902105, GO:0006636, GO:0071624, GO:0055085, GO:0010959, GO:0005923, GO:0030001, GO:0002237, GO:0009607, GO:0002699, GO:0005261, GO:0015281, GO:0015338, GO: 1903522, GO:0043408, GO:0008324, GO:0015711, GO:0071622, GO:0070665, GO:0002683,GO:0010543, GO:0050730, GO:0007189, GO:0010579, GO:0010580, GO:0016338, GO:0050671, GO:0015318, GO:0050777, GO:0050793, GO:0030054, GO:0022610, GO:0032946, GO:0043300, GO:0042102, GO:0001817, GO:0002275, GO:0032844, GO:0060429, GO:0001653, GO:0031347, GO:0048646, GO:0042981, GO:0051345, GO:0002690, GO:0043302, GO:0098660, GO:0009719, GO:0048018, GO:0071884, GO:0009116, GO:0043168, GO:0002444, GO:0043296, GO:0065007, GO:0098662, GO:0043299, GO:0030193, GO:0042119, GO:0050921, GO:0002688, GO:0043410, GO:0022836, GO:0090022, GO:0002888, GO:0002821, GO: 1900046, GO:0042509, GO:0042510, GO:0042513, GO:0042516, GO:0042519, GO:0042522, GO:0042525, GO:0042528, GO:0035295, GO:0043235, GO:0022839, GO:0090023, GO:0043065, GO:0046718, GO:0019063, GO:0043067, GO:0043070, GO:0030545, GO:0001816, GO:0003382, GO:0044409, GO:0051806, GO:0030260, GO:0051828, GO:0036230, GO:0010941, GO:0009725, GO:0002476, GO:0002526, GO:0051384, GO:0050790, GO:0048552, GO:0051247, GO:0008285, GO:0097755, GO:0045909, GO:0031960, GO:0070374, GO:0002824, GO:0030728, GO:0007155, GO:0098602, GO:0035556, GO:0007242, GO: 0007243, GO:0023013, GO:0023034, GO:0010942, GO:0070372, GO:0051046, GO:0043068, GO:0043071, GO:1902107, GO:0002283, GO:0005509, GO:0050818, GO:0051336, GO:0009119, GO:0003073, GO:0036018, GO:0046635, GO:2000026, GO:0006082, GO:0001819, GO:0004175, GO:0016809, GO:0050764, GO:0043436, GO:0005201, GO:0097028, GO:0008528, GO:0045055, GO:0016477, GO:0030168, GO:0035239, GO:0070820, GO:0031349, GO:0001932, GO:0098797, GO:0045137, GO:0043312, GO:0002446, GO:0052547, GO:0048585, GO:0009070, GO:0009113, GO:0034764, GO:0022600, GO:0016323, GO:0045597, GO:0042803, GO:0016324, GO:0045177, GO:0008406, GO:0006887, GO:0016194, GO:0016195, GO:0008236, GO:0072358, GO:0001944, GO:0002521, GO:1902624, GO:0044283, GO:0048519, GO:0043118, GO:0045684, GO:0006690, GO:0010522, GO:0022890, GO:0015082, GO:0019752, GO:0071396, GO:0001525, GO:0050731, GO:0036017, GO:0042609, GO:0050817, GO:0070252, GO:0060670, GO:0019369, GO:0019229, GO:0009164, GO:0017171, GO:0045907, GO:0008289, GO:1902622, GO:0050920, GO:0051047, GO:0046649, GO:0032270, GO:0009991, GO:0033628, GO:0004715, GO:0045776, GO:0042454, GO:0005515, GO:0001948, GO:0045308, GO:0002706, GO:1903530, GO:1901657, GO:0030322, GO:0042270, GO:0045088, GO:0046717, GO:0016661, GO:0008584, GO:0002428, GO:1901568, GO:0042325, GO:0044433, GO:0044057, GO:0031638, GO:0006953, GO:0050729, GO:0046546, GO:0042531, GO:0042511, GO:0042515, GO:0042517, GO:0042520, GO:0042523, GO:0042526, GO:0042529, GO:0046850, GO:0005178, GO:0048514, GO:0045682, GO:0003674, GO:0005554, GO:0046634, GO:0061041, GO:0008016, GO:0043407, GO:0046456, GO:0007596, GO:0045606, GO:0014070, GO:0048870, GO:0051674, GO:0002704, GO:0007584, GO:0070228, GO:0002675, GO:0052548,GO:0001664, GO:0090330, GO:0045117, GO:0034340, GO:0044853, GO:0032587, GO:0007586, GO:0097529, GO:0045595, GO:0040012, GO:0050866, GO:0010035, GO:0034767, GO:0098801, GO:0015079, GO:0015388, GO:0022817, GO:0044706, GO:1901605, GO:0009636,GO : 0007599 ,GO : 0002705 , GO:2000145, GO:0034103, GO:0032642, GO:0098805, GO:0051209, GO:1901137, GO:0090066, GO:0098641, GO:0032409, GO:0007589, GO:0046128, GO:0061134, GO:0015893, GO:0001726, GO:0001893, GO:0030334, GO:0042398 or any combination thereof.

[0219] In embodiments, the gene expression profile information for the desirable determined dopaminergic precursor cell includes decreased gene expression levels relative to a pluripotent stem cell for a second gene set, wherein the second gene set includes at least one decreased gene within one or more second gene ontologies selected from the group consisiting of GO:0044459 ,GO:0071944, GO:0005886, GO:0005904, GO:0031226, GO:0005887, GO:0042127, GO:0005576, GO:0044421, GO:0070887, GO:0034097, GO:0050896, GO:0051869, GO:0071345, GO:0048856, GO:0010033, 00:0044425, GO:0007166, GO:0032501, GO:0044707, GO:0050874, GO:0023052, GO:0023046, 00:0044700, GO:0031982, GO:0031988, GO:0032502, GO:0044767, GO:0007154, GO:0071310, GO:0005615, GO:0042221, GO:0031224, GO:0051049, GO:0019221, GO:0048583, GO:0008284,GO: 0007275, GO:0023051, GO:0010646, GO:0048584, GO:0051239, GO:0032879, GO:0006954, GO:0007165, GO:0023033, GO:0043230, GO:0098771, GO:0055065, GO:0016021, GO:1903561, GO:0009966, GO:0035466, GO:0050801, GO:0010647, GO:0006811, GO:0065008, GO:0051240, GO:0098590, GO:0055082, GO:0055080, GO:0023056, GO:0006875, GO:0070062, GO:0051716, GO:0048878, GO:0043269, GO:0065009, GO:0051050, GO:0050865, GO:0098857, GO:0006873, GO:0048518, GO:0043119, GO:0030003, GO:0048731, GO:0042592, GO:0045121, GO:0006952, GO:0002217, GO:0042829, GO:0048522, GO:0051242, GO:0046903, GO:0005102, GO:0030154, GO:0019725, GO:0001775, GO:0009967, GO:0035468, GO:0002376, GO:0072503, GO:0045321, GO:0050863, GO:0050878, GO:0048869, GO:0002703,GO:0050670, GO:0022407, GO:0032944, GO:0016020, GO: 1902533, GO:0010740, GO:0043270, GO:0045785, GO:0072507, GO:0009888,GO: 0022409, GO:0042493, GO:0017035, GO:0002682, GO:0006874, GO:0032101, GO:0070663, GO:0007204, GO:1902531, GO:0010627, GO:1903039, GO:1903037, GO:0002694, GO:0031012, GO:0009605, GO:0044281, GO:2000021, GO:0055074, GO:0035296, GO:0097746, GO:0042312, 00:0044093, 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, 00:0044763, 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, 00:0044409, 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, 00:0044706, GO: 1901605, GO:0009636, GO:0007599,GO:0002705, GO:2000145, GO:0034103, GO:0032642, GO:0098805, GO:0051209, GO:1901137, GO:0090066, GO:0098641, GO:0032409, GO:0007589, GO:0046128, GO:0061134, GO:0015893, GO:0001726, GO:0001893, GO:0030334, GO:0042398 and any combination thereof.

[0220] In embodiments, the second gene set includes about 1-1000 decreased genes within one or more of the second gene ontologies. In embodiments, the second gene set includes about 2-1000 decreased genes within one or more of the second gene ontologies. In embodiments, the second gene set includes about 3-1000 decreased genes within one or more of the second gene ontologies. In embodiments, the second gene set includes about 4-1000 decreased genes within one or more of the second gene ontologies. In embodiments, the second gene set includes about 5-1000 decreased genes within one or more of the second gene ontologies. In embodiments, the second gene set includes about 6-1000 decreased genes within one or more of the second gene ontologies. In embodiments, the second gene set includes about 7-1000 decreased genes within one or more of the second gene ontologies. In embodiments, the second gene set includes about 8-1000 decreased genes within one or more of the second gene ontologies. In embodiments, the second gene set includes about 9-1000 decreased genes within one or more of the second gene ontologies. In embodiments, the second gene set includes about 10-1000 decreased genes within one or more of the second gene ontologies.

[0221] In embodiments, the second gene set includes about 15-1000 decreased genes within one or more of the second gene ontologies. In embodiments, the second gene set includes about 20-1000 decreased genes within one or more of the second gene ontologies. In embodiments, the second gene set includes about 25-1000 decreased genes within one or more of the second gene ontologies. In embodiments, the second gene set includes about 30-1000 decreased genes within one or more of the second gene ontologies. In embodiments, the second gene set includes about 35-1000 decreased genes within one or more of the second gene ontologies. In embodiments, the second gene set includes about 40-1000 decreased genes within one or more of the second gene ontologies. In embodiments, the second gene set includes about 45-1000 decreased genes within one or more of the second gene ontologies. Inembodiments, the second gene set includes about 50-1000 decreased genes within one or more of the second gene ontologies. In embodiments, the second gene set includes about 55-1000 decreased genes within one or more of the second gene ontologies. In embodiments, the second gene set includes about 60-1000 decreased genes within one or more of the second gene ontologies. In embodiments, the second gene set includes about 65-1000 decreased genes within one or more of the second gene ontologies. In embodiments, the second gene set includes about 70-1000 decreased genes within one or more of the second gene ontologies. In embodiments, the second gene set includes about 75-1000 decreased genes within one or more of the second gene ontologies. In embodiments, the second gene set includes about 80-1000 decreased genes within one or more of the second gene ontologies. In embodiments, the second gene set includes about 85-1000 decreased genes within one or more of the second gene ontologies. In embodiments, the second gene set includes about 90-1000 decreased genes within one or more of the second gene ontologies. In embodiments, the second gene set includes about 95-1000 decreased genes within one or more of the second gene ontologies. In embodiments, the second gene set includes about 100-1000 decreased genes within one or more of the second gene ontologies.

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

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

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

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

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

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

[0228] In embodiments, the second gene set includes about 705-1000 decreased genes within one or more of the second gene ontologies. In embodiments, the second gene set includes about 715-1000 decreased genes within one or more of the second gene ontologies. In embodiments, the second gene set includes about 720-1000 decreased genes within one or more of the second gene ontologies. In embodiments, the second gene set includes about 725-1000 decreased genes within one or more of the second gene ontologies. In embodiments, the second gene set includes about 730-1000 decreased genes within one or more of the second gene ontologies. In embodiments, the second gene set includes about 735-1000 decreased genes within one or more of the second gene ontologies. In embodiments, the second gene set includes about 740-1000 decreased genes within one or more of the second gene ontologies. In embodiments, the second gene set includes about 745-1000 decreased genes within one or more of the second gene ontologies. In embodiments, the second gene set includes about 750-1000 decreased genes within one or more of the second gene ontologies. In embodiments, the second gene set includes about 755-1000 decreased genes within one or more of the second gene ontologies. In embodiments, the second gene set includes about 760-1000 decreased genes within one or more of the second gene ontologies. In embodiments, the second gene set includes about 765-1000 decreased genes within one or more of the second gene ontologies. In embodiments, the second gene set includes about770-1000 decreased genes within one or more of the second gene ontologies. In embodiments, the second gene set includes about 775-1000 decreased genes within one or more of the second gene ontologies. In embodiments, the second gene set includes about 780-1000 decreased genes within one or more of the second gene ontologies. In embodiments, the second gene set includes about 785-1000 decreased genes within one or more of the second gene ontologies. In embodiments, the second gene set includes about 790-1000 decreased genes within one or more of the second gene ontologies. In embodiments, the second gene set includes about 795-1000 decreased genes within one or more of the second gene ontologies. In embodiments, the second gene set includes about 800-1000 decreased genes within one or more of the second gene ontologies.

[0229] In embodiments, the second gene set includes about 805-1000 decreased genes within one or more of the second gene ontologies. In embodiments, the second gene set includes about 815-1000 decreased genes within one or more of the second gene ontologies. In embodiments, the second gene set includes about 820-1000 decreased genes within one or more of the second gene ontologies. In embodiments, the second gene set includes about 825-1000 decreased genes within one or more of the second gene ontologies. In embodiments, the second gene set includes about 830-1000 decreased genes within one or more of the second gene ontologies. In embodiments, the second gene set includes about 835-1000 decreased genes within one or more of the second gene ontologies. In embodiments, the second gene set includes about 840-1000 decreased genes within one or more of the second gene ontologies. In embodiments, the second gene set includes about 845-1000 decreased genes within one or more of the second gene ontologies. In embodiments, the second gene set includes about 850-1000 decreased genes within one or more of the second gene ontologies. In embodiments, the second gene set includes about 855-1000 decreased genes within one or more of the second gene ontologies. In embodiments, the second gene set includes about 860-1000 decreased genes within one or more of the second gene ontologies. In embodiments, the second gene set includes about 865-1000 decreased genes within one or more of the second gene ontologies. In embodiments, the second gene set includes about 870-1000 decreased genes within one or more of the second gene ontologies. In embodiments, the second gene set includes about 875-1000 decreased genes within one or more of the second gene ontologies. In embodiments, the second gene set includes about 880-1000 decreased genes within one or more of the second gene ontologies. In embodiments, the second gene set includes about 885-1000 decreased genes within one or more of the second gene ontologies. In embodiments, the second gene set includes about 890-1000 decreased genes within one or more of the second gene ontologies. In embodiments, the second gene set includes about 895-1000 decreased genes within one or more of the second gene ontologies. In embodiments, the second gene set includes about 900-1000 decreased genes within one or more of the second gene ontologies.

[0230] In embodiments, the second gene set includes about 905-1000 decreased genes within one or more of the second gene ontologies. In embodiments, the second gene set includes about 915-1000decreased genes within one or more of the second gene ontologies. In embodiments, the second gene set includes about 920-1000 decreased genes within one or more of the second gene ontologies. In embodiments, the second gene set includes about 925-1000 decreased genes within one or more of the second gene ontologies. In embodiments, the second gene set includes about 930-1000 decreased genes within one or more of the second gene ontologies. In embodiments, the second gene set includes about 935-1000 decreased genes within one or more of the second gene ontologies. In embodiments, the second gene set includes about 940-1000 decreased genes within one or more of the second gene ontologies. In embodiments, the second gene set includes about 945-1000 decreased genes within one or more of the second gene ontologies. In embodiments, the second gene set includes about 950-1000 decreased genes within one or more of the second gene ontologies. In embodiments, the second gene set includes about 955-1000 decreased genes within one or more of the second gene ontologies. In embodiments, the second gene set includes about 960-1000 decreased genes within one or more of the second gene ontologies. In embodiments, the second gene set includes about 965-1000 decreased genes within one or more of the second gene ontologies. In embodiments, the second gene set includes about 970-1000 decreased genes within one or more of the second gene ontologies. In embodiments, the second gene set includes about 975-1000 decreased genes within one or more of the second gene ontologies. In embodiments, the second gene set includes about 980-1000 decreased genes within one or more of the second gene ontologies. In embodiments, the second gene set includes about 985-1000 decreased genes within one or more of the second gene ontologies. In embodiments, the second gene set includes about 990-1000 decreased genes within one or more of the second gene ontologies. In embodiments, the second gene set includes about 995-1000 decreased genes within one or more of the second gene ontologies.

[0231] In embodiments, the second gene set 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, 120,121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141,142, 143, 144, 145, 146, 147 ,148 ,149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162,163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183,184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204,205, 206, 207 ,208, 209, 210, 211 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246,247 ,248 ,249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267,268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288,289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307 ,308, 309,310, 311 312, 313, 314, 315 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347 ,348 ,349, 350, 351,352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372,373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, 391, 392, 393,394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407 ,408, 409, 410, 411 412, 413, 414,415 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447 ,448 ,449, 450, 451, 452, 453, 454, 455, 456,457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477,478, 479, 480, 481, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 492, 493, 494, 495, 496, 497, 498,499, 500, 501, 502, 503, 504, 505, 506, 507 ,508, 509, 510, 511 512, 513, 514, 515, 516, 517, 518, 519,520, 521, 522, 523, 524, 525, 526, 527, 528, 529, 530, 231, 532, 533, 534, 535, 536, 537, 538, 539, 540,541, 542, 543, 544, 545, 546, 547 ,548 ,549, 550, 551, 552, 553, 554, 555, 556, 557, 558, 559, 560, 561,562, 563, 564, 565, 566, 567, 568, 569, 570, 571, 572, 573, 574, 575, 576, 577, 578, 579, 580, 581, 582,583, 584, 585, 586, 587, 588, 589, 590, 591, 592, 593, 594, 595, 596, 597, 598, 599, 600, 601, 605, 603,604, 605, 606, 607 ,608, 609, 610, 611 612, 613, 614, 615 616, 617, 618, 619, 620, 621, 622, 623, 624, 625, 626, 627, 628, 629, 630, 631, 632, 633, 634, 635, 636, 637, 638, 639, 640, 641, 642, 643, 644, 645,646, 647 ,648 ,649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666,667, 668, 669, 670, 671, 672, 673, 674, 675, 676, 677, 678, 679, 680, 681, 682, 683, 684, 685, 686, 687,688, 689, 690, 691, 692, 693, 694, 695, 696, 697, 698, 699, 700, 701, 702, 703, 704, 705, 706, 707 ,708,709, 710, 711 712, 713, 717, 715 716, 714, 718, 719, 720, 721, 722, 723, 724, 725, 726, 727, 728, 729, 730, 731, 732, 733, 734, 735, 736, 737, 738, 739, 740, 741, 742, 743, 744, 745, 746, 747 ,748 ,749, 750,751, 752, 753, 757, 755, 756, 754, 758, 759, 760, 761, 762, 763, 764, 765, 766, 767, 768, 769, 770, 771,772, 773, 774, 775, 776, 777, 778, 779, 780, 781, 782, 783, 784, 785, 786, 787, 788, 789, 790, 791, 792,793, 794, 795, 796, 797, 798, 799, 800, 801, 802, 803, 804, 805, 806, 807 ,808, 809, 810, 811 812, 813,817, 815 816, 814, 818, 819, 820, 821, 822, 823, 824, 825, 826, 827, 828, 829, 830, 831, 832, 833, 834,835, 836, 837, 838, 839, 840, 841, 842, 843, 845, 846, 847 ,848 ,849, 850, 851, 852, 853, 854, 855, 856,854, 858, 859, 860, 861, 862, 863, 864, 865, 866, 867, 868, 869, 870, 871, 872, 873, 874, 875, 876, 877,878, 879, 880, 881, 882, 883, 884, 885, 886, 887, 888, 889, 890, 891, 892, 893, 894, 895, 896, 897, 898,899, 900, 901, 902, 903, 904, 905, 906, 907 ,908, 909, 910, 911 912, 913, 917, 915 916, 914, 918, 919,920, 921, 922, 923, 924, 925, 926, 927, 928, 929, 930, 931, 932, 933, 934, 935, 936, 937, 938, 939, 940,941, 942, 943, 945, 946, 947 ,948 ,949, 950, 951, 952, 953, 954, 955, 956, 954, 958, 959, 960, 961, 962,963, 964, 965, 966, 967, 968, 969, 970, 971, 972, 973, 974, 975, 976, 977, 978, 979, 980, 981, 982, 983,984, 985, 986, 987, 988, 989, 990, 991, 992, 993, 994, 995, 996, 997, 998, 999, or 1000 decreased genes within one or more of the second gene ontologies.

[0232] The gene expression profile information for the desirable determined dopaminergic precursor cell may include decreased gene expression levels relative to a pluripotent stem cell for a second geneset, wherein the second gene set includes at least one decreased gene within one or more second gene ontologies of Table 8.“One or more” as described herein in the context of second gene ontologies refers to at least one, for example, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, etc. of second gene ontologies.

[0233] In embodiments, the second gene set includes about 1-500 decreased genes within 1-1000 of the second gene ontologies. In embodiments, the second gene set includes about 1-500 decreased genes within 50-1000 of the second gene ontologies. In embodiments, the second gene set includes about 1- 500 decreased genes within 100-1000 of the second gene ontologies. In embodiments, the second gene set includes about 1-500 decreased genes within 150-1000 of the second gene ontologies. In embodiments, the second gene set includes about 1-500 decreased genes within 200-1000 of the second gene ontologies. In embodiments, the second gene set includes about 250-500 decreased genes within 50-1000 of the second gene ontologies. In embodiments, the second gene set includes about 1-500 decreased genes within 300-1000 of the second gene ontologies. In embodiments, the second gene set includes about 1-500 decreased genes within 350-1000 of the second gene ontologies. In embodiments, the second gene set includes about 1-500 decreased genes within 400-1000 of the second gene ontologies. In embodiments, the second gene set includes about 1-500 decreased genes within 450-1000 of the second gene ontologies. In embodiments, the second gene set includes about 1-500 decreased genes within 500-1000 of the second gene ontologies. In embodiments, the second gene set includes about 1-500 decreased genes within 550-1000 of the second gene ontologies. In embodiments, the second gene set includes about 1-500 decreased genes within 600-1000 of the second gene ontologies. In embodiments, the second gene set includes about 1-500 decreased genes within 650-1000 of the second gene ontologies. In embodiments, the second gene set includes about 1-500 decreased genes within 700- 1000 of the second gene ontologies. In embodiments, the second gene set includes about 1-500 decreased genes within 750-1000 of the second gene ontologies. In embodiments, the second gene set includes about 1-500 decreased genes within 800-1000 of the second gene ontologies. In embodiments, the second gene set includes about 1-500 decreased genes within 850-1000 of the second gene ontologies. In embodiments, the second gene set includes about 1-500 decreased genes within 900-1000 of the second gene ontologies. In embodiments, the second gene set includes about 1-500 decreased genes within 950-1000 of the second gene ontologies.

[0234] In embodiments, the second gene set includes about 1-500 decreased genes within 1-300 of the second gene ontologies. In embodiments, the second gene set includes about 1-500 decreased genes within 10-300 of the second gene ontologies. In embodiments, the second gene set includes about 1-500 decreased genes within 20-300 of the second gene ontologies. In embodiments, the second gene set includes about 1-500 decreased genes within 30-300 of the second gene ontologies. In embodiments, the second gene set includes about 1-500 decreased genes within 40-300 of the second gene ontologies. In embodiments, the second gene set includes about 1-500 decreased genes within 50-300 of the secondgene ontologies. In embodiments, the second gene set includes about 1-500 decreased genes within 60- 300 of the second gene ontologies. In embodiments, the second gene set includes about 1-500 decreased genes within 70-300 of the second gene ontologies. In embodiments, the second gene set includes about 1-500 decreased genes within 80-300 of the second gene ontologies. In embodiments, the second gene set includes about 1-500 decreased genes within 90-300 of the second gene ontologies. In embodiments, the second gene set includes about 1-500 decreased genes within 100-300 of the second gene ontologies. In embodiments, the second gene set includes about 1-500 decreased genes within 110-300 of the second gene ontologies. In embodiments, the second gene set includes about 1-500 decreased genes within 120- 300 of the second gene ontologies. In embodiments, the second gene set includes about 1-500 decreased genes within 130-300 of the second gene ontologies. In embodiments, the second gene set includes about 1-500 decreased genes within 140-300 of the second gene ontologies. In embodiments, the second gene set includes about 1-500 decreased genes within 150-300 of the second gene ontologies. Inembodiments, the second gene set includes about 1-500 decreased genes within 160-300 of the second gene ontologies. In embodiments, the second gene set includes about 1-500 decreased genes within 170- 300 of the second gene ontologies. In embodiments, the second gene set includes about 1-500 decreased genes within 180-300 of the second gene ontologies. In embodiments, the second gene set includes about 1-500 decreased genes within 190-300 of the second gene ontologies. In embodiments, the second gene set includes about 1-500 decreased genes within 200-300 of the second gene ontologies. Inembodiments, the second gene set includes about 1-500 decreased genes within 210-300 of the second gene ontologies. In embodiments, the second gene set includes about 1-500 decreased genes within 220- 300 of the second gene ontologies. In embodiments, the second gene set includes about 1-500 decreased genes within 230-300 of the second gene ontologies. In embodiments, the second gene set includes about 1-500 decreased genes within 240-300 of the second gene ontologies. In embodiments, the second gene set includes about 1-500 decreased genes within 250-300 of the second gene ontologies. Inembodiments, the second gene set includes about 1-500 decreased genes within 260-300 of the second gene ontologies. In embodiments, the second gene set includes about 1-500 decreased genes within 270- 300 of the second gene ontologies. In embodiments, the second gene set includes about 1-500 decreased genes within 280-300 of the second gene ontologies. In embodiments, the second gene set includes about 1-500 decreased genes within 290-300 of the second gene ontologies.

[0235] In embodiments, the second gene set includes about 1-500 decreased genes within 1-290 of the second gene ontologies. In embodiments, the second gene set includes about 1-500 decreased genes within 1-280 of the second gene ontologies. In embodiments, the second gene set includes about 1-500 decreased genes within 1-270 of the second gene ontologies. In embodiments, the second gene set includes about 1-500 decreased genes within 1-260 of the second gene ontologies. In embodiments, the second gene set includes about 1-500 decreased genes within 1-250 of the second gene ontologies. In embodiments, the second gene set includes about 1-500 decreased genes within 1-240 of the second geneontologies. In embodiments, the second gene set includes about 1-500 decreased genes within 1-230 of the second gene ontologies. In embodiments, the second gene set includes about 1-500 decreased genes within 1-220 of the second gene ontologies. In embodiments, the second gene set includes about 1-500 decreased genes within 1-210 of the second gene ontologies. In embodiments, the second gene set includes about 1-500 decreased genes within 1-200 of the second gene ontologies. In embodiments, the second gene set includes about 1-500 decreased genes within 1-190 of the second gene ontologies. In embodiments, the second gene set includes about 1-500 decreased genes within 1-180 of the second gene ontologies. In embodiments, the second gene set includes about 1-500 decreased genes within 1-170 of the second gene ontologies. In embodiments, the second gene set includes about 1-500 decreased genes within 1-160 of the second gene ontologies. In embodiments, the second gene set includes about 1-500 decreased genes within 1-150 of the second gene ontologies. In embodiments, the second gene set includes about 1-500 decreased genes within 1-140 of the second gene ontologies. In embodiments, the second gene set includes about 1-500 decreased genes within 1-130 of the second gene ontologies. In embodiments, the second gene set includes about 1-500 decreased genes within 1-120 of the second gene ontologies. In embodiments, the second gene set includes about 1-500 decreased genes within 1-110 of the second gene ontologies. In embodiments, the second gene set includes about 1-500 decreased genes within 1-100 of the second gene ontologies. In embodiments, the second gene set includes about 1-500 decreased genes within 1-90 of the second gene ontologies. In embodiments, the second gene set includes about 1-500 decreased genes within 1-80 of the second gene ontologies. In embodiments, the second gene set includes about 1-500 decreased genes within 1-70 of the second gene ontologies. In embodiments, the second gene set includes about 1-500 decreased genes within 1-60 of the second gene ontologies. In embodiments, the second gene set includes about 1-500 decreased genes within 1-50 of the second gene ontologies. In embodiments, the second gene set includes about 1-500 decreased genes within 1-40 of the second gene ontologies. In embodiments, the second gene set includes about 1-500 decreased genes within 1-30 of the second gene ontologies. In embodiments, the second gene set includes about 1-500 decreased genes within 1-20 of the second gene ontologies. In embodiments, the second gene set includes about 1-500 decreased genes within 1-10 of the second gene ontologies. In embodiments, the second gene set includes about 1-500 decreased genes within 1-5 of the second gene ontologies.

[0236] In embodiments, the second gene set includes at least one decreased gene within 1, 2, 3, 4, 5, 6, 7 ,8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47 ,48 ,49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107 ,108, 109, 110, 111 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147 ,148 ,149, 150, 151, 152, 153, 154, 155,156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176,177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197,198, 199, 200, 201, 202, 203, 204, 205, 206, 207 ,208, 209, 210, 211 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239,240, 241, 242, 243, 244, 245, 246, 247 ,248 ,249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260,261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281,282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302,303, 304, 305, 306, 307 ,308, 309, 310, 311 312, 313, 314, 315 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344,345, 346, 347 ,348 ,349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365,366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 386,387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407,408, 409, 410, 411 412, 413, 414, 415 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447 ,448 ,449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, or 463 second gene ontologies of Table 8.

[0237] In embodiments, the second gene set 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, 120,121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141,142, 143, 144, 145, 146, 147 ,148 ,149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162,163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183,184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204,205, 206, 207 ,208, 209, 210, 211 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246,247 ,248 ,249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267,268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288,289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307 ,308, 309,310, 311 312, 313, 314, 315 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347 ,348 ,349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372,373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, 391, 392, 393,394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407 ,408, 409, 410, 411 412, 413, 414, 415 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435,436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447 ,448 ,449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477,478, 479, 480, 481, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 492, 493, 494, 495, 496, 497, 498,499, 500, 501, 502, 503, 504, 505, 506, 507 ,508, 509, 510, 511 512, 513, 514, 515, 516, 517, 518, 519, 520, 521, 522, 523, 524, 525, 526, 527, 528, 529, 530, 231, 532, 533, 534, 535, 536, 537, 538, 539, 540,541, 542, 543, 544, 545, 546, 547 ,548 ,549, 550, 551, 552, 553, 554, 555, 556, 557, 558, 559, 560, 561,562, 563, 564, 565, 566, 567, 568, 569, 570, 571, 572, 573, 574, 575, 576, 577, 578, 579, 580, 581, 582,583, 584, 585, 586, 587, 588, 589, 590, 591, 592, 593, 594, 595, 596, 597, 598, 599, 600, 601, 605, 603,604, 605, 606, 607 ,608, 609, 610, 611 612, 613, 614, 615 616, 617, 618, 619, 620, 621, 622, 623, 624, 625, 626, 627, 628, 629, 630, 631, 632, 633, 634, 635, 636, 637, 638, 639, 640, 641, 642, 643, 644, 645,646, 647 ,648 ,649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666,667, 668, 669, 670, 671, 672, 673, 674, 675, 676, 677, 678, 679, 680, 681, 682, 683, 684, 685, 686, 687,688, 689, 690, 691, 692, 693, 694, 695, 696, 697, 698, 699, 700, 701, 702, 703, 704, 705, 706, 707 ,708,709, 710, 711 712, 713, 717, 715 716, 714, 718, 719, 720, 721, 722, 723, 724, 725, 726, 727, 728, 729, 730, 731, 732, 733, 734, 735, 736, 737, 738, 739, 740, 741, 742, 743, 744, 745, 746, 747 ,748 ,749, 750,751, 752, 753, 757, 755, 756, 754, 758, 759, 760, 761, 762, 763, 764, 765, 766, 767, 768, 769, 770, 771,772, 773, 774, 775, 776, 777, 778, 779, 780, 781, 782, 783, 784, 785, 786, 787, 788, 789, 790, 791, 792,793, 794, 795, 796, 797, 798, 799, 800, 801, 802, 803, 804, 805, 806, 807 ,808, 809, 810, 811 812, 813,817, 815 816, 814, 818, 819, 820, 821, 822, 823, 824, 825, 826, 827, 828, 829, 830, 831, 832, 833, 834, 835, 836, 837, 838, 839, 840, 841, 842, 843, 845, 846, 847 ,848 ,849, 850, 851, 852, 853, 854, 855, 856, 854, 858, 859, 860, 861, 862, 863, 864, 865, 866, 867, 868, 869, 870, 871, 872, 873, 874, 875, 876, 877,878, 879, 880, 881, 882, 883, 884, 885, 886, 887, 888, 889, 890, 891, 892, 893, 894, 895, 896, 897, 898,899, 900, 901, 902, 903, 904, 905, 906, 907 ,908, 909, 910, 911 912, 913, 917, 915 916, 914, 918, 919, 920, 921, 922, 923, 924, 925, 926, 927, 928, 929, 930, 931, 932, 933, 934, 935, 936, 937, 938, 939, 940,941, 942, 943, 945, 946, 947 ,948 ,949, 950, 951, 952, 953, 954, 955, 956, 954, 958, 959, 960, 961, 962,963, 964, 965, 966, 967, 968, 969, 970, 971, 972, 973, 974, 975, 976, 977, 978, 979, 980, 981, 982, 983,984, 985, 986, 987, 988, 989, 990, 991, 992, 993, 994, 995, 996, 997, 998, 999, or 1000 decreased genes within 1, 2, 3, 4, 5, 6, 7 ,8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29,30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47 ,48, 49, 50, 51, 52, 53, 54, 55, 56, 57,58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85,86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107 ,108, 109,110, 111 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147 ,148 ,149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172,173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193,194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207 ,208, 209, 210, 211 212, 213, 214,215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235,236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247 ,248 ,249, 250, 251, 252, 253, 254, 255, 256,257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277,278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298,299, 300, 301, 302, 303, 304, 305, 306, 307 ,308, 309, 310, 311 312, 313, 314, 315 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340,341, 342, 343, 344, 345, 346, 347 ,348 ,349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361,362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382,383, 384, 385, 386, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403,404, 405, 406, 407 ,408, 409, 410, 411 412, 413, 414, 415 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447 ,448 ,449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, or 463 second gene ontologies of Table 8.

[0238] In embodiments, the second gene ontologies are any one of the gene ontologies listed in Table 8. In embodiments, the second gene ontologies are any one of GO:0044459 ,GO:0071944, GO:0005886, GO:0005904, GO:0031226, GO:0005887, GO:0042127, GO:0005576, GO:0044421, GO:0070887, GO:0034097, GO:0050896, GO:0051869, GO:0071345, GO:0048856, GO:0010033, 00:0044425, GO:0007166, GO:0032501, GO:0044707, GO:0050874, GO:0023052, GO:0023046, 00:0044700, GO:0031982, GO:0031988, GO:0032502, GO:0044767, GO:0007154, GO:0071310, GO:0005615, GO:0042221, GO:0031224, GO:0051049, GO:0019221, GO:0048583, GO:0008284,GO: 0007275, GO:0023051, GO:0010646, GO:0048584, GO:0051239, GO:0032879, GO:0006954, GO:0007165, GO:0023033, GO:0043230, GO:0098771, GO:0055065, GO:0016021, GO:1903561, GO:0009966, GO:0035466, GO:0050801, GO:0010647, GO:0006811, GO:0065008, GO:0051240, GO:0098590, GO:0055082, GO:0055080, GO:0023056, GO:0006875, GO:0070062, GO:0051716, GO:0048878, GO:0043269, GO:0065009, GO:0051050, GO:0050865, GO:0098857, GO:0006873, GO:0048518, GO:0043119, GO:0030003, GO:0048731, GO:0042592, GO:0045121, GO:0006952, GO:0002217, GO:0042829, GO:0048522, GO:0051242, GO:0046903, GO:0005102, GO:0030154, GO:0019725, GO:0001775, GO:0009967, GO:0035468, GO:0002376, GO:0072503, GO:0045321, GO:0050863, GO:0050878, GO:0048869, GO:0002703,GO:0050670, GO:0022407, GO:0032944, GO:0016020, GO: 1902533, GO:0010740, GO:0043270, GO:0045785, GO:0072507, GO:0009888,GO: 0022409, GO:0042493, GO:0017035, GO:0002682, GO:0006874, GO:0032101, GO:0070663, GO:0007204, GO:1902531, GO:0010627, GO:1903039, GO:1903037, GO:0002694, GO:0031012, GO:0009605, GO:0044281, GO:2000021, GO:0055074, GO:0035296, GO:0097746, GO:0042312, 00:0044093, 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, 00:0044763, 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, 00:0044409, 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, 00:0044706, GO: 1901605, GO:0009636, GO:0007599,GO:0002705, GO:2000145, GO:0034103, GO:0032642, GO:0098805, GO:0051209, GO:1901137, GO:0090066, GO:0098641, GO:0032409, GO:0007589, GO:0046128, GO:0061134, GO:0015893, GO:0001726, GO:0001893, GO:0030334, GO:0042398 or any combination thereof.

[0239] In embodiments, the gene expression profile information for the desirable determined dopaminergic precursor cell includes decreased gene expression levels relative to a pluripotent stem cell for a second gene set, wherein the second gene set includes at least one decreased gene within one or more second gene ontologies selected from the group consisting of: G00070887, G00044459 and G00044281. In embodiments, the gene expression profile information for the desirable determined dopaminergic precursor cell includes decreased gene expression levels relative to a pluripotent stem cell for a second gene set, wherein the second gene set includes at least one decreased gene within one or more second gene ontologies of: G00070887, G00044459, or G00044281. In embodiments, the gene expression profile information for the desirable determined dopaminergic precursor cell includes decreased gene expression levels relative to a pluripotent stem cell for a second gene set, wherein the second gene set includes at least one decreased gene within one or more second gene ontologies selected from the group consisting of: G00042127, G0006954, and G00032502 and any combination thereof. In embodiments, the gene expression profile information for the desirable determined dopaminergic precursor cell includes decreased gene expression levels relative to a pluripotent stem cell for a secondgene set, wherein the second gene set includes at least one decreased gene within one or more second gene ontologies of: G00042127, G0006954, G00032502 or any combination thereof.

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

[0241] In embodiments, the second gene set includes at least one (e.g., 1, 2, 3, 4, 5, 6 etc.) decreased gene of Table 9. In embodiments, the at least one (e.g., 1, 2, 3, 4, 5, 6 etc.) decreased gene is DYSF, RASAL3, AKR1C3, CGREF1, SULT2B1, CAV2, IL12A, HMGA1 , HHLA2, HMX2, CARD11, TSPO, IRF6, CEBPB, BCL11B, CASR, INPP5D, FGF21, NODAL, TNFRSF1B, HPSE, GRPR, TNMD, SPINT2, IER5, CAV1, JAML, SOX10, SFN, NPY5R, MYB, HMOX1, CDH5, HEY2, CLDN7, CXCR2, FGF2, APELA, FLT3LG, CD22, CDCA7L, NPM1, STYK1, SKOR2, LRRC32, HRG, CDH3, IL4R, TERT, ANG, RAB25, NRK, ADM, MARVELD3, DPP4, CD4, LTF, FGF4, ERBB3, IFITM1, P3H2, BAX, WNT11, CEBPA, AVPR1A, PTPRZ1, EIF5A, EPO, NPR1, NQ02, FGF16, EPHA1, CCL26, NR1D1, SYK, PTGES, TCIRG1, HCLS1, RAC2, NME2, TESC, HCK, FZD5, ETS1, APLN, TRIM71, ADA, MYC, GCNT2, SFRP1, FGFR4, EMX1, KDR, RARG, CD74, DRD3, PDPN, TRNP1, HPN, PLAU, TNFSF12, GAS 6, SRPX, FGF19, PROK2, TSLP, SHMT2, PIM2, GHRHR, EBI3, ADORA1, NOS3, LIF, PINX1, TNFRSF8, FA2H, LECT1, CHRM1, NME1, SOX15, S100A11, NCCRP1, CD40, SERPINB3, RARRES3, LIN28A, TCL1A, ICOSLG, HYAL1, AIF1, LEP, EEF1E1, PRKCH, VIPR1, IL34, SH2B3, SPINT1, ESRP2, PYCARD, CLEC4G, MATK, EAF2, TACR1, EGFL7, CCNI2, GAL, FERMT1, SFRP5, PPP1R16B, MLXIPL, OVOL1, CD9, TNFSF9, KDF1, MST1R, IL23A, FLT1, FLT3, HLA-G, AD AMTS 8, GUCY2C, MMP9, ALOX15B, VDR, SIX4, LGALS3, LAMC2, CCNE1, NPPC, CLC, APOE, MAP3K5, CCND1, XCL1, PTPN6, GUI, TCL1B, PIM1, ARG2, LYN, NRARP, ELL3, TDGF1, FOSL1, CDCA7, NANOG, CCKBR, BNC1, PNP, TRIB1, HPGD, PRTN3, KIAA1462, HTR1A, BTK, FZD7, IFNLR1, JAK3, CD55, TFAP4, SLA, FBX02, RBPMS2, OSMR, IL12RB2, EPCAM, IL6, IDOl, CHP2, PTAFR, CXCL1, SFRP2, PF4, CCDC88B, PRKCQ, CXCL5, TGFA,GJA1, FZD9, RPA3, TACSTD2, TNFRSF11A, CNN1, or PTGER2.

[0242] In embodiments, the at least one (e.g., 1, 2, 3, 4, 5, 6 etc.) decreased gene is selected from the group consisting of DYSF, RASAL3, AKR1C3, CGREF1, SULT2B 1, CAV2, IL12A, HMGA1, HHLA2, HMX2, CARD11, TSPO, IRF6, CEBPB, BCL11B, CASR, INPP5D, FGF21, NODAL, TNFRSF1B, HPSE, GRPR, TNMD, SPINT2, IER5, CAV1, JAML, SOX10, SFN, NPY5R, MYB, HMOX1, CDH5, HEY2, CLDN7, CXCR2, FGF2, APELA, FLT3LG, CD22, CDCA7L, NPM1, STYK1, SKOR2, LRRC32, HRG, CDH3, IL4R, TERT, ANG, RAB25, NRK, ADM, MARVELD3, DPP4, CD4, LTF, FGF4, ERBB3, IFITM1, P3H2, BAX, WNT11, CEBPA, AVPR1A, PTPRZ1, EIF5A, EPO, NPR1, NQ02, FGF16, EPHA1, CCL26, NR1D1, SYK, PTGES, TCIRG1, HCLS1, RAC2, NME2, TESC,HCK, FZD5, ETS1, APLN, TRIM71, ADA, MYC, GCNT2, SFRP1, FGFR4, EMX1, KDR, RARG, CD74, DRD3, PDPN, TRNP1, HPN, PLAU, TNFSF12, GAS6, SRPX, FGF19, PROK2, TSLP, SHMT2, PIM2, GHRHR, EBI3, ADORA1, NOS3, LIF, PINX1, TNFRSF8, FA2H, LECT1, CHRM1, NME1,SOX15, S100A11, NCCRP1, CD40, SERPINB3, RARRES3, LIN28A, TCL1A, ICOSLG, HYAL1, AIF1, LEP, EEF1E1, PRKCH, VIPR1, IL34, SH2B3, SPINT1, ESRP2, PYCARD, CLEC4G, MATK, EAF2, TACR1, EGFL7, CCNI2, GAL, FERMT1, SFRP5, PPP1R16B, MLXIPL, OVOL1, CD9, TNFSF9, KDF1, MST1R, IL23A, FLT1, FLT3, HLA-G, AD AMTS 8, GUCY2C, MMP9, ALOX15B, VDR, SIX4, LGALS3, LAMC2, CCNE1, NPPC, CLC, APOE, MAP3K5, CCND1, XCL1, PTPN6,GLI1, TCL1B, PIM1, ARG2, LYN, NRARP, ELL3, TDGF1, FOSL1, CDCA7, NANOG, CCKBR, BNC1, PNP, TRIB 1, HPGD, PRTN3, KIAA1462, HTR1A, BTK, FZD7, IFNLR1, JAK3, CD55,TFAP4, SLA, FBX02, RBPMS2, OSMR, IL12RB2, EPCAM, IL6, IDOl, CHP2, PTAFR, CXCL1, SFRP2, PF4, CCDC88B, PRKCQ, CXCL5, TGFA, GJA1, FZD9, RPA3, TACSTD2, TNFRSF11A, CNN1, and PTGER2.

[0243] In embodiments, the second gene set includes at least one (e.g., 1, 2, 3, 4, 5, 6 etc.) decreased gene of Table 10. In embodiments, the at least one (e.g., 1, 2, 3, 4, 5, 6 etc.) decreased gene is C3, AFAP1L2, PTGDR, CMKLR1, CEBPB, NFKBID, TNFRSF1B, SMPDL3B, F2RL1, HMOX1, CXCR2, FPR2, IL17RE, CHST4, IL4R, NFKBIZ, RELB, ADM, ALOX5, SPP1, SIGIRR, EPO, CCL26, SYK, PTGES, TFR2, AHCY, TCIRG1, CHI3L1, UGT1A1, NLRP10, HCK, RARRES2, KLKB 1, CXCL2,F12, ALOX15, PROK2, ELF3, ADORA1, CXCL6, CD40, ,HYAL1, AIF1, ADGRE2, IL34, AHSG, THEMIS2, MMP25, PLSCR1, NMI, PYCARD, TACR1, LBP, GAL, F11R, LY75, IL23A, NRROS, XCL1, ASS1, LYN, BTK, TNFAIP6, IL6, IDOl, PTAFR, CXCL1, PF4, PRKCQ, IL17C, CXCL5, GJA1, CXCL3, PLA2G4C, ICAM1, ORM2, SDC1, PTGER2, or TLR3.

[0244] In embodiments, the at least one (e.g., 1, 2, 3, 4, 5, 6 etc.) decreased gene is selected from the group consisting of C3, AFAP1L2, PTGDR, CMKLR1, CEBPB, NFKBID, TNFRSF1B, SMPDL3B, F2RL1, HMOX1, CXCR2, FPR2, IL17RE, CHST4, IL4R, NFKBIZ, RELB, ADM, ALOX5, SPP1, SIGIRR, EPO, CCL26, SYK, PTGES, TFR2, AHCY, TCIRG1, CHI3L1, UGT1A1, NLRP10, HCK, RARRES2, KLKB1, CXCL2, F12, ALOX15, PROK2, ELF3, ADORA1, CXCL6, CD40, ,HYAL1,AIF1, ADGRE2, IL34, AHSG, THEMIS2, MMP25, PLSCR1, NMI, PYCARD, TACR1, LBP, GAL, F11R, LY75, IL23A, NRROS, XCL1, ASS1, LYN, BTK, TNFAIP6, IL6, IDOl, PTAFR, CXCL1, PF4, PRKCQ, IL17C, CXCL5, GJA1, CXCL3, PLA2G4C, ICAM1, ORM2, SDC1, PTGER2, and TLR3.

[0245] In embodiments, the second gene set includes at least one (e.g., 1, 2, 3, 4, 5, 6 etc.) decreased gene of Table 11. In embodiments, the at least one (e.g., 1, 2, 3, 4, 5, 6 etc.) decreased gene is C3, MOG, FOXI3, ACTN3, P2RX2, TWIST2, DYSF, MYBPC2, VSIG1, AKR1C3, CAV2, COL23A1, PTGDR, SLC2A4, RNF43, SHROOM1, BCAN, FGR, LFNG, KRTDAP, GCM2, SEMA4A, SYNGR3,COL13A1, SAMHD1, PDCD1, HMGA1, DSC3, GCNT4, FGF22, SNTG2, HMX2, CARD11, TSPO, IRF6, KLF15, ALAS2, KLK7, ...

Claims

1.         A computer implemented method of classifying an in vitro population of neuronal progenitor cells, the method comprising:receiving a test dataset comprising (a) gene expression levels, and (b) expression levels of one or more metagenes for a cell or a plurality of cells comprised in an in vitro population of neuronal progenitor cells, wherein the one or more metagenes are determined based on correlated gene expression levels of reference cells in a reference database, wherein the reference cells are neuronal cells at one or more different stages of differentiation;applying the expression levels of the one or more metagenes as input to a process configured to determine a probability of the cell or the plurality of cells having metagene expression levels of a determined dopaminergic precursor cell; determining a deviation score for the cell or the plurality of cells, wherein the deviation score indicates 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, wherein the one or more reference cells are at a stage of differentiation indicating a determined dopaminergic precursor cell; andoutputting, based on the probability and the deviation score, a computed label classification comprising an indication of whether said cell or said plurality of cells from the in vitro population of neuronal progenitor cells is a determined dopaminergic precursor cell.2.         A computer implemented method of training a process to determine a probability of a cell or a plurality of cells having metagene expression levels of a determined dopaminergic precursor cell, the method comprising training a supervised classification model using (i) expression levels of one or more metagenes, wherein the one or more metagenes are determined based on correlated gene expression levels of reference cells in a reference database, wherein the reference cells are neuronal cells at one or more different stages of differentiation; and (ii) class labels indicating each of the one or more different stages of differentiation for reference cells in the reference database, to determine a probability of a cell or a plurality of cells having metagene expression levels of a determined dopaminergic precursor cell.3.         The computer implemented method of any of claims 1-2, wherein the reference cells are an in vitro population of neuronal progenitor cells.4.         The computer implemented method of claim 3, wherein said in vitro population of neuronal progenitor cells is formed by culturing one or more induced pluripotent stem cells (iPSC) in vitro for a period of time under conditions capable of differentiating the one or more iPSCs to a neuronal progenitor cell.5.         The computer implemented method of claim 4, wherein the culturing is for period of time that is between 2 and 25 days.6.         The computer implemented method of any of claims 1-2, wherein the reference database comprises gene expression levels determined from one or more reference cell populations, wherein each of the one or more reference cell populations are formed by culturing one or more iPSC in vitro for a different period of time, each under conditions capable of differentiating the one or more iPSCs to a neuronal progenitor cell. 7.         The computer implemented method of any of claims 1-2, wherein the one or more metagenes and the 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 database.8.         The computer implemented method of any of claims 1-2, wherein the class label indicating each of the one or more different stages of differentiation of the reference cells is determined using an in vivo method.9.         The computer implemented method of claim 8, wherein the in vivo method comprises:transplanting the in vitro population of neuronal progenitor cells comprising a 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 transplantation on the animal model; anddesignating the class label as a determined dopaminergic precursor cell if the transplantation results in the occurrence of the outcome associated with a therapeutic effect; ordesignating the class label as not a determined dopaminergic precursor cell if the transplantation does not result in the occurrence of the outcome associated with a therapeutic effect.10.     The computer implemented method of any of claims 1-2, wherein the class label indicating each of the one or more different stages of differentiation of the reference cells is determined using an in vitro method.11.     The computer implemented method of claim 10, wherein:the in vitro method comprises assessing dopamine production levels of a reference cell population; and the class label is designated as a determined dopaminergic precursor cell if the dopamine production levels are increased relative to a pluripotent stem cell.12.     The computer implemented method of any of claims 1-2, wherein the expression levels of the one or more metagenes in the test dataset is 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.     The computer implemented method of claim 12, wherein the expression levels of the one or more metagenes in the test dataset is determined using 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.     The computer implemented method of any of claims 1-2, wherein the expression levels of the one or more metagenes in the test dataset is 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 on the updated reference database.15.     The computer implemented method of any of claims 1-2, wherein the number of the one or more metagenes is chosen based on evaluating one or more metrics determined from performing the dimensionality reduction technique using multiple candidate numbers of metagenes.16.     The computer implemented method of any of claims 1-2, wherein the computed label classification indicates that said cell or plurality of cells from the in vitro population of neuronal progenitor cells is a determined dopaminergic precursor cell if the probability of the cell or the plurality of cells having metagene expression levels of the determined dopaminergic precursor cell is greater than a threshold probability value.17.     The computer implemented method of claim 16, wherein:the threshold probability value is set such that a determined dopaminergic precursor cell is identified with greater than or greater than 75% sensitivity; and / or the threshold probability value is set such that a determined dopaminergic precursor cell is identified with greater than or greater than 95% specificity.18.     The computer implemented method of any of claims 1-2, wherein the deviation score for the cell or the plurality of cells is determined using a single-gene deviation score for each of one or more genes in the test dataset. 19.     The computer implemented method of claim 18, wherein the single-gene deviation scores are determined using differences between the gene expression levels of the test dataset and the gene expression levels in one or more reference cells in the reference database.20.     The computer implemented method of claim 18, wherein the single-gene deviation scores are determined using standard deviations of gene expression levels in one or more of the one or more reference cells.21.     The computer implemented method of claim 18, wherein the single-gene deviation scores are z-scores determined using:the differences 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; andthe standard deviations of gene expression levels in one or more of the one or more reference cells of the reference database.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 regression analysis based on (i) the expression levels of the one or more metagenes in the test dataset and (ii) the gene expression levels in the test dataset.23.     The computer implemented method of claim 18, wherein the deviation score is a summary statistic based on all single-gene deviation scores. 24.     The computer implemented method of claim 18, wherein the deviation score is a summary statistic based on single-gene deviation scores for one or more marker genes. 25.     The computer implemented method of claim 23, wherein the summary statistic is a sum or a percentile value.26.     The computer implemented method of claim 25, wherein:the percentile value is between the 50% percentile and the 100% percentile; and / orthe percentile value is the 90% percentile. 27.     The computer implemented method of claim 24, wherein the marker genes comprise radial glial cell markers, early neuronal development genes, pluripotency specific markers, intermediate to late 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 development of neurons, genes controlling dopaminergic neuron development, genes regulating identity and fate of neuronal progenitor cells, dopaminergic neuron markers, astrocyte markers, forebrain markers, hindbrain markers, subthalamic nucleus markers, radial glial markers, cell cycle markers, or any combination of any of the foregoing.28.     The computer implemented method of claim 24, wherein the marker genes are or 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 the foregoing. 29.     The computer implemented method of any of claims 1-2, wherein the computed label classification indicates that said cell or plurality of cells from the in vitro population of neuronal progenitor cells is a determined dopaminergic precursor cell if the deviation score indicates that at least 50% of gene expression levels in the test dataset are no more than five standard deviations away from gene expression levels of the one or more reference cells in the reference database.30.     The computer implemented method of any of claims 1-2, wherein the computed label classification indicates that said cell or plurality of cells from the in vitro population of neuronal progenitor cells is a determined dopaminergic precursor cell if the deviation score indicates that at least 95% of gene expression levels in the test dataset are no more than 10, 9, 8, 7, 6, or 5 standard deviations away from the gene expression levels of the one or more reference cells in the reference database.31.     The computer implemented method of claim 16, wherein the computed label classification indicates that said cell or plurality of cells from the in vitro population of neuronal progenitor cells is a determined dopaminergic precursor cell if:the probability of the cell or the plurality of cells having metagene expression levels of the determined dopaminergic precursor cell is greater than the threshold probability value; andthe deviation score indicates that at least 50% of gene expression levels in the test dataset are no more than five standard deviations away from the gene expression levels of the one or more reference cells in the reference database.32.     The computer implemented method of claim 24, wherein the computed label classification indicates that said cell or plurality of cells from the in vitro population of neuronal progenitor cells is a determined dopaminergic precursor cell if the differences in expression of the marker genes between the test dataset and reference cells of the reference database is statistically insignificant based on a multiple-comparison corrected significance level. 33.     The computer implemented method of claim 32, wherein the multiple-comparison corrected significance level is a Bonferroni corrected significance level or a false discover rate corrected significance level.34.     The computer implemented method of one of claims 1-2, wherein said gene expression levels are obtained from microarray analysis of cellular RNA, RNA sequencing, or both.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.     The computer implemented method of claim 34, wherein the RNA sequencing is performed on RNA from the single cells or a single reference cell.37.     The computer implemented method of claim 1, wherein receiving said test dataset comprises receiving input from an array analysis system.38.     The computer implemented method of claim 1, wherein said one or more reference databases forms part of a storage medium.39.     The computer implemented method of claim 1, comprising repeating the receiving, applying, determining, and outputting steps if the computed label classification indicates that said cell or plurality of cells is not a determined dopaminergic neuronal cell.40.     A population of determined dopaminergic precursor cells identified by the method of claim 1.41.     The method of claim 4, wherein the neuronal progenitor cell is one or more of a floor plate midbrain progenitor cell, determined dopaminergic precursor cell, or dopamine (DA) neurons. 42.     The method of claim 6, wherein the neuronal progenitor cell is one or more of a floor plate midbrain progenitor cell, determined dopaminergic precursor cell, or dopamine (DA) neurons. 43.     The method of claim 39, wherein the steps are repeated using the same or a different in vitro population of neuronal progenitor cells.

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