Modeling amyotrophic lateral sclerosis (ALS) with human induced pluripotent stem cells (iPSCs) aims to reenact embryogenesis, maturation and aging of spinal motor neurons (spMNs) in vitro. As the maturity of spMNs grown in vitro compared to spMNs in vivo remains largely unaddressed, it is unclear to what extent this in vitro system captures critical aspects of spMN development and molecular signatures associated with ALS. Here, we compared transcriptomes among iPSC-derived spMNs, fetal spinal tissues and adult spinal tissues. This approach produced a maturation scale revealing that iPSC-derived spMNs were more similar to fetal spinal tissue than to adult spMNs. Additionally, we resolved gene networks and pathways associated with spMN maturation and aging. These networks enriched for pathogenic familial ALS genetic variants and were disrupted in sporadic ALS spMNs. Altogether, our findings suggest that developing strategies to further mature and age iPSC-derived spMNs will provide more effective iPSC models of ALS pathology.
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Hardiman, O., van den Berg, L.H. & Kiernan, M.C. Clinical diagnosis and management of amyotrophic lateral sclerosis. Nat. Rev. Neurol. 7, 639–649 (2011).
Dimos, J.T. et al. Induced pluripotent stem cells generated from patients with ALS can be differentiated into motor neurons. Science 321, 1218–1221 (2008).
Sterneckert, J.L., Reinhardt, P. & Schöler, H.R. Investigating human disease using stem cell models. Nat. Rev. Genet. 15, 625–639 (2014).
Jessell, T.M. Neuronal specification in the spinal cord: inductive signals and transcriptional codes. Nat. Rev. Genet. 1, 20–29 (2000).
Wichterle, H., Lieberam, I., Porter, J.A. & Jessell, T.M. Directed differentiation of embryonic stem cells into motor neurons. Cell 110, 385–397 (2002).
Sances, S. et al. Modeling ALS with motor neurons derived from human induced pluripotent stem cells. Nat. Neurosci. 16, 542–553 (2016).
Arbab, M., Baars, S. & Geijsen, N. Modeling motor neuron disease: the matter of time. Trends Neurosci. 37, 642–652 (2014).
Cox, L.E. et al. Mutations in CHMP2B in lower motor neuron predominant amyotrophic lateral sclerosis (ALS). PLoS One 5, e9872 (2010).
Kirby, J. et al. Phosphatase and tensin homologue/protein kinase B pathway linked to motor neuron survival in human superoxide dismutase 1-related amyotrophic lateral sclerosis. Brain 134, 506–517 (2011).
Sareen, D. et al. Targeting RNA foci in iPSC-derived motor neurons from ALS patients with a C9ORF72 repeat expansion. Sci. Transl. Med. 5, 208ra149 (2013).
Amoroso, M.W. et al. Accelerated high-yield generation of limb-innervating motor neurons from human stem cells. J. Neurosci. 33, 574–586 (2013).
Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci. USA 102, 15545–15550 (2005).
Devlin, A.C. et al. Human iPSC-derived motoneurons harbouring TARDBP or C9ORF72 ALS mutations are dysfunctional despite maintaining viability. Nat. Commun. 6, 5999 (2015).
Wojcik-Stanaszek, L., Gregor, A. & Zalewska, T. Regulation of neurogenesis by extracellular matrix and integrins. Acta Neurobiol. Exp. (Warsz.) 71, 103–112 (2011).
Langfelder, P. & Horvath, S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 9, 559 (2008).
Das, M.M. & Svendsen, C.N. Astrocytes show reduced support of motor neurons with aging that is accelerated in a rodent model of ALS. Neurobiol. Aging 36, 1130–1139 (2015).
Landrum, M.J. et al. ClinVar: public archive of relationships among sequence variation and human phenotype. Nucleic Acids Res. 42, D980–D985 (2014).
Ince, P.G. et al. Molecular pathology and genetic advances in amyotrophic lateral sclerosis: an emerging molecular pathway and the significance of glial pathology. Acta Neuropathol. 122, 657–671 (2011).
Schymick, J.C., Talbot, K. & Traynor, B.J. Genetics of sporadic amyotrophic lateral sclerosis. Hum. Mol. Genet. 16 Spec No. 2: R233–R242 (2007).
Kiskinis, E. et al. Pathways disrupted in human ALS motor neurons identified through genetic correction of mutant SOD1. Cell Stem Cell 14, 781–795 (2014).
Rabin, S.J. et al. Sporadic ALS has compartment-specific aberrant exon splicing and altered cell-matrix adhesion biology. Hum. Mol. Genet. 19, 313–328 (2010).
Langfelder, P., Luo, R., Oldham, M.C. & Horvath, S. Is my network module preserved and reproducible? PLoS Comput. Biol. 7, e1001057 (2011).
Abdelalim, E.M. & Emara, M.M. Advances and challenges in the differentiation of pluripotent stem cells into pancreatic β cells. World J. Stem Cells 7, 174–181 (2015).
Batalov, I. & Feinberg, A.W. Differentiation of cardiomyocytes from human pluripotent stem cells using monolayer culture. Biomark. Insights 10 (suppl. 1), 71–76 (2015).
Lapasset, L. et al. Rejuvenating senescent and centenarian human cells by reprogramming through the pluripotent state. Genes Dev. 25, 2248–2253 (2011).
Sarnat, H.B. Clinical neuropathology practice guide 5-2013: markers of neuronal maturation. Clin. Neuropathol. 32, 340–369 (2013).
Bender, A.C., Morse, R.P., Scott, R.C., Holmes, G.L. & Lenck-Santini, P.P. SCN1A mutations in Dravet syndrome: impact of interneuron dysfunction on neural networks and cognitive outcome. Epilepsy Behav. 23, 177–186 (2012).
Wapinski, O.L. et al. Hierarchical mechanisms for direct reprogramming of fibroblasts to neurons. Cell 155, 621–635 (2013).
Chanda, S. et al. Generation of induced neuronal cells by the single reprogramming factor ASCL1. Stem Cell Rep. 3, 282–296 (2014).
Shi, Y., Kirwan, P. & Livesey, F.J. Directed differentiation of human pluripotent stem cells to cerebral cortex neurons and neural networks. Nat. Protoc. 7, 1836–1846 (2012).
Hawrylycz, M. et al. Canonical genetic signatures of the adult human brain. Nat. Neurosci. 18, 1832–1844 (2015).
Kang, H.J. et al. Spatio-temporal transcriptome of the human brain. Nature 478, 483–489 (2011).
Almeida, S. et al. Induced pluripotent stem cell models of progranulin-deficient frontotemporal dementia uncover specific reversible neuronal defects. Cell Rep. 2, 789–798 (2012).
Bilican, B. et al. Mutant induced pluripotent stem cell lines recapitulate aspects of TDP-43 proteinopathies and reveal cell-specific vulnerability. Proc. Natl. Acad. Sci. USA 109, 5803–5808 (2012).
Miller, J.D. et al. Human iPSC-based modeling of late-onset disease via progerin-induced aging. Cell Stem Cell 13, 691–705 (2013).
Nguyen, H.N. et al. LRRK2 mutant iPSC-derived DA neurons demonstrate increased susceptibility to oxidative stress. Cell Stem Cell 8, 267–280 (2011).
Shi, Y. et al. A human stem cell model of early Alzheimer's disease pathology in Down syndrome. Sci. Transl. Med. 4, 124ra29 (2012).
Studer, L., Vera, E. & Cornacchia, D. Programming and reprogramming cellular age in the era of induced pluripotency. Cell Stem Cell 16, 591–600 (2015).
Mertens, J. et al. Directly reprogrammed human neurons retain aging-associated transcriptomic signatures and reveal age-related nucleocytoplasmic defects. Cell Stem Cell 17, 705–718 (2015).
Liu, M.L. et al. Small molecules enable neurogenin 2 to efficiently convert human fibroblasts into cholinergic neurons. Nat. Commun. 4, 2183 (2013).
Son, E.Y. et al. Conversion of mouse and human fibroblasts into functional spinal motor neurons. Cell Stem Cell 9, 205–218 (2011).
Cady, J. et al. Amyotrophic lateral sclerosis onset is influenced by the burden of rare variants in known amyotrophic lateral sclerosis genes. Ann. Neurol. 77, 100–113 (2015).
Kennedy, S.R., Loeb, L.A. & Herr, A.J. Somatic mutations in aging, cancer and neurodegeneration. Mech. Ageing Dev. 133, 118–126 (2012).
Lin, J. et al. Specific electron transport chain abnormalities in amyotrophic lateral sclerosis. J. Neurol. 256, 774–782 (2009).
Chung, W.S., Welsh, C.A., Barres, B.A. & Stevens, B. Do glia drive synaptic and cognitive impairment in disease? Nat. Neurosci. 18, 1539–1545 (2015).
O'Rourke, J.G. et al. C9orf72 is required for proper macrophage and microglial function in mice. Science 351, 1324–1329 (2016).
Albert, R., Jeong, H. & Barabasi, A.L. Error and attack tolerance of complex networks. Nature 406, 378–382 (2000).
Langfelder, P., Mischel, P.S. & Horvath, S. When is hub gene selection better than standard meta-analysis? PLoS One 8, e61505 (2013).
Wainger, B.J. et al. Intrinsic membrane hyperexcitability of amyotrophic lateral sclerosis patient-derived motor neurons. Cell Rep. 7, 1–11 (2014).
Zhang, K. et al. The C9orf72 repeat expansion disrupts nucleocytoplasmic transport. Nature 525, 56–61 (2015).
Chin, M.H. et al. Induced pluripotent stem cells and embryonic stem cells are distinguished by gene expression signatures. Cell Stem Cell 5, 111–123 (2009).
Maherali, N. et al. A high-efficiency system for the generation and study of human induced pluripotent stem cells. Cell Stem Cell 3, 340–345 (2008).
Roth, R.B. et al. Gene expression analyses reveal molecular relationships among 20 regions of the human CNS. Neurogenetics 7, 67–80 (2006).
Brockington, A. et al. Unravelling the enigma of selective vulnerability in neurodegeneration: motor neurons resistant to degeneration in ALS show distinct gene expression characteristics and decreased susceptibility to excitotoxicity. Acta Neuropathol. 125, 95–109 (2013).
Lu, J., Kerns, R.T., Peddada, S.D. & Bushel, P.R. Principal component analysis-based filtering improves detection for Affymetrix gene expression arrays. Nucleic Acids Res. 39, e86 (2011).
Zhang, B. & Horvath, S. A general framework for weighted gene co-expression network analysis. Stat. Appl. Genet. Mol. Biol. 4, e17 (2005).
The authors gratefully acknowledge B. Shelley, L. Garcia and L. Ornelas for assistance with experiments and reagent organization; B. Berman and D. Rushton for statistical and programming advice; and B. Berman, V. Mattis and S. Svendsen for critical reading and comments on the manuscript. This work was supported by the following grants: NIH/NINDS (U54NS091046-01) (C.N.S.) and the ALS Association (R.H. and C.N.S.). Project ALS supported work done by H.W. and M.W.A.
The authors declare no competing financial interests.
Integrated supplementary information
(a) Principal component analysis of 10,605 mRNA transcripts in n = 43 samples. Y-axis depicts sample coordinate along each principal component. The percent contribution to the total variance of the data by each principal component is shown along the x-axis. In order to reduce obscuration, data points are jittered randomly along the x-axis within each bin. The color legend for tissue types is indicated to the left. (b) Regression analysis of linear models generated by using 6 sample traits as predictors and coordinates along 42 principal component as dependent variables. Heat map indicates F-statistic. Bonferroni-corrected P-values thresholds are indicated. Sample coordinates along PC1 to 4 are best explained by tissue type. For additional information, see Supplementary Tables 1a (for sample meta data) and 1b (for linear regression test statistics).
Supplementary Figure 2 Gene co-expression modules that correlate with age, spMN maturation and embryonic spMN development enrich for distinct and overlapping pathways (see Fig. 2).
(a) Weighted gene co-expression network analysis (WGCNA) clustered 10,605 genes across human pluripotent cells, iMNs, fetal spinal tissues, and adult spinal tissues based on similar expression patterns. Six ALS spMN samples were left out so that only non-ALS samples contributed to the network building (n = 34 samples). WGCNA grouped tightly networked genes into 15 modules. Upper and middle panel: Summary expression values for each module, known as module eigengenes, are correlated using Pearson’s method in a pair-wise manner to external traits of the samples, such as the sex, post mortem interval, or age of adult tissue donor, or correlated to the principal component coordinate of each sample along PC1 and PC3. n = number of samples for which there is data for the indicated sample trait, and thus used in the correlation. Colors on heat map indicate Pearson’s pairwise correlation between module eigengene and sample trait. Correlations outlined in black denote values with a Bonferroni-corrected P-value < 0.01. Lower panel: Gene variants associated with diseases in the ClinVar database were tested against each of the 15 modules for enrichment. n = number of genes with variants associated with the indicated disease, represented on the human microarray platform, and thus used in the enrichment analysis. The Benjamini-Hochberg corrected negative log10 P-value from each hypergeometric test is indicated by the green heat map. Corrected P-values < 0.005 are called significant and outlined in black. Gene variants associated with “MN disease” and “Amyotrophic Lateral Sclerosis” are enriched in the yellow module, which significantly correlated with spMN maturation and age, while gene variants associated with other age-related diseases are not significantly enriched in any modules. (b) Chow-Ruskey diagram illustrating the number of overlapping and distinct GO terms (Bonferroni-corrected P-value < 0.05) enriched in modules identified as significantly correlated or anti-correlated to age (AGEpos or AGEneg, respectively) or embryonic spMN development (PC3pos or PC3neg, respectively). Representative pathways are listed in grey boxes extending from the diagram, along with the lowest Bonferroni-corrected P-values across all modules. (c) As in b, except illustrating the number of overlapping and distinct GO terms in spMN maturation (PC1pos or PC1neg, respectively) or embryonic spMN development (PC3pos or PC3neg, respectively). For additional information, see Supplementary Tables 2c (for module assignments and properties), 2d, and 2e (for gene set enrichments and P-values).
Supplementary Figure 3 Combined principal component and weighted gene co-expression network analyses reduce the number of key spMN maturation and embryonic development markers (see Fig. 3).
(a) mRNA expression values compared between RT-qPCR and microarray for an iPSC line and its iMN derivative, as well as for fetal spinal cord (fSC) samples at gestational days 52 and 53. Data were obtained from the same sample of RNA used in both platforms. RT-qPCR expression values based on the average of n = 3 technical replicates. (b) Gene expression density plot for 6,640 overlapping genes represented in the training data set as well as in the validation data sets, totaling 120 samples. Each line represents the gene expression distribution from one sample. Colors denote the study from which they were obtained. Black line represents the quantile normalized distribution of all samples. (c) Principal component analysis of 6,640 mRNA transcripts from training and validation samples (n = 120 samples) illustrates the major features that define the samples with respect to one another. The y-axis depicts the coordinate along each principal component for each sample. The percent contribution to the total variance of the data by each principal component is shown along the x-axis. In order to reduce obscuration, data points are jittered randomly along the x-axis within each bin. Sample legend is shown on the far right. Colors of data points indicate general sample type, and shapes of data points indicate the study from which the data were obtained. Microarray platforms are also indicated. (d) Principal component analysis performed on 20 gene expression values across 77 samples represented in b without the 43 samples in the training set. Samples are plotted by their coordinates along PC1 and PC2. Sample legend is the same as for c. (e – g) ROC analysis performed on four methods classifying samples in the validation data set (in addition to two human fibroblast samples, n = 79 samples) as pluripotent stem cells (e), fetal spinal cord-like cells (f), or adult spinal cord cells (g). Classifications were based on Pearson’s correlation of the sample to the median expression values of target cell types in the training data set using 6,640 genes (orange) or 20 genes (green) or based on sample coordinates along the spMN maturation or embryonic development principal components using 6,640 genes (black) or 20 genes (blue). The area under the curve (AUC) is shown next to each like-colored curve, and summarizes the overall performance of each classification method. For additional information, see Supplementary Tables 1a (for sample meta data), 2c (for module assignments and properties), 2f–i (for gene scoring properties), 3a (for qPCR primer sequences), and 3b (for normalized linear expression values used in validation analyses).
Supplementary Figure 4 Gene co-expression modules in spMNs correlate with sALS disease status (see Fig. 5).
(a) Principal component analysis of 15,614 mRNA transcripts from sALS (n = 12) and control (n = 10) spMN samples illustrates the major features that define the samples with respect to one another. The y-axis depicts the coordinate along each principal component for each sample. The percent contribution to the total variance of the data by each principal component is shown along the x-axis. In order to reduce obscuration, data points are jittered randomly along the x-axis within each bin. (b) Regression analysis of linear models generated by using 6 sample traits as predictors and coordinates along 21 principal components as dependent variables. Heat map indicates F-statistic. The nominal P-value threshold is indicated. No P-values passed Bonferroni-corrected significance. Sample coordinates along PC1 are best explained by sALS disease status, therefore PC1 is referred to as the sALS component. (c) WGCNA clustered 15,614 genes across sALS and control spMNs (n = 22 samples) based on similar network topology. Height metric on dendrogram indicates topological overlap (TO) distance between genes. A dynamic tree-cutting algorithm grouped tightly networked genes, illustrated as low hanging branches, into 52 modules represented by arbitrary colors directly below the branches. Genes falling onto the predominant light grey color are not classified into any module. (d) Summary expression values for each of the 52 modules, known as module eigengenes, are correlated using Pearson’s method in a pair-wise manner to external traits of the samples, such as the sex, sALS disease status, site of ALS onset, age of tissue donor, disease course, or post mortem interval, or correlated to the principal component coordinate of each sample along PC1 (the sALS component). n = number of samples for which there is data for the indicated sample trait, and thus used in the correlation. Colors on heat map indicate Pearson’s pairwise correlation between module eigengene and sample trait. For PC1, correlations outlined in black denote values with a Bonferroni-corrected P-value < 0.01, and these modules were kept for subsequent GO analysis. For sALS disease status, Benjamini-Hochberg-corrected P-values < 0.05 are outlined in black. Some modules uniquely correlate or anti-correlate with a sample trait, while others correlate to more than one sample trait. This data is also shown in Fig. 5b. For additional information, see Supplementary Tables 5a (for linear expression values of sALS data set), 1c (for linear regression test statistics), 5b (for full lists of significantly enriched gene sets and P-values), and 5c (for module assignments and properties).
(a) Measure of how well 55 modules defined in the iMN data set are preserved in the sALS data set. The Z-summary statistic (y-axis) for is plotted against module size (x-axis). Data points reflect module color. Dashed green line indicate threshold at Z = 10, and dashed blue line indicate threshold at Z = 2. For the likelihood of module preservation, Z-summary > 10 indicates strong evidence; 10 > Z-summary > 2 indicates moderate to weak evidence, and 2 > Z-summary indicates no evidence. (b) Measure of how well 55 modules defined in the iMN data set are preserved in the sALS data set, as a relative comparison among modules. The median rank statistic (y-axis) is plotted against module size (x-axis). Data points reflect module color. Low median rank values indicate a high preservation. (c) As in a, except applied to 52 modules defined in the sALS data set and tested for preservation in the iMN data set. (d) As in b, except applied to 52 modules defined in the sALS data set and tested for preservation in the iMN data set. (e) Pathogenic ALS genetic variants have higher intramodule membership than genes not affect by variants. Among the 4,711 genes classified into modules, genes were divided by either those affect by pathogenic variants versus unaffected genes for either motor neuron disease (first and second bins, respectively with n = 139 and n = 4,572) or ALS (third and fourth bins, respectively with n = 67 and n = 4,644). Significance was tested using the Wilcoxon rank-sum test. For MN, W = 339,949, for ALS, W = 182,087, and P-values are indicated above comparisons. The median intramodule membership of MN disease variants is higher than unaffected genes, but not significantly. The ALS variants have a significantly higher intramodule membership than unaffected genes. For additional information, see Supplementary Tables 2c (for iMN module assignments and properties), 5c (for sALS module assignments and properties), and 5d (for module preservation statistics).
Supplementary Figure 6 Gene co-expression network modules in spMNs associated with sALS enrich for pathways similar to those enriched in spMN maturation- and age-associates modules (see Fig. 5).
Chow-Ruskey diagrams illustrating the number of overlapping and distinct GO terms enriched in modules identified as significantly correlated or anti-correlated with the sALS component (sALSpos or sALSneg, respectively) and (a) age (AGEpos or AGEneg), (b) spMN maturation (PC1pos or PC1neg), or (c) embryonic spMN development (PC3pos or PC3neg). Pathways are listed in grey boxes extending from the diagrams, along with Bonferroni-corrected P-values of enrichment in sALS modules. For additional information, see Supplementary Tables 2c (for iMN module assignments and properties), 2b, 2d, 2e (for iMN gene set enrichments and P-values), 5c (for sALS module assignments and properties), 5b, 5e, and 5f (for for full lists of significantly enriched gene sets and P-values).
Supplementary Figures 1–6. (PDF 1838 kb)
Summary of RNA expression profile data used in this study, Related to all figures. (XLSX 73 kb)
Gene expression analysis of Affymetrix Human Genome U133 Plus 2.0 Array samples used for hierarchical clustering, PCA and WGCNA, Related to all figures. (XLSX 10898 kb)
Gene expression of multiple microarray samples used for Pearson correlation, PCA, and ROC analysis, Related to Figure 3 and Supplementary Figure 3. (XLSX 9746 kb)
Gene expression of samples from mtSOD1 and control spMNs  and iMNs  used for MA plots, Related to Figure 4. (XLSX 3119 kb)
Gene expression analysis of sALS and control spMNs  used for hierarchical clustering, PCA and WGCNA, Related to Figures 5, 6, Supplementary Figures 5, and 6. (XLSX 7508 kb)
Gene property analysis of Age, spMN maturation, and sALS modules, Related to Figure 6. (XLSX 9 kb)
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Ho, R., Sances, S., Gowing, G. et al. ALS disrupts spinal motor neuron maturation and aging pathways within gene co-expression networks. Nat Neurosci 19, 1256–1267 (2016). https://doi.org/10.1038/nn.4345
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