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Variation among intact tissue samples reveals the core transcriptional features of human CNS cell classes

Nature Neuroscience volume 21, pages 1171–1184 (2018)Cite this article

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Abstract

It is widely assumed that cells must be physically isolated to study their molecular profiles. However, intact tissue samples naturally exhibit variation in cellular composition, which drives covariation of cell-class-specific molecular features. By analyzing transcriptional covariation in 7,221 intact CNS samples from 840 neurotypical individuals, representing billions of cells, we reveal the core transcriptional identities of major CNS cell classes in humans. By modeling intact CNS transcriptomes as a function of variation in cellular composition, we identify cell-class-specific transcriptional differences in Alzheimer’s disease, among brain regions, and between species. Among these, we show that PMP2 is expressed by human but not mouse astrocytes and significantly increases mouse astrocyte size upon ectopic expression in vivo, causing them to more closely resemble their human counterparts. Our work is available as an online resource (http://oldhamlab.ctec.ucsf.edu/) and provides a generalizable strategy for determining the core molecular features of cellular identity in intact biological systems.

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Fig. 1: Rationale and workflow.
Fig. 2: Integrative gene coexpression analysis of intact CNS transcriptomes reveals consensus transcriptional profiles of human astrocytes, oligodendrocytes, microglia, and neurons.
Fig. 3: The core transcriptional identities of human astrocytes, oligodendrocytes, microglia, and neurons include known and novel biomarkers.
Fig. 4: Variation among intact tissue samples reveals transcriptional signatures of human cholinergic neurons, midbrain dopaminergic neurons, endothelial cells, and ependymal cells.
Fig. 5: Variation in cellular abundance predicts gene expression in transcriptomes from intact CNS samples.
Fig. 6: Gene expression modeling offers new avenues for studying human CNS diseases.
Fig. 7: Regional expression fidelity and predictive modeling reveal astrocyte heterogeneity in the human brain.
Fig. 8: Gene expression modeling identifies cell-class-specific transcriptional differences between humans and mice.

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Data availability

All gene expression data sets analyzed in this study are publicly available (accession codes and URLs are provided in Supplementary Table 1). Genome-wide estimates of expression fidelity for major human CNS cell classes are provided on our web site (http://oldhamlab.ctec.ucsf.edu/). All other data that support the findings of this study are available from the corresponding author upon reasonable request.

References

  1. Darmanis, S. et al. A survey of human brain transcriptome diversity at the single cell level. Proc. Natl. Acad. Sci. USA 112, 7285–7290 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Paul, G. et al. The adult human brain harbors multipotent perivascular mesenchymal stem cells. PLoS One 7, e35577 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Zhang, Y. et al. Purification and characterization of progenitor and mature human astrocytes reveals transcriptional and functional differences with mouse. Neuron 89, 37–53 (2016).

    Article  CAS  PubMed  Google Scholar 

  4. Habib, N. et al. Massively parallel single-nucleus RNA-seq with DroNc-seq. Nat. Methods 14, 955–958 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Lake, B. B. et al. Neuronal subtypes and diversity revealed by single-nucleus RNA sequencing of the human brain. Science 352, 1586–1590 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Liu, S. & Trapnell, C. Single-cell transcriptome sequencing: recent advances and remaining challenges. F1000Res. https://doi.org/10.12688/f1000research.7223.1 (2016).

  7. Azevedo, F. A. et al. Equal numbers of neuronal and nonneuronal cells make the human brain an isometrically scaled-up primate brain. J. Comp. Neurol. 513, 532–541 (2009).

    Article  PubMed  Google Scholar 

  8. Oldham, M.C. Transcriptomics: from differential expression to coexpression. In The OMICs: Applications in Neurosciences. (ed. Coppola, G.) 85–113 (Oxford, 2014).

  9. Abbas, A. R., Wolslegel, K., Seshasayee, D., Modrusan, Z. & Clark, H. F. Deconvolution of blood microarray data identifies cellular activation patterns in systemic lupus erythematosus. PLoS One 4, e6098 (2009).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  10. Gong, T. & Szustakowski, J. D. DeconRNASeq: a statistical framework for deconvolution of heterogeneous tissue samples based on mRNA-Seq data. Bioinformatics 29, 1083–1085 (2013).

    Article  CAS  PubMed  Google Scholar 

  11. Kuhn, A., Thu, D., Waldvogel, H. J., Faull, R. L. & Luthi-Carter, R. Population-specific expression analysis (PSEA) reveals molecular changes in diseased brain. Nat. Methods 8, 945–947 (2011).

    Article  CAS  PubMed  Google Scholar 

  12. Newman, A. M. et al. Robust enumeration of cell subsets from tissue expression profiles. Nat. Methods 12, 453–457 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Shen-Orr, S. S. et al. Cell type-specific gene expression differences in complex tissues. Nat. Methods 7, 287–289 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Zhong, Y., Wan, Y. W., Pang, K., Chow, L. M. & Liu, Z. Digital sorting of complex tissues for cell type-specific gene expression profiles. BMC Bioinformatics 14, 89 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  15. Zuckerman, N. S., Noam, Y., Goldsmith, A. J. & Lee, P. P. A self-directed method for cell-type identification and separation of gene expression microarrays. PLOS Comput. Biol. 9, e1003189 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Oldham, M. C. et al. Functional organization of the transcriptome in human brain. Nat. Neurosci. 11, 1271–1282 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Fertuzinhos, S. et al. Laminar and temporal expression dynamics of coding and noncoding RNAs in the mouse neocortex. Cell Rep. 6, 938–950 (2014).

    Article  CAS  PubMed  Google Scholar 

  18. Ponomarev, I., Rau, V., Eger, E. I., Harris, R. A. & Fanselow, M. S. Amygdala transcriptome and cellular mechanisms underlying stress-enhanced fear learning in a rat model of posttraumatic stress disorder. Neuropsychopharmacology 35, 1402–1411 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  19. Hilliard, A. T., Miller, J. E., Fraley, E. R., Horvath, S. & White, S. A. Molecular microcircuitry underlies functional specification in a basal ganglia circuit dedicated to vocal learning. Neuron 73, 537–552 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Bakken, T. E. et al. A comprehensive transcriptional map of primate brain development. Nature 535, 367–375 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Hawrylycz, M. et al. Canonical genetic signatures of the adult human brain. Nat. Neurosci. 18, 1832–1844 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Cahoy, J. D. et al. A transcriptome database for astrocytes, neurons, and oligodendrocytes: a new resource for understanding brain development and function. J. Neurosci. 28, 264–278 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Hickman, S. E. et al. The microglial sensome revealed by direct RNA sequencing. Nat. Neurosci. 16, 1896–1905 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Horvath, S. & Dong, J. Geometric interpretation of gene coexpression network analysis. PLOS Comput. Biol. 4, e1000117 (2008).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  25. Langfelder, P. & Horvath, S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 9, 559 (2008).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  26. Tasic, B. et al. Adult mouse cortical cell taxonomy revealed by single cell transcriptomics. Nat. Neurosci. 19, 335–346 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Hodges, A. et al. Regional and cellular gene expression changes in human Huntington’s disease brain. Hum. Mol. Genet. 15, 965–977 (2006).

    Article  CAS  PubMed  Google Scholar 

  28. Berchtold, N. C. et al. Gene expression changes in the course of normal brain aging are sexually dimorphic. Proc. Natl. Acad. Sci. USA 105, 15605–15610 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Kang, H. J. et al. Spatio-temporal transcriptome of the human brain. Nature 478, 483–489 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Hernandez, D. G. et al. Integration of GWAS SNPs and tissue specific expression profiling reveal discrete eQTLs for human traits in blood and brain. Neurobiol. Dis. 47, 20–28 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Li, J. Z. et al. Circadian patterns of gene expression in the human brain and disruption in major depressive disorder. Proc. Natl. Acad. Sci. USA 110, 9950–9955 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Ramasamy, A. et al. Genetic variability in the regulation of gene expression in ten regions of the human brain. Nat. Neurosci. 17, 1418–1428 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. GTEx Consortium. Human genomics. The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans. Science 348, 648–660 (2015).

    Article  PubMed Central  CAS  Google Scholar 

  34. Lake, B. B. et al. Integrative single-cell analysis of transcriptional and epigenetic states in the human adult brain. Nat. Biotechnol. 36, 70–80 (2018).

    Article  CAS  PubMed  Google Scholar 

  35. Lek, M. et al. Analysis of protein-coding genetic variation in 60,706 humans. Nature 536, 285–291 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Binder, J. X. et al. COMPARTMENTS: unification and visualization of protein subcellular localization evidence. Database (Oxford) 2014, bau012 (2014).

    Article  CAS  Google Scholar 

  37. Szklarczyk, D. et al. STRINGv10: protein-protein interaction networks, integrated over the tree of life. Nucleic Acids Res. 43, D447–D452 (2015).

    Article  CAS  PubMed  Google Scholar 

  38. Lein, E. S. et al. Genome-wide atlas of gene expression in the adult mouse brain. Nature 445, 168–176 (2007).

    Article  CAS  PubMed  Google Scholar 

  39. Yu, W., Clyne, M., Khoury, M. J. & Gwinn, M. Phenopedia and Genopedia: disease-centered and gene-centered views of the evolving knowledge of human genetic associations. Bioinformatics 26, 145–146 (2010).

    Article  CAS  PubMed  Google Scholar 

  40. Cribbs, D. H. et al. Extensive innate immune gene activation accompanies brain aging, increasing vulnerability to cognitive decline and neurodegeneration: a microarray study. J. Neuroinflammation 9, 179 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Zhang, B. et al. Integrated systems approach identifies genetic nodes and networks in late-onset Alzheimer’s disease. Cell 153, 707–720 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Hokama, M. et al. Altered expression of diabetes-related genes in Alzheimer’s disease brains: the Hisayama study. Cereb. Cortex 24, 2476–2488 (2014).

    Article  PubMed  Google Scholar 

  43. Oberheim, N. A., Goldman, S. A. & Nedergaard, M. Heterogeneity of astrocytic form and function. Methods Mol. Biol. 814, 23–45 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Zenker, J. et al. A role of peripheral myelin protein 2 in lipid homeostasis of myelinating Schwann cells. Glia 62, 1502–1512 (2014).

    Article  PubMed  Google Scholar 

  45. Bozek, K. et al. Exceptional evolutionary divergence of human muscle and brain metabolomes parallels human cognitive and physical uniqueness. PLoS Biol. 12, e1001871 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  46. Mittelbronn, M., Dietz, K., Schluesener, H. J. & Meyermann, R. Local distribution of microglia in the normal adult human central nervous system differs by up to one order of magnitude. Acta Neuropathol. 101, 249–255 (2001).

    CAS  PubMed  Google Scholar 

  47. Zhang, Y. et al. An RNA-sequencing transcriptome and splicing database of glia, neurons, and vascular cells of the cerebral cortex. J. Neurosci. 34, 11929–11947 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Butovsky, O. et al. Identification of a unique TGF-β-dependent molecular and functional signature in microglia. Nat. Neurosci. 17, 131–143 (2014).

    Article  CAS  PubMed  Google Scholar 

  49. Storey, J. D. & Tibshirani, R. Statistical significance for genomewide studies. Proc. Natl. Acad. Sci. USA 100, 9440–9445 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Irizarry, R. A. et al. Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics 4, 249–264 (2003).

    Article  PubMed  Google Scholar 

  51. Dai, M. et al. Evolving gene/transcript definitions significantly alter the interpretation of GeneChip data. Nucleic Acids Res. 33, e175 (2005).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  52. Zhang, J., Finney, R. P., Clifford, R. J., Derr, L. K. & Buetow, K. H. Detecting false expression signals in high-density oligonucleotide arrays by an in silico approach. Genomics 85, 297–308 (2005).

    Article  CAS  PubMed  Google Scholar 

  53. Gautier, L., Cope, L., Bolstad, B. M. & Irizarry, R. A. affy--analysis of Affymetrix GeneChip data at the probe level. Bioinformatics 20, 307–315 (2004).

    Article  CAS  PubMed  Google Scholar 

  54. Davis, S. & Meltzer, P. S. GEOquery: a bridge between the Gene Expression Omnibus (GEO) and BioConductor. Bioinformatics 23, 1846–1847 (2007).

    Article  PubMed  CAS  Google Scholar 

  55. Oldham, M. C., Langfelder, P. & Horvath, S. Network methods for describing sample relationships in genomic datasets: application to Huntington’s disease. BMC Syst. Biol. 6, 63 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  56. Johnson, W. E., Li, C. & Rabinovic, A. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics 8, 118–127 (2007).

    Article  PubMed  Google Scholar 

  57. Bolstad, B. M., Irizarry, R. A., Astrand, M. & Speed, T. P. A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics 19, 185–193 (2003).

    Article  CAS  PubMed  Google Scholar 

  58. Molofsky, A. V. et al. Expression profiling of Aldh1l1-precursors in the developing spinal cord reveals glial lineage-specific genes and direct Sox9-Nfe2l1 interactions. Glia 61, 1518–1532 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  59. Lui, J. H. et al. Radial glia require PDGFD-PDGFRβ signalling in human but not mouse neocortex. Nature 515, 264–268 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Hardin, J., Mitani, A., Hicks, L. & VanKoten, B. A robust measure of correlation between two genes on a microarray. BMC Bioinformatics 8, 220 (2007).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  61. Song, L., Langfelder, P. & Horvath, S. Comparison of co-expression measures: mutual information, correlation, and model based indices. BMC Bioinformatics 13, 328 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Zeisel, A. et al. Brain structure. Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq. Science 347, 1138–1142 (2015).

    Article  CAS  PubMed  Google Scholar 

  63. Butler, L. M. et al. Analysis of body-wide unfractionated tissue data to identify a core human endothelial transcriptome. Cell Syst. 3, 287–301.e3 (2016).

    Article  CAS  PubMed  Google Scholar 

  64. La Manno, G. et al. Molecular diversity of midbrain development in mouse, human, and stem cells. Cell 167, 566–580.e19 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  65. Kuhn, A. et al. Cell population-specific expression analysis of human cerebellum. BMC Genomics 13, 610 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  66. Fisher, R. A. Statistical Methods for Research Workers. (Hafner Publishing Company, New York, 1970).

    Google Scholar 

  67. Doyle, J. P. et al. Application of a translational profiling approach for the comparative analysis of CNS cell types. Cell 135, 749–762 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Mancarci, B.O. et al. Cross-laboratory analysis of brain cell type transcriptomes with applications to interpretation of bulk tissue data. eNeuro https://doi.org/10.1523/eneuro.0212-17.2017 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  69. Gokce, O. et al. Cellular taxonomy of the mouse striatum as revealed by single-cell RNA-seq. Cell Rep. 16, 1126–1137 (2016).

    Article  CAS  PubMed  Google Scholar 

  70. He, L. et al. Analysis of the brain mural cell transcriptome. Sci. Rep. 6, 35108 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Wickham, H. lot2: Elegant Graphics for Data Analysis. (Springer-Verlag, New York, 2009; ggp.

    Google Scholar 

  72. Trapnell, C. et al. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat. Biotechnol. 32, 381–386 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Yin, P. F. & Estimating, X. R2 shrinkage in multiple regression: a comparison of different analytical methods. J. Exp. Educ. 69, 203–224 (2001).

    Article  Google Scholar 

  74. Troyanskaya, O. et al. Missing value estimation methods for DNA microarrays. Bioinformatics 17, 520–525 (2001).

    Article  CAS  PubMed  Google Scholar 

  75. Benoit, J., Ayoub, A. & Rakic, P. Epigenetic stability in the adult mouse cortex under conditions of pharmacologically induced histone acetylation. Brain Struct. Funct. 221, 3963–3978 (2016).

    Article  CAS  PubMed  Google Scholar 

  76. Benton, C. S. et al. Evaluating genetic markers and neurobiochemical analytes for fluoxetine response using a panel of mouse inbred strains. Psychopharmacology (Berl.) 221, 297–315 (2012).

    Article  CAS  Google Scholar 

  77. Chu, P. L., Keum, S. & Marchuk, D. A. A novel genetic locus modulates infarct volume independently of the extent of collateral circulation. Physiol. Genomics 45, 751–763 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Iancu, O. D. et al. Cosplicing network analysis of mammalian brain RNA-seq data utilizing WGCNA and Mantel correlations. Front. Genet. 6, 174 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  79. Jiang, P. et al. A systems approach identifies networks and genes linking sleep and stress: implications for neuropsychiatric disorders. Cell Rep. 11, 835–848 (2015).

    Article  CAS  PubMed  Google Scholar 

  80. Kasukawa, T. et al. Quantitative expression profile of distinct functional regions in the adult mouse brain. PLoS One 6, e23228 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Kleiman, R. J. et al. Dendritic spine density deficits in the hippocampal CA1 region of young Tg2576 mice are ameliorated with the PDE9A inhibitor PF-04447943. Alzheimers Dement. 6, S563–S564 (2010).

    Article  Google Scholar 

  82. Ling, K. H. et al. Functional transcriptome analysis of the postnatal brain of the Ts1Cje mouse model for Down syndrome reveals global disruption of interferon-related molecular networks. BMC Genomics 15, 624 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  83. Mackiewicz, M. et al. Macromolecule biosynthesis: a key function of sleep. Physiol. Genomics 31, 441–457 (2007).

    Article  CAS  PubMed  Google Scholar 

  84. Matarin, M. et al. A genome-wide gene-expression analysis and database in transgenic mice during development of amyloid or tau pathology. Cell Rep. 10, 633–644 (2015).

    Article  CAS  PubMed  Google Scholar 

  85. Parente, M. K., Rozen, R., Cearley, C. N. & Wolfe, J. H. Dysregulation of gene expression in a lysosomal storage disease varies between brain regions implicating unexpected mechanisms of neuropathology. PLoS One 7, e32419 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  86. Peixoto, L. L. et al. Memory acquisition and retrieval impact different epigenetic processes that regulate gene expression. BMC Genomics 16, S5 (2015). Suppl 5.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  87. Segall, S. K. et al. Comt1 genotype and expression predicts anxiety and nociceptive sensitivity in inbred strains of mice. Genes Brain Behav. 9, 933–946 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. Stark, K. L. et al. Altered brain microRNA biogenesis contributes to phenotypic deficits in a 22q11-deletion mouse model. Nat. Genet. 40, 751–760 (2008).

    Article  CAS  PubMed  Google Scholar 

  89. Stevens, S. L. et al. Multiple preconditioning paradigms converge on interferon regulatory factor-dependent signaling to promote tolerance to ischemic brain injury. J. Neurosci. 31, 8456–8463 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  90. Vanderlinden, L. A. et al. Whole brain and brain regional coexpression network interactions associated with predisposition to alcohol consumption. PLoS One 8, e68878 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  91. Wes, P. D. et al. Tau overexpression impacts a neuroinflammation gene expression network perturbed in Alzheimer’s disease. PLoS One 9, e106050 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  92. Wolen, A. R. et al. Genetic dissection of acute ethanol responsive gene networks in prefrontal cortex: functional and mechanistic implications. PLoS One 7, e33575 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  93. Enard, W. et al. Intra- and interspecific variation in primate gene expression patterns. Science 296, 340–343 (2002).

    Article  CAS  PubMed  Google Scholar 

  94. Khaitovich, P. et al. Regional patterns of gene expression in human and chimpanzee brains. Genome Res. 14, 1462–1473 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  95. Cáceres, M. et al. Elevated gene expression levels distinguish human from non-human primate brains. Proc. Natl. Acad. Sci. USA 100, 13030–13035 (2003).

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  96. Fraser, H. B., Khaitovich, P., Plotkin, J. B., Pääbo, S. & Eisen, M. B. Aging and gene expression in the primate brain. PLoS Biol. 3, e274 (2005).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  97. Khaitovich, P. et al. Parallel patterns of evolution in the genomes and transcriptomes of humans and chimpanzees. Science 309, 1850–1854 (2005).

    Article  CAS  PubMed  Google Scholar 

  98. Franz, H. et al. Systematic analysis of gene expression in human brains before and after death. Genome Biol. 6, R112 (2005).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  99. Khaitovich, P. et al. Positive selection on gene expression in the human brain. Curr. Biol. 16, R356–R358 (2006).

    Article  CAS  PubMed  Google Scholar 

  100. Somel, M. et al. Transcriptional neoteny in the human brain. Proc. Natl. Acad. Sci. USA 106, 5743–5748 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  101. Bernard, A. et al. Transcriptional architecture of the primate neocortex. Neuron 73, 1083–1099 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  102. Chen, J., Bardes, E. E., Aronow, B. J. & Jegga, A. G. ToppGene Suite for gene list enrichment analysis and candidate gene prioritization. Nucleic Acids Res. 37, W305–11 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  103. Yu, W. et al. GAPscreener: an automatic tool for screening human genetic association literature in PubMed using the support vector machine technique. BMC Bioinformatics 9, 205 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  104. Uhlén, M. et al. Proteomics. Tissue-based map of the human proteome. Science 347, 1260419 (2015).

    Article  CAS  PubMed  Google Scholar 

  105. Viswanathan, S. et al. High-performance probes for light and electron microscopy. Nat. Methods 12, 568–576 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We are grateful to B. Dispensa (UCSF), J. Hesse (UCSF), D. Kleinhesselink (UCSF), and J. Jed (UCSF) for technical support. We thank A. Molinaro (UCSF) for statistical consultations, D. Rowitch (UCSF) for astrocyte discussions, and E. Huang (UCSF) and M. Paredes (UCSF) for human brain samples. We apologize that many relevant and important publications are not cited, due to space limitations. This work was supported by the UCSF Program for Breakthrough Biomedical Research (to M.C.O.), which is funded in part by the Sandler Foundation, a Scholar Award from the UCSF Weill Institute for Neurosciences (to M.C.O.), a Research Grant from The Shurl and Kay Curci Foundation (to M.C.O.), NIMH R01MH113896 (to M.C.O.), a Pew Scholars Award (to A.V.M.), NIMH K08MH104417 (to A.V.M.), the Burroughs Wellcome Fund (to A.V.M.), and National Institute of General Medical Sciences (NIGMS) Medical Scientist Training Program grant #T32GM007618.

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Authors and Affiliations

  1. Department of Neurological Surgery, University of California at San Francisco, San Francisco, CA, USA

    Kevin W. Kelley & Michael C. Oldham

  2. The Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California at San Francisco, San Francisco, CA, USA

    Kevin W. Kelley, Hiromi Nakao-Inoue, Anna V. Molofsky & Michael C. Oldham

  3. Weill Institute for Neurosciences, University of California at San Francisco, San Francisco, CA, USA

    Kevin W. Kelley, Hiromi Nakao-Inoue, Anna V. Molofsky & Michael C. Oldham

  4. Department of Psychiatry, University of California at San Francisco, San Francisco, CA, USA

    Kevin W. Kelley, Hiromi Nakao-Inoue & Anna V. Molofsky

  5. Medical Scientist Training Program and Neuroscience Graduate Program, University of California at San Francisco, San Francisco, CA, USA

    Kevin W. Kelley

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Contributions

K.W.K. and M.C.O. conceived and designed the analytical strategies and wrote the manuscript. K.W.K. performed most data analyses and histological experiments. K.W.K. and H.N.-I. performed PMP2 expression experiments under supervision from A.V.M.

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Correspondence to Michael C. Oldham.

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Integrated supplementary information

Supplementary Figure 1 Cell-class-specific gene coexpression relationships accurately predict variation in cellular composition among heterogeneous samples (related to Figs. 1–8).

Cell-class module eigengenes were calculated for astrocytes, oligodendrocytes, microglia, and neurons from gene coexpression analysis of synthetic mixtures of single-cell RNA-seq data from adult human brain1 (A) or adult mouse brain2 (B). Each cell-class module eigengene was defined as the 1st principal component of the synthetic coexpression module that was maximally enriched with the corresponding cell-class markers (Methods). C) kME values for synthetic cell-class modules from adult mouse brains accurately predicted the results of differential expression analysis for each cell class (n=10 synthetic datasets; ‘up’ / ‘down’ denote up- and down-regulated genes for each cell class). Data are from Tasic et al2. 1. Darmanis, S., et al. A survey of human brain transcriptome diversity at the single cell level. PNAS 112, 7285-7290 (2015). 2. Tasic, B., et al. Adult mouse cortical cell taxonomy revealed by single cell transcriptomics. Nat. Neurosci. 19, 335-346 (2016).

Supplementary Figure 2 Gene expression fidelity is robust to the choice of gene set used for enrichment analysis (related to Figs. 1 and 2).

Consensus expression fidelity was calculated as described in Fig. 1c-g using four independent gene sets for each cell class. Astrocyte and oligodendrocyte expression fidelity 1 - 4 were calculated using the respective markers from: 11, 22, 33, and 44. Microglia expression fidelity 1 - 3 were calculated using the respective markers from: 15, 26, 32, and 4 was from the immune system phenotype pathway (MP:0005378)7. Neuron expression fidelity 1 - 4 were calculated using the respective markers from: 11, 22, 33, and 48. 1. Cahoy, J.D., et al. A transcriptome database for astrocytes, neurons, and oligodendrocytes: a new resource for understanding brain development and function. J. Neurosci. 28, 264-278 (2008). 2. Zhang, Y., et al. An RNA-sequencing transcriptome and splicing database of glia, neurons, and vascular cells of the cerebral cortex. J. Neurosci. 34, 11929-11947 (2014). 3. Lein, E.S., et al. Genome-wide atlas of gene expression in the adult mouse brain. Nature 445, 168-176 (2007). 4. Doyle, J.P., et al. Application of a translational profiling approach for the comparative analysis of CNS cell types. Cell 135, 749-762 (2008). 5. Hickman, S.E., et al. The microglial sensome revealed by direct RNA sequencing. Nat. Neurosci. 16, 1896-1905 (2013). 6. Butovsky, O., et al. Identification of a unique TGF-beta-dependent molecular and functional signature in microglia. Nat. Neurosci. 17, 131-143 (2014). 7. Zhang, Y., et al. Systematic analysis, comparison, and integration of disease based human genetic association data and mouse genetic phenotypic information. BMC Med. Genomics 3, 1 (2010). 8. Collins, M.O., et al. Molecular characterization and comparison of the components and multiprotein complexes in the postsynaptic proteome. J. Neurochem. 97 Suppl 1, 16-23 (2006).

Supplementary Figure 3 Concordant high-fidelity and single-nucleus RNA-seq genes are more likely to be detected in single-nucleus data than discordant genes (related to Fig. 2).

A-D) Proportion of cells expressing concordant and discordant genes (Fig. 2f-i) for astrocytes, oligodendrocytes, microglia, and neurons. Data are from Habib et al.1 (n=1909 astrocyte, 2965 oligodendrocyte, 389 microglia, and 7735 neuron independent single-nucleus samples). E-H) Proportion of cells expressing concordant and discordant genes (Fig. 2f-i) for astrocytes, oligodendrocytes, microglia, and neurons. Data are from Lake et al.2 (n=2524 astrocyte, 4369 oligodendrocyte, 756 microglia, and 25854 neuron independent single-nucleus samples). 1. Habib, N., et al. Massively parallel single-nucleus RNA-seq with DroNc-seq. Nat Methods 14, 955-958 (2017). 2. Lake, B.B., et al. Integrative single-cell analysis of transcriptional and epigenetic states in the human adult brain. Nat. Biotechnol. 36, 70-80 (2018).

Supplementary Figure 4 The genes with the highest expression fidelity for human CNS cell classes are consistently coexpressed across CNS regions and technology platforms (related to Figs. 1–4).

Expression patterns of the top 10 high-fidelity genes for astrocytes, oligodendrocytes, microglia, and neurons in four human CNS regions. Transcriptomes in each region were analyzed using different technology platforms. Columns 1-4 correspond to the following datasets: Hs.FCX.RNAseq, Hs.HIP.GSE11882, Hs.AMY.ABI, and Hs.DI.GSE46707 (Table S1).

Supplementary Figure 5 Human brain histological validation of high-fidelity genes (related to Fig. 3).

A) NeuN, DBNDD2, and PON2 are expressed in separate cells in adult human frontal cortex (FCX). B) APBB1IP1 is coexpressed with AIF1 and absent in GFAP+ astrocytes in adult human subcortical white matter (WM). Scale bar: 50μm. Immunostaining was repeated at least twice on independent samples with similar results.

Supplementary Figure 6 Variation among intact tissue samples reveals transcriptional signatures of human choroid plexus cells, mural cells, oligodendrocyte precursor cells (OPCs), and Purkinje neurons (related to Fig. 4).

A-D) Top: high-fidelity genes for each cell class (top 10 are shown) are consistently coexpressed in independent datasets. Middle: consensus gene expression fidelity distributions for each cell class with canonical markers of major cell classes labeled in green (neurons), red (astrocytes), blue (oligodendrocytes), and black (microglia). Gene expression fidelity distributions for published sets of markers (Al, A2, O1, O2, M1, M2, N1, N2, Cp1, Cp2, Mu1, Mu2, Op1, Op2, P1, P2; Methods) were cross-referenced with high-fidelity genes (top three percentiles). Gray shading: significant enrichment (one-sided Fisher's exact test). Note that Cp1, Mu1, Op1, and P1 were the gene sets used for module enrichment analysis (Table S2). The number of independent samples used to calculate fidelity for each gene is provided in Table S3. Bottom: mouse in situ hybridization data from the Allen Mouse Brain Atlas1 for high-fidelity genes in lateral ventricle (A), ventral midbrain/hypothalamus (B), cortex (C), and cerebellum (D). Scale bar: 200μm; inset scale bar: 500μm. 1. Lein, E.S., et al. Genome-wide atlas of gene expression in the adult mouse brain. Nature 445, 168-176 (2007).

Supplementary Figure 7 Variation in cellular abundance predicts gene expression in transcriptomes from intact CNS samples (related to Fig. 5).

Total percent variance explained (mean adj. r2) for ~9600 genes whose expression levels were modeled as a function of inferred astrocyte, oligodendrocyte, microglia, and neuron abundance in each of 47 regional datasets (subset to n=40 samples; values are mean ± 2 s.e.m., 10 iterations). Gene expression modeling results were essentially identical when up to 90% of the data was excluded prior to determining the high-fidelity genes used for estimating relative cellular abundance.

Supplementary Figure 8 Gene expression modeling in AD (related to Fig. 6).

A,B) Expression patterns of the top 10 high-fidelity genes for each cell class were used to estimate the relative abundance of neurons, astrocytes, microglia, and oligodendrocytes in samples from control (CTRL) and AD subjects as illustrated in Fig. 5a. Data in (A) consist of FCX, TCX, PCX, and HIP samples1 (n=71 CTRL and 71 AD independent samples). Data in (B) consist of FCX, TCX, and HIP samples2 (n=32 CTRL and 32 AD independent samples). P-values indicate the significance of differences in estimated cellular abundance between CTRL and AD (two-sided Wilcoxon rank-sum test). C) T-values of AD risk genes3 from cell-class models calculated for CTRL and AD samples in three independent datasets: GSE383501, GSE447704, GSE369802. Gene order was determined by hierarchical clustering. D) Enrichment analysis (one-sided Fisher’s exact test) of significant (≥ 2 datasets) up-regulated neuron genes in AD. E) Enrichment analysis (one-sided Fisher’s exact test) of significant (≥ 2 datasets) up-regulated microglia genes in AD. Benjamini and Hochberg corrected q-values are displayed. F,G) Expression levels for select genes show cell-intrinsic expression increases in AD neurons (F) or microglia (G) relative to CTRL after controlling for variation in cellular abundance. Black lines in (D,E) denote q-value = 0.05. 1. Cribbs, D.H., et al. Extensive innate immune gene activation accompanies brain aging, increasing vulnerability to cognitive decline and neurodegeneration: a microarray study. J. Neuroinflammation 9, 179 (2012). 2. Hokama, M., et al. Altered expression of diabetes-related genes in Alzheimer's disease brains: the Hisayama study. Cereb. Cortex 24, 2476-2488 (2014). 3. Karch, C.M., Cruchaga, C. & Goate, A.M. Alzheimer's disease genetics: from the bench to the clinic. Neuron 83, 11-26 (2014). 4. Zhang, B., et al. Integrated systems approach identifies genetic nodes and networks in late-onset Alzheimer's disease. Cell 153, 707-720 (2013).

Supplementary Figure 9 Cell-class module eigengenes (ME) accurately reflect cellular abundance and are robust to gene expression perturbations (related to Fig. 6).

MEs were constructed from the top 10 astrocyte, oligodendrocyte, microglia, and neuron genes from two synthetic datasets, simulating a condition and control scenario, each consisting of 100 samples of randomly aggregated single-cell RNA-seq data from human brain1 (Methods). In one of the two synthetic datasets, a subset of the top 10 genes was systematically perturbed by the indicated fold-changes. Pearson correlation coefficients between the perturbed module eigengenes and actual cellular abundance for astrocytes, oligodendrocytes, microglia, and neurons are shown. 1. Darmanis, S., et al. A survey of human brain transcriptome diversity at the single cell level. PNAS 112, 7285-7290 (2015).

Supplementary Figure 10 Gene expression modeling identifies neuronal expression differences between CNS regions (related to Fig. 7).

A) Genes conservatively predicted to be expressed by human neurons in restricted brain regions: FCX, STR, HIP, DI, MID. Depicted genes were differentially modeled (same criteria as Fig. 7f-i) and differentially expressed by greater than 50 %-tile units in region 1 vs. region 2. B) Enrichment analysis of regional neuron genes (one-sided Fisher’s exact test). Benjamini and Hochberg corrected q-values are shown. Black line denotes q-value = 0.05. V.G.: Voltage gated; sig.: signaling. C-H) Examples of differentially modeled regional neuron genes. Gene expression was modeled via linear regression as a function of estimated neuronal abundance in samples from each brain region. Data in (C, D, G) are from the Allen Institute1. Data in (E, F) are from GSE467062. Data in (H) are from GTEx3. Also displayed are Allen Mouse Brain Atlas in situ hybridization data4 for the indicated genes. Scale bar: 500μm. 1. Hawrylycz, M., et al. Canonical genetic signatures of the adult human brain. Nat. Neurosci. 18, 1832-1844 (2015). 2. Ramasamy, A., et al. Genetic variability in the regulation of gene expression in ten regions of the human brain. Nat. Neurosci. 17, 1418-1428 (2014). 3. GTExConsortium. Human genomics. The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans. Science 348, 648-660 (2015). 4. Lein, E.S., et al. Genome-wide atlas of gene expression in the adult mouse brain. Nature 445, 168-176 (2007).

Supplementary Figure 11 Integrative gene coexpression analysis of mouse brain transcriptomes reveals consensus transcriptional profiles of astrocytes, oligodendrocytes, microglia, and neurons (related to Fig. 8).

A-D) Left: consensus gene expression fidelity distributions for mouse astrocytes (A), oligodendrocytes (O), microglia (M), and neurons (N). Canonical markers of each cell class are labeled in red (A), blue (O), black (M), and green (N). Right: gene expression fidelity distributions for published cell-class markers (A1, O1, M1, N1: Zhang et al.1; A2, O2, N2: Cahoy et al.2; M2: Butovsky et al.3; A3, O3, N3: Lein et al.4; M3: Hickman et al.5) were cross-referenced with high-fidelity genes from each consensus signature (z-score > 20). Gray shading: significant enrichment (one-sided Fisher's exact test). Note that A2, O2, M3, and N2 were the gene sets used for module enrichment analysis (Table S2). The number of independent samples used to calculate fidelity for each gene is provided in Table S7. E-H) The top 50 genes ranked by consensus expression fidelity for mouse astrocytes, oligodendrocytes, microglia, or neurons. Expression levels represent averages of mean percentile ranks for all datasets where gene data were present. Mutation intolerance data were obtained from ExAC6. PubMed citations were obtained by queries with gene symbol and cell class (for example gene symbol and 'neuron'). Cellular localization data were extracted from COMPARTMENTS7. Predicted protein-protein interactions (PPI) were obtained from STRING8. A link is shown if the combined score between two proteins was >350. 1. Zhang, Y., et al. An RNA-sequencing transcriptome and splicing database of glia, neurons, and vascular cells of the cerebral cortex. J. Neurosci. 34, 11929-11947 (2014). 2. Cahoy, J.D., et al. A transcriptome database for astrocytes, neurons, and oligodendrocytes: a new resource for understanding brain development and function. J. Neurosci. 28, 264-278 (2008). 3. Butovsky, O., et al. Identification of a unique TGF-beta-dependent molecular and functional signature in microglia. Nat. Neurosci. 17, 131-143 (2014). 4. Lein, E.S., et al. Genome-wide atlas of gene expression in the adult mouse brain. Nature 445, 168-176 (2007). 5. Hickman, S.E., et al. The microglial sensome revealed by direct RNA sequencing. Nat. Neurosci. 16, 1896-1905 (2013). 6. Lek, M., et al. Analysis of protein-coding genetic variation in 60,706 humans. Nature 536, 285-291 (2016). 7. Binder, J.X., et al. COMPARTMENTS: unification and visualization of protein subcellular localization evidence. Database Feb 25, bau012 (2014). 8. Szklarczyk, D., et al. STRING v10: protein-protein interaction networks, integrated over the tree of life. Nucleic Acids Res. 43, D447-452 (2015).

Supplementary Figure 12 Gene expression modeling identifies cell-class-specific transcriptional differences between humans and mice (related to Fig. 8).

A) Examples of linear regression modeling results using human, chimpanzee, macaque, and mouse brain transcriptomes (Human: PCX1; Chimp: FCX2–5; Macaque: CTX6; Mouse: CTX, HIP, and CB7). SLC1A3 is predicted to be expressed by astrocytes in all species, MRVI1 by astrocytes in primates but not mice, and PLA2G7 by astrocytes in mice but not primates. B) Astrocyte modeling results and mean expression percentiles for genes in (A) from all brain transcriptomes in all species. Bars denote median values and error bars denote s.e.m. The number of independent samples and datasets for each species are provided in Table S1. C-D) Single-molecule FISH for MRVI1 and ALDH1L1 (C) and PLA2G7 and ALDH1L1 (D) in human and mouse cerebral cortex. Scale bar: 20μm. FISH experiments were only performed once. E) PMP2 is expressed by mouse Schwann cells in sciatic nerve from a postnatal day 42 animal. Scale bar: 40μm. Immunostaining was performed twice with similar results. F) Top: lentivirus construct. Left: representative examples of control (CTRL) and PMP2-positive astrocytes from mouse somatosensory cortex. Scale bar: 10μm. Right: Quantification of maximum diameter in CTRL and PMP2-positive astrocytes. n=4 animals per group, n=37 CTRL and 31 PMP2-infected astrocytes, bars denote mean ± s.e.m., with significance determined by a one-sided Welch’s t-test on animal averages. 1. Hawrylycz, M., et al. Canonical genetic signatures of the adult human brain. Nat. Neurosci. 18, 1832-1844 (2015). 2. Khaitovich, P., et al. Parallel patterns of evolution in the genomes and transcriptomes of humans and chimpanzees. Science 309, 1850-1854 (2005). 3. Franz, H., et al. Systematic analysis of gene expression in human brains before and after death. Genome Biol. 6, R112 (2005). 4. Khaitovich, P., et al. Positive selection on gene expression in the human brain. Curr. Biol. 16, R356-358 (2006). 5. Somel, M., et al. Transcriptional neoteny in the human brain. PNAS 106, 5743-5748 (2009). 6. Bernard, A., et al. Transcriptional architecture of the primate neocortex. Neuron 73, 1083-1099 (2012). 7. Matarin, M., et al. A genome-wide gene-expression analysis and database in transgenic mice during development of amyloid or tau pathology. Cell Rep 10, 633-644 (2015).

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–12

Reporting Summary

Supplementary Note

Supplementary Table 1 CNS transcriptomes analyzed in this study.

Dataset information including CNS region(s), sample preparation, technology platform, quality control metrics, sample size, and reference for each dataset analyzed in this study.

Supplementary Table 2 Enrichment p-values for cell-class gene sets in human gene coexpression modules.

One-sided Fisher’s exact test p-value enrichments for cell-class gene sets across human regional datasets.

Supplementary Table 3 Genome-wide expression fidelity for major CNS cell classes in humans.

Gene identifiers, data set representations, sample sizes, cell-class fidelity metrics, and mean expression percentiles for 18451 genes.

Supplementary Table 4 Genome-wide comparison of expression fidelity for human CNS cell classes and differential expression results from SN RNA-seq data.

Cell-class fidelity metrics and SN differential expression statistics for astrocytes, microglia, oligodendrocytes, and neurons for each of the 15494 shared genes between this study and Habib et al.1 (n=1909 astrocyte, 2965 oligodendrocyte, 389 microglia, and 7735 neuron independent single-nucleus samples) and Lake et al.2 (n=2524 astrocyte, 4369 oligodendrocyte, 756 microglia, and 25854 neuron independent single-nucleus samples). 1. Habib, N., et al. Massively parallel single-nucleus RNA-seq with DroNc-seq. Nat Methods 14, 955-958 (2017).2. Lake, B.B., et al. Integrative single-cell analysis of transcriptional and epigenetic states in the human adult brain. Nat. Biotechnol. 36, 70-80 (2018).

Supplementary Table 5 Cell-class-specific transcriptional differences between CTRL and AD.

Significant (p<.05) cell-class-specific expression differences between CTRL and AD after controlling for differences in cellular abundance in three independent datasets: GSE383501, GSE447702, GSE369803. Statistical significance was determined for each gene by comparing the differences in t-values (obtained by linear regression for each cell class) between CTRL and AD to differences observed after permuting sample labels (n=1000 permutations). 1. Cribbs, D.H., et al. Extensive innate immune gene activation accompanies brain aging, increasing vulnerability to cognitive decline and neurodegeneration: a microarray study. J. Neuroinflammation 9, 179 (2012). 2. Zhang, B., et al. Integrated systems approach identifies genetic nodes and networks in late-onset Alzheimer's disease. Cell 153, 707-720 (2013). 3. Hokama, M., et al. Altered expression of diabetes-related genes in Alzheimer's disease brains: the Hisayama study. Cereb. Cortex 24, 2476-2488 (2014).

Supplementary Table 6 Cell-class-specific transcriptional differences among human CNS regions.

Significant (p<2.67x10−8: Bonferroni correction for total # of gene models) cell-class-specific expression differences among human CNS regions (workflow shown in Fig. 7F).

Supplementary Table 7 Genome-wide expression fidelity for major CNS cell classes in mice.

Gene identifiers, dataset representations, sample sizes, cell-class fidelity metrics, and mean expression percentiles for 18739 genes.

Supplementary Table 8 Cell-class-specific transcriptional differences among species.

Predicted cell-class-specific transcriptional differences between humans and mice with gene identifier information, cell-class modeling statistics, and expression percentile values.

Supplementary Table 9 RNAscope single-molecule FISH probe information.

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Kelley, K.W., Nakao-Inoue, H., Molofsky, A.V. et al. Variation among intact tissue samples reveals the core transcriptional features of human CNS cell classes. Nat Neurosci 21, 1171–1184 (2018). https://doi.org/10.1038/s41593-018-0216-z

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