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Nature Genetics
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Multiomic single-cell profiling identifies critical regulators of postnatal brain

Nature Geneticsvolume 57, pages591–603 (2025)Cite this article

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Abstract

Human brain development spans from embryogenesis to adulthood, with dynamic gene expression controlled by cell-type-specificcis-regulatory element activity and three-dimensional genome organization. To advance our understanding of postnatal brain development, we simultaneously profiled gene expression and chromatin accessibility in 101,924 single nuclei from four brain regions across ten donors, covering five key postnatal stages from infancy to late adulthood. Using this dataset and chromosome conformation capture data, we constructed enhancer-based gene regulatory networks to identify cell-type-specific regulators of brain development and interpret genome-wide association study loci for ten main brain disorders. Our analysis connected 2,318 cell-specific loci to 1,149 unique genes, representing 41% of loci linked to the investigated traits, and highlighted 55 genes influencing several disease phenotypes. Pseudotime analysis revealed distinct stages of postnatal oligodendrogenesis and their regulatory programs. These findings provide a comprehensive dataset of cell-type-specific gene regulation at critical timepoints in postnatal brain development.

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Fig. 1: Multimodal profiling and multivariate exploration of the postnatal human brain.
Fig. 2: Cell-type level eGRN analysis in postnatal human brain.
Fig. 3: Summary of cell-type-specific disease heritability and disease regulome landscape.
Fig. 4: Annotation of GWAS prioritized genes.
Fig. 5: Characterization of oligodendrogenesis by integrative pseudotime analysis.
Fig. 6: Identification of key regulatory programs in oligodendrogenesis.

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Data availability

Raw data from single-nuclei multiome are available in NHIM Data Archive under collection C5371:https://nda.nih.gov/edit_collection.html?id=5371. The preprocessed single-cell data described within this paper are available through the interactive web browser interface CELLxGENE Discover Census Portal platform under accession link:https://cellxgene.cziscience.com/collections/f406a653-c079-4bf9-aab6-85846c27571d, as well as on Synapse under the accession code syn62750396. Corresponding Hi-C data utilized for ABC-calculation are uploaded under the same accession code on Synapse. The microglial59, endothelial cell147 and neuronal (GABAergic and GLUtamatergic)/OL/astrocyte Hi-C data were downloaded from the following:https://doi.org/10.7303/syn26207321,https://www.encodeproject.org/experiments/ENCSR507AHE/ andhttps://doi.org/10.7303/syn63888657, respectively. The mutational constraint maps were downloaded fromhttps://gnomad.broadinstitute.org/downloads (section ExAC: Constraint).

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References

  1. Wilson, S. W. & Houart, C. Early steps in the development of the forebrain.Dev. Cell6, 167–181 (2004).

    Article CAS PubMed PubMed Central  Google Scholar 

  2. Kang, H. J. et al. Spatio-temporal transcriptome of the human brain.Nature478, 483–489 (2011).

    Article CAS PubMed PubMed Central  Google Scholar 

  3. Li, M. et al. Integrative functional genomic analysis of human brain development and neuropsychiatric risks.Science362, eaat7615 (2018).

    Article CAS PubMed PubMed Central  Google Scholar 

  4. Nord, A. S. & West, A. E. Neurobiological functions of transcriptional enhancers.Nat. Neurosci.23, 5–14 (2020).

    Article CAS PubMed  Google Scholar 

  5. Fan, X. et al. Single-cell transcriptome analysis reveals cell lineage specification in temporal–spatial patterns in human cortical development.Sci. Adv.6, eaaz2978 (2020).

    Article CAS PubMed PubMed Central  Google Scholar 

  6. Eze, U. C., Bhaduri, A., Haeussler, M., Nowakowski, T. J. & Kriegstein, A. R. Single-cell atlas of early human brain development highlights heterogeneity of human neuroepithelial cells and early radial glia.Nat. Neurosci.24, 584–594 (2021).

    Article CAS PubMed PubMed Central  Google Scholar 

  7. Kelley, K. W. & Pașca, S. P. Human brain organogenesis: toward a cellular understanding of development and disease.Cell185, 42–61 (2022).

    Article CAS PubMed  Google Scholar 

  8. Ziats, M. N. & Rennert, O. M. Identification of differentially expressed microRNAs across the developing human brain.Mol. Psychiatry19, 848–852 (2014).

    Article CAS PubMed  Google Scholar 

  9. Herring, C. A. et al. Human prefrontal cortex gene regulatory dynamics from gestation to adulthood at single-cell resolution.Cell185, 4428–4447.e28 (2022).

    Article CAS PubMed  Google Scholar 

  10. Ma, S. et al. Chromatin potential identified by shared single-cell profiling of RNA and chromatin.Cell183, 1103–1116.e20 (2020).

    Article CAS PubMed PubMed Central  Google Scholar 

  11. Zhu, K. et al. Multi-omic profiling of the developing human cerebral cortex at the single-cell level.Sci. Adv.9, eadg3754 (2023).

    Article CAS PubMed PubMed Central  Google Scholar 

  12. Hauberg, M. E. et al. Common schizophrenia risk variants are enriched in open chromatin regions of human glutamatergic neurons.Nat. Commun.11, 5581 (2020).

    Article CAS PubMed PubMed Central  Google Scholar 

  13. Fang, M., Chen, L., Tang, T., Qiu, M. & Xu, X. The committed oligodendrocyte precursor cell, a newly-defined intermediate progenitor cell type in oligodendroglial lineage.Glia71, 2499–2510 (2023).

    Article CAS PubMed  Google Scholar 

  14. Siletti, K. et al. Transcriptomic diversity of cell types across the adult human brain.Science382, eadd7046 (2023).

    Article CAS PubMed  Google Scholar 

  15. Mathys, H. et al. Single-cell atlas reveals correlates of high cognitive function, dementia, and resilience to Alzheimer’s disease pathology.Cell186, 4365–4385.e27 (2023).

    Article CAS PubMed PubMed Central  Google Scholar 

  16. Hoffman, G. E. & Schadt, E. E. variancePartition: interpreting drivers of variation in complex gene expression studies.BMC Bioinf.17, 483 (2016).

    Article  Google Scholar 

  17. Hoffman, G. Count ratio uncertainty modeling based linear regression.GitHubhttps://github.com/GabrielHoffman/crumblr (2024).

  18. McKenzie, A. T. et al. Brain cell type specific gene expression and co-expression network architectures.Sci Rep.8, 8868 (2018).

    Article PubMed PubMed Central  Google Scholar 

  19. Langlieb, J. et al. The cell type composition of the adult mouse brain revealed by single cell and spatial genomics. Preprint atbioRxivhttps://doi.org/10.1101/2023.03.06.531307 (2023).

  20. Pei, G. et al. Gene expression imputation and cell-type deconvolution in human brain with spatiotemporal precision and its implications for brain-related disorders.Genome Res.31, 146–158 (2021).

    Article PubMed PubMed Central  Google Scholar 

  21. Kundakovic, M. & Tickerhoof, M. Epigenetic mechanisms underlying sex differences in the brain and behavior.Trends Neurosci.47, 18–35 (2024).

    Article CAS PubMed  Google Scholar 

  22. Marrocco, J., Einhorn, N. R. & McEwen, B. S. Environmental epigenetics of sex differences in the brain.Handb. Clin. Neurol.175, 209–220 (2020).

    Article PubMed  Google Scholar 

  23. Bravo González-Blas, C. et al. SCENIC+: single-cell multiomic inference of enhancers and gene regulatory networks.Nat. Methods20, 1355–1367 (2023).

    Article PubMed PubMed Central  Google Scholar 

  24. Zhang, K. et al. The oligodendrocyte transcription factor 2 OLIG2 regulates transcriptional repression during myelinogenesis in rodents.Nat. Commun.13, 1423 (2022).

    Article CAS PubMed PubMed Central  Google Scholar 

  25. Emery, B. et al. Myelin gene regulatory factor is a critical transcriptional regulator required for CNS myelination.Cell138, 172–185 (2009).

    Article CAS PubMed PubMed Central  Google Scholar 

  26. Braccioli, L., Vervoort, S. J., Puma, G., Nijboer, C. H. & Coffer, P. J. SOX4 inhibits oligodendrocyte differentiation of embryonic neural stem cells in vitro by inducing Hes5 expression.Stem Cell Res.33, 110–119 (2018).

    Article CAS PubMed  Google Scholar 

  27. Shiga, H., Asou, H. & Ito, E. Advancement of differentiation of oligodendrocyte progenitor cells by a cascade including protein kinase A and cyclic AMP-response element binding protein.Neurosci. Res.53, 436–441 (2005).

    Article CAS PubMed  Google Scholar 

  28. Sakurai, K. & Osumi, N. The neurogenesis-controlling factor, Pax6, inhibits proliferation and promotes maturation in murine astrocytes.J. Neurosci.28, 4604–4612 (2008).

    Article CAS PubMed PubMed Central  Google Scholar 

  29. Byun, J. S. et al. The transcription factor PITX1 drives astrocyte differentiation by regulating the SOX9 gene.J. Biol. Chem.295, 13677–13690 (2020).

    Article CAS PubMed PubMed Central  Google Scholar 

  30. Smith, A. M. et al. The transcription factor PU.1 is critical for viability and function of human brain microglia.Glia61, 929–942 (2013).

    Article PubMed  Google Scholar 

  31. Jones, R. E., Andrews, R., Holmans, P., Hill, M. & Taylor, P. R. Modest changes in Spi1 dosage reveal the potential for altered microglial function as seen in Alzheimer’s disease.Sci Rep.11, 14935 (2021).

    Article CAS PubMed PubMed Central  Google Scholar 

  32. Saeki, K., Pan, R., Lee, E., Kurotaki, D. & Ozato, K. IRF8 defines the epigenetic landscape in postnatal microglia, thereby directing their transcriptome programs.Nat. Immunol.25, 1928–1942 (2024).

    Article CAS PubMed  Google Scholar 

  33. Masuda, T. et al. IRF8 is a critical transcription factor for transforming microglia into a reactive phenotype.Cell Rep.1, 334–340 (2012).

    Article CAS PubMed PubMed Central  Google Scholar 

  34. Zusso, M. et al. Regulation of postnatal forebrain amoeboid microglial cell proliferation and development by the transcription factor Runx1.J. Neurosci.32, 11285–11298 (2012).

    Article CAS PubMed PubMed Central  Google Scholar 

  35. Rosenbauer, F. & Tenen, D. G. Transcription factors in myeloid development: balancing differentiation with transformation.Nat. Rev. Immunol.7, 105–117 (2007).

    Article CAS PubMed  Google Scholar 

  36. Uribe, R. A. & Bronner, M. E. Meis3 is required for neural crest invasion of the gut during zebrafish enteric nervous system development.Mol. Biol. Cell26, 3728–3740 (2015).

    Article CAS PubMed PubMed Central  Google Scholar 

  37. Lin, C.-H. et al. The dosage of the neuroD2 transcription factor regulates amygdala development and emotional learning.Proc. Natl Acad. Sci. USA102, 14877–14882 (2005).

    Article CAS PubMed PubMed Central  Google Scholar 

  38. Runge, K. et al. Disruption of NEUROD2 causes a neurodevelopmental syndrome with autistic features via cell-autonomous defects in forebrain glutamatergic neurons.Mol. Psychiatry26, 6125–6148 (2021).

    Article CAS PubMed PubMed Central  Google Scholar 

  39. Chen, F. et al. The transcription factor NeuroD2 coordinates synaptic innervation and cell intrinsic properties to control excitability of cortical pyramidal neurons.J. Physiol.594, 3729–3744 (2016).

    Article CAS PubMed PubMed Central  Google Scholar 

  40. Hevner, R. F., Hodge, R. D., Daza, R. A. M. & Englund, C. Transcription factors in glutamatergic neurogenesis: conserved programs in neocortex, cerebellum, and adult hippocampus.Neurosci. Res.55, 223–233 (2006).

    Article CAS PubMed  Google Scholar 

  41. Yang, X.-F. The FOX transcription factors regulate vascular pathology diabetes and Tregs.Front. Biosci.S1, s35 (2009).

    Article  Google Scholar 

  42. Miyashita, H., Kanemura, M., Yamazaki, T., Abe, M. & Sato, Y. Vascular endothelial zinc finger 1 is involved in the regulation of angiogenesis: possible contribution of stathmin/OP18 as a downstream target gene.Arterioscler. Thromb. Vasc. Biol.24, 878–884 (2004).

    Article CAS PubMed  Google Scholar 

  43. de Leeuw, C. A., Mooij, J. M., Heskes, T. & Posthuma, D. MAGMA: generalized gene-set analysis of GWAS data.PLoS Comput. Biol.11, e1004219 (2015).

    Article PubMed PubMed Central  Google Scholar 

  44. Finucane, H. K. et al. Partitioning heritability by functional annotation using genome-wide association summary statistics.Nat. Genet.47, 1228–1235 (2015).

    Article CAS PubMed PubMed Central  Google Scholar 

  45. Bentsen, M. et al. ATAC-seq footprinting unravels kinetics of transcription factor binding during zygotic genome activation.Nat. Commun.11, 4267 (2020).

    Article CAS PubMed PubMed Central  Google Scholar 

  46. Buniello, A. et al. The NHGRI-EBI GWAS Catalog of published genome-wide association studies, targeted arrays and summary statistics 2019.Nucleic Acids Res.47, D1005–D1012 (2019).

    Article CAS PubMed  Google Scholar 

  47. Kim, S. S. et al. Leveraging single-cell ATAC-seq and RNA-seq to identify disease-critical fetal and adult brain cell types.Nat. Commun.15, 563 (2024).

    Article CAS PubMed PubMed Central  Google Scholar 

  48. Corces, M. R. et al. Single-cell epigenomic analyses implicate candidate causal variants at inherited risk loci for Alzheimer’s and Parkinson’s diseases.Nat. Genet.52, 1158–1168 (2020).

    Article CAS PubMed PubMed Central  Google Scholar 

  49. Li, Y. E. et al. A comparative atlas of single-cell chromatin accessibility in the human brain.Science382, eadf7044 (2023).

    Article CAS PubMed PubMed Central  Google Scholar 

  50. Skene, N. G. et al. Genetic identification of brain cell types underlying schizophrenia.Nat. Genet.50, 825–833 (2018).

    Article CAS PubMed PubMed Central  Google Scholar 

  51. Schmidt, T. et al. A system to determine risk of osteoporosis in patients with autoimmune hepatitis.Clin. Gastroenterol. Hepatol.18, 226–233.e3 (2020).

    Article PubMed  Google Scholar 

  52. Mattheisen, M. et al. Identification of shared and differentiating genetic architecture for autism spectrum disorder, attention-deficit hyperactivity disorder and case subgroups.Nat. Genet.54, 1470–1478 (2022).

    Article CAS PubMed PubMed Central  Google Scholar 

  53. Birey, F., Kokkosis, A. G. & Aguirre, A. Oligodendroglia-lineage cells in brain plasticity, homeostasis and psychiatric disorders.Curr. Opin. Neurobiol.47, 93–103 (2017).

    Article CAS PubMed PubMed Central  Google Scholar 

  54. Lee, D. et al. Plasticity of human microglia and brain perivascular macrophages in aging and Alzheimer’s disease. Preprint atmedRxivhttps://doi.org/10.1101/2023.10.25.23297558 (2023).

  55. Scheiblich, H., Trombly, M., Ramirez, A. & Heneka, M. T. Neuroimmune connections in aging and neurodegenerative diseases.Trends Immunol.41, 300–312 (2020).

    Article CAS PubMed  Google Scholar 

  56. Fulco, C. P. et al. Activity-by-contact model of enhancer–promoter regulation from thousands of CRISPR perturbations.Nat. Genet.51, 1664–1669 (2019).

    Article CAS PubMed PubMed Central  Google Scholar 

  57. Chen, S. et al. A genomic mutational constraint map using variation in 76,156 human genomes.Nature625, 92–100 (2024).

    Article CAS  Google Scholar 

  58. Nasser, J. et al. Genome-wide enhancer maps link risk variants to disease genes.Nature593, 238–243 (2021).

    Article CAS PubMed PubMed Central  Google Scholar 

  59. Kosoy, R. et al. Genetics of the human microglia regulome refines Alzheimer’s disease risk loci.Nat. Genet.54, 1145–1154 (2022).

    Article CAS PubMed PubMed Central  Google Scholar 

  60. Nott, A. & Holtman, I. R. Genetic insights into immune mechanisms of Alzheimer’s and Parkinson’s disease.Front. Immunol.14, 1168539 (2023).

    Article CAS PubMed PubMed Central  Google Scholar 

  61. Distéfano-Gagné, F., Bitarafan, S., Lacroix, S. & Gosselin, D. Roles and regulation of microglia activity in multiple sclerosis: insights from animal models.Nat. Rev. Neurosci.24, 397–415 (2023).

    Article PubMed  Google Scholar 

  62. Malek, T. R. & Castro, I. Interleukin-2 receptor signaling: at the interface between tolerance and immunity.Immunity33, 153–165 (2010).

    Article CAS PubMed PubMed Central  Google Scholar 

  63. Trubetskoy, V. et al. Mapping genomic loci implicates genes and synaptic biology in schizophrenia.Nature604, 502–508 (2022).

    Article CAS PubMed PubMed Central  Google Scholar 

  64. Singh, T. et al. Rare coding variants in ten genes confer substantial risk for schizophrenia.Nature604, 509–516 (2022).

    Article CAS PubMed PubMed Central  Google Scholar 

  65. Harrison, P. J. & Bannerman, D. M. GRIN2A (NR2A): a gene contributing to glutamatergic involvement in schizophrenia.Mol. Psychiatry28, 3568–3572 (2023).

    Article CAS PubMed PubMed Central  Google Scholar 

  66. McLaughlin, R. L. et al. Genetic correlation between amyotrophic lateral sclerosis and schizophrenia.Nat. Commun.8, 14774 (2017).

    Article CAS PubMed PubMed Central  Google Scholar 

  67. van Rheenen, W. et al. Common and rare variant association analyses in amyotrophic lateral sclerosis identify 15 risk loci with distinct genetic architectures and neuron-specific biology.Nat. Genet.53, 1636–1648 (2021).

    Article PubMed PubMed Central  Google Scholar 

  68. O’Rourke, J. G. et al. C9orf72 is required for proper macrophage and microglial function in mice.Science351, 1324–1329 (2016).

    Article PubMed PubMed Central  Google Scholar 

  69. Le Fevre, A. K. et al. FOXP1 mutations cause intellectual disability and a recognizable phenotype.Am. J. Med. Genet. A.161A, 3166–3175 (2013).

    Article PubMed  Google Scholar 

  70. Sollis, E. et al. Identification and functional characterization of de novo FOXP1 variants provides novel insights into the etiology of neurodevelopmental disorder.Hum. Mol. Genet.25, 546–557 (2015).

    Article PubMed  Google Scholar 

  71. Bacon, C. & Rappold, G. A. The distinct and overlapping phenotypic spectra of FOXP1 and FOXP2 in cognitive disorders.Hum. Genet.131, 1687–1698 (2012).

    Article CAS PubMed PubMed Central  Google Scholar 

  72. Watson, H. J. et al. Genome-wide association study identifies eight risk loci and implicates metabo-psychiatric origins for anorexia nervosa.Nat. Genet.51, 1207–1214 (2019).

    Article CAS PubMed PubMed Central  Google Scholar 

  73. Bakshi, A. et al. Fast set-based association analysis using summary data from GWAS identifies novel gene loci for human complex traits.Sci Rep.6, 32894 (2016).

    Article CAS PubMed PubMed Central  Google Scholar 

  74. Demontis, D. et al. Author correction: Genome-wide analyses of ADHD identify 27 risk loci, refine the genetic architecture and implicate several cognitive domains.Nat. Genet.55, 730 (2023).

    Article CAS PubMed  Google Scholar 

  75. van Bruggen, D. et al. Developmental landscape of human forebrain at a single-cell level identifies early waves of oligodendrogenesis.Dev. Cell57, 1421–1436.e5 (2022).

    Article PubMed  Google Scholar 

  76. Gibson, E. M. et al. Neuronal activity promotes oligodendrogenesis and adaptive myelination in the mammalian brain.Science344, 1252304 (2014).

    Article PubMed PubMed Central  Google Scholar 

  77. Fine, D. et al. A syndrome of congenital microcephaly, intellectual disability and dysmorphism with a homozygous mutation in FRMD4A.Eur. J. Hum. Genet.23, 1729–1734 (2014).

    Article PubMed PubMed Central  Google Scholar 

  78. Lambert, J.-C. et al. Genome-wide haplotype association study identifies the FRMD4A gene as a risk locus for Alzheimer’s disease.Mol. Psychiatry18, 461–470 (2012).

    Article PubMed PubMed Central  Google Scholar 

  79. Beiter, R. M. et al. Evidence for oligodendrocyte progenitor cell heterogeneity in the adult mouse brain.Sci Rep.12, 12921 (2022).

    Article CAS PubMed PubMed Central  Google Scholar 

  80. Bonfanti, E. et al. The role of oligodendrocyte precursor cells expressing the GPR17 receptor in brain remodeling after stroke.Cell Death Dis.8, e2871 (2017).

    Article CAS PubMed PubMed Central  Google Scholar 

  81. Braun, E. et al. Comprehensive cell atlas of the first-trimester developing human brain.Science382, eadf1226 (2023).

    Article CAS PubMed  Google Scholar 

  82. Wang, F. et al. RNAscope: a novel in situ RNA analysis platform for formalin-fixed, paraffin-embedded tissues.J. Mol. Diagn.14, 22–29 (2012).

    Article CAS PubMed PubMed Central  Google Scholar 

  83. Cao, J. et al. The single-cell transcriptional landscape of mammalian organogenesis.Nature566, 496–502 (2019).

    Article CAS PubMed PubMed Central  Google Scholar 

  84. Setty, M. et al. Characterization of cell fate probabilities in single-cell data with Palantir.Nat. Biotechnol.37, 451–460 (2019).

    Article CAS PubMed PubMed Central  Google Scholar 

  85. DeTomaso, D. & Yosef, N. Hotspot identifies informative gene modules across modalities of single-cell genomics.Cell Syst.12, 446–456.e9 (2021).

    Article CAS PubMed  Google Scholar 

  86. Tiane, A. et al. From OPC to oligodendrocyte: an epigenetic journey.Cells8, 1236 (2019).

    Article CAS PubMed PubMed Central  Google Scholar 

  87. Doumpas, N. et al. TCF/LEF dependent and independent transcriptional regulation of Wnt/β-catenin target genes.EMBO J.38, e98873 (2019).

    Article PubMed  Google Scholar 

  88. Palazuelos, J., Klingener, M. & Aguirre, A. TGFβ signaling regulates the timing of CNS myelination by modulating oligodendrocyte progenitor cell cycle exit through SMAD3/4/FoxO1/Sp1.J. Neurosci.34, 7917–7930 (2014).

    Article CAS PubMed PubMed Central  Google Scholar 

  89. Li, Y. et al. A novel long noncoding RNA lnc158 promotes the differentiation of mouse neural precursor cells into oligodendrocytes by targeting nuclear factor-IB.Neuroreport29, 1121–1128 (2018).

    Article CAS PubMed  Google Scholar 

  90. Zhao, Y., Liu, H., Zhang, Q. & Zhang, Y. The functions of long non-coding RNAs in neural stem cell proliferation and differentiation.Cell Biosci.10, 74 (2020).

    Article PubMed PubMed Central  Google Scholar 

  91. Ciceri, G. et al. An epigenetic barrier sets the timing of human neuronal maturation.Nature626, 881–890 (2024).

    Article CAS PubMed PubMed Central  Google Scholar 

  92. Eicher, J. D. et al. Genome-wide association study of shared components of reading disability and language impairment.Genes Brain Behav.12, 792–801 (2013).

    Article CAS PubMed  Google Scholar 

  93. Wang, Q. et al. Single-cell transcriptomic atlas of the human substantia nigra in Parkinson’s disease. Preprint atbioRxivhttps://doi.org/10.1101/2022.03.25.485846 (2022).

  94. Qin, Z. et al. ZNF536, a novel zinc finger protein specifically expressed in the brain, negatively regulates neuron differentiation by repressing retinoic acid-induced gene transcription.Mol. Cell. Biol.29, 3633–3643 (2009).

    Article CAS PubMed PubMed Central  Google Scholar 

  95. Lorsch, Z. S. et al. Stress resilience is promoted by a Zfp189-driven transcriptional network in prefrontal cortex.Nat. Neurosci.22, 1413–1423 (2019).

    Article CAS PubMed PubMed Central  Google Scholar 

  96. Truby, N. L. et al. A zinc finger transcription factor enables social behaviors while controlling transposable elements and immune response in prefrontal cortex.Transl. Psychiatry14, 59 (2024).

    Article CAS PubMed PubMed Central  Google Scholar 

  97. Huang, Y. et al. Longevity-associated transcription factor ATF7 promotes healthspan by suppressing cellular senescence and systematic inflammation.Aging Dis.14, 1374–1389 (2023).

    PubMed PubMed Central  Google Scholar 

  98. Sun, M.-H. et al. ATF7-dependent epigenetic changes induced by high temperature during early porcine embryonic development.Cell Prolif.56, e13352 (2023).

    Article CAS PubMed  Google Scholar 

  99. Campbell, C. E. et al. The transcription factor Nfix is essential for normal brain development.BMC Dev. Biol.8, 52 (2008).

    Article PubMed PubMed Central  Google Scholar 

  100. Heng, Y. H. E. et al. NFIX regulates neural progenitor cell differentiation during hippocampal morphogenesis.Cereb. Cortex24, 261–279 (2014).

    Article PubMed  Google Scholar 

  101. García-Corzo, L. et al. The transcription factor LEF1 interacts with NFIX and switches isoforms during adult hippocampal neural stem cell quiescence.Front Cell Dev Biol.10, 912319 (2022).

    Article PubMed PubMed Central  Google Scholar 

  102. Wang, S. et al. Activation of transcription factor EB alleviates tubular epithelial cell injury via restoring lysosomal homeostasis in diabetic nephropathy.Oxid. Med. Cell. Longev.2022, 2812493 (2022).

    PubMed PubMed Central  Google Scholar 

  103. Arotcarena, M.-L. et al. Transcription factor EB overexpression prevents neurodegeneration in experimental synucleinopathies.JCI Insight4, e129719 (2019).

    Article PubMed PubMed Central  Google Scholar 

  104. Wamsley, B. et al. Molecular cascades and cell type-specific signatures in ASD revealed by single-cell genomics.Science384, eadh2602 (2024).

    Article PubMed  Google Scholar 

  105. Lai, C. S. L., Gerrelli, D., Monaco, A. P., Fisher, S. E. & Copp, A. J. FOXP2 expression during brain development coincides with adult sites of pathology in a severe speech and language disorder.Brain126, 2455–2462 (2003).

    Article PubMed  Google Scholar 

  106. Moore, J. E., Pratt, H. E., Purcaro, M. J. & Weng, Z. A curated benchmark of enhancer-gene interactions for evaluating enhancer-target gene prediction methods.Genome Biol.21, 17 (2020).

    Article PubMed PubMed Central  Google Scholar 

  107. Fumagalli, M. et al. Phenotypic changes, signaling pathway, and functional correlates of GPR17-expressing neural precursor cells during oligodendrocyte differentiation.J. Biol. Chem.286, 10593–10604 (2011).

    Article CAS PubMed PubMed Central  Google Scholar 

  108. Boccazzi, M. et al. G protein-coupled receptor 17 is regulated by WNT pathway during oligodendrocyte precursor cell differentiation.Neurobiol. Dis.187, 106315 (2023).

    Article CAS PubMed  Google Scholar 

  109. O’Leary, A. et al. Behavioural and functional evidence revealing the role of RBFOX1 variation in multiple psychiatric disorders and traits.Mol. Psychiatry27, 4464–4473 (2022).

    Article PubMed PubMed Central  Google Scholar 

  110. Wang, Y., Lin, L., Lai, H., Parada, L. F. & Lei, L. Transcription factor Sox11 is essential for both embryonic and adult neurogenesis.Dev. Dyn.242, 638–653 (2013).

    Article CAS PubMed  Google Scholar 

  111. Stolt, C. C. et al. SoxD proteins influence multiple stages of oligodendrocyte development and modulate SoxE protein function.Dev. Cell11, 697–709 (2006).

    Article CAS PubMed  Google Scholar 

  112. Baroti, T. et al. Transcription factors Sox5 and Sox6 exert direct and indirect influences on oligodendroglial migration in spinal cord and forebrain.Glia64, 122–138 (2016).

    Article PubMed  Google Scholar 

  113. Yokoyama, T. et al. Smad2Δexon3 and Smad3 have distinct properties in signal transmission leading to TGF-β-induced cell motility.J. Biol. Chem.299, 102820 (2023).

    Article CAS PubMed  Google Scholar 

  114. Askar, P. et al. The Etv1/Er81 transcription factor coordinates myelination-related genes to regulate Schwann cell differentiation and myelination.Ann Transl Med.10, 875 (2022).

    Article CAS PubMed PubMed Central  Google Scholar 

  115. Braccioli, L. et al. FOXP1 promotes embryonic neural stem cell differentiation by repressing Jagged1 expression.Stem Cell Reports9, 1530–1545 (2017).

    Article CAS PubMed PubMed Central  Google Scholar 

  116. Zhou, B., Zhu, Z., Ransom, B. R. & Tong, X. Oligodendrocyte lineage cells and depression.Mol. Psychiatry26, 103–117 (2021).

    Article PubMed  Google Scholar 

  117. Sacchet, M. D. & Gotlib, I. H. Myelination of the brain in major depressive disorder: an in vivo quantitative magnetic resonance imaging study.Sci Rep.7, 2200 (2017).

    Article PubMed PubMed Central  Google Scholar 

  118. Fessel, J. Abnormal oligodendrocyte function in schizophrenia explains the long latent interval in some patients.Transl. Psychiatry12, 120 (2022).

    Article PubMed PubMed Central  Google Scholar 

  119. Falkai, P. et al. Disturbed oligodendroglial maturation causes cognitive dysfunction in schizophrenia: a new hypothesis.Schizophr. Bull.49, 1614–1624 (2023).

    Article PubMed PubMed Central  Google Scholar 

  120. Manichaikul, A. et al. Robust relationship inference in genome-wide association studies.Bioinformatics26, 2867–2873 (2010).

    Article CAS PubMed PubMed Central  Google Scholar 

  121. Butler, A., Hoffman, P., Smibert, P., Papalexi, E. & Satija, R. Integrating single-cell transcriptomic data across different conditions, technologies, and species.Nat. Biotechnol.36, 411–420 (2018).

    Article CAS PubMed PubMed Central  Google Scholar 

  122. Stuart, T., Srivastava, A., Madad, S., Lareau, C. A. & Satija, R. Single-cell chromatin state analysis with Signac.Nat. Methods18, 1333–1341 (2021).

    Article CAS PubMed PubMed Central  Google Scholar 

  123. Zhang, Y. et al. Model-based analysis of ChIP–Seq (MACS).Genome Biol.9, R137 (2008).

    Article PubMed PubMed Central  Google Scholar 

  124. Amemiya, H. M., Kundaje, A. & Boyle, A. P. The ENCODE blacklist: identification of problematic regions of the genome.Sci Rep.9, 9354 (2019).

    Article PubMed PubMed Central  Google Scholar 

  125. Wolock, S. L., Lopez, R. & Klein, A. M. Scrublet: computational identification of cell doublets in single-cell transcriptomic data.Cell Syst8, 281–291.e9 (2019).

    Article CAS PubMed PubMed Central  Google Scholar 

  126. Hafemeister, C. & Satija, R. Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression.Genome Biol.20, 296 (2019).

    Article CAS PubMed PubMed Central  Google Scholar 

  127. Cusanovich, D. A. et al. A single-cell atlas of in vivo mammalian chromatin accessibility.Cell174, 1309–1324.e18 (2018).

    Article CAS PubMed PubMed Central  Google Scholar 

  128. Hao, Y. et al. Integrated analysis of multimodal single-cell data.Cell184, 3573–3587.e29 (2021).

    Article CAS PubMed PubMed Central  Google Scholar 

  129. Waltman, L. & van Eck, N. J. A smart local moving algorithm for large-scale modularity-based community detection.Eur. Phys. J. B86, 471 (2013).

    Article  Google Scholar 

  130. Becht, E. et al. Dimensionality reduction for visualizing single-cell data using UMAP.Nat. Biotechnol.37, 38–44 (2018).

    Article  Google Scholar 

  131. Aran, D. et al. Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage.Nat. Immunol.20, 163–172 (2019).

    Article CAS PubMed PubMed Central  Google Scholar 

  132. Li, B. et al. Cumulus provides cloud-based data analysis for large-scale single-cell and single-nucleus RNA-seq.Nat. Methods17, 793–798 (2020).

    Article CAS PubMed PubMed Central  Google Scholar 

  133. Hoffman, G. E. GabrielHoffman/crumblr: freeze to zenodo.Zenodohttps://doi.org/10.5281/zenodo.12752106 (2024).

  134. 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 

  135. Kuleshov, M. V. et al. Enrichr: a comprehensive gene set enrichment analysis web server 2016 update.Nucleic Acids Res.44, W90–W97 (2016).

    Article CAS PubMed PubMed Central  Google Scholar 

  136. Wu, T. et al. clusterProfiler 4.0: a universal enrichment tool for interpreting omics data.Innovation (Camb).2, 100141 (2021).

    CAS PubMed PubMed Central  Google Scholar 

  137. Wolf, F. A., Angerer, P. & Theis, F. J. SCANPY: large-scale single-cell gene expression data analysis.Genome Biol.19, 15 (2018).

    Article PubMed PubMed Central  Google Scholar 

  138. Bravo González-Blas, C. et al. cisTopic: cis-regulatory topic modeling on single-cell ATAC-seq data.Nat. Methods16, 397–400 (2019).

    Article PubMed  Google Scholar 

  139. Friedman, J. H. Greedy function approximation: a gradient boosting machine.Ann. Stat.29, 1189–1232 (2001).

    Article  Google Scholar 

  140. Moerman, T. et al. GRNBoost2 and Arboreto: efficient and scalable inference of gene regulatory networks.Bioinformatics35, 2159–2161 (2019).

    Article CAS PubMed  Google Scholar 

  141. Aibar, S. et al. SCENIC: single-cell regulatory network inference and clustering.Nat. Methods14, 1083–1086 (2017).

    Article CAS PubMed PubMed Central  Google Scholar 

  142. van de Sande, B. et al. A scalable SCENIC workflow for single-cell gene regulatory network analysis.Nat. Protoc.15, 2247–2276 (2020).

    Article CAS PubMed  Google Scholar 

  143. Suo, S. et al. Revealing the critical regulators of cell identity in the mouse cell atlas.Cell Rep.25, 1436–1445.e3 (2018).

    Article CAS PubMed PubMed Central  Google Scholar 

  144. Bulik-Sullivan, B. K. et al. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies.Nat. Genet.47, 291–295 (2015).

    Article CAS PubMed PubMed Central  Google Scholar 

  145. Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing.J. R. Stat. Soc. B57, 289–300 (1995).

    Article  Google Scholar 

  146. ENCODE Project Consortium. An integrated encyclopedia of DNA elements in the human genome.Nature489, 57–74 (2012).

    Article  Google Scholar 

  147. Lajoie, B. R., Dekker, J. & Kaplan, N. The Hitchhiker’s guide to Hi-C analysis: practical guidelines.Methods72, 65–75 (2015).

    Article CAS PubMed  Google Scholar 

  148. Clarence, T. Multiomic single-cell profiling reveals critical regulators of postnatal brain.Zenodohttps://doi.org/10.5281/zenodo.13991329 (2024).

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Acknowledgements

We would like to express our gratitude to the patients and families who kindly donated material for these studies. We appreciate the computational resources and staff expertise provided by the Scientific Computing group at the Icahn School of Medicine at Mount Sinai. This work was supported by the National Institute of Mental Health, NIH grants RF1-MH128970 (to G.-C.Y. and P.R.), RF1-MH133703 (to G.-C.Y. and P.R.), R01-MH110921 (to P.R.), U01-MH116442 (to P.R., S.A., S.D.), U01-DA048279 (to P.R., S.A.), R01-MH122590 (to S.D.), R01-MH125246 (to P.R.), R01-MH109677 (to P.R.) and the National Institute on Aging, NIH grants R01-AG050986 (to P.R.), R01-AG067025 (to P.R., V.H.), R01-AG065582 (to P.R., V.H.), R01-AG082185 (to P.R., V.H., D.L.) and U24-AG087563 (to P.R.), and the National Institute of Neurological Disorders and Stroke, NIH grant U24-AG087563 (to P.R.). This study was supported by the Veterans Affairs Merit grants BX-005160 (to S.D.) and BX-002395 (to P.R.). This work was supported in part through the computational and data resources and staff expertise provided by Scientific Computing and Data at the Icahn School of Medicine at Mount Sinai and supported by the Clinical and Translational Science Awards (CTSA) grant UL1TR004419 from the National Center for Advancing Translational Sciences. Research reported in this publication was also supported by the Office of Research Infrastructure of the National Institutes of Health under award numbers S10OD026880 and S10OD030463. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

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Authors and Affiliations

  1. Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Tereza Clarence, Jaroslav Bendl, Xuan Cao, Xinyi Wang, Gabriel E. Hoffman, Aram Hong, Sarah Murphy, Alexander Yu, John F. Fullard, Donghoon Lee & Panos Roussos

  2. Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Tereza Clarence, Jaroslav Bendl, Xuan Cao, Xinyi Wang, Gabriel E. Hoffman, Alexey Kozlenkov, Aram Hong, Marina Iskhakova, Manoj K. Jaiswal, Sarah Murphy, Alexander Yu, Vahram Haroutunian, Stella Dracheva, Schahram Akbarian, John F. Fullard, Donghoon Lee & Panos Roussos

  3. Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Tereza Clarence, Jaroslav Bendl, Xuan Cao, Xinyi Wang, Gabriel E. Hoffman, Alexey Kozlenkov, Aram Hong, Marina Iskhakova, Manoj K. Jaiswal, Sarah Murphy, Alexander Yu, Vahram Haroutunian, Stella Dracheva, Schahram Akbarian, John F. Fullard, Donghoon Lee & Panos Roussos

  4. Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Tereza Clarence, Jaroslav Bendl, Xuan Cao, Xinyi Wang, Shiwei Zheng, Gabriel E. Hoffman, Aram Hong, Sarah Murphy, Alexander Yu, John F. Fullard, Guo-Cheng Yuan, Donghoon Lee & Panos Roussos

  5. Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Marina Iskhakova, Vahram Haroutunian & Schahram Akbarian

  6. Mental Illness Research Education and Clinical Center, James J. Peters VA Medical Center, Bronx, NY, USA

    Manoj K. Jaiswal, Vahram Haroutunian, Stella Dracheva & Panos Roussos

  7. Center for Precision Medicine and Translational Therapeutics, James J. Peters VA Medical Center, Bronx, NY, USA

    Panos Roussos

Authors
  1. Tereza Clarence

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  2. Jaroslav Bendl

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  3. Xuan Cao

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  4. Xinyi Wang

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  5. Shiwei Zheng

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  6. Gabriel E. Hoffman

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  7. Alexey Kozlenkov

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  8. Aram Hong

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  9. Marina Iskhakova

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  10. Manoj K. Jaiswal

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  11. Sarah Murphy

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  12. Alexander Yu

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  13. Vahram Haroutunian

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  14. Stella Dracheva

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  15. Schahram Akbarian

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  16. John F. Fullard

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  17. Guo-Cheng Yuan

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  18. Donghoon Lee

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  19. Panos Roussos

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Contributions

P.R. conceived and designed the project. V.H. performed tissue dissections for the Mount Sinai Brain Bank specimens. A.K., M.K.J. and S.D. performed tissue processing and FANS for Hi-C experiments. M.I. and S.A. generated Hi-C data. A.H. and J.F.F. processed tissue and generated single-nucleus multiome data. T.C., J.B., X.C., D.L. and P.R. designed analytical strategies. T.C. conducted initial bioinformatics, sample processing and quality control for single-nucleus data and taxonomy. T.C. developed the computational scheme and performed the downstream analysis. J.B. performed the GWAS enrichment analysis and mapping of GWAS loci to causal genes. X.C. performed SCENIC+ analysis. S.Z. supported the initial part of pseudotime analysis. G.E.H. provided support on cell-type composition analysis. X.W. performed RNAScope experiments. S.M. and A.Y. helped with preparation for RNAscope experiments. Data analysis was supervised by G.-C.Y., D.L. and P.R. T.C., J.B., J.F.F., D.L. and P.R. wrote the paper with input from all authors.

Corresponding authors

Correspondence toTereza Clarence orPanos Roussos.

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The authors declare no competing interests.

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Nature Genetics thanks Junyue Cao, Kushal Dey and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.Peer reviewer reports are available.

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Extended data

Extended Data Fig. 1 Representative FANS sorting for HiC and genotype control for multiome.

a, Sample identity check based on the pairwise comparison of genotypes called from RNA-seq (left) and ATAC-seq (right) reads showing a clear distinction between samples originating from the same donor (each donor was profiled in 4 brain regions) versus different donors. The Kinship scores were calculated by King software (Methods).b, Gating with forward and side scatter was used to identify DAPI+ singlets.c, DAPI+ singlets were gated and assessed for binding of both NeuN and Sox10. This allowed for the identification and subsequent collection of oligodendrocyte (DAPI+ NeuN- SOX10+) and microglia/astrocyte (DAPI+ NeuN- SOX10-) nuclei. In parallel, the NeuN+ population was gated and assessed for binding of Sox6, enabling the isolation of nuclei from both GABAergic (DAPI+ NeuN+ SOX6+) and glutamatergic neurons (DAPI+ NeuN+ SOX6−).d, Representative yield of nuclei from different cell types using the sorting strategy described inMethods.

Extended Data Fig. 2 Annotation of human brain single-nucleus multiome dataset.

a, RNA (left) and ATAC (right) representation of the full dataset colored by main cell types.b-c, Cosine similarity correlation on pseudobulk level of RNA between cell types defined in our multiome dataset and cell types from published datasets9,14,15.d, Geneset enrichment analysis (GSEA) of top 500 genes within highest contribution to gene expression variability based on age, brain region and sex, colored by P-value (one-sided Fisher’s Exact test) and size corresponding to number of genes within Gene Ontology (GO) term category.

Extended Data Fig. 3 Disease heritability enrichment patterns for cell type - TF regulator pairs.

Enrichment of common risk variants associated with selected disease traits in regulatory elements (peaks) linked to predicted regulatory targets (genes) of 146 TFs. For each cell type, co-localization was estimated between common risk variants and the peaks linked by ABC approach56 using LD-score partitioned heritability (Methods). For each cell-type and disease trait, up to 5 of the most specific FDR-significant TF regulators are labeled with their names. SCZ = schizophrenia, PD = Parkinson’s disease, MS = multiple sclerosis, MDD = major depressive disorder, BD = bipolar disorder, ASD = autism spectrum disorder, AN = anorexia nervosa, ALS = amyotrophic lateral sclerosis, AD = Alzheimer’s disease, ADHD = attention deficit hyperactivity disorder). ‘#’: indicates significance for enrichment in MAGMA LDSR calculation of P-value after FDR (Benjamini-Hochberg) correction of multiple testing across all tests in plot (Methods)145; ‘.’: Nominally significant for enrichment.

Extended Data Fig. 4 Comparison of GWAS trait enrichments across cell types between our study and other studies.

The heatmaps and scatter plots provide complementary visualizations of the same data. In the scatter plots, the blue line represents the best-fit regression line from a linear model. For Li et al.49, enrichments in the original study were reported at the cell subtype level (for example, SST and PVAL interneurons) rather than at the broader cell type level (for example, GABAergic neurons). To facilitate comparison, we ran the comparison against the cell subtype with the lowest P-value. SCZ = schizophrenia, PD = Parkinson’s disease, MS = multiple sclerosis, MDD = major depressive disorder, BD = bipolar disorder, ASD = autism spectrum disorder, AN = anorexia nervosa, ALS = amyotrophic lateral sclerosis, AD = Alzheimer’s disease, ADHD = attention deficit hyperactivity disorder. ‘#’: indicates significance for enrichment at BH-adjusted P-value < 0.05; ‘·’: Nominally significant for enrichment.

Extended Data Fig. 5 Pseudotime analysis OPC and OL population.

a, OPC and OL subtype-specific marker gene expression projected on RNA-UMAP.b, Percentage of nuclei corresponding to each brain region across distinct OPC and OL subtypes.c, UMAP projection of RNA-seq and ATAC-seq for the OPC/OL population, colored by OPC and OL subtypes.d, RNA-UMAP colored by normalized pseudotime values from monocle383, palantir84 and PC1 approach (upper row). Normalized expression patterns of selected OPC and OL subtype markers across pseudotime progression from each method.e, per-nuclei Pearson correlation of pseudotime values across selected methods.f, distribution of pseudotime values from monocle3 across distinct brain regions; ACC (n = 11,585 nuclei), DLPFC (n = 12,226 nuclei), CN (n = 12,933 nuclei) and Hipp (n = 10,489 nuclei). The center line represents the median, the box captures the interquartile range, and the whiskers show the maximum and minimum values within 1.5 times the interquartile range.

Extended Data Fig. 6 RNAscope validation of transcriptionally distinct OPC and oligodendrocyte populations.

a-b, Representative 20x stitched images of RNAscope in situ hybridization of dentate gyrus region (n = 2 samples), using probes targetingPDGFRA andATRNL1 (marking OPC Type I),BMPER (marking OPC Type II) (a),GPR37 (marking OL Type I), andRBFOX1 andACTN2 (marking OL Type II) (b), and DAPI for nuclear staining, with the scale bar corresponding to 200 µm.c,PDGFRA,ATRNL1 andBMPER (for OPC Type I and II, respectively) gene expression, visualized on the UMAP of OPC and OL populations.d-e, zoom in images showing representative OPC Type I cells co-expressingPDGFRA andATRNL1 (d), and OPC Type II cells expressingBMPER (e), with scale bars corresponding to 20 µm.f,GPR37 andRBFOX1,ACTN2 (for OL Type I and II, respectively) gene expression, visualized on the UMAP of OPC and OL populations.g-h, zoom in images showing representative OL Type I cells expressingGPR37 (g), and OL Type II cells co-expressingRBFOX1 andACTN2 (h), with scale bars corresponding to 20 µm.

Supplementary information

Supplementary Information

Supplementary Figs. 1–18, Note 1 and References.

Supplementary Data 1

Gene and peak markers with cell type specificity.

Supplementary Data 2

GSEA of top 500 genes contributing to highest variability in gene expression based on covariate selection.

Supplementary Data 3

Metadata output from SCENIC+ analysis after filtering criteria.

Supplementary Data 4

Overlap between common risk genetic variants associated with ten brain-related traits and both genes (MAGMA method) and enhancer OCRs (S-LDSC method) associated with cell-specific TF regulators.

Supplementary Data 5

Overlap between common risk genetic variants associated with ten brain-related traits and enhancer OCRs (S-LDSC method) associated with all TF regulators separately per each cell type.

Supplementary Data 6

RSS score of selected shared eRegulons.

Supplementary Data 7

Overlap between common risk genetic variants associated with ten brain-related traits and cell-specific marker genes and peaks as calculated by MAGMA (genes) and S-LDSC (peaks) methods.

Supplementary Data 8

E–P links connected to GWAS loci as predicted by the ABC-MAX approach.

Supplementary Data 9

E–P links connected to SCZ GWAS loci as predicted by the ABC approach.

Supplementary Data 10

GSEA for the top ten pathways in MS.

Supplementary Data 11

Targeted GSEA using the SynGO in SCZ.

Supplementary Data 12

Per gene summary of E–P links connected to GWAS loci as predicted by ABC-MAX approach.

Supplementary Data 13

Gene markers of OPC and OL subtypes.

Supplementary Data 14

Hotspot assignment on single-cell level, as well as per cell score for all detected Hotspot modules with further information on genes forming each Hotspot module.

Supplementary Data 15

Results from GSEA for each pseudotime trendline.

Supplementary Data 16

MAGMA enrichment results for both cell-type-specific regulators and OLa-eRegulons.

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Clarence, T., Bendl, J., Cao, X.et al. Multiomic single-cell profiling identifies critical regulators of postnatal brain.Nat Genet57, 591–603 (2025). https://doi.org/10.1038/s41588-025-02083-8

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