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Activity-dependent regulome of human GABAergic neurons reveals new patterns of gene regulation and neurological disease heritability

Abstract

Neuronal activity-dependent gene expression is essential for brain development. Although transcriptional and epigenetic effects of neuronal activity have been explored in mice, such an investigation is lacking in humans. Because alterations in GABAergic neuronal circuits are implicated in neurological disorders, we conducted a comprehensive activity-dependent transcriptional and epigenetic profiling of human induced pluripotent stem cell-derived GABAergic neurons similar to those of the early developing striatum. We identified genes whose expression is inducible after membrane depolarization, some of which have specifically evolved in primates and/or are associated with neurological diseases, including schizophrenia and autism spectrum disorder (ASD). We define the genome-wide profile of human neuronal activity-dependent enhancers, promoters and the transcription factors CREB and CRTC1. We found significant heritability enrichment for ASD in the inducible promoters. Our results suggest that sequence variation within activity-inducible promoters of developing human forebrain GABAergic neurons contributes to ASD risk.

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Fig. 1: hGNs express RNA signatures of developing forebrain GNs.
Fig. 2: Neuronal activity-dependent gene expression of hGNs by total RNAseq.
Fig. 3: Neuronal activity-dependent gene expression of hGNs by scRNAseq.
Fig. 4: hGN activity-dependent promoters and enhancers.
Fig. 5: Genome-wide binding of the activity-dependent CREB complex.
Fig. 6: Disease heritability enrichment in hGN promoter and enhancer regions.

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Data availability

The sequencing data that support the findings of this study are available under Gene Expression Omnibus accession number GSE136656. Publicly available datasets include ENSEMBL’s Comparative Genomics Tool (https://www.ensembl.org/info/genome/compara/index.html), GenTree (http://gentree.ioz.ac.cn/index.php), HomoloGene (https://www.ncbi.nlm.nih.gov/homologene), BrainSpan atlas (http://www.brainspan.org/static/download.html), UCSC Genome Browser (https://genome.ucsc.edu/), SFARI ASD-associated genes 11-21-2018 release (https://gene-archive.sfari.org/) and the Schizophrenia Working Group of the Psychiatric Genomics Consortium (https://www.med.unc.edu/pgc/). Source data are provided with this paper.

References

  1. Geschwind, D. H. & Rakic, P. Cortical evolution: judge the brain by its cover. Neuron 80, 633–647 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Sousa, A. M. M. M., Meyer, K. A., Santpere, G., Gulden, F. O. & Sestan, N. Evolution of the human nervous system function, structure, and development. Cell 170, 226–247 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Lui, J. H., Hansen, D. V. & Kriegstein, A. R. Development and evolution of the human neocortex. Cell 146, 18–36 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Petanjek, Z. et al. Extraordinary neoteny of synaptic spines in the human prefrontal cortex. Proc. Natl Acad. Sci. USA 108, 13281–13286 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Hensch, T. K. Critical period regulation. Annu. Rev. Neurosci. 27, 549–579 (2004).

    Article  CAS  PubMed  Google Scholar 

  6. Ataman, B. et al. Evolution of Osteocrin as an activity-regulated factor in the primate brain. Nature 539, 242 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  7. Lim, L., Mi, D., Llorca, A. & Marín, O. Development and functional diversification of cortical interneurons. Neuron 100, 294–313 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Medina, L., Abellán, A., Vicario, A. & Desfilis, E. Evolutionary and developmental contributions for understanding the organization of the basal ganglia. Brain Behav. Evol. 83, 112–125 (2014).

    Article  PubMed  Google Scholar 

  9. Shepherd, G. M. Corticostriatal connectivity and its role in disease. Nat. Rev. Neurosci. 14, 278 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Haythornthwaite, A. et al. Characterizing human ion channels in induced pluripotent stem cell-derived neurons. J. Biomol. Screen. 17, 1264–1272 (2012).

    Article  PubMed  Google Scholar 

  11. Berry, B. J. et al. Morphological and functional characterization of human induced pluripotent stem cell-derived neurons (iCell Neurons) in defined culture systems. Biotechnol. Prog. 31, 1613–1622 (2015).

  12. Hochbaum, D. R. et al. All-optical electrophysiology in mammalian neurons using engineered microbial rhodopsins. Nat. Methods 11, 825–833 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Onorati, M. et al. Molecular and functional definition of the developing human striatum. Nat. Neurosci. 17, 1804–1815 (2014).

    Article  CAS  PubMed  Google Scholar 

  14. Mayer, C. et al. Developmental diversification of cortical inhibitory interneurons. Nature 555, 457–462 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Allaway, K. C. & Machold, R. Developmental specification of forebrain cholinergic neurons. Dev. Biol. 421, 1–7 (2017).

    Article  CAS  PubMed  Google Scholar 

  16. Wei, B. et al. The onion skin-like organization of the septum arises from multiple embryonic origins to form multiple adult neuronal fates. Neuroscience 222, 110–123 (2012).

    Article  CAS  PubMed  Google Scholar 

  17. Spiegel, I. et al. Npas4 regulates excitatory-inhibitory balance within neural circuits through cell-type-specific gene programs. Cell 157, 1216–1229 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Hrvatin, S. et al. Single-cell analysis of experience-dependent transcriptomic states in the mouse visual cortex. Nat. Neurosci. 21, 120–129 (2018).

    Article  CAS  PubMed  Google Scholar 

  19. Pruunsild, P., Bengtson, P. C. & Bading, H. Networks of cultured iPSC-derived neurons reveal the human synaptic activity-regulated adaptive gene program. Cell Rep. 18, 122–135 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Ingi, T. et al. Dynamic regulation of RGS2 suggests a novel mechanism in G-protein signaling and neuronal plasticity. J. Neurosci. 18, 7178–7188 (1998).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Nedivi, E., Hevroni, D., Naot, D., Israeli, D. & Citri, Y. Numerous candidate plasticity-related genes revealed by differential cDNA cloning. Nature 363, 718–722 (1993).

    Article  CAS  PubMed  Google Scholar 

  22. Fujioka, H., Dairyo, Y., Yasunaga, K.-I. & Emoto, K. Neural functions of matrix metalloproteinases: plasticity, neurogenesis, and disease. Biochem. Res. Int. 2012, 789083 (2012).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  23. Zeisel, A. et al. Molecular architecture of the mouse nervous system. Cell 174, 999–1014 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Miller, J. A. et al. Transcriptional landscape of the prenatal human brain. Nature 508, 199–206 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Kapusta, A. et al. Transposable elements are major contributors to the origin, diversification, and regulation of vertebrate long noncoding RNAs. PLoS Genet. 9, e1003470 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Tadepally, H. D., Burger, G. & Aubry, M. Evolution of C2H2-zinc finger genes and subfamilies in mammals: species-specific duplication and loss of clusters, genes and effector domains. BMC Evol. Biol. 8, 176 (2008).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  28. Ebert, D. H. & Greenberg, M. E. Activity-dependent neuronal signalling and autism spectrum disorder. Nature 493, 327–337 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Meur, L. N. et al. MEF2C haploinsufficiency caused by either microdeletion of the 5q14.3 region or mutation is responsible for severe mental retardation with stereotypic movements, epilepsy and/or cerebral malformations. J. Med. Genet. 47, 22 (2010).

    Article  PubMed  CAS  Google Scholar 

  30. Schizophrenia Working Group of the Psychiatric Genomics Consortium. Biological insights from 108 schizophrenia-associated genetic loci. Nature 511, 421–427 (2014).

    Article  PubMed Central  CAS  Google Scholar 

  31. Bipolar Disorder and Schizophrenia Working Group of the Psychiatric Genomics Consortium. Genomic dissection of bipolar disorder and schizophrenia, including 28 subphenotypes. Cell 173, 1705–1715 (2018).

  32. Grove, J. et al. Identification of common genetic risk variants for autism spectrum disorder. Nat. Genet. 51, 431–444 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Francis, K. R. et al. Modeling Smith–Lemli–Opitz syndrome with induced pluripotent stem cells reveals a causal role for Wnt/β-catenin defects in neuronal cholesterol synthesis phenotypes. Nat. Med. 22, 388–396 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Riazuddin, S. et al. Alterations of the CIB2 calcium- and integrin-binding protein cause Usher syndrome type 1J and nonsyndromic deafness DFNB48. Nat. Genet. 44, 1265–1271 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Zhou, Y. et al. Atypical behaviour and connectivity in SHANK3-mutant macaques. Nature 570, 326–331 (2019).

    Article  CAS  PubMed  Google Scholar 

  36. Robertson, C. E. & Baron-Cohen, S. Sensory perception in autism. Nat. Rev. Neurosci. 18, 671–684 (2017).

    Article  CAS  PubMed  Google Scholar 

  37. Buraei, Z. & Yang, J. The β subunit of voltage-gated Ca2+ channels. Physiol. Rev. 90, 1461–1506 (2010).

    Article  CAS  PubMed  Google Scholar 

  38. Rada-Iglesias, A. et al. A unique chromatin signature uncovers early developmental enhancers in humans. Nature 470, 279 (2011).

    Article  CAS  PubMed  Google Scholar 

  39. Malik, A. N. et al. Genome-wide identification and characterization of functional neuronal activity-dependent enhancers. Nat. Neurosci. 17, nn.3808 (2014).

    Article  CAS  Google Scholar 

  40. 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 

  41. Lonze, B. E. & Ginty, D. D. Function and regulation of CREB family transcription factors in the nervous system. Neuron 35, 605–623 (2002).

    Article  CAS  PubMed  Google Scholar 

  42. Impey, S. et al. Defining the CREB regulon: a genome-wide analysis of transcription factor regulatory regions. Cell 119, 1041–1054 (2004).

    CAS  PubMed  Google Scholar 

  43. Lalonde, J., Lachance, P. & Chaudhuri, A. Developmental and activity‐dependent genomic occupancy profiles of CREB in monkey area V1. Genes Brain Behav. 8, 149–160 (2009).

    Article  CAS  PubMed  Google Scholar 

  44. Kornhauser, J. M. et al. CREB transcriptional activity in neurons is regulated by multiple, calcium-specific phosphorylation events. Neuron 34, 221–233 (2002).

    Article  CAS  PubMed  Google Scholar 

  45. Ch’ng, T. H. et al. Activity-dependent transport of the transcriptional coactivator CRTC1 from synapse to nucleus. Cell 150, 207–221 (2012).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  46. Bulger, M. & Groudine, M. Functional and mechanistic diversity of distal transcription enhancers. Cell 144, 327–339 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Vercauteren, K., Pasko, R. A., Gleyzer, N., Marino, V. M. & Scarpulla, R. C. PGC-1-related coactivator: immediate early expression and characterization of a CREB/NRF-1 binding domain associated with cytochrome c promoter occupancy and respiratory growth. Mol. Cell. Biol. 26, 7409–7419 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Li, M. et al. Integrative functional genomic analysis of human brain development and neuropsychiatric risks. Science 362, eaat7615 (2018).

  49. Satterstrom, F. K. et al. Large-scale exome sequencing study implicates both developmental and functional changes in the neurobiology of autism. Cell 180, 568–584 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. An, J.-Y. Y. et al. Genome-wide de novo risk score implicates promoter variation in autism spectrum disorder. Science 362, eaat6576 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  51. Klein, A. M. et al. Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells. Cell 161, 1187–1201 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Langmead, B., Trapnell, C., Pop, M. & Salzberg, S. L. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 10, R25 (2009).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  53. 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 

  54. Satija, R., Farrell, J. A., Gennert, D., Schier, A. F. & Regev, A. Spatial reconstruction of single-cell gene expression data. Nat. Biotechnol. 33, 495–502 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Qiu, X. et al. Single-cell mRNA quantification and differential analysis with Census. Nat. Methods 14, 309–315 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Liu, F., Thompson, M., Wagner, S., Greenberg, M. & Green, M. Activating transcription factor-1 can mediate Ca2+- and cAMP-inducible transcriptional activation. J. Biol. Chem. 268, 6714–6720 (1993).

    Article  CAS  PubMed  Google Scholar 

  57. Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25, 1754–1760 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Zhang, X. et al. Genome-wide analysis of cAMP-response element binding protein occupancy, phosphorylation, and target gene activation in human tissues. Proc. Natl Acad. Sci. USA 102, 4459–4464 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).

    Article  CAS  PubMed  Google Scholar 

  60. Reilly, S. K. et al. Evolutionary changes in promoter and enhancer activity during human corticogenesis. Science 347, 1155–1159 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Vermunt, M. W. et al. Large-scale identification of coregulated enhancer networks in the adult human brain. Cell Rep. 9, 767–779 (2014).

    Article  CAS  PubMed  Google Scholar 

  62. Vermunt, M. W. et al. Epigenomic annotation of gene regulatory alterations during evolution of the primate brain. Nat. Neurosci. 19, 494–503 (2016).

    Article  CAS  PubMed  Google Scholar 

  63. Sharma, N. et al. ARNT2 tunes activity-dependent gene expression through NCoR2-mediated repression and NPAS4-mediated activation. Neuron 102, 390–406 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Heinz, S. et al. Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol. Cell 38, 576–589 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. 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 

  67. Rizzardi, L. F. et al. Neuronal brain-region-specific DNA methylation and chromatin accessibility are associated with neuropsychiatric trait heritability. Nat. Neurosci. 22, 307–316 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We thank M. Chahrour, J. L. Hecht, J. Partlow and C. A. Walsh for assistance with human tissue collection; C. C. Harwell, C. Mayer and M. T. Garcia for guidance on single-cell sequencing and cell-type identification; K. D. Hansen and A. P. Feinberg for providing additional LDSC brain-associated control category annotations; and A. N. Chang, E. E. Duffy, G. Fishell, D. Malhotra and D. Reich for helpful discussions. We acknowledge the large body of previous work that informed this study and regret omission of relevant citations due to space constraints. This work was supported by the National Institues of Health: P50MH106933 and R01NS028829 (to M.E.G.), F32NS086270 (to G.L.B.) and T32GM007753 (to E.D.); the ROADS Program funded by F. Hoffmann-La Roche (to M.E.G); and the Paul G. Allen Frontiers Group (to M.E.G). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute of General Medical Sciences or the National Institutes of Health.

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Authors and Affiliations

Authors

Contributions

G.L.B., E.D., B.A. and M.E.G. conceived of the experiments. G.L.B., E.D., A.C.C., D.A.H., B.A. and K.M. performed total RNAseq. G.L.B., K.M., D.R.H. and S.H. performed scRNAseq. E.D. performed 1G ChIPseq and initial disease enrichment analysis. G.L.B. and A.C.C. performed 4G ChIPseq. G.L.B., A.C.C. and K.M. performed ATACseq. M.A.S. performed LDSC heritability enrichment analysis. K.M. performed luciferase assays, TaqMan assays and statistical analyses. B.A. performed evolutionary conservation analysis. G.L.B. and B.A. performed qPCR and immunocytochemistry with assistance from M.R.B. D.A.H. performed human BrainSpan data visualizations and correlations and statistical analyses. A.J.G. performed electrophysiology. J.M.E. performed ABC modeling. M.G.Y. assisted with IVRL comparisons. G.L.B. performed, directed or assisted with all experiments and analyses. G.L.B., M.E.G. and E.C.G. wrote the manuscript with input from all authors.

Corresponding authors

Correspondence to Gabriella L. Boulting or Michael E. Greenberg.

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The authors declare no competing interests.

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Peer review information Nature Neuroscience thanks Andrew Jaffe and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 The gene expression profile of hGNs is similar to that of developing human telencephalic GABAergic neurons.

a. Image from DIV14 1G hGN culture immunostained for neural progenitor marker protein NESTIN, post-mitotic neuronal marker protein MAP2 showing the majority of hGN cells expressing MAP2 and very few express NESTIN. Representative image of 4 independent experiments. b. Whole-cell patch clamp measurements of DIV14-15 hGN cells reveals immature neuronal firing and membrane properties. Frequency and amplitude of the spontaneous inhibitory post-synaptic currents (sIPSCs), which confirm that hGNs form synapses in these culture conditions. c. mRNA expression levels of regional and developmental marker genes in unstimulated 1G and 4 G hGN cultures by total RNAseq (1G: n = 6; 4G: n = 4; both: box center = mean; box minima/maxima = mean±1 SE; whiskers minima/maxima = mean±1 SD). Adapted from Straccia M., et al., 2015. d. DIV14 4G hGN cultures immunostained for NESTIN and MAP2. The majority of hGN cells express MAP2 and very few express NESTIN in hGNs from genotypes 1434 and 1501. No NESTIN-positive cells were detected in hGN cultures from genotypes 1505 and CW20049. For 1434: representative image of 4 independent experiments. For other three genotypes: images from one independent experiment.

Extended Data Fig. 2 Single-cell RNA-sequencing of hGNs reveals developing ventral forebrain cell types.

a. – f. UMAP visualizations of the entire scRNAseq data set with individual cells colored purple if expression of a given marker gene was detected. g. – j. Brainspan human developmental brain expression data for marker genes within different brain regions. Loess curves interpolated through the means of available data at each age, with error bands on either side interpolated through values, at each age, 1.96 standard errors above or below the mean.

Extended Data Fig. 3 Activity-dependent gene expression patterns detected by total RNAseq.

a. mRNA expression level changes measured by total RNAseq of 4 G hGNs at 15 minutes (n = 4), 1 hour (n = 3), 2 hours (n = 4), and 4 hours (n = 4) after membrane depolarization compared to expression in unstimulated cultures (n = 4) represented by MA-plot. Genes with a significantly different gene expression level and a minimum fold-change magnitude of 1.5 after depolarization are marked in red. Genes having inducible expression fall above y=0 and example gene names are labeled. b. Time courses of previously known activity-inducible gene transcript levels in unstimulated and stimulated 1 G hGNs. Means±s.e.m. Timepoints at which the transcript was significantly induced compared to unstimulated cultures are marked with an asterisk. c. qPCR Fold-induction of mRNA transcripts in 1 G hGNs cultures of genes detected to be inducible by total and single cell RNAseq. Sensitivity limitations of qPCR prevent detection of significant differences when a gene’s expression is modestly induced in a minority of the cells (for example MMP1 and SHANK3). d. The percentage of 1 G and 4 G hGN inducible genes whose gene products have known subcellular localizations. Early inducible genes (15 min. - 1 hr.) have a greater percentage of nuclear localized products than gene transcripts induced at later stimulation timepoints, when the percentage of induced genes in the membrane-bound or secreted category increases.

Extended Data Fig. 4 Activity-dependent gene expression patterns detected by single-cell RNAseq.

a. One biological replicate (rep1) of unstimulated (0 h), 1 hour, and 2 hour membrane depolarized hGN cultures, and two biological replicates (rep2 and 3) of unstimulated, 1 hour, 2 hour, and 4 hour membrane depolarized hGN cultures contributed to all cell clusters identified by single cell RNA sequencing. Replicate – timepoint contribution is calculated for each cluster in the table below b. Selected genes previously known to have inducible expression are also inducible in hGNs after membrane depolarization, as measured by single-cell RNAseq. This includes genes with induction in many hGN cell types (for example FOS, LINC00473, NPAS4) and genes showing hGN cell type-restricted induction (for example BDNF, NPTX2). MMP1 expression was identified to be activity-inducible in this study by total RNAseq, and single-cell RNAseq shows that it is only induced in cluster 9 striatal-like neurons of hGN cultures. The magnitude of fold-change of mRNA levels in each cell cluster at different timepoints after membrane depolarization compared to unstimulated mRNA levels is indicated by dot color. The percentage of cells in each cluster from which expression was detected is represented by the size of the dot. Only timepoints with significantly induced mRNA levels (compared to unstimulated) are shown.

Extended Data Fig. 5 hGN activity-dependent enhancers and promoters.

hGNs from four independent iPSC donors (4G) were cultured in parallel and H3K27ac ChIPseq was performed in unstimulated cultures, at 15 minutes, and two hours after membrane depolarization for each of them in duplicate (technical replicates). Using each of the four genotypes as biological replicates, differential H3K27ac enrichment analysis was performed to identify promoters and enhancers that underwent histone modification changes in response to neuronal depolarization. a. MA-plots representing all 4G H3K27ac peaks identified with those in red having increased significantly in H3K27ac and those in blue having decreased significantly at 15 minutes or 2 hours. b. The total number of reproducible 4G H3K27ac peaks that were called across the four genotypes of hGNs and the subset that were significantly inducible. As with the 1G H3K27ac peaks, 4G H3K27ac peaks are highly represented in the in vivo reference list, and most inducible peaks were inducible at either 15 min or 2 hours but not at both time points. c. Proportions of H3K27ac peaks that intersect a TSS (promoter regions) or do not intersect a TSS (enhancer regions). As with the 1G H3K27ac peaks, 4G 15 minute inducible H3K27ac peaks are significantly enriched for promoter regions. d. As with the 1G H3K27ac peaks, the CREB binding sequence is the most enriched sequence motif within 15 min 4G inducible H3K27ac regions, and the AP-1 motif is the most enriched within 2 hour 4G inducible H3K27ac regions. e. 1G inducible H3K27ac promoter and enhancer regions were cloned into the pGL4.11 (promoter regions) or NUED2 (enhancer regions) plasmids and transfected into embryonic mouse cortical cultures. Luciferase assays were performed to test for these sequences’ ability to drive luciferase reporter gene expression in response to membrane depolarization. 15/15 constructs that show significant reporter expression induction compared to the corresponding empty control plasmid condition are indicated with an asterisk. n = 2–10 independent replicates (neuronal dissection, plasmid transfection, and luminometer reading); box and whisker plot showing median and interquartile range. f. Pearson correlations showing similar levels of H3K27ac induction between hGNs from independent genetic donors of the 4G data set. g. Hierarchical clustering of H3K27ac induction across all 4G samples.

Extended Data Fig. 6 Detection of CREB, pCREB, and CRTC1.

a. 15 minutes after membrane depolarization is the timepoint at which the greatest number of hGNs immunostain positive using the phosphorylated ser133 CREB antibody used for ChIPseq. Median and interquartile range of counts from six images for each condition from a single immunostaining event of one differentiation lot of 1G neurons. b. Western blot for CRTC1 and GAPDH from hGN cell lysates after 6 or 9 days of non-targeting (NT) or CRTC1-targeting siRNA blotted with the antibody that was used for CRTC1 ChIPseq. Blot displays the two independent experiments performed for this quantification. 9 day treatments were used for siRNA treatments in this study, which resulted in an average 64.1% protein knockdown. c. Western blot of hGN cell lysates using the CREB antibody used for CREB ChIPseq. Image is representative of two independent experiments. d. Immunostaining for CRTC1 in hGN cultures before (unstimulated) and 15 minutes after membrane depolarization. Membrane depolarization causes CRTC1 to translocate to the nucleus. Scale bar = 100 µm. Images are representative of 3 independent experiments.

Extended Data Fig. 7 Disease heritability enrichment in hGN promoter and enhancer regions.

a. Heritability enrichments (top) and p-values of heritability enrichment (bottom) of the 1G hGN H3K27ac promoter and enhancer regions across neurological and non-neurological disorders. b. p-values of heritability enrichment of 4G hGN H3K27ac promoter and enhancer regions across neurological and non-neurological disorders. c. Heritability enrichments (left) and p-values of heritability enrichment (right) of the inducible and constitutive H3K27ac enhancer regions across neurological diseases for both 4G (top) and 1G (bottom), showing significant enrichment for only constitutive enhancer regions. d. p-values of heritability enrichment of 4G (top) hGN inducible and constitutive enhancers (central 500 bp of 4G ATACseq peaks within 4G H3K27ac enhancer regions) across neurological disorders, and (bottom) 1G heritability enrichments (left) and p-values of heritability enrichment (right), showing significant enrichment for only constitutive enhancers. e. p-values of heritability enrichment of 4G (left) hGN inducible and constitutive promoters across neurological disorders, and 1G heritability enrichments (center) and p-values of heritability enrichment (right), showing significant ASD and BP heritability enrichment in inducible promoters but not constitutive promoters. f. Heritability enrichment (top) p-values of heritability enrichment (bottom) of the inducible and constitutive 1G (left) and 4G (right) H3K27ac promoter regions across neurological diseases. g. Proportions of each hGN scRNAseq cluster’s inducible gene list that have inducible promoters by 1G H3K27ac ChIPseq. Proportions are calculated at multiple gene expression fold-change cut-off thresholds. Cluster 10 (striatal spiny projection neuron-like cells) inducible genes have the highest proportion of SFARI ASD-associated genes, regardless of gene expression fold-change cut-off threshold. h. LDSC heritability enrichments were calculated using summary statistics from two schizophrenia (SCZ) GWASs: Psychiatric Genomics Consortium (PGC), 2014 and Pardiñas, et al., 2018. The latter incorporates 40.5 K cases compared to 37 K in the 2014 PGC study and uses fewer controls (63 K vs 113 K). i. LDSC heritability enrichment calculations were repeated for all neurological disorders in parallel with Alzheimer’s Disease (AD), using the summary statistics from Lambert, J., Ibrahim-Verbaas, C., Harold, D. et al., Nature Genetics, 2013. Please note that the GWASs for the other neurological disorders included in our analyses were uniformly processed by the PGC whereas the AD GWAS was processed differently, which may contribute to the relatively large error bars associated with the AD analysis. All LDSC Plots are color coded according to Fig. 6a. Each heritability enrichment value is provided in bar plot (+/- std. error). p-values of heritability enrichment are multi-test corrected and heritability enrichment bars with p = or > 0.05 are cross-hatched. Exact p-values are provided in Supplementary Data 14.

Extended Data Fig. 8 ABC model predictions of inducible enhancer-gene associations.

a. The number of inducible genes and H3K27ac enhancer regions, as defined by this study (1G), predicted to interact by the Activity-by-Contact (ABC) model described in Fulco et al., Nat. Genetics, 2019. b. An example of one ASD-associated and neuronal activity-dependent gene locus, KCNQ3, as visualized in the IGV genome browser, with chromatin data presented in this resource (1G tracks below) and ABC model predictions of enhancer associations with the KCNQ3 promoter (arcs above). These include enhancer associations that remain constitutively predicted across all stimulation conditions (purple), those that are potentially transient but not necessarily activity-dependent (yellow), and those that are likely to be activity-dependent (red). In this case, at 15 minutes and 2 hours after neuronal depolarization, the KCNQ3 promoter is predicted to interact with an activity-dependent intronic enhancer (shaded), which increases in H3K27ac enrichment and becomes associated with the activated CREB-complex after depolarization. See Supplementary Data 13 for the full list of ABC model predicted enhancer-gene associations.

Supplementary information

Supplementary Information

Supplementary Tables 1–3.

Reporting Summary

Supplementary Data 1–5 and 7–10

Differential gene expression levels for depolarization-inducible and decreased hGN genes. Each tab describes different subsets of genes.

Supplementary Data 6

All differential gene expression output tables from Monocle analysis of hGN scRNAseq data.

Supplementary Data 11 and 12

Differential enrichment output tables from EdgeR and DESeq2 analysis of hGN H3K27ac ChIPseq data

Supplementary Data 13

Activity-by-contact (ABC) model predictions of 1G enhancer–gene interactions associated with Extended Data Fig. 8.

Supplementary Data 14

LDSC P values associated with Fig. 6 and Extended Data Fig. 7.

Source data

Source Data Fig. 1

Unprocessed western blot images.

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Boulting, G.L., Durresi, E., Ataman, B. et al. Activity-dependent regulome of human GABAergic neurons reveals new patterns of gene regulation and neurological disease heritability. Nat Neurosci 24, 437–448 (2021). https://doi.org/10.1038/s41593-020-00786-1

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