Abstract
Single-cell spatial transcriptomes have demonstrated molecular and cellular diversity in the brain, but gene regulatory mechanisms underlying transcriptomic profiles and disease pathogenesis remain largely unknown in primates. Here we performed single-nucleus Assay for Transposase-Accessible Chromatin followed by sequencing (snATAC-seq) for ~1.6 million cells from 142 cortical regions of two male cynomolgus monkeys (Macaca fascicularis), and identified distinct chromatin accessibility profiles of cis-regulatory elements (CREs) for various cell types. By integrative analysis with large-scale spatial transcriptome data, we found that these CREs showed laminar and regional preferences, with their regional accessibility exhibiting striking dependence on the region’s hierarchical level. Cross-species comparison of snATAC-seq data revealed human/macaque-enriched layer-4 glutamatergic neurons and LAMP5/LHX6-expressing GABAergic neurons as well as human/macaque-biased CREs for genes related to neurodevelopment and psychiatric diseases. Importantly, risk single-nucleotide polymorphisms for many brain disorders strongly associated with human/macaque-biased CREs in glutamatergic neuronal types and those for Alzheimer’s disease strongly associated with CREs exclusively in microglia. Our results provided the basis for understanding the spatial gene regulatory mechanisms underlying cellular diversity and disease pathogenesis in the primate cortex.
Similar content being viewed by others
Data availability
For snATAC-seq data in this paper, all raw data are available in CNGB Nucleotide Sequence Archive under accession code CNP0005530. The processed data ready for exploration could be accessed and downloaded via https://macaque.digital-brain.cn/chromatin-accessibility. The stereo-seq data are from a previous study and available via https://macaque.digital-brain.cn/spatial-omics/singleCellData?project=neocortex. The public snATAC-seq data of human and mouse are accessible via https://catlas.org/catlas/. Source data are provided with this paper.
Code availability
All data were analyzed with standard programs and packages, as detailed above, and the corresponding codes were accessible through https://github.com/JuneMeng/snATAC_MacaqueCTX_Atlas and at the Zenodo (https://doi.org/10.5281/zenodo.18431761)100. Custom code of cell-cell pairing of snATAC-seq and spatial transcriptome data is available at https://github.com/sunyk740/RMGE/ and at the Zenodo (https://doi.org/10.5281/zenodo.18432489)101. Additional information required to reanalyze the data reported in this paper is available from the lead contact upon request.
References
Lein, E., Borm, L. E. & Linnarsson, S. The promise of spatial transcriptomics for neuroscience in the era of molecular cell typing. Science 358, 64–69 (2017).
Zhu, Y. et al. Spatiotemporal transcriptomic divergence across human and macaque brain development. Science 362, (2018).
Khrameeva, E. et al. Single-cell-resolution transcriptome map of human, chimpanzee, bonobo, and macaque brains. Genome Res 30, 776–789 (2020).
Krienen, F. M. et al. Innovations present in the primate interneuron repertoire. Nature 586, 262–269 (2020).
Berg, J. et al. Human neocortical expansion involves glutamatergic neuron diversification. Nature 598, 151–158 (2021).
Bakken, T. E. et al. Comparative cellular analysis of motor cortex in human, marmoset and mouse. Nature 598, 111–119 (2021).
(BICCN) BICCN. A multimodal cell census and atlas of the mammalian primary motor cortex. Nature 598, 86-102 (2021).
Yao, Z. et al. A transcriptomic and epigenomic cell atlas of the mouse primary motor cortex. Nature 598, 103–110 (2021).
Preissl, S. et al. Single-nucleus analysis of accessible chromatin in developing mouse forebrain reveals cell-type-specific transcriptional regulation. Nat. Neurosci. 21, 432–439 (2018).
Cusanovich, D. A. et al. A Single-Cell Atlas of In Vivo Mammalian Chromatin Accessibility. Cell 174, 1309–1324 e1318 (2018).
Zu, S. et al. Single-cell analysis of chromatin accessibility in the adult mouse brain. Nature 624, 378–389 (2023).
Li, Y. E. et al. An atlas of gene regulatory elements in adult mouse cerebrum. Nature 598, 129–136 (2021).
Villar, D. et al. Enhancer evolution across 20 mammalian species. Cell 160, 554–566 (2015).
Won, H. et al. Chromosome conformation elucidates regulatory relationships in developing human brain. Nature 538, 523–527 (2016).
Li, Y. E. et al. A comparative atlas of single-cell chromatin accessibility in the human brain. Science 382, eadf7044 (2023).
Zemke, N. R. et al. Conserved and divergent gene regulatory programs of the mammalian neocortex. Nature 624, 390–402 (2023).
Yin, S. et al. Transcriptomic and open chromatin atlas of high-resolution anatomical regions in the rhesus macaque brain. Nat. Commun. 11, 474 (2020).
Chiou, K. L. et al. A single-cell multi-omic atlas spanning the adult rhesus macaque brain. Sci. Adv. 9, eadh1914 (2023).
Lei, Y. et al. Spatially resolved gene regulatory and disease-related vulnerability map of the adult Macaque cortex. Nat. Commun. 13, 6747 (2022).
Han, L. et al. Cell transcriptomic atlas of the non-human primate Macaca fascicularis. Nature 604, 723–731 (2022).
Chen, A. et al. Single-cell spatial transcriptome reveals cell-type organization in the macaque cortex. Cell 186, 3726–3743.e3724 (2023).
Hodge, R. D. et al. Conserved cell types with divergent features in human versus mouse cortex. Nature 573, 61–68 (2019).
Yao, Z. et al. A taxonomy of transcriptomic cell types across the isocortex and hippocampal formation. Cell 184, 3222–3241.e3226 (2021).
Zhang, Y. et al. Model-based analysis of ChIP-Seq (MACS). Genome Biol. 9, R137 (2008).
Su, Y. et al. Neuronal activity modifies the chromatin accessibility landscape in the adult brain. Nat. Neurosci. 20, 476–483 (2017).
Pliner, H. A. et al. Cicero Predicts cis-Regulatory DNA Interactions from Single-Cell Chromatin Accessibility Data. Mol. Cell 71, 858–871.e858 (2018).
Reiprich, S. et al. Transcription factor Sox10 regulates oligodendroglial Sox9 levels via microRNAs. Glia 65, 1089–1102 (2017).
Kamimoto, K. et al. Dissecting cell identity via network inference and in silico gene perturbation. Nature 614, 742–751 (2023).
Basu, S. et al. The Mef2c Gene Dose-Dependently Controls Hippocampal Neurogenesis and the Expression of Autism-Like Behaviors. J Neurosci 44, (2024).
Pandya, D., Seltzer, B., Petrides, M. & Cipolloni, P. B. Cerebral Cortex: Architecture, Connections, and the Dual Origin Concept. (Oxford University Press, 2015).
Shibata, M. et al. Hominini-specific regulation of CBLN2 increases prefrontal spinogenesis. Nature 598, 489–494 (2021).
Crow, M., Paul, A., Ballouz, S., Huang, Z. J. & Gillis, J. Characterizing the replicability of cell types defined by single cell RNA-sequencing data using MetaNeighbor. Nat. Commun. 9, 884 (2018).
Manitt, C. et al. dcc orchestrates the development of the prefrontal cortex during adolescence and is altered in psychiatric patients. Transl. Psychiatry 3, e338 (2013).
Gabitto, M. I. et al. Integrated multimodal cell atlas of Alzheimer’s disease. Nat. Neurosci. 27, 2366–2383 (2024).
Seo, M. H., Lim, S. & Yeo, S. Association of decreased triadin expression level with apoptosis of dopaminergic cells in Parkinson’s disease mouse model. BMC Neurosci. 22, 65 (2021).
Chuong, E. B., Elde, N. C. & Feschotte, C. Regulatory activities of transposable elements: from conflicts to benefits. Nat. Rev. Genet 18, 71–86 (2017).
Tasic, B. et al. Shared and distinct transcriptomic cell types across neocortical areas. Nature 563, 72–78 (2018).
Gouwens, N. W. et al. Integrated Morphoelectric and Transcriptomic Classification of Cortical GABAergic Cells. Cell 183, 935–953.e919 (2020).
Wei, J. R. et al. Identification of visual cortex cell types and species differences using single-cell RNA sequencing. Nat. Commun. 13, 6902 (2022).
Chen, W. G. et al. Upstream stimulatory factors are mediators of Ca2+-responsive transcription in neurons. J. Neurosci. 23, 2572–2581 (2003).
Flavell, S. W. et al. Genome-wide analysis of MEF2 transcriptional program reveals synaptic target genes and neuronal activity-dependent polyadenylation site selection. Neuron 60, 1022–1038 (2008).
Micali, N. et al. Molecular programs of regional specification and neural stem cell fate progression in macaque telencephalon. Science 382, eadf3786 (2023).
Jahangir, M., Shah, S. M., Zhou, J. S., Lang, B. & Wang, X. P. Parvalbumin interneurons in the anterior cingulate cortex exhibit distinct processing patterns for fear and memory in rats. Heliyon 11, e41218 (2025).
Qi, C. et al. Anterior cingulate cortex parvalbumin and somatostatin interneurons shape social behavior in male mice. Nat. Commun. 16, 4156 (2025).
Fang, R. et al. Conservation and divergence of cortical cell organization in human and mouse revealed by MERFISH. Science 377, 56–62 (2022).
Shi, Y. et al. Mouse and human share conserved transcriptional programs for interneuron development. Science 374, eabj6641 (2021).
Urban-Ciecko, J. & Barth, A. L. Somatostatin-expressing neurons in cortical networks. Nat. Rev. Neurosci. 17, 401–409 (2016).
Berto, S. et al. Accelerated evolution of oligodendrocytes in the human brain. Proc. Natl. Acad. Sci. USA 116, 24334–24342 (2019).
Miller, D. J. et al. Prolonged myelination in human neocortical evolution. Proc. Natl. Acad. Sci. USA 109, 16480–16485 (2012).
Caglayan, E. et al. Molecular features driving cellular complexity of human brain evolution. Nature 620, 145–153 (2023).
Felleman, D.J. & Van Essen, D.C. Distributed hierarchical processing in the primate cerebral cortex. Cerebral cortex (New York, NY: 1991) 1, 1–47 (1991).
Ghorbani, S. et al. Fibulin-2 is an extracellular matrix inhibitor of oligodendrocytes relevant to multiple sclerosis. J Clin Invest 134, (2024).
Li, Y. et al. Npas3 deficiency impairs cortical astrogenesis and induces autistic-like behaviors. Cell Rep. 40, 111289 (2022).
Cummings, A. C. et al. Genome-wide association and linkage study in the Amish detects a novel candidate late-onset Alzheimer disease gene. Ann. Hum. Genet 76, 342–351 (2012).
Schizophrenia Working Group of the Psychiatric Genomics, C. Biological insights from 108 schizophrenia-associated genetic loci. Nature 511, 421–427 (2014).
Loo, S. K. et al. Genome-wide association study of intelligence: additive effects of novel brain expressed genes. J. Am. Acad. Child Adolesc. Psychiatry 51, 432–440.e432 (2012).
Seshadri, S. et al. Genetic correlates of brain aging on MRI and cognitive test measures: a genome-wide association and linkage analysis in the Framingham Study. BMC Med Genet 8, S15 (2007).
Zhang, G. et al. Construction of single-cell cross-species chromatin accessibility landscapes with combinatorial-hybridization-based ATAC-seq. Dev. Cell 59, 793–811.e798 (2024).
Pollard, K. S., Hubisz, M. J., Rosenbloom, K. R. & Siepel, A. Detection of nonneutral substitution rates on mammalian phylogenies. Genome Res 20, 110–121 (2010).
Bipolar, D. Schizophrenia Working Group of the Psychiatric Genomics Consortium. Electronic address drve, Bipolar D, Schizophrenia Working Group of the Psychiatric Genomics C. Genomic Dissection of Bipolar Disorder and Schizophrenia, Including 28 Subphenotypes. Cell 173, e1716 (2018).
Merenlender-Wagner, A. et al. Autophagy has a key role in the pathophysiology of schizophrenia. Mol. Psychiatry 20, 126–132 (2015).
Liu, X., Bipolar Genome Study (BiGS), Kelsoe, J. R. & Greenwood, T. A. A genome-wide association study of bipolar disorder with comorbid eating disorder replicates the SOX2-OT region. J. Affect Disord. 189, 141–149 (2016).
Kahler, A. K. et al. Association analysis of schizophrenia on 18 genes involved in neuronal migration: MDGA1 as a new susceptibility gene. Am. J. Med Genet B Neuropsychiatr. Genet 147B, 1089–1100 (2008).
Banfi, F. et al. SETBP1 accumulation induces P53 inhibition and genotoxic stress in neural progenitors underlying neurodegeneration in Schinzel-Giedion syndrome. Nat. Commun. 12, 4050 (2021).
Yamazaki, K. et al. ABCA7 Gene Expression and Genetic Association Study in Schizophrenia. Neuropsychiatr. Dis. Treat. 16, 441–446 (2020).
Gotoh, L. et al. Association analysis of adenosine A1 receptor gene (ADORA1) polymorphisms with schizophrenia in a Japanese population. Psychiatr. Genet 19, 328–335 (2009).
Takahashi, N. et al. Association between chromogranin A gene polymorphism and schizophrenia in the Japanese population. Schizophr. Res 83, 179–183 (2006).
Pozhidaev, I. V. et al. Association of cholinergic muscarinic M4 receptor gene polymorphism with schizophrenia. Appl Clin. Genet 13, 97–105 (2020).
Li, M. et al. Novel genetic susceptibility loci identified by family based whole exome sequencing in Han Chinese schizophrenia patients. Transl. Psychiatry 10, 5 (2020).
Zhang, Z. et al. The Schizophrenia Susceptibility Gene OPCML Regulates Spine Maturation and Cognitive Behaviors through Eph-Cofilin Signaling. Cell Rep. 29, 49–61.e47 (2019).
Ventriglia, M. et al. Allelic variation in the human prodynorphin gene promoter and schizophrenia. Neuropsychobiology 46, 17–21 (2002).
Juraeva, D. et al. Integrated pathway-based approach identifies association between genomic regions at CTCF and CACNB2 and schizophrenia. PLoS Genet 10, e1004345 (2014).
Tiihonen, J. et al. Molecular signaling pathways underlying schizophrenia. Schizophr. Res 232, 33–41 (2021).
Le Hellard, S. et al. Polymorphisms in SREBF1 and SREBF2, two antipsychotic-activated transcription factors controlling cellular lipogenesis, are associated with schizophrenia in German and Scandinavian samples. Mol. Psychiatry 15, 463–472 (2010).
Aberg, K. A. et al. Methylome-wide association study of schizophrenia: identifying blood biomarker signatures of environmental insults. JAMA Psychiatry 71, 255–264 (2014).
Souza, R. P. et al. Genetic association of the GDNF alpha-receptor genes with schizophrenia and clozapine response. J. Psychiatr. Res 44, 700–706 (2010).
Pinacho, R. et al. Altered CSNK1E, FABP4 and NEFH protein levels in the dorsolateral prefrontal cortex in schizophrenia. Schizophr. Res 177, 88–97 (2016).
Shim, K. H. et al. Subsequent correlated changes in complement component 3 and amyloid beta oligomers in the blood of patients with Alzheimer’s disease. Alzheimers Dement 20, 2731–2741 (2024).
Colonna, M. & Wang, Y. TREM2 variants: new keys to decipher Alzheimer disease pathogenesis. Nat. Rev. Neurosci. 17, 201–207 (2016).
Wang, Y. et al. Key variants via the Alzheimer’s Disease Sequencing Project whole genome sequence data. Alzheimers Dement 20, 3290–3304 (2024).
Joshi, H. et al. Decreased Expression of Cerebral Dopamine Neurotrophic Factor in Platelets of Probable Alzheimer Patients. Alzheimer Dis. Assoc. Disord. 36, 269–271 (2022).
Corraliza-Gomez, M. et al. Insulin-degrading enzyme (IDE) as a modulator of microglial phenotypes in the context of Alzheimer’s disease and brain aging. J. Neuroinflammation 20, 233 (2023).
Chang, J. Y. & Chang, N. S. WWOX dysfunction induces sequential aggregation of TRAPPC6ADelta, TIAF1, tau and amyloid beta, and causes apoptosis. Cell Death Discov. 1, 15003 (2015).
Chiba-Falek, O., Gottschalk, W. K. & Lutz, M. W. The effects of the TOMM40 poly-T alleles on Alzheimer’s disease phenotypes. Alzheimers Dement 14, 692–698 (2018).
Jackson, R. J., Hyman, B. T. & Serrano-Pozo, A. Multifaceted roles of APOE in Alzheimer disease. Nat. Rev. Neurol. 20, 457–474 (2024).
Reynolds, L. M. et al. DCC Receptors Drive Prefrontal Cortex Maturation by Determining Dopamine Axon Targeting in Adolescence. Biol. Psychiatry 83, 181–192 (2018).
Li, H. J. et al. Further confirmation of netrin 1 receptor (DCC) as a depression risk gene via integrations of multi-omics data. Transl. Psychiatry 10, 98 (2020).
Vosberg, D. E., Leyton, M. & Flores, C. The Netrin-1/DCC guidance system: dopamine pathway maturation and psychiatric disorders emerging in adolescence. Mol. Psychiatry 25, 297–307 (2020).
Tricoire, L. et al. Common origins of hippocampal Ivy and nitric oxide synthase expressing neurogliaform cells. J. Neurosci. 30, 2165–2176 (2010).
Kosoy, R. et al. Genetics of the human microglia regulome refines Alzheimer’s disease risk loci. Nat. Genet 54, 1145–1154 (2022).
Mathys, H. et al. Single-cell atlas reveals correlates of high cognitive function, dementia, and resilience to Alzheimer’s disease pathology. Cell 186, 4365–4385.e4327 (2023).
Bakken, T. E. et al. Single-nucleus and single-cell transcriptomes compared in matched cortical cell types. PloS one 13, e0209648 (2018).
Granja, J. M. et al. ArchR is a scalable software package for integrative single-cell chromatin accessibility analysis. Nat. Genet. 53, 403–411 (2021).
Zeisel, A. et al. Molecular Architecture of the Mouse Nervous System. Cell 174, 999–1014.e1022 (2018).
Yu, G., Wang, L. G. & He, Q. Y. ChIPseeker: an R/Bioconductor package for ChIP peak annotation, comparison and visualization. Bioinformatics 31, 2382–2383 (2015).
van Heeringen, S. J. & Veenstra, G. J. GimmeMotifs: a de novo motif prediction pipeline for ChIP-sequencing experiments. Bioinformatics 27, 270–271 (2011).
Schep, A. N., Wu, B., Buenrostro, J. D. & Greenleaf, W. J. chromVAR: inferring transcription-factor-associated accessibility from single-cell epigenomic data. Nat. Methods 14, 975–978 (2017).
McLean, C. Y. et al. GREAT improves functional interpretation of cis-regulatory regions. Nat. Biotechnol. 28, 495–501 (2010).
Gu, Z. & Hübschmann, D. rGREAT: an R/bioconductor package for functional enrichment on genomic regions. Bioinformatics 39, (2023).
JuneMeng & chenc. JuneMeng/snATAC_MacaqueCTX_Atlas: Single-cell spatial map of cis-regulatory elements for disease-related genes in the macaque cortex (v1.0). Zenodo. https://doi.org/10.5281/zenodo.18431762 (2026).
sunyk740. sunyk740/RMGE: RMGE V1.0(v1.0.2). Zenodo. https://doi.org/10.5281/zenodo.18432489 (2026).
Acknowledgements
The project was supported by National Key Research and Development Program of China (No. 2024YFC3408000), National Science and Technology Innovation 2030 Major Program (STI2030-2021ZD0200101 and 2021ZD0200103), National Natural Science Foundation of China (NSFC) Outstanding Youth Foundation (No. T2422026), National Key R&D Program of China No.2022YEF0203200, National Natural Science Foundation of China (No. U23A6010), Lingang Laboratory (No. LGL-6672-07) and Shanghai Science and Technology Development Funds (23QA1410400) to Y-D.S.
Author information
Authors and Affiliations
Contributions
Methodology, J.M., C.C., Z.Z., Y.S., K.H. and T.F.; Investigation, J.M., Y.L., C.C., Z.Z., Y.B.; Formal Analysis, J.M., C.C., Z.Z., Y.S. and Y.H.; Validation, J.F., L.W., J.M. and Li.L.; Writing – Original Draft, Y.L. and Y-D.S.; Writing – Review & Editing, J.M., T.F., Z.L., Ch.L., Z.S., L-Q.L., C-Y.L., T.S., C-R.L., and M.P.; Conceptualization and supervision, S.L., Y.L. and Y-D.S.
Corresponding authors
Ethics declarations
Competing interests
The authors declare no competing interests.
Peer review
Peer review information
Nature Communications thanks Yang Yu and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. A peer review file is available.
Additional information
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary information
Source data
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
About this article
Cite this article
Meng, J., Chen, C., Zhu, Z. et al. Single-cell spatial map of cis-regulatory elements for disease-related genes in the macaque cortex. Nat Commun (2026). https://doi.org/10.1038/s41467-026-70497-x
Received:
Accepted:
Published:
DOI: https://doi.org/10.1038/s41467-026-70497-x
