James, S. L. et al. Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. The Lancet 392, 1789–1858 (2018).


Google Scholar
 

Cui, L. et al. Major depressive disorder: hypothesis, mechanism, prevention and treatment. Signal Transduct. Target. Ther. 9, 30 (2024).

CAS 

Google Scholar
 

Nagy, C. et al. Astrocytic abnormalities and global DNA methylation patterns in depression and suicide. Mol. Psychiatry 20, 320–328 (2015).

CAS 

Google Scholar
 

Edgar, N. & Sibille, E. A putative functional role for oligodendrocytes in mood regulation. Transl. Psychiatry 2, e109 (2012).

CAS 

Google Scholar
 

Yirmiya, R., Rimmerman, N. & Reshef, R. Depression as a microglial disease. Trends Neurosci. 38, 637–658 (2015).

CAS 

Google Scholar
 

Pantazatos, S. P. et al. Whole-transcriptome brain expression and exon-usage profiling in major depression and suicide: evidence for altered glial, endothelial and ATPase activity. Mol. Psychiatry 22, 760–773 (2017).

CAS 

Google Scholar
 

Howard, D. M. et al. Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions. Nat. Neurosci. 22, 343–352 (2019).

CAS 

Google Scholar
 

Als, T. D. et al. Depression pathophysiology, risk prediction of recurrence and comorbid psychiatric disorders using genome-wide analyses. Nat. Med. 29, 1832–1844 (2023).

CAS 

Google Scholar
 

Chen, Y. et al. The interaction of early life factors and depression-associated loci affecting the age at onset of the depression. Transl. Psychiatry 12, 294 (2022).

CAS 

Google Scholar
 

Ochi, S. & Dwivedi, Y. Dissecting early life stress-induced adolescent depression through epigenomic approach. Mol. Psychiatry 28, 141–153 (2023).

CAS 

Google Scholar
 

Maurano, M. T. et al. Systematic localization of common disease-associated variation in regulatory DNA. Science 337, 1190–1195 (2012).

CAS 

Google Scholar
 

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).

CAS 

Google Scholar
 

Nagy, C. et al. Single-nucleus transcriptomics of the prefrontal cortex in major depressive disorder implicates oligodendrocyte precursor cells and excitatory neurons. Nat. Neurosci. 23, 771–781 (2020).

CAS 

Google Scholar
 

Maitra, M. et al. Cell type specific transcriptomic differences in depression show similar patterns between males and females but implicate distinct cell types and genes. Nat. Commun. 14, 2912 (2023).

CAS 

Google Scholar
 

Buenrostro, J. D. et al. Single-cell chromatin accessibility reveals principles of regulatory variation. Nature 523, 486–490 (2015).

CAS 

Google Scholar
 

Chawla, A., Nagy, C. & Turecki, G. Chromatin profiling techniques: exploring the chromatin environment and its contributions to complex traits. Int. J. Mol. Sci. 22, 7612 (2021).

CAS 

Google Scholar
 

The 1000 Genomes Project Consortium. A global reference for human genetic variation. Nature 526, 68–74 (2015).


Google Scholar
 

Granja, J. M. et al. ArchR is a scalable software package for integrative single-cell chromatin accessibility analysis. Nat. Genet. 53, 403–411 (2021).

CAS 

Google Scholar
 

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

CAS 

Google Scholar
 

Morabito, S. et al. Single-nucleus chromatin accessibility and transcriptomic characterization of Alzheimer’s disease. Nat. Genet. 53, 1143–1155 (2021).

CAS 

Google Scholar
 

Girdhar, K. et al. Cell-specific histone modification maps in the human frontal lobe link schizophrenia risk to the neuronal epigenome. Nat. Neurosci. 21, 1126–1136 (2018).

CAS 

Google Scholar
 

Gutiérrez-Sacristán, A. et al. PsyGeNET: a knowledge platform on psychiatric disorders and their genes. Bioinformatics 31, 3075–3077 (2015).


Google Scholar
 

Malik, A. N. et al. Genome-wide identification and characterization of functional neuronal activity-dependent enhancers. Nat. Neurosci. 17, 1330–1339 (2014).

CAS 

Google Scholar
 

Gosselin, D. et al. An environment-dependent transcriptional network specifies human microglia identity. Science 356, eaal3222 (2017).


Google Scholar
 

Lutz, P.-E. et al. Non-CG methylation and multiple histone profiles associate child abuse with immune and small GTPase dysregulation. Nat. Commun. 12, 1132 (2021).

CAS 

Google Scholar
 

Lutz, P.-E. et al. Association of a history of child abuse with impaired myelination in the anterior cingulate cortex: convergent epigenetic, transcriptional, and morphological evidence. Am. J. Psychiatry 174, 1185–1194 (2017).


Google Scholar
 

Klemm, S. L., Shipony, Z. & Greenleaf, W. J. Chromatin accessibility and the regulatory epigenome. Nat. Rev. Genet. 20, 207–220 (2019).

CAS 

Google Scholar
 

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).

CAS 

Google Scholar
 

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).

CAS 

Google Scholar
 

West, A. E. & Greenberg, M. E. Neuronal activity-regulated gene transcription in synapse development and cognitive function. Cold Spring Harb. Perspect. Biol. 3, a005744 (2011).


Google Scholar
 

Dennis, D. J., Han, S. & Schuurmans, C. bHLH transcription factors in neural development, disease, and reprogramming. Brain Res. 1705, 48–65 (2019).

CAS 

Google Scholar
 

Harris, H. K. et al. Disruption of RFX family transcription factors causes autism, attention-deficit/hyperactivity disorder, intellectual disability, and dysregulated behavior. Genet. Med. 23, 1028–1040 (2021).

CAS 

Google Scholar
 

Holtman, I. R., Skola, D. & Glass, C. K. Transcriptional control of microglia phenotypes in health and disease. J. Clin. Invest. 127, 3220–3229 (2017).


Google Scholar
 

Wehrspaun, C. C., Haerty, W. & Ponting, C. P. Microglia recapitulate a hematopoietic master regulator network in the aging human frontal cortex. Neurobiol. Aging 36, 2443.e9–2443.e20 (2015).

CAS 

Google Scholar
 

Kierdorf, K. & Prinz, M. Factors regulating microglia activation. Front. Cell. Neurosci. 7, 44 (2013).

CAS 

Google Scholar
 

BRAIN Initiative Cell Census Network (BICCN). A multimodal cell census and atlas of the mammalian primary motor cortex. Nature 598, 86–102 (2021).


Google Scholar
 

Peng, H. et al. Morphological diversity of single neurons in molecularly defined cell types. Nature 598, 174–181 (2021).

CAS 

Google Scholar
 

Niu, M. et al. Claustrum mediates bidirectional and reversible control of stress-induced anxiety responses. Sci. Adv. 8, eabi6375 (2022).

CAS 

Google Scholar
 

Jankovic, J., Chen, S. & Le, W. D. The role of Nurr1 in the development of dopaminergic neurons and Parkinson’s disease. Prog. Neurobiol. 77, 128–138 (2005).

CAS 

Google Scholar
 

Torretta, S. et al. NURR1 and ERR1 modulate the expression of genes of a DRD2 coexpression network enriched for schizophrenia risk. J. Neurosci. 40, 932–941 (2020).

CAS 

Google Scholar
 

Eells, J. B., Lipska, B. K., Yeung, S. K., Misler, J. A. & Nikodem, V. M. Nurr1-null heterozygous mice have reduced mesolimbic and mesocortical dopamine levels and increased stress-induced locomotor activity. Behav. Brain Res. 136, 267–275 (2002).

CAS 

Google Scholar
 

Imura, T., Kobayashi, Y., Suzutani, K., Ichikawa‐Tomikawa, N. & Chiba, H. Differential expression of a stress‐regulated gene Nr4a2 characterizes early‐ and late‐born hippocampal granule cells. Hippocampus 29, 539–549 (2019).

CAS 

Google Scholar
 

Campos-Melo, D., Galleguillos, D., Sánchez, N., Gysling, K. & Andrés, M. E. Nur transcription factors in stress and addiction. Front. Mol. Neurosci. 6, 44 (2013).

CAS 

Google Scholar
 

Helbling, J.-C., Minni, A. M., Pallet, V. & Moisan, M.-P. Stress and glucocorticoid regulation of NR4A genes in mice. J. Neurosci. Res. 92, 825–834 (2014).

CAS 

Google Scholar
 

Carpentier, R., Sacchetti, P., Ségard, P., Staels, B. & Lefebvre, P. The glucocorticoid receptor is a co-regulator of the orphan nuclear receptor Nurr1. J. Neurochem. 104, 777–789 (2008).

CAS 

Google Scholar
 

Rojas, P., Joodmardi, E., Perlmann, T. & Ögren, S. O. Rapid increase of Nurr1 mRNA expression in limbic and cortical brain structures related to coping with depression-like behavior in mice. J. Neurosci. Res. 88, 2284–2293 (2010).

CAS 

Google Scholar
 

He, Y. et al. Protective effect of Nr4a2 (Nurr1) against LPS-induced depressive-like behaviors via regulating activity of microglia and CamkII neurons in anterior cingulate cortex. Pharmacol. Res. 191, 106717 (2023).

CAS 

Google Scholar
 

Xing, G., Zhang, L., Russell, S. & Post, R. Reduction of dopamine-related transcription factors Nurr1 and NGFI-B in the prefrontal cortex in schizophrenia and bipolar disorders. Schizophr. Res. 84, 36–56 (2006).


Google Scholar
 

Gammie, S. C. Creation of a gene expression portrait of depression and its application for identifying potential treatments. Sci. Rep. 11, 3829 (2021).

CAS 

Google Scholar
 

Loupe, J. M. et al. Multiomic profiling of transcription factor binding and function in human brain. Nat. Neurosci. https://doi.org/10.1038/s41593-024-01658-8 (2024).

Van Der Poel, M. et al. Transcriptional profiling of human microglia reveals grey–white matter heterogeneity and multiple sclerosis-associated changes. Nat. Commun. 10, 1139 (2019).


Google Scholar
 

Maynard, K. R. et al. Transcriptome-scale spatial gene expression in the human dorsolateral prefrontal cortex. Nat. Neurosci. 24, 425–436 (2021).

CAS 

Google Scholar
 

Scheepstra, K. W. F. et al. Microglia transcriptional profiling in major depressive disorder shows inhibition of cortical gray matter microglia. Biol. Psychiatry https://doi.org/10.1016/j.biopsych.2023.04.020 (2023).

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


Google Scholar
 

Nguyen, T.-D. et al. Genetic heterogeneity and subtypes of major depression. Mol. Psychiatry 27, 1667–1675 (2022).

CAS 

Google Scholar
 

Shi, H., Kichaev, G. & Pasaniuc, B. Contrasting the genetic architecture of 30 complex traits from summary association data. Am. J. Hum. Genet. 99, 139–153 (2016).

CAS 

Google Scholar
 

Levey, D. F. et al. Bi-ancestral depression GWAS in the Million Veteran Program and meta-analysis in >1.2 million individuals highlight new therapeutic directions. Nat. Neurosci. 24, 954–963 (2021).

CAS 

Google Scholar
 

Lee, D. et al. A method to predict the impact of regulatory variants from DNA sequence. Nat. Genet. 47, 955–961 (2015).

CAS 

Google Scholar
 

Shrikumar, A., Prakash, E. & Kundaje, A. GkmExplain: fast and accurate interpretation of nonlinear gapped k-mer SVMs. Bioinformatics 35, i173–i182 (2019).

CAS 

Google Scholar
 

Lee, D. LS-GKM: a new gkm-SVM for large-scale datasets. Bioinformatics 32, 2196–2198 (2016).

CAS 

Google Scholar
 

Hu, B. et al. Neuronal and glial 3D chromatin architecture informs the cellular etiology of brain disorders. Nat. Commun. 12, 3968 (2021).

CAS 

Google Scholar
 

Yao, X. et al. Integrative analysis of genome-wide association studies identifies novel loci associated with neuropsychiatric disorders. Transl. Psychiatry 11, 69 (2021).

CAS 

Google Scholar
 

Martins-de-Souza, D., Guest, P. C., Vanattou-Saifoudine, N., Rahmoune, H. & Bahn, S. Phosphoproteomic differences in major depressive disorder postmortem brains indicate effects on synaptic function. Eur. Arch. Psychiatry Clin. Neurosci. 262, 657–666 (2012).


Google Scholar
 

Matt, L., Kim, K., Chowdhury, D. & Hell, J. W. Role of palmitoylation of postsynaptic proteins in promoting synaptic plasticity. Front. Mol. Neurosci. 12, 8 (2019).

CAS 

Google Scholar
 

Callejas-Marin, A. et al. Gli2-mediated Shh signaling is required for thalamocortical projection guidance. Front. Neuroanat. 16, 830758 (2022).


Google Scholar
 

Pappas, A. L. et al. Deficiency of Shank2 causes mania-like behavior that responds to mood stabilizers. JCI Insight 2, e92052 (2017).


Google Scholar
 

Hong, C. et al. Constitutive activation of LXR in macrophages regulates metabolic and inflammatory gene expression: identification of ARL7 as a direct target. J. Lipid Res. 52, 531–539 (2011).

CAS 

Google Scholar
 

van de Geijn, B., McVicker, G., Gilad, Y. & Pritchard, J. K. WASP: allele-specific software for robust molecular quantitative trait locus discovery. Nat. Methods 12, 1061–1063 (2015).


Google Scholar
 

Wilson, P. C. et al. Multimodal single cell sequencing implicates chromatin accessibility and genetic background in diabetic kidney disease progression. Nat. Commun. 13, 5253 (2022).

CAS 

Google Scholar
 

Herring, C. A. et al. Human prefrontal cortex gene regulatory dynamics from gestation to adulthood at single-cell resolution. Cell 185, 4428–4447.e28 (2022).

CAS 

Google Scholar
 

Eells, J. B., Misler, J. A. & Nikodem, V. M. Early postnatal isolation reduces dopamine levels, elevates dopamine turnover and specifically disrupts prepulse inhibition in Nurr1-null heterozygous mice. Neuroscience 140, 1117–1126 (2006).

CAS 

Google Scholar
 

Ouimet, C. C., Miller, P. E., Hemmings, H. C., Walaas, S. I. & Greengard, P. DARPP-32, a dopamine- and adenosine 3′:5′-monophosphate-regulated phosphoprotein enriched in dopamine-innervated brain regions. III. Immunocytochemical localization. J. Neurosci. 4, 111–124 (1984).

CAS 

Google Scholar
 

Santana, N., Mengod, G. & Artigas, F. Quantitative analysis of the expression of dopamine D1 and D2 receptors in pyramidal and GABAergic neurons of the rat prefrontal cortex. Cereb. Cortex 19, 849–860 (2009).


Google Scholar
 

Baik, J.-H. Stress and the dopaminergic reward system. Exp. Mol. Med. 52, 1879–1890 (2020).

CAS 

Google Scholar
 

Fang, C., Wang, H. & Naumann, R. K. Developmental patterning and neurogenetic gradients of Nurr1 positive neurons in the rat claustrum and lateral cortex. Front. Neuroanat. 15, 786329 (2021).

CAS 

Google Scholar
 

Català-Solsona, J. et al. Activity-dependent Nr4a2 induction modulates synaptic expression of AMPA receptors and plasticity via a Ca2+/CRTC1/CREB pathway. J. Neurosci. 43, 3028–3041 (2023).

Bleakman, D., Alt, A. & Witkin, J. AMPA receptors in the therapeutic management of depression. CNS Neurol. Disord. Drug Targets 6, 117–126 (2007).

CAS 

Google Scholar
 

Wang, Q., Roy, B., Turecki, G., Shelton, R. C. & Dwivedi, Y. Role of complex epigenetic switching in tumor necrosis factor-α upregulation in the prefrontal cortex of suicide subjects. Am. J. Psychiatry 175, 262–274 (2018).


Google Scholar
 

O’Connor, J. C. et al. Lipopolysaccharide-induced depressive-like behavior is mediated by indoleamine 2,3-dioxygenase activation in mice. Mol. Psychiatry 14, 511–522 (2009).


Google Scholar
 

Snijders, G. J. L. J. et al. Distinct non-inflammatory signature of microglia in post-mortem brain tissue of patients with major depressive disorder. Mol. Psychiatry 26, 3336–3349 (2021).

CAS 

Google Scholar
 

Böttcher, C. et al. Single-cell mass cytometry of microglia in major depressive disorder reveals a non-inflammatory phenotype with increased homeostatic marker expression. Transl. Psychiatry 10, 310 (2020).


Google Scholar
 

Zhang, J., Kaye, A. P., Wang, J. & Girgenti, M. J. Transcriptomics of the depressed and PTSD brain. Neurobiol. Stress 15, 100408 (2021).

CAS 

Google Scholar
 

Hannestad, J. et al. The neuroinflammation marker translocator protein is not elevated in individuals with mild-to-moderate depression: a [11C]PBR28 PET study. Brain. Behav. Immun. 33, 131–138 (2013).

CAS 

Google Scholar
 

Jaffe, A. E. et al. Decoding shared versus divergent transcriptomic signatures across cortico-amygdala circuitry in PTSD and depressive disorders. Am. J. Psychiatry 179, 673–686 (2022).


Google Scholar
 

Zhang, Y. et al. Microglia-specific transcriptional repression of interferon-regulated genes after prolonged stress in mice. Neurobiol. Stress 21, 100495 (2022).

CAS 

Google Scholar
 

Bolton, J. L. et al. Early stress-induced impaired microglial pruning of excitatory synapses on immature CRH-expressing neurons provokes aberrant adult stress responses. Cell Rep. 38, 110600 (2022).

CAS 

Google Scholar
 

Wray, N. R. et al. Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression. Nat. Genet. 50, 668–681 (2018).

CAS 

Google Scholar
 

Howard, D. M. et al. Genome-wide association study of depression phenotypes in UK Biobank identifies variants in excitatory synaptic pathways. Nat. Commun. 9, 1470 (2018).


Google Scholar
 

Sey, N. Y. A. et al. A computational tool (H-MAGMA) for improved prediction of brain-disorder risk genes by incorporating brain chromatin interaction profiles. Nat. Neurosci. 23, 583–593 (2020).

CAS 

Google Scholar
 

Li, X. et al. Transcriptome-wide association study identifies new susceptibility genes and pathways for depression. Transl. Psychiatry 11, 306 (2021).

CAS 

Google Scholar
 

Dall’Aglio, L., Lewis, C. M. & Pain, O. Delineating the genetic component of gene expression in major depression. Biol. Psychiatry 89, 627–636 (2021).


Google Scholar
 

Gandal, M. J. et al. Shared molecular neuropathology across major psychiatric disorders parallels polygenic overlap. Science 359, 693–697 (2018).

CAS 

Google Scholar
 

Dumais, A. et al. Risk factors for suicide completion in major depression: a case-control study of impulsive and aggressive behaviors in men. Am. J. Psychiatry 162, 2116–2124 (2005).

CAS 

Google Scholar
 

Maitra, M. et al. Extraction of nuclei from archived postmortem tissues for single-nucleus sequencing applications. Nat. Protoc. 16, 2788–2801 (2021).

CAS 

Google Scholar
 

Lareau, C. A., Ma, S., Duarte, F. M. & Buenrostro, J. D. Inference and effects of barcode multiplets in droplet-based single-cell assays. Nat. Commun. 11, 866 (2020).

CAS 

Google Scholar
 

Huang, Y., McCarthy, D. J. & Stegle, O. Vireo: Bayesian demultiplexing of pooled single-cell RNA-seq data without genotype reference. Genome Biol. 20, 273 (2019).


Google Scholar
 

Korsunsky, I. et al. Fast, sensitive and accurate integration of single-cell data with Harmony. Nat. Methods 16, 1289–1296 (2019).

CAS 

Google Scholar
 

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


Google Scholar
 

Hagenauer, M. H. et al. Inference of cell type content from human brain transcriptomic datasets illuminates the effects of age, manner of death, dissection, and psychiatric diagnosis. PLoS ONE 13, e0200003 (2018).


Google Scholar
 

Stuart, T., Srivastava, A., Madad, S., Lareau, C. A. & Satija, R. Single-cell chromatin state analysis with Signac. Nat. Methods 18, 1333–1341 (2021).

CAS 

Google Scholar
 

Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902.e21 (2019).

CAS 

Google Scholar
 

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).


Google Scholar
 

Law, C. W., Chen, Y., Shi, W. & Smyth, G. K. voom: precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biol. 15, R29 (2014).


Google Scholar
 

Crowell, H. L. et al. muscat detects subpopulation-specific state transitions from multi-sample multi-condition single-cell transcriptomics data. Nat. Commun. 11, 6077 (2020).

CAS 

Google Scholar
 

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).

CAS 

Google Scholar
 

Yu, G., Wang, L.-G., Han, Y. & He, Q.-Y. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS 16, 284–287 (2012).

CAS 

Google Scholar
 

Nagel, M. et al. Meta-analysis of genome-wide association studies for neuroticism in 449,484 individuals identifies novel genetic loci and pathways. Nat. Genet. 50, 920–927 (2018).

CAS 

Google Scholar
 

Yengo, L. et al. Meta-analysis of genome-wide association studies for height and body mass index in ~700000 individuals of European ancestry. Hum. Mol. Genet. 27, 3641–3649 (2018).

CAS 

Google Scholar
 

Trubetskoy, V. et al. Mapping genomic loci implicates genes and synaptic biology in schizophrenia. Nature 604, 502–508 (2022).

CAS 

Google Scholar
 

Stahl, E. A. et al. Genome-wide association study identifies 30 loci associated with bipolar disorder. Nat. Genet. 51, 793–803 (2019).

CAS 

Google Scholar
 

Demontis, D. et al. Discovery of the first genome-wide significant risk loci for attention deficit/hyperactivity disorder. Nat. Genet. 51, 63–75 (2019).

CAS 

Google Scholar
 

Watanabe, K. et al. Genome-wide meta-analysis of insomnia prioritizes genes associated with metabolic and psychiatric pathways. Nat. Genet. 54, 1125–1132 (2022).

CAS 

Google Scholar
 

Mullins, N. et al. Dissecting the shared genetic architecture of suicide attempt, psychiatric disorders, and known risk factors. Biol. Psychiatry 91, 313–327 (2022).


Google Scholar
 

Kerimov, N. et al. A compendium of uniformly processed human gene expression and splicing quantitative trait loci. Nat. Genet. 53, 1290–1299 (2021).

CAS 

Google Scholar
 

Emani, P. S. et al. Single-cell genomics and regulatory networks for 388 human brains. Science 384, eadi5199 (2024).

CAS 

Google Scholar
 

Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).


Google Scholar
 

Chawla, A., Cakmakci, D., Denniston, R. & MGSSdouglas. MGSSdouglas/snATAC_MDD: snATAC_MDD v0.2. Zenodo https://zenodo.org/records/15320132 (2025).