The emergence of multiomic single-cell Hi-C methods, which simultaneously profile chromatin conformation and other modalities such as gene expression or DNA methylation, creates tremendous opportunities for studying the genome's structure-function relationships. Existing tools for processing multiomic single-cell Hi-C datasets have certain limitations for downstream bioinformatics analysis. We present map3C, a software tool designed to address these limitations. We demonstrate that map3C improves the quality of multiomic single-cell Hi-C data for analysis and its utility for identifying structural variant locations in the genome.

Higher-order chromatin structure and DNA methylation are critical for gene regulation, but how these vary across the human body remains unclear. We performed multiomic profiling of three-dimensional (3D) genome structure and DNA methylation for 86,689 single nuclei across 16 tissues, identifying 35 major and 206 cell subtypes. We revealed extensive changes in CG and non-CG methylation across cell types and characterized 3D chromatin structure at an unprecedented cellular resolution. Extensive discrepancies exist between cell types delineated by DNA methylation and genome structure, which indicates that the role of distinct epigenomic features in maintaining cell identity may vary by lineage. This study expands our understanding of the diversity of DNA methylation and chromatin structure and offers a reference for exploring gene regulation in human health and disease.

Noncoding genetic variation contributes to brain disorder risk, but the mechanisms through which it acts in specific brain cell types remain unclear. DNA methylation (DNAm), a highly cell type-specific regulatory layer in the brain, may mediate noncoding genetic risk, yet whether methylation at CG (mCG) and neuron-enriched non-CG (mCH) dinucleotides contribute differently to that risk remains unknown. Here we develop a deep learning framework that predicts DNAm from DNA sequence and estimates variant effects across 186 brain cell subtypes in both mCG and mCH, leveraging single-nucleus DNAm profiles from 46 brain regions. The models reveal distinct transcription factor (TF) programs underlying the two methylation contexts, with mCH-associated TFs showing stronger evolutionary constraint. Predicted variant effects agree closely with cell type-matched mQTLs in both direction and magnitude. Common variants predicted to affect mCG, particularly in excitatory neurons, show substantially greater heritability enrichment for brain-related traits than variants affecting mCH. By contrast, noncoding de novo mutations in autism preferentially perturb mCH, but not mCG, at conserved neuronal regulatory regions. This pattern is replicated across two independent cohorts totaling 5,782 probands and 4,053 unaffected siblings. Together, these findings indicate that common and rare noncoding variants contribute to brain disorders through distinct DNA methylation mechanisms.

Autism spectrum disorder (ASD) is a heterogeneous neurodevelopmental condition. Studies of postmortem ASD brain tissue have revealed convergent molecular changes across the cortex. Whether these features are reflected in cell-type-specific epigenetic signatures is unknown. Here, we present a single-cell analysis of DNA methylation (DNAm) coupled with transcriptomics in ASD. Using snmCT-seq, we profiled DNAm and transcript levels from over 60,000 nuclei derived from the prefrontal cortex of 49 donors. We identified over 30,000 differentially methylated regions (DMRs) in ASD that were enriched in promoters and cell-type-specific regulatory elements active across the lifespan. ASD-related methylation changes were uncorrelated with transcript levels and were small in magnitude compared with age-associated effects. Age-DMRs were concentrated in excitatory neurons and revealed distinct roles for CG and non-CG methylation. Age-varying methylation signatures of ASD identified neuron projection development as a key process perturbed in ASD, highlighting the heterogeneous impact of ASD across the lifespan.

The human basal ganglia (BG), subcortical nuclei fundamental to motor regulation and cognitive modulation, is constructed from neurons produced during gestation in the adjacent ganglionic eminences (GEs). GEs are transient structures in the ventral prenatal brain that also generate GABAergic inhibitory neurons which migrate to destinations in the BG, cortex and other destinations. This study aims to elucidate the epigenomic and 3D-genomic dynamics involved in the specification and maturation of GEs and GE-derived neurons, using single-nucleus methyl-3C sequencing (snm3C-seq), highly-multiplexed spatial transcriptomics, and chromatin+RNA single-molecule imaging. Our multi-modal data support a heterogeneous temporal progression across GE subregions, with the lateral GE (LGE) showing declining neurogenic activity in mid-gestation and caudal GE (CGE) exhibiting ongoing developmental progression through infancy. We identified regulatory programs that specify subtypes of BG principal cells, medium spiny neurons (MSN), via synchronized maturation of the 3D-epigenome. In infant brains, we found a transient short-range enriched (SE) chromatin conformation during the transition between oligodendrocyte progenitors (OPCs) and oligodendrocytes (ODCs), and a temporary shift toward Long-range Enriched (LE) chromatin conformation in projection neurons, extending previous works showing the differentiation of neurons and glial cells is associated with permanent SE and LE conformation, respectively. Lastly, we found that gene regulatory regions active in MSNs were enriched in loci associated with genetic risk for neuropsychiatric disease. Our study delineates the highly complex, lineage-specific 3D genomic dynamics in ventral progenitors and basal ganglia populations of the perinatal human brain.

The large diversity of neuronal and glial cell types in the human brain is underpinned by foundational cell populations known as neural progenitor cells (NPCs). The dentate gyrus (DG) of the hippocampus, a key structure in learning and memory, maintains a tightly organized NPC population into adulthood across many mammalian species. However, the emergence, organization and persistence of NPCs in the human hippocampus remain poorly characterized. Reports of NPCs in the juvenile, adult, and aged periods have been variable, reflecting differences in identification criteria and highlighting the need for a unified framework across development. In this study, we provide a spatial and molecular map of the developmental trajectory of NPCs in the human DG, combining multimodal transcriptomic analysis within a neuroanatomical context. At mid-gestation, we observed changes in the structural and cellular arrangement of the hippocampus, coinciding with the emergence of a multicellular NPC layer within the DG, herein named the granular-hilar progenitor zone (GHPZ). Neurogenic transcriptomic signatures in the GHPZ were diminished by early infancy, coinciding with a reduction in NPC number as they progressed toward an astrocytic program. At childhood, the GHPZ dissolved with only sparse radial NPCs remaining in the DG. Lastly, we validated WNT signaling pathway-associated genes as NPC identity markers in the developing human DG, observing a decline in their expression after infancy. Our study defines the steep decline of NPCs from gestation to the postnatal period, identifies their progression to an astrocytic nature, and sets the molecular blueprint for NPC identification in the human DG. Highlights:Multimodal mapping of neural progenitor cells from gestational to postnatal stages in the human hippocampusFormation of the granular-hilar progenitor zone within the dentate gyrus at mid-gestationNeurogenic potential declines sharply from the prenatal period to childhood, with radial glia cells progressively acquiring astrocytic featuresDevelopmental modulation of the WNT signaling pathway accompanies radial glia cell transitions.

Obesity impairs subcutaneous adipose tissue function, which predisposes to chronic cardiometabolic comorbidities and accelerated biological aging. However, regulatory variants, their target genes and epigenomic landscape underlying this predisposition in each subcutaneous adipose tissue cell-type remain elusive. Our subcutaneous adipose tissue cell-type level cis-expression quantitative trait and colocalization analyses reveal cis-expression quantitative trait locus variants, regulating 279 genes for 33 cardiometabolic disease and aging traits. Most of these genes are cell-type-specific (90%), led by adipocytes (55%), and missed in previous bulk tissue colocalization studies. Conducting subcutaneous adipose tissue cell-type level epigenome analysis, we discover that the vast majority (81%) of these colocalized cardiometabolic disease and aging risk variants map to the active chromatin compartments that comprise only 45% of the human genome, revealing three-dimensional epigenome in the center of cardiometabolic disease and aging risk. These findings uncover genetic and epigenomic regulation of genes underlying 33 cardiometabolic disease and aging traits in subcutaneous adipose tissue cell-types and offer critical insights into the principal role of three-dimensional chromatin in disease risk.

Single cell technologies have advanced at a rapid pace, providing assays for various molecular phenotypes. Droplet-based single cell technologies, particularly those based on nuclei isolation, such as simultaneous RNA+ATAC single-cell multiome, are susceptible to exogenous ambient molecule contamination, which can increase noise in cell type-level associations. We reasoned that genotype-based sample multiplexing can provide an opportunity to infer this ambient contamination by leveraging DNA variation in sequenced reads. Thus, we developed ambimux, a likelihood-based method to estimate ambient fractions and demultiplex single-cell multiome experiments using genotype-level data. Ambimux models the ambient or nuclear probability at the read level and thus can classify empty droplets and estimate droplet-specific ambient molecule fractions in each modality. We first evaluated our method using simulated data sets across a range of parameters. We found that ambimux closely estimated the ground truth droplet contamination fractions in the RNA (MAE=0.048) and ATAC (MAE=0.042) modalities. As a result, ambimux maintained high specificity (>95%) and was able to correctly assign singlets at considerably high ambient fractions (up to 60%) for both RNA and ATAC modalities. In comparison with models that do not consider ambient contamination, these only maintained similar sensitivity levels at considerably lower ambient fractions (up to 25%). We then generated a real data set of seven visceral adipose tissue biopsies run on a single 10x Multiome channel. We ran ambimux and detected 4,986 singlets, capturing similar numbers as other methods. Then, we sought to evaluate the fidelity of the ambient fraction estimates from ambimux. We split singlets into ambient-enriched (>5% contamination in both modalities) or nuclear-enriched (<5% in both) droplets and performed gene-peak linkage analysis. Low ambient droplets resulted in more significant hits with gene-peak links enriched at the transcription start site relative to high ambient droplets, suggesting that the ambient droplets identified by ambimux hamper the identification of biologically meaningful signals. In summary, we developed a joint single-cell multiome demultiplexing method, ambimux, that accurately models and estimates ambient molecule contamination in each modality.

The cell-type-level epigenomic landscape of human subcutaneous adipose tissue (SAT) is not well characterized. Here, we elucidate the epigenomic landscape across SAT cell types using snm3C-seq. We find that SAT CG methylation (mCG) displays pronounced hypermethylation in myeloid cells and hypomethylation in adipocytes and adipose stem and progenitor cells, driving nearly half of the 705,063 differentially methylated regions (DMRs). Moreover, TET1 and DNMT3A are identified as plausible regulators of the cell-type-level mCG profiles. Both global mCG profiles and chromosomal compartmentalization reflect SAT cell-type lineage. Notably, adipocytes display more short-range chromosomal interactions, forming complex local 3D genomic structures that regulate transcriptional functions, including adipogenesis. Furthermore, adipocyte DMRs and A compartments are enriched for abdominal obesity genome-wide association study (GWAS) variants and polygenic risk, while myeloid A compartments are enriched for inflammation. Together, we characterize the SAT single-cell-level epigenomic landscape and link GWAS variants and partitioned polygenic risk of abdominal obesity and inflammation to the SAT epigenome.

Arabidopsis MBD5, MBD6, and MBD7 are CG-specific methyl-readers with opposite functions: MBD5 and MBD6 (MBD5/6) repress methylated loci in pollen vegetative nuclei (VN), while MBD7 prevents transgene silencing, possibly by promoting DNA demethylation. Here we show that loss of MBD7 rescues transcriptional defects at a large subset of MBD5/6-bound loci. Using simultaneous profiling of DNA methylation and transcription in single pollen nuclei, we found that MBD5/6-bound loci that are actively demethylated in immature VN lose additional methylation in mbd5/6, prior to transcriptional derepression. A subset of these loci is also bound by MBD7, correlating with demethylation and transcriptional derepression in mbd5/6 that are both reversed by loss of MBD7. Conversely, ectopically recruiting the MBD7 complex to MBD5/6 targets causes partial demethylation and upregulation. We propose that MBD5/6 maintain silencing in VN in part by preventing the MBD7 complex from enhancing the active demethylation that occurs during VN maturation.

Sample multiplexing has become an increasingly common design choice in droplet-based single-nucleus multi-omic sequencing experiments to reduce costs and remove technical variation. Genotype-based demultiplexing is one popular class of methods that was originally developed for single-cell RNA-seq, but has not been rigorously benchmarked in other assays, such as snATAC-seq and joint snRNA/snATAC assays, especially in the context of variable ambient RNA/DNA contamination. To address this, we develop ambisim, a genotype-aware read-level simulator that can flexibly control ambient molecule proportions and generate realistic joint snRNA/snATAC data. We use ambisim to evaluate demultiplexing methods across several important parameters: doublet rate, number of multiplexed donors, and coverage levels. Our simulations reveal that methods are variably impacted by ambient contamination in both modalities. We then applied the demultiplexing methods to two joint snRNA/snATAC datasets and found highly variable concordance between methods in both modalities. Finally, we develop a new metric, variant consistency, which we show is correlated with cell-level ambient molecule fractions in singlets. Applying our metric to two multiplexed joint snRNA/snATAC datasets reveals variable ambient contamination across experiments and modalities. We conclude that improved modelling of ambient material in demultiplexing algorithms will increase both sensitivity and specificity.

Autism spectrum disorder (ASD) is a common, genetically and clinically heterogeneous neurodevelopmental condition. Despite this diversity, studies of postmortem brain tissue have revealed convergent molecular changes across the cortex, including reduced synaptic function in subsets of excitatory and inhibitory neurons and increased glial reactivity. Whether these features are reflected in cell type-specific epigenetic signatures remains unknown. Here, we present the first single-cell analysis of DNA methylation (DNAm) coupled with transcriptomics in ASD. Using snmCT- seq, we profiled DNAm and gene expression from over 60,000 nuclei across 49 donors. We identified thousands of differentially methylated regions (DMRs) in ASD, enriched in promoters and regulatory elements active during both prenatal development and in adult cortex. ASD-related methylation changes were spatially localized but uncorrelated with gene expression, and were small in magnitude compared to robust age-associated effects. Age-DMRs were concentrated in excitatory neurons, enriched in known cognitive aging pathways, and revealed distinct roles for CG and non- CG methylation in the aging brain. Finally, we explored age-by-diagnosis interactions, identifying a reduction in inhibitory neuron abundance with age in ASD relative to controls, highlighting this area as a promising direction for future research. Highlights:We generate a single cell multi-omic dataset, jointly profiling DNA methylation and gene expression in autistic and neurotypical donorsWe identify thousands of cell type informed differentially methylated regions (DMRs) in ASD, particularly in excitatory neurons from superficial cortical lamina and microgliaASD-DMRs are enriched in promoters and known regulatory regions, but not strongly tied to gene expressionAge effects on DNA methylation are profound, cell type specific, and concentrated in excitatory neurons.

Higher-order chromatin structure and DNA methylation are critical for gene regulation, but how these vary across the human body remains unclear. We performed multi-omic profiling of 3D genome structure and DNA methylation for 86,689 single nuclei across 16 human tissues, identifying 35 major and 206 cell subtypes. We revealed extensive changes in CG and non-CG methylation across almost all cell types and characterized 3D chromatin structure at an unprecedented cellular resolution. Intriguingly, extensive discrepancies exist between cell types delineated by DNA methylation and genome structure, indicating that the role of distinct epigenomic features in maintaining cell identity may vary by lineage. This study expands our understanding of the diversity of DNA methylation and chromatin structure and offers an extensive reference for exploring gene regulation in human health and disease.

Large scale genome-wide association studies (GWAS) have identified hundreds of risk loci for major depression disorder (MDD) with their functional understanding being largely unknown. We integrate MDD polygenic risk from GWAS with methylation at a single cell level resolution to gain insights into the role of methylation in driving MDD risk. We introduce a new approach that leverages the polygenic risk of disease with single-cell methylation data to provide a methylation single cell disease relevance score (met-scDRS) for every cell in a single-cell methylation-seq experiment. We analyzed human atlas single cell methylation data to find 54.0% of layer 2/3 intratelencephalic (L2/3-IT) neurons and 46.5% of layer 5 extratelencelphalic (L5-ET) neurons in the dataset showing significant met-scDRS enrichment. We identified gradient of met-scDRS from inferior temporal gyrus to middle temporal gyrus and variations in posterior to anterior brain axis within L2/3-IT neurons. Met-scDRS identifies functional pathways such as synaptic cellular component, somato-dendritic compartment, post-synapse, cell junction organization that are implicated in diseases and identifies genes that are more disease associated. We contrasted met-scDRS for MDD across 75 other traits including brain, immune/blood, metabolism, and other trait categories to identify diverging and converging cell types and prioritized pathways across different traits. Finally, we demonstrated that met-scDRS is portable across non-CpG and CpG methylation data in providing robust signal. ### Competing Interest Statement The authors have declared no competing interest.

DNA methylation (DNAm) is a key epigenetic mark with essential roles in gene regulation, mammalian development, and human diseases. Single-cell technologies enable profiling DNAm at cytosines in individual cells, but they often suffer from low coverage for CpG sites. We introduce scMeFormer, a transformer-based deep learning model for imputing DNAm states at each CpG site in single cells. Comprehensive evaluations across five single-nucleus DNAm datasets from human and mouse demonstrate scMeFormer's superior performance over alternative models, achieving high-fidelity imputation even with coverage reduced to 10% of original CpG sites. Applying scMeFormer to a single-nucleus DNAm dataset from the prefrontal cortex of patients with schizophrenia and controls identified thousands of schizophrenia-associated differentially methylated regions that would have remained undetectable without imputation and added granularity to our understanding of epigenetic alterations in schizophrenia. We anticipate that scMeFormer will be a valuable tool for advancing single-cell DNAm studies.

Human subcutaneous adipose tissue (SAT) contains a diverse array of cell-types; however, the epigenomic landscape among the SAT cell-types has remained elusive. Our integrative analysis of single-cell resolution DNA methylation and chromatin conformation profiles (snm3C-seq), coupled with matching RNA expression (snRNA-seq), systematically cataloged the epigenomic, 3D topology, and transcriptomic dynamics across the SAT cell-types. We discovered that the SAT CG methylation (mCG) landscape is characterized by pronounced hyper-methylation in myeloid cells and hypo-methylation in adipocytes and adipose stem and progenitor cells (ASPCs), driving nearly half of the 705,063 detected differentially methylated regions (DMRs). In addition to the enriched cell-type-specific transcription factor binding motifs, we identifiedTET1andDNMT3Aas plausible candidates for regulating cell-type level mCG profiles. Furthermore, we observed that global mCG profiles closely correspond to SAT lineage, which is also reflected in cell-type-specific chromosome compartmentalization. Adipocytes, in particular, display significantly more short-range chromosomal interactions, facilitating the formation of complex local 3D genomic structures that regulate downstream transcriptomic activity, including those associated with adipogenesis. Finally, we discovered that variants in cell-type level DMRs and A compartments significantly predict and are enriched for variance explained in abdominal obesity. Together, our multimodal study characterizes human SAT epigenomic landscape at the cell-type resolution and links partitioned polygenic risk of abdominal obesity to SAT epigenome.

The human hippocampus and prefrontal cortex play critical roles in learning and cognition1,2, yet the dynamic molecular characteristics of their development remain enigmatic. Here we investigated the epigenomic and three-dimensional chromatin conformational reorganization during the development of the hippocampus and prefrontal cortex, using more than 53,000 joint single-nucleus profiles of chromatin conformation and DNA methylation generated by single-nucleus methyl-3C sequencing (snm3C-seq3)3. The remodelling of DNA methylation is temporally separated from chromatin conformation dynamics. Using single-cell profiling and multimodal single-molecule imaging approaches, we have found that short-range chromatin interactions are enriched in neurons, whereas long-range interactions are enriched in glial cells and non-brain tissues. We reconstructed the regulatory programs of cell-type development and differentiation, finding putatively causal common variants for schizophrenia strongly overlapping with chromatin loop-connected, cell-type-specific regulatory regions. Our data provide multimodal resources for studying gene regulatory dynamics in brain development and demonstrate that single-cell three-dimensional multi-omics is a powerful approach for dissecting neuropsychiatric risk loci. Using a single-nucleus multi-omics approach, a study jointly profiles the reorganization of the epigenome and the three-dimensional chromatin conformation during the development of the human hippocampus and prefrontal cortex.

DNA methylation (DNAm), a crucial epigenetic mark, plays a key role in gene regulation, mammalian development, and various human diseases. Single-cell technologies enable the profiling of DNAm states at cytosines within the DNA sequence of individual cells, but they often suffer from limited coverage of CpG sites. In this study, we introduce scMeFormer, a transformer-based deep learning model designed to impute DNAm states for each CpG site in single cells. Through comprehensive evaluations, we demonstrate the superior performance of scMeFormer compared to alternative models across four single-nucleus DNAm datasets generated by distinct technologies. Remarkably, scMeFormer exhibits high-fidelity imputation, even when dealing with significantly reduced coverage, as low as 10% of the original CpG sites. Furthermore, we applied scMeFormer to a single-nucleus DNAm dataset generated from the prefrontal cortex of four schizophrenia patients and four neurotypical controls. This enabled the identification of thousands of differentially methylated regions associated with schizophrenia that would have remained undetectable without imputation and added granularity to our understanding of epigenetic alterations in schizophrenia within specific cell types. Our study highlights the power of deep learning in imputing DNAm states in single cells, and we expect scMeFormer to be a valuable tool for single-cell DNAm studies.

Knowing the genes involved in quantitative traits provides an entry point to understanding the biological bases of behavior, but there are very few examples where the pathway from genetic locus to behavioral change is known. To explore the role of specific genes in fear behavior, we mapped three fear-related traits, tested fourteen genes at six quantitative trait loci (QTLs) by quantitative complementation, and identified six genes. Four genes, Lamp, Ptprd, Nptx2, and Sh3gl, have known roles in synapse function; the fifth, Psip1, was not previously implicated in behavior; and the sixth is a long non-coding RNA, 4933413L06Rik, of unknown function. Variation in transcriptome and epigenetic modalities occurred preferentially in excitatory neurons, suggesting that genetic variation is more permissible in excitatory than inhibitory neuronal circuits. Our results relieve a bottleneck in using genetic mapping of QTLs to uncover biology underlying behavior and prompt a reconsideration of expected relationships between genetic and functional variation.

Few neuropsychiatric disorders have replicable biomarkers, prompting high-resolution and large-scale molecular studies. However, we still lack consensus on a more foundational question: whether quantitative shifts in cell types—the functional unit of life—contribute to neuropsychiatric disorders. Leveraging advances in human brain single-cell methylomics, we deconvolve seven major cell types using bulk DNA methylation profiling across 1270 postmortem brains, including from individuals diagnosed with Alzheimer’s disease, schizophrenia, and autism. We observe and replicate cell-type compositional shifts for Alzheimer’s disease (endothelial cell loss), autism (increased microglia), and schizophrenia (decreased oligodendrocytes), and find age- and sex-related changes. Multiple layers of evidence indicate that endothelial cell loss contributes to Alzheimer’s disease, with comparable effect size to APOE genotype among older people. Genome-wide association identified five genetic loci related to cell-type composition, involving plausible genes for the neurovascular unit ( P2RX5 and TRPV3 ) and excitatory neurons ( DPY30 and MEMO1 ). These results implicate specific cell-type shifts in the pathophysiology of neuropsychiatric disorders.