Biology subjects

Welch, J. D.

Publications and source records attributed to Welch, J. D..

At least 19 recordsLinked to original sources

bioRxiv2026

Human and Mouse Models of the Bipolar Disorder-Associated ANK3 p.W1989R Variant Display Altered Somatostatin Interneuron Connectivity and Function

Bipolar disorder (BD) is a debilitating neuropsychiatric disorder characterized by recurrent mood perturbations, cognitive and functional impairment, and increased risk of suicide. Genome-wide association studies (GWAS) have repeatedly identified the gene ANK3, which encodes the neuronal scaffolding protein ankyrin-G, as a risk locus for BD. Ankyrin-G interacts directly with the GABAA receptor-associated protein (GABARAP) to stabilize inhibitory synapses, providing a potential mechanistic link between ANK3 variation and GABAergic dysfunction observed in BD. Previously, we identified an ANK3 p.W1989R variant in a family with BD and demonstrated that this variant abolishes the ankyrin-G/GABARAP interaction, resulting in inhibitory signaling deficits in mice. Here, we investigated the effects of this variant in human induced pluripotent stem cell (hiPSC)-derived neurons generated from an affected individual heterozygous for the ANK3 p.W1989R variant. CRISPR correction of the variant increased inhibitory synapse density on both glutamatergic and GABAergic patient-derived neurons. Integrated spatial transcriptomics and calcium imaging identified cell-type-dependent changes in gene expression and calcium dynamics. ANK3 p.W1989R somatostatin (SST) positive interneurons displayed altered calcium dynamics and reduced SST expression. To determine whether the variant impacts SST interneuron-mediated inhibition of cortical circuits, we selectively stimulated prefrontal cortical SST interneurons in heterozygous Ank3 p.W1989R mice and recorded optically evoked inhibitory postsynaptic currents (oIPSCs) from cortical pyramidal neurons. SST interneuron-mediated oIPSC peak amplitude, charge transfer, and decay times were reduced in variant mice, without a significant change in the paired-pulse ratio. These findings, using complementary human and mouse models, identify novel roles for ankyrin-G in SST interneuron-mediated inhibitory signaling and demonstrate that altered inhibitory connectivity and SST interneuron function are associated with ANK3 p.W1989R. This work provides mechanistic insight into how disrupted ankyrin-G-dependent synaptic function may contribute to inhibitory signaling deficits relevant to BD.

neuroscience↗

bioRxiv2026

Empirical Estimation of Ambient Contamination in Combinatorial Single-Cell Methods Using Multi-Reference Mapping

Droplet-based microfluidics and combinatorial indexing (scifi-ATAC and scifi-RNA) have made single-cell experiments massively scalable. However, higher-order multiplexing complicates data quality, introduces noise, and affects the potential for biological discovery. Here, we show that ambient chromatin accumulates through the experimental workflow and distorts chromatin profiles, most drastically in low-depth nuclei and in minority populations. Standard cell calling relies heavily on read count thresholds, while existing decontamination methods generally operate on aggregated count matrices rather than the underlying reads. We introduce scifi-demux, for preprocessing scifi-ATAC libraries, and AmbientMapper, a generative model that maps reads competitively against multiple references, learns the ambient profile from empty and low-complexity barcodes, and separates nuclei from background and singlets from doublets by Bayesian Information Criterion. Using interspecies ground truth experiments, published multi-genotype libraries, and simulated read-level synthetic barcodes in which every contaminating read is traceable, we show that calls are robust to parameter variation and stable across designs, achieving a wrong-genome rate of 0.19% on a 26-genome reference panel. Finally, we evaluated the impact of removing contaminants, showcasing how AmbientMapper rescues low-depth nuclei discarded by standard pipelines and restores biological structure obscured by contamination.

bioinformatics↗

bioRxiv2026

LIGER2: Scalable Single-Cell Integration with On-Disk Datasets

Correcting batch effects and integrating single-cell sequencing datasets has been a crucial step in large-scale biological studies. Many methods have been published for this task, and excel in various scenarios. Our previous work, LIGER, leveraging integrative non-negative matrix factorization (iNMF), stands out in providing an interpretable low-dimensional representation. To adapt to the modern need for integrating millions of cells, we developed a highly-optimized parallel factorization solution with on-demand loading from disk. The upgraded LIGER algorithm shows significant improvements in time and memory efficiency for single-cell data integration. We also developed a new downstream embedding alignment method significantly improved performance in conserving biological variation while still aligning corresponding cell types across datasets.

bioinformatics↗

bioRxiv2026

Nuclear Speckles Regulate Splicing During Muscle Stem Cell Activation and Aging

Skeletal muscle contains a population of adult stem cells called satellite cells or muscle stem cells (MuSCs) that are responsible for regeneration after injury. MuSCs utilize gene expression programs to maintain quiescence and differentiate after injury and a key regulator of gene expression is splicing, which uniquely changes when transcripts interact with nuclear speckles (NS). NS are membrane-less biomolecular condensates that phase separate proteins, RNAs and chromatin, but how these organelles regulate molecular processes in MuSCs remains unknown. Herein, we build a comprehensive and systems-level understanding of NS influence on alternative splicing, transcriptional regulation and stem cell function before and after injury and in aging. We establish that NS increased in size and number in MuSCs following injury and influence MuSC activation dynamics. We generated a catalog of isoform-resolved splicing events and linked how RNA interactions with NS amplify splicing completion during the injury response. In old age, MuSCs lose NS, yet shifted towards longer, more completely spliced isoforms enriched for RNA binding protein motifs and multivalency. Our studies unveil evidence that RNA interactions with NS shape stem cell state and regenerative responses but are attenuated in old age. HIGHLIGHTSO_LI3D super-resolution imaging of nuclear speckles in muscle stem cells before and after muscle injury shows intricate relationship with activation C_LIO_LIIsoform-resolved profiling of muscle stem cells shows increases in gene expression and splicing during injury response C_LIO_LIMapping RNA interactions with nuclear speckles shows RNAs undergo strongest splicing when proximal to nuclear speckles C_LIO_LIOld aged muscle stem cells lose nuclear speckles and display aberrant splicing, with longer transcripts, more exons, and increased RNA binding protein motifs C_LI

genomics↗

bioRxiv2026

Mechanisms of HIV Latency in Hematopoietic Progenitors: GFI1 as a Key Regulator

Durable control of HIV infection is challenged by persistent latent reservoirs, including hematopoietic stem and progenitor cells (HSPCs), which provide a unique niche for proviral silencing. Mechanisms of HIV latency in HSPCs remain poorly studied. Here, we utilized a dual-reporter HIV (89.6 VT1) and single-cell RNA sequencing to identify host factors governing latency in HSPCs. Transcriptomic profiling revealed elevated expression of several genes in latently infected cells, among them the transcriptional repressor GFI1. Functional studies showed that GFI1 suppresses HIV gene expression by binding a conserved sequence near the primer binding site within the long terminal repeat (LTR). Disruption of GFI1 DNA-binding or corepressor recruitment domains diminished its silencing effect. GFI1 also antagonized Tat and NF-{kappa}B-mediated activation, and reversal of GFI1-mediated suppression was most robust with combined HDAC inhibition and NF-{kappa}B activation. These findings position GFI1 as an important regulator of HIV latency in HSPCs.

microbiology↗

bioRxiv2026

MIF-mediated reprogramming of myeloid lineage within the glioma tumor microenvironment impacts the efficacy of immune stimulatory gene therapy

Gliomas with mutant isocitrate dehydrogenase 1 (mIDH1) represent a distinct subgroup of brain tumors characterized by unique metabolic and immunological profiles compared to wildtype IDH1 (wtIDH1) gliomas. Despite recent progress, the cellular mechanisms underlying tumor progression and immune modulation in these subtypes remain poorly understood. In this study, we employed single-cell RNA sequencing (scRNA-seq) to characterize the cellular heterogeneity of wtIDH1 and mIDH1 gliomas, with a particular focus on myeloid cell populations. Our analyses revealed a marked reduction of monocyte-derived tumor-associated macrophages (Mo-TAMs) and lower expression of macrophage migration inhibitory factor (MIF) in mIDH1 gliomas, which was attributable to epigenetic reprogramming. Mechanistic studies using MIF and CD74 knockout mice demonstrated that the MIF-CD74 axis plays a crucial role in regulating the glioma immune microenvironment, thereby driving tumor growth and progression. Importantly, the combination of immune-stimulatory gene therapy (HSV1-thymidine kinase/Fms-like tyrosine kinase 3 ligand; TK/Flt3L) with MIF inhibition significantly extended survival in models of wtIDH1 glioma. These findings highlight the therapeutic potential of targeting the MIF-CD74 pathway and underscore the importance of integrating immunomodulatory strategies for the treatment of glioma. HighlightsO_LIMutant IDH1 gliomas exhibit fewer Mo-TAMs and increased Mg-TAMs C_LIO_LIMutant IDH1 gliomas have less MIF expression via epigenetic reprogramming. C_LIO_LIMesenchymal wtIDH1 glioma cells are main source of MIF. C_LIO_LIMIF inhibition plus immune stimulatory gene therapy extends survival wtIDH1 glioma. C_LI

cancer biology↗

bioRxiv2025

Reprogramming the Immune Suppressive Tumor Microenvironment in Glioma Enhances the Efficacy of Immune-Mediated Gene Therapy

Gliomas account for ~80% of primary malignant brain tumors. Many CNS WHO grade 2-3 and some grade 4 gliomas harbor mutant isocitrate dehydrogenase 1 (mIDH1), which causes a gain of function mutation (IDH1 R132H) leading to the production of 2-hydroxyglutarate (2HG). Mutant IDH1-induced 2HG, through epigenetic reprogramming elicits an immune-permissive tumor microenvironment (TME). An immunosuppressive mechanism in the glioma TME involves adenosine production via the ectoenzyme CD73. This study investigates mIDH1s influence on CD73 expression and adenosine levels. We demonstrate that mIDH1 glioma cells exhibit reduced CD73 expression, driven by DNA hypermethylation, leading to reduced adenosine levels. Since wtIDH1 gliomas have high CD73 expression, we evaluated CD73 blockade as an immunotherapy target. We show that CD73 inhibition used as monotherapy, did not improve survival in wtIDH1 glioma-bearing mice. However, when combined with immune-stimulatory Ad-TK (adenoviral vectors encoding herpes simplex virus thymidine kinase) and Ad-Flt3L (adenoviral vectors encoding FMS-like tyrosine kinase 3 ligand) gene therapy, CD73 blockade significantly enhanced therapeutic efficacy and increased anti-glioma effector T cell activity. These findings reveal that CD73 inhibition used in combination with immune stimulatory Ad-TK/Ad-Flt3L gene therapy may be an effective treatment for wtIDH1 gliomas, which could be readily translated to the clinical arena.

cancer biology↗

bioRxiv2025

VISTA-FISH: Video Imaging with Spatial-Temporal Analysis by Fluorescent In Situ Hybridization

Performing live-cell microscopy and high-dimensional gene expression measurements on the same cells is crucial for unraveling the molecular mechanisms underlying complex temporal phenotypes, yet this remains challenging using traditional approaches. To address these limitations, we developed Video Imaging with Spatial-Temporal Analysis by FISH (VISTA-FISH), a technique that matches live-cell recordings of cultured cells with the expression of thousands of genes in the same cell at the end of the video. Moreover, VISTA-FISH can simultaneously detect CRISPR guide RNAs in pooled screens, enabling investigation into the fundamental underpinnings of cellular activity, organelle transport, and other pivotal cellular functions. Using VISTA-FISH, we measured gene expression and neuron activity in the same differentiating neurons. This combined single-cell transcriptomic and video data allowed us to link differentiation stage, subcellular transcript localization, and cell subtype with neuron activity. Using these measurements, we built a model to predict activity from expression. Finally, we performed a pooled CRISPR interference screen with live-cell lysosome imaging, identifying alterations in lysosome movement and morphology as well as gene expression in perturbed neurons. Collectively, these studies position VISTA-FISH as a powerful new tool for elucidating the molecular mechanisms underlying neuron activity, organelle trafficking, and other complex temporal phenotypes.

molecular biology↗

bioRxiv2025

Evidence for Transcriptomic Conservation Between the Main Cells of the Drosophila Prostate-Like Accessory Gland and Basal Cells of the Mammalian Prostate.

The Drosophila accessory gland performs functions analogous to the mammalian prostate in production of seminal fluid components that are essential for male fertility. The mammalian prostate and Drosophila accessory glands share a similar tissue organization and structure. Both organs contain secretory epithelial cells forming a gland lumen, surrounded by a stroma with extracellular matrix enveloped by innervated muscle for organ contraction and fluid release. However, the Drosophila accessory gland secretory epithelium is postmitotic, polyploid and binucleate, and lacks a known stem cell population. By contrast, the mammalian prostate epithelium is made up of diploid luminal secretory cells and diploid basal cells that are maintained by at least two stem cell populations. Despite the differences in the tissues, it has been argued these tissues may share a deep homology based on the expression of conserved genes during development. Here we performed a cross-species comparative analysis using single-cell RNA sequencing data from adult tissues using data from the Drosophila Fly Cell Atlas and mammalian adult prostate single-cell datasets. Our analysis provides additional evidence of transcriptomic similarity between the main epithelial cells of the Drosophila prostate-like accessory gland and the basal epithelial cells of the mammalian prostate. While we do not know whether these similarities reflect shared evolutionary homology, or independently derived features due to shared tissue functions, our results strengthen the arguments that the Drosophila accessory gland can be used to effectively model aspects of human prostate biology and disease.

genomics↗

bioRxiv2025

HPV integration in head and neck cancer: downstream splicing events and expression ratios linked with poor outcomes

HPV integration (HPVint) is associated with carcinogenesis and tumor progression in HPV-associated cancers, including head and neck squamous cell carcinomas (HNSCC). While its impact on human DNA has been well characterized, its relationship with clinical outcomes remains unconfirmed. Here we investigate the consequences of HPVint both with respect to human and HPV characteristics by analyzing 261 HPV-associated HNSCC bulk and single-cell RNA-seq samples from five cohorts, and DNA HPVint events from 102 HPV+ participants in two of the cohorts. By leveraging this large meta-cohort, we first reveal an oncogenic network based on the recurrent HPV integration locations in HNSCC. We then classify HPVint-positive (HPVint(+)) participants by HPV RNA features, specifically based on spliced HPV-human fusion transcripts and ratios of HPV gene transcripts, showing that subsets of participants have worse clinical outcomes. Our analyses, focused mainly on RNA instead of DNA, expand our understanding of the carcinogenic mechanisms of HPVint, partially addressing the conflicting findings of whether HPVint is associated with aggressive phenotypes and worse clinical consequences, and provide potential biomarkers to advance precision oncology in HPV-associated HNSCC.

cancer biology↗

bioRxiv2024

Single-cell multi-omic analysis of post-transplant mesenchymal cells reveals molecular signatures and putative regulators of lung fibrosis

Survival after lung transplantation is limited by chronic progressive graft failure, termed chronic lung allograft dysfunction (CLAD). Graft-resident mesenchymal cells (MCs) drive CLAD pathogenesis and exhibit stable, dysregulated signaling; however, the transcriptomic and epigenomic drivers behind this fibrogenic transformation remain elusive. Here, we utilize single-cell multi-omic technologies to study gene expression and chromatin accessibility of MCs from the lavage fluid of lung transplant recipients with and without CLAD, obtained either early post-transplantation or after disease onset. MCs obtained after CLAD onset (CLAD-MCs) demonstrated a unique transcriptomic signature compared to non-CLAD controls; a logistic regression model trained on these profiles classified the disease status of individual cells with > 98% accuracy using a set of signature genes. Chromatin accessibility and motif scan analysis identified the CCAAT-enhancer-binding proteins family of transcription factors, specifically CEBPD, as a key marker of the CLAD-enriched subtype. Footprint analysis of early time-point MCs revealed minimal differences in accessibility, suggesting that CEBPD-associated regulatory changes emerge over time after transplantation. Integration and unsupervised clustering identified 8 distinct cell states, and a compositional shift was noted uniquely in CLAD-MCs. Knocking down CEBPD with siRNA in CLAD-MCs partially reverted the CLAD transcriptomic signature, confirming its importance in the dysregulated molecular state of CLAD-associated MCs. scRNA-seq analysis on human lung CLAD tissue provided in situ validation of key genes and CEBPD expression changes noted in CLAD-MCs. Our results provide deeper insights into the transcriptomic and epigenomic changes in post-transplant MCs, nominating biomarkers and disease-associated factors with implications for future therapeutic efforts.

bioinformatics↗

bioRxiv2024

LUMIC: Latent diffUsion for Multiplexed Images of Cells

The rapid advancement of high-content, single-cell technologies like robotic confocal microscopy with multiplexed dyes (morphological profiling) can be leveraged to reveal fundamental biology, ranging from microbial and abiotic stress to organ development. Specifically, heterogeneous cell systems can be perturbed genetically or with chemical treatments to allow for inference of causal mechanisms. An exciting strategy to navigate the high-dimensional space of possible perturbation and cell type combinations is to use generative models as priors to anticipate high-content outcomes in order to design informative experiments. Towards this goal, we present the Latent diffUsion for Multiplexed Images of Cells (LUMIC) framework that can generate high quality and high fidelity images of cells. LUMIC combines diffusion models with DINO (self-Distillation with NO labels), a vision-transformer based, self-supervised method that can be trained on images to learn feature embeddings, and HGraph2Graph, a hierarchical graph encoder-decoder to represent chemicals. To demonstrate the ability of LUMIC to generalize across cell lines and treatments, we apply it to a dataset of[~] 27,000 images of two cell lines treated with 306 chemicals and stained with three dyes from the JUMP Pilot dataset and a newly-generated dataset of[~] 3,000 images of five cell lines treated with 61 chemicals and stained with three dyes. To quantify prediction quality, we evaluate the DINO embeddings, Kernel Inception Distance (KID) score, and recovery of morphological feature distributions. LUMIC significantly outperforms previous methods and generates realistic out-of-sample images of cells across unseen compounds and cell types.

molecular biology↗

bioRxiv2024

Lung CD4+ resident memory T cells use airway secretory cells to stimulate and regulate neutrophilic allergic airways disease.

Neutrophilic asthma is a vexing disease, but mechanistic and therapeutic advancement will require better models of allergy-induced airway neutrophilia. Here, we find that periodic ovalbumin (OVA) inhalation in sensitized mice elicits allergic airway inflammation and pathophysiology mimicking neutrophilic asthma. OVA-experienced murine lungs harbor diverse clusters of CD4+ resident memory TRM cells, including unconventional ROR{gamma}tnegative/low TH17 cells. Acute OVA challenge instigates IL-17A secretion from these TRM cells, driving CXCL5 production from Muc5achigh airway secretory cells, leading to destructive airway neutrophilia. The TRM- and epithelial-cell signals discovered herein are also observed in adult human asthmatic airways. Epithelial antigen presentation regulates this biology by skewing TRM cells towards TH2 and TH1 fates, so that the TH1-related IFN-{gamma} suppresses IL-17A-driven, CXCL5-mediated airway neutrophilia. Concordantly, in vivo IFN-{gamma} supplementation improves disease outcomes. Thus, using our model of neutrophilic asthma we identify lung epithelial-CD4+ TRM cell crosstalk as a key rheostat of allergic airway neutrophilia. HighlightsO_LIRecurrent OVA inhalation experience predisposes mice to allergic airways neutrophilia C_LIO_LINeutrophil-prone lungs harbor CD4+ TRM cells including ROR{gamma}tnegative/low TH17 cells C_LIO_LIMuc5achigh secretory cells instruct CD4+ TRM fates and neutrophilia via MHC-II and CXCL5, respectively C_LIO_LIProphylactic or therapeutic delivery of IFN-{gamma} curbs allergic airway neutrophilia C_LI

immunology↗

bioRxiv2024

CytoSignal Detects Locations and Dynamics of Ligand-Receptor Signaling at Cellular Resolution from Spatial Transcriptomic Data

Nearby cells within tissues communicate through ligand-receptor signaling interactions. Emerging spatial transcriptomic technologies provide a tremendous opportunity to systematically detect ligand-receptor signaling, but no method operates at cellular resolution in the spatial context. We developed CytoSignal to infer the locations and dynamics of cell-cell communication at cellular resolution from spatial transcriptomic data. CytoSignal is based on the simple insight that signaling is a protein-protein interaction that occurs at a specific tissue location when ligand and receptor are expressed in close spatial proximity. Our cellular-resolution, spatially-resolved signaling scores allow several novel types of analyses: we identify spatial gradients in signaling strength; separately quantify the locations of contact-dependent and diffusible interactions; and detect signaling-associated differentially expressed genes. Additionally, we can predict the temporal dynamics of a signaling interaction at each spatial location. CytoSignal is compatible with nearly every kind of spatial transcriptomic technology including FISH-based protocols and spot-based protocols without deconvolution. We experimentally validate our results in situ by proximity ligation assay, confirming that CytoSignal scores closely match the tissue locations of ligand-receptor protein-protein interactions. Our work addresses the fields current need for a robust and scalable tool to detect cell-cell signaling interactions and their dynamics at cellular resolution from spatial transcriptomic data.

bioinformatics↗

bioRxiv2024

Integrating single-cell multimodal epigenomic data using 1D-convolutional neural networks

Recent experimental developments enable single-cell multimodal epigenomic profiling, which measures multiple histone modifications and chromatin accessibility within the same cell. Such parallel measurements provide exciting new opportunities to investigate how epigenomic modalities vary to-gether across cell types and states. A pivotal step in using this type of data is integrating the epigenomic modalities to learn a unified representation of each cell, but existing approaches are not designed to model the unique nature of this data type. Our key insight is to model single-cell multimodal epigenome data as a multi-channel sequential signal. Based on this insight, we developed ConvNet-VAEs, a novel framework that uses 1D-convolutional variational autoencoders (VAEs) for single-cell multimodal epigenomic data integration. We evaluated ConvNet-VAEs on nano-CT and scNTT-seq data generated from juvenile mouse brain and human bone marrow. We found that ConvNet-VAEs can perform dimension reduction and batch correction better than previous architectures while using significantly fewer parameters. Furthermore, the performance gap between convolutional and fully-connected architectures increases with the number of modalities, and deeper convolutional architectures can increase performance while performance degrades for deeper fully-connected architectures. Our results indicate that convolutional autoencoders are a promising method for integrating current and future single-cell multimodal epigenomic datasets.

bioinformatics↗

bioRxiv2024

Mapping Cell Fate Transition in Space and Time

Cell fate transition is fundamentally a spatiotemporal process, but previous work has largely neglected the spatial dimension. Incorporating both space and time into models of cell fate transition would be a key step toward characterizing how interactions among neighboring cells, the presence of local niche factors, and physical migration of cells contribute to tissue development. To realize this potential, we propose topological velocity inference (TopoVelo), a model for jointly inferring spatial and temporal dynamics of cell fate transition from spatial transcriptomic data. TopoVelo extends the RNA velocity framework to model single-cell gene expression dynamics of an entire tissue with spatially coupled differential equations. Our principled probabilistic approach enables the incorporation of time point labels and multiple slices. We further introduce the idea of cell velocity, which is defined as the physical direction of cell maturation and migration. Simulated data analysis indicates that incorporating spatial coordinates significantly improves the accuracy of velocity and time inference. When evaluated on real Slide-Seq and Stereo-Seq data, TopoVelo significantly improves the spatial coherence of inferred cell ordering compared to previous methods. Furthermore, TopoVelo accurately recovers the expected directions of cell differentiation and migration in the embryonic mouse cerebral cortex, gut, and lung. Our work introduces a new dimension into the study of cell fate transitions and lays a foundation for modeling the collective dynamics of cells comprising an entire tissue.

bioinformatics↗

bioRxiv2023

Predicting the Structural Impact of Human Alternative Splicing

Protein structure prediction with neural networks is a powerful new method for linking protein sequence, structure, and function, but structures have generally been predicted for only a single isoform of each gene, neglecting splice variants. To investigate the structural implications of alternative splicing, we used AlphaFold2 to predict the structures of more than 11,000 human isoforms. We employed multiple metrics to identify splicing-induced structural alterations, including template matching score, secondary structure composition, surface charge distribution, radius of gyration, accessibility of post-translational modification sites, and structure-based function prediction. We identified examples of how alternative splicing induced clear changes in each of these properties. Structural similarity between isoforms largely correlated with degree of sequence identity, but we identified a subset of isoforms with low structural similarity despite high sequence similarity. Exon skipping and alternative last exons tended to increase the surface charge and radius of gyration. Splicing also buried or exposed numerous post-translational modification sites, most notably among the isoforms of BAX. Functional prediction nominated numerous functional differences among isoforms of the same gene, with loss of function compared to the reference predominating. Finally, we used single-cell RNA-seq data from the Tabula Sapiens to determine the cell types in which each structure is expressed. Our work represents an important resource for studying the structure and function of splice isoforms across the cell types of the human body.

bioinformatics↗

bioRxiv2023

Population-scale skeletal muscle single-nucleus multi-omic profiling reveals extensive context specific genetic regulation

Skeletal muscle, the largest human organ by weight, is relevant in several polygenic metabolic traits and diseases including type 2 diabetes (T2D). Identifying genetic mechanisms underlying these traits requires pinpointing cell types, regulatory elements, target genes, and causal variants. Here, we use genetic multiplexing to generate population-scale single nucleus (sn) chromatin accessibility (snATAC-seq) and transcriptome (snRNA-seq) maps across 287 frozen human skeletal muscle biopsies representing nearly half a million nuclei. We identify 13 cell types and integrate genetic variation to discover >7,000 expression quantitative trait loci (eQTL) and >100,000 chromatin accessibility QTLs (caQTL) across cell types. Learning patterns of e/caQTL sharing across cell types increased precision of effect estimates. We identify high-resolution cell-states and context-specific e/caQTL with significant genotype by context interaction. We identify nearly 2,000 eGenes colocalized with caQTL and construct causal directional maps for chromatin accessibility and gene expression. Almost 3,500 genome-wide association study (GWAS) signals across 38 relevant traits colocalize with sn-e/caQTL, most in a cell-specific manner. These signals typically colocalize with caQTL and not eQTL, highlighting the importance of population-scale chromatin profiling for GWAS functional studies. Finally, our GWAS-caQTL colocalization data reveal distinct cell-specific regulatory paradigms. Our results illuminate the genetic regulatory architecture of human skeletal muscle at high resolution epigenomic, transcriptomic, and cell-state scales and serve as a template for population-scale multi-omic mapping in complex tissues and traits.

genomics↗