Biology subjects

Parker, S. C. J.

Publications and source records attributed to Parker, S. C. J..

At least 19 recordsLinked to original sources

bioRxiv2026

The Human Pancreas Cell Atlas defines a healthy reference framework for disease contextualization and translational benchmarking

A central challenge in single-cell biology is distinguishing disease-associated remodeling from normal cellular heterogeneity. Addressing this challenge requires healthy reference frameworks that capture cellular diversity across individuals, technologies, and biological contexts. Here we present the Human Pancreas Cell Atlas (HPCA), a reference atlas of the healthy human pancreas integrating 815,126 single-cell and single-nucleus transcriptomes from 109 donors across 12 studies, diverse technologies, and demographics. Using benchmarked integration and community-driven annotations, HPCA defines 94 cell types and transcriptional states spanning endocrine, exocrine, immune, and stromal compartments. The atlas identifies rare endocrine populations, including a putative, spatially supported polyhormonal alpha-beta-delta state, and provides a unified framework for interpreting pancreatic cellular variation across diverse biological and demographic covariates. Projection of disease and model-system datasets onto HPCA contextualized endocrine and epithelial remodeling relative to healthy pancreatic states. Diabetes-associated endocrine cells remained embedded within the healthy endocrine state space while exhibiting disease-specific changes, as supported by spatial and eQTL concordance analyses. Integration with a pancreatic ductal adenocarcinoma atlas resolved injury-associated and malignant epithelial ecosystem regions across donors. Finally, the HPCA enables quantitative benchmarking of murine diabetes models and stem-cell-derived islets against human pancreatic reference states. Together, the HPCA establishes a healthy transcriptional coordinate system for interpreting disease-associated pathophysiology, experimental perturbation, and regenerative fidelity, illustrating how reference atlases can function as analytical frameworks rather than static cell catalogs.

bioinformatics↗

bioRxiv2026

PanKbase Integrated Single-Cell Map: A Comprehensive Atlas of Human Pancreatic Islets

Single-cell RNA sequencing (scRNA-seq) of human pancreatic islet tissue is a powerful tool for investigating type 1 diabetes (T1D). However, individual datasets are limited in size and fragmented across donors, laboratories, and experimental conditions. To address this, we constructed a comprehensive, integrated scRNA-seq atlas of isolated human pancreatic islets by collating publicly available data generated from tissue provided by resources including the Human Pancreas Analysis Program, the Integrated Islet Distribution Program, and Prodo Labs. Systematic quality controls were implemented to select high-quality samples, reads, and cells. During integration, we accounted for important variables such as age, sex, body mass index, origin study, treatments, islet distribution resources, and sequencing chemistry. Our single-cell atlas comprises 191 high-quality samples from 140 donors (59 female, 81 male) across five phenotypic groups: no diabetes (controls, n=69), autoantibody positivity without diabetes (n=12), pre-diabetes (n=11), T1D (n=12), and type 2 diabetes (T2D) (n=36). In total, the atlas contains 448,935 cells, capturing 13 distinct populations, including alpha cells (43.3%) and beta cells (26.8%), as well as groups such as immune cells (0.6%). Publicly available at www.pankbase.org, this atlas provides a platform for hypothesis-driven investigation of diabetes pathophysiology and, given rigorous quality control, is well-suited for downstream machine-learning applications. Article HighlightsO_LICurrent scRNA-seq datasets of pancreatic islet tissue are limited in size and scattered across donors, laboratories, and experimental conditions, underscoring the need for a consolidated resource. C_LIO_LIWe harmonized datasets from multiple sources to build a comprehensive single-cell map of isolated human pancreatic islets. C_LIO_LIOur atlas captures 448,935 cells from 191 high-quality samples across 140 donors and multiple phenotypic groups, identifying 13 distinct cell populations. C_LIO_LIAvailable at www.pankbase.org, the atlas provides a scalable, rigorously curated platform to support hypothesis-driven diabetes research and can enable a broad range of downstream computational applications. C_LI

genomics↗

bioRxiv2026

Donor-matched iPSC model reveals context-dependent T2D genetic signals in fibro-adipogenic progenitors

Fibro-adipogenic progenitors (FAPs) in skeletal muscle have been implicated in type 2 diabetes (T2D), yet their heterogeneity and context-dependent regulation remain poorly understood. Here, we establish induced pluripotent stem cell (iPSC)-derived FAPs as a model of primary FAPs by leveraging a unique resource: iPSC lines and skeletal muscle biopsies obtained from 34 individuals, a subset of 30 profiled using single-nucleus multiomics. Donor-matched comparisons reveal iPSC-FAPs recapitulate the transcriptome, epigenome, and subtype composition of primary FAPs. Using single-nucleus multiomics, we show that high-insulin exposure drives iPSC-FAPs toward an adipogenic fate - and this adipogenic subtype is enriched for T2D GWAS signals, an enrichment undetectable at baseline. We map the T2D-associated rs3814707 non-coding signal to LTBP3, a gene that influences FAP adipogenic differentiation. These findings reveal how disease-relevant regulatory mechanisms can be masked in unstimulated cells and establish iPSC-FAPs as a powerful platform for dissecting the state-dependent biology of complex metabolic disease.

genetics↗

bioRxiv2025

Opposite directions of association of higher physical activity and higher insulin resistance with human skeletal muscle cell type abundance and fiber type-level gene expression

To investigate the interplay between physical activity and cardiometabolic traits in human skeletal muscle, we characterized gene expression and chromatin accessibility across skeletal muscle cell types in 263 Finnish individuals from the FUSION Tissue Biopsy Study. We analyzed skeletal muscle single-nucleus RNA-seq data (168,309 nuclei, 23,849 genes), ATAC-seq data (242,069 nuclei, 927,588 peaks), and bulk RNA-seq data (22,309 genes). Lower insulin resistance (HOMA-IR) and higher total physical activity were both associated with higher proportions of Type 1 nuclei and lower proportions of Type 2x nuclei. We identified cell-type-level and tissue-level gene expression-trait and gene set-trait associations for cardiometabolic and physical activity traits, and a smaller proportion of cell-type-level chromatin accessibility-trait associations. Traits typically associated with better health--lower trait values of cardiometabolic traits (BMI, HOMA-IR, normal glucose tolerance vs. type 2 diabetes, 2-hour plasma glucose) and higher physical activity levels (total and vigorous)--were associated with higher expression of energy metabolism genes and lower expression of signaling pathway genes across muscle fiber types, total pseudobulk, and to some extent in bulk tissue. For HOMA-IR and physical activity, these directions of association remained when adjusting for both traits in the same model, indicating apparently independent associations in the same pathways.

genomics↗

bioRxiv2025

Integrative multi-omics uncovers skeletal muscle enhancer programming of cardiorespiratory fitness

Cardiorespiratory fitness (CRF) is a heritable trait associated with improved metabolic health and longevity. To identify regulatory mechanisms underlying CRF, we integrated 546 transcriptomic and epigenomic profiles from skeletal muscle of 128 genetically heterogeneous rats selectively bred for high and low running capacity, a model that mirrors CRF-associated traits in humans. Selection drove genetic convergence in coordinated skeletal muscle enhancer networks linked to lipid metabolism and angiogenesis genes. We validated thousands of these genetic effects through integration of 426 genotype, gene expression, and chromatin accessibility profiles in an independent HCRxLCR F2 population (n=147). These 972 multi-omics profiles show that CRF-associated genetic variation reshapes the chromatin landscape to support energy metabolism and oxygen delivery, offering a molecular framework for identifying targets to reduce cardiometabolic disease risk. Glossary of TermsO_LIModality/modalities: essentially a dataset. A modality can refer to one or multiple of ATAC-Seq, H3K27ac & additional histone modifications, chromatin states such as Strong Enhancer, and/or RNA-Seq C_LIO_LIPeaks: only refers to ATAC-Seq and CUT&Tag data C_LIO_LIFeatures: can collectively refer to genomic regions of one or more modality classes (genes, peaks, and/or chromatin state regions) C_LIO_LIVariant(s): SNP(s) C_LI O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/679855v2_ufig1.gif" ALT="Figure 1"> View larger version (56K): org.highwire.dtl.DTLVardef@1ee52eorg.highwire.dtl.DTLVardef@5848b7org.highwire.dtl.DTLVardef@debaforg.highwire.dtl.DTLVardef@1ec43ba_HPS_FORMAT_FIGEXP M_FIG C_FIG

genomics↗

bioRxiv2024

Functional dissection of metabolic trait-associated gene regulation in steady state and stimulated human skeletal muscle cells

Type 2 diabetes (T2D) is a common metabolic disorder characterized by dysregulation of glucose metabolism. Genome-wide association studies have defined hundreds of signals associated with T2D and related metabolic traits, predominantly in noncoding regions. While pancreatic islets have been a focal point given their central role in insulin production and glucose homeostasis, other metabolic tissues, including liver, adipose, and skeletal muscle, also contribute to T2D pathogenesis and risk. Here, we examined context-specific genetic regulation under basal and stimulated states. Using LHCN-M2 human skeletal muscle cells, we generated transcriptomic profiles and characterized regulatory activity of 327 metabolic trait-associated variants via a massively parallel reporter assay (MPRA). To identify condition-specific effects, we compared four different conditions: (1) undifferentiated, or (2) differentiated with basal media, (3) media supplemented with the AMP analog AICAR (to simulate exercise) or (4) media containing sodium palmitate (to induce insulin resistance). RNA-seq revealed these treatments extensively perturbed transcriptional regulation, with 498-3,686 genes showing significant differential expression between pairs of conditions. Among differentially expressed genes, we observed enrichment of relevant biological pathways including muscle differentiation (undifferentiated vs. differentiated), oxidoreductase activity (differentiated vs. AICAR), and glycogen binding (differentiated vs. palmitate). The results of our MPRA found broadly different levels of activity between all conditions. Our MPRA screen revealed a shared set of 7 variants with significant allelic activity across all conditions, along with a proportional number of variants showing condition-specific allelic bias and the total number of active oligos per condition. We found that a lead variant for serum triglyceride levels, rs490972, overlaps SP transcription factor motifs and has differential regulatory activity between conditions. Comparison of MPRA activity with paired gene expression data allowed us to predict that regulatory activity at this locus is mediated by SP1 transcription factor binding. While several of the MPRA variants have been previously characterized in other metabolic tissues, none have been studied in these stimulated states. Together, this work uncovers context-dependent transcriptomic and regulatory dynamics of T2D- and metabolic trait-associated variants in skeletal muscle cells, offering new insights into their functional roles in metabolic processes.

genomics↗

bioRxiv2024

Combining functional annotation and multi-trait fine-mapping methods improves fine-mapping resolution at glycaemic trait loci

The Meta-Analysis of Glucose and Insulin-related traits Consortium (MAGIC) identified 242 loci associated with glycaemic traits fasting insulin (FI), fasting glucose (FG), 2h-Glucose (2hGlu), and glycated haemoglobin (HbA1c). However, for the majority, the causal variant(s) remain(s) unknown. Modelling multiple traits and integrating functional annotations have each been shown to improve fine-mapping resolution. Here, we aimed to determine whether combining these techniques would further improve fine-mapping resolution. Using single-trait fine-mapping results from FINEMAP as input, we performed multi-trait fine-mapping with flashfm at 50 loci significantly associated with more than one glycaemic trait. We used fGWAS to build models of enriched annotations by considering 32 cell-type specific and 28 static annotations. We used the prior probabilities from these models to perform annotation informed fine-mapping with both FINEMAP (single-trait) and flashfm (multi-trait). Multi-trait fine-mapping of 106 locus-trait associations significantly (p=1.23 x 10-17) reduced the median size of the credible sets accounting for 99% of the posterior probability of being causal (99CS) to 21.5 variants compared to the 60.5 variants in single-trait fine-mapping. Annotation informed single-trait fine-mapping of 211 locus-trait associations reduced (p=4.24x10-12) the median 99CS size from 72 in agnostic single-trait fine-mapping to 52 variants. Annotation informed multi-trait fine-mapping of 110 locus-trait associations led to a further significant (p=2.69x10-18) decrease in median 99CS size to 14.5 variants compared to 51.0 in annotation informed single-trait fine-mapping. In conclusion, we found that multi-trait and annotation informed fine-mapping can help to further narrow down likely causal variants at glycaemic trait loci, both separately and when combined. Author SummaryLarge-scale studies such as the Meta-analysis of glucose and insulin-related traits consortium (MAGIC) identified regions in our DNA which affect glycaemic measures related to type 2 diabetes, such as glucose and insulin levels measured after fasting overnight. However, these regions contain many DNA changes within them that are close to each other and that are inherited together. This makes identifying the DNA change responsible for the effect on glycaemic measures challenging. Fine-mapping is a statistical approach that helps to narrow down the list of DNA changes that are likely to be responsible for the association with glycaemic measures, referred to as causal DNA changes. Here we used multiple types of fine-mapping approaches together to combine their advantages. We showed that both combining data from multiple related glycaemic measures as well as including information about the likely function of DNA changes helps to narrow down the number of likely causal DNA changes. Combining both approaches led to the biggest improvement in the list of likely causal DNA changes. While this study focuses on glycaemic measures, the approaches can be applied to other measures used to monitor human health, for example blood pressure or cholesterol levels. This study highlights the benefits of combining multiple approaches to find likely causal DNA changes.

genetics↗

bioRxiv2024

Human Vascularized Macrophage-Islet Organoids to Model Immune-Mediated Pancreatic β cell Pyroptosis upon Viral Infection

There is a paucity of human models to study immune-mediated host damage. Here, we utilized the GeoMx spatial multi-omics platform to analyze immune cell changes in COVID-19 pancreatic autopsy samples, revealing an accumulation of proinflammatory macrophages. Single cell RNA-seq analysis of human islets exposed to SARS-CoV-2 or Coxsackievirus B4 (CVB4) viruses identified activation of proinflammatory macrophages and {beta} cell pyroptosis. To distinguish viral versus proinflammatory macrophage-mediated {beta} cell pyroptosis, we developed human pluripotent stem cell (hPSC)-derived vascularized macrophage-islet (VMI) organoids. VMI organoids exhibited enhanced marker expression and function in both {beta} cells and endothelial cells compared to separately cultured cells. Notably, proinflammatory macrophages within VMI organoids induced {beta} cell pyroptosis. Mechanistic investigations highlighted TNFSF12-TNFRSF12A involvement in proinflammatory macrophage-mediated {beta} cell pyroptosis. This study established hPSC- derived VMI organoids as a valuable tool for studying immune cell-mediated host damage and uncovered mechanism of {beta} cell damage during viral exposure.

developmental biology↗

bioRxiv2023

Adipose tissue eQTL meta-analysis reveals the contribution of allelic heterogeneity to gene expression regulation and cardiometabolic traits

Complete characterization of the genetic effects on gene expression is needed to elucidate tissue biology and the etiology of complex traits. Here, we analyzed 2,344 subcutaneous adipose tissue samples and identified 34K conditionally distinct expression quantitative trait locus (eQTL) signals in 18K genes. Over half of eQTL genes exhibited at least two eQTL signals. Compared to primary signals, non-primary signals had lower effect sizes, lower minor allele frequencies, and less promoter enrichment; they corresponded to genes with higher heritability and higher tolerance for loss of function. Colocalization of eQTL with conditionally distinct genome-wide association study signals for 28 cardiometabolic traits identified 3,605 eQTL signals for 1,861 genes. Inclusion of non-primary eQTL signals increased colocalized signals by 46%. Among 30 genes with [≥]2 pairs of colocalized signals, 21 showed a mediating gene dosage effect on the trait. Thus, expanded eQTL identification reveals more mechanisms underlying complex traits and improves understanding of the complexity of gene expression regulation.

genomics↗

bioRxiv2023

Using a modular massively parallel reporter assay to discover context-specific regulatory grammars in type 2 diabetes

Most genome-wide association signals for complex disease reside in the noncoding genome, where defining function is nontrivial. MPRAs (massively parallel reporter assays) offer a scalable means to identify functional regulatory elements, but are typically conducted without regard to cell type, pairing cloned fragments with a generic housekeeping promoter. To explore the context-sensitivity of MPRAs, we screened enhancer activity across a panel of nearly 12,000 198-bp fragments spanning over 300 type 2 diabetes- and metabolic trait-associated regions in the 832/13 rat insulinoma beta cell line, a relevant model of pancreatic beta cells. We explored these fragments context sensitivity by comparing their activities when placed up- or downstream of a reporter gene, and in combination with either a synthetic housekeeping promoter (SCP1) or a more biologically relevant promoter corresponding to the human insulin (INS) gene. We identified clear effects of MPRA construct design on enhancer activity. Specifically, a subset of fragments (n = 702/11,656) displayed positional bias, evenly distributed across up- and downstream preference. Promoter choice also influenced MPRA activity (n = 698/11,656), mostly biased towards the cell-specific INS promoter (73.4%). To identify sequence features associated with promoter preference, we used Lasso regression with 562 genomic annotations and discovered that fragments with INS promoter-biased activity are enriched for HNF1 motifs. HNF1 family transcription factors are key regulators of glucose metabolism disrupted in maturity onset diabetes of the young (MODY), suggesting genetic convergence between rare coding variants that cause MODY and common T2D-associated regulatory regions. We designed a follow-up MPRA containing HNF1 motif-enriched fragments and observed several instances where deletion or mutation of HNF1 motifs disrupted the INS promoter-biased enhancer activity, specifically in the beta cell model but not in a skeletal muscle cell line, another diabetes-relevant cell type. Together, our study suggests that cell-specific regulatory activity is partially influenced by enhancer-promoter compatibility and indicates that careful attention should be paid when designing MPRA libraries to capture context-specific regulatory processes at disease-associated genetic signals.

genomics↗

bioRxiv2022

Single-cell gene expression and chromatin accessibility profiling of human pancreatic islets at basal and stimulatory conditions nominates mechanisms of type 1 diabetes genetic risk

Type 1 diabetes (T1D) is a complex autoimmune disease characterized by the loss of pancreatic islet beta cells. The mechanisms of T1D genetic risk remain poorly understood. Here, we present a multi-omic integrative study of single-cell/nucleus molecular profiles of gene expression and chromatin accessibility in the same biological samples from healthy and beta cell autoantibody+ (AAB+) human pancreatic islets to characterize mechanisms of islet-mediated T1D genetic risk. We additionally performed single-cell/nucleus multi-omic profiling of healthy islets under two stimulatory conditions used as in vitro models of T1D (cytokine cocktail and CVB4 infection) to evaluate how environmental exposures recapitulate multi-omic signatures of T1D. In total, we analyzed 121,272 cells/nuclei across 34 libraries, identifying 10 distinct cell types. We identified cell-type-specific and disease-associated cis-regulatory elements and nominated likely target genes. We provide evidence that T1D genetic risk is mediated through multiple pancreatic cell populations, including islet endocrine cells (beta, alpha, gamma, and delta), exocrine acinar and ductal cells, and immune cells. Finally, we identified three independent T1D risk variants acting through pancreatic islet endocrine cells at the TOX, RASGRP1, and DLK1/MEG3 loci. Together, this work improves our understanding of how non-coding genetic variants encode T1D risk through a complex interplay of different cell types in the pancreas.

genomics↗

bioRxiv2022

Three-dimensional chromatin re-organization during muscle stem cell aging

Age-related skeletal muscle atrophy or sarcopenia is a significant societal problem that is becoming amplified as the worlds population continues to increase. A critical contributor to sarcopenia is the loss in the number and function of muscle stem cells, which maintain tissue homeostasis and regenerate damage. The molecular mechanisms that govern muscle stem cell aging encompass changes across multiple regulatory layers and are integrated by the three-dimensional organization of the genome. To quantitatively understand how hierarchical chromatin architecture changes during muscle stem cell aging, we generated 3D chromatin conformation maps (Hi-C) and integrated these datasets with multi-omic (chromatin accessibility and transcriptome) profiles from bulk populations and single cells. We observed that muscle stem cells display static behavior at global scales of chromatin organization during aging and extensive rewiring of local contacts at finer scales that were associated with variations in transcription factor binding and aberrant gene expression. These data provide insights into genome topology as a regulator of molecular function in stem cell aging.

cell biology↗

bioRxiv2022

Retrograde mitochondrial signaling governs the identity and maturity of metabolic tissues

Mitochondrial damage is a hallmark of metabolic diseases, including diabetes and metabolic dysfunction-associated steatotic liver disease, yet the consequences of impaired mitochondria in metabolic tissues are often unclear. Here, we report that dysfunctional mitochondrial quality control engages a retrograde (mitonuclear) signaling program that impairs cellular identity and maturity across multiple metabolic tissues. Surprisingly, we demonstrate that defects in the mitochondrial quality control machinery, which we observe in pancreatic {beta} cells of humans with type 2 diabetes, cause reductions of {beta} cell mass due to dedifferentiation, rather than apoptosis. Utilizing transcriptomic profiling, lineage tracing, and assessments of chromatin accessibility, we find that targeted deficiency anywhere in the mitochondrial quality control pathway (e.g., genome integrity, dynamics, or turnover) activate the mitochondrial integrated stress response and promote cellular immaturity in {beta} cells, hepatocytes, and brown adipocytes. Intriguingly, pharmacologic blockade of mitochondrial retrograde signaling in vivo restores {beta} cell mass and identity to ameliorate hyperglycemia following mitochondrial damage. Thus, we observe that a shared mitochondrial retrograde response controls cellular identity across metabolic tissues and may be a promising target to treat or prevent metabolic disorders.

cell biology↗

bioRxiv2022

Fixing reference errors efficiently improves sequencing results

The GRCh38 reference is the current standard in human genomics research and clinical applications, but includes errors across 33 protein-coding genes, including 12 with medical relevance. Current studies rely on the correctness of this reference genome and require an accurate and cost-effective way to improve variant calling and expression analysis across these erroneous loci. We identified likely artifacts in GTEx, gnomAD, 1000 Genomes Project, and other important genomic resources leading to wrong interpretations for these genes. Here, we present FixItFelix together with a modified GRCh38 version that improves the subsequent analysis across these genes within minutes for an existing BAM/CRAM file. We showcase these improvements over multi-ethnic control samples across short and long-read DNA-, and RNA-sequencing. Furthermore, applying our approach across thousands of genomes demonstrates improvements for population variant calling as well as eQTL studies. Still, some genes e.g., DUSP22 indicate mixed results due to their complexity.

genomics↗

bioRxiv2021

RFX6-mediated dysregulation defines human β cell dysfunction in early type 2 diabetes

A hallmark of type 2 diabetes (T2D), a major cause of world-wide morbidity and mortality, is dysfunction of insulin-producing pancreatic islet {beta} cells1-3. T2D genome-wide association studies (GWAS) have identified hundreds of signals, mostly in the non-coding genome and overlapping {beta} cell regulatory elements, but translating these into biological mechanisms has been challenging4-6. To identify early disease-driving events, we performed single cell spatial proteomics, sorted cell transcriptomics, and assessed islet physiology on pancreatic tissue from short-duration T2D and control donors. Here, through integrative analyses of these diverse modalities, we show that multiple gene regulatory modules are associated with early-stage T2D {beta} cell-intrinsic defects. One notable example is the transcription factor RFX6, which we show is a highly connected {beta} cell hub gene that is reduced in T2D and governs a gene regulatory network associated with insulin secretion defects and T2D GWAS variants. We validated the critical role of RFX6 in {beta} cells through direct perturbation in primary human islets followed by physiological and single nucleus multiome profiling, which showed reduced dynamic insulin secretion and large-scale changes in the {beta} cell transcriptome and chromatin accessibility landscape. Understanding the molecular mechanisms of complex, systemic diseases necessitates integration of signals from multiple molecules, cells, organs, and individuals and thus we anticipate this approach will be a useful template to identify and validate key regulatory networks and master hub genes for other diseases or traits with GWAS data.

cell biology↗

bioRxiv2021

Combinatorial transcription factor profiles predict mature and functional human islet α and β cells

Islet-enriched transcription factors (TFs) exert broad control over cellular processes in pancreatic and {beta} cells and changes in their expression are associated with developmental state and diabetes. However, the implications of heterogeneity in TF expression across islet cell populations are not well understood. To define this TF heterogeneity and its consequences for cellular function, we profiled >40,000 cells from normal human islets by scRNA-seq and stratified and {beta} cells based on combinatorial TF expression. Subpopulations of islet cells co-expressing ARX/MAFB ( cells) and MAFA/MAFB ({beta} cells) exhibited greater expression of key genes related to glucose sensing and hormone secretion relative to subpopulations expressing only one or neither TF. Moreover, all subpopulations were identified in native pancreatic tissue from multiple donors. By Patch-seq, MAFA/MAFB co-expressing {beta} cells showed enhanced electrophysiological activity. Thus, these results indicate combinatorial TF expression in islet and {beta} cells predicts highly functional, mature subpopulations.

genomics↗

bioRxiv2020

Human and rat skeletal muscle single-nuclei multi-omic integrative analyses nominate causal cell types, regulatory elements, and SNPs for complex traits

BackgroundSkeletal muscle accounts for the largest proportion of human body mass, on average, and is a key tissue in complex diseases, mobility, and quality of life. It is composed of several different cell and muscle fiber types. ResultsHere, we optimize single-nucleus ATAC-seq (snATAC-seq) to map skeletal muscle cell-specific chromatin accessibility landscapes in frozen human and rat samples, and single-nucleus RNA-seq (snRNA-seq) to map cell-specific transcriptomes in human. We capture type I and type II muscle fiber signatures, which are generally missed by existing single-cell RNA-seq methods. We perform cross-modality and cross-species integrative analyses on 30,531 nuclei, representing 11 libraries, profiled in this study, and identify seven distinct cell types ranging in abundance from 63% (type II fibers) to 0.9% (muscle satellite cells) of all nuclei. We introduce a regression-based approach to infer cell types by comparing transcription start site-distal ATAC-seq peaks to reference enhancer maps and show consistency with RNA-based marker gene cell type assignments. We find heterogeneity in enrichment of genetic variants linked to complex phenotypes from the UK Biobank and diabetes genome wide association studies in cell-specific ATAC-seq peaks, with the most striking enrichment patterns in muscle mesenchymal stem cells ([~]3% of nuclei). Finally, we overlay these chromatin accessibility maps on GWAS data to nominate causal cell types, SNPs, and transcription factor motifs for creatinine levels and type 2 diabetes signals. ConclusionsThese chromatin accessibility profiles for human and rat skeletal muscle cell types are a useful resource for investigating specific cell types and nominating causal GWAS SNPs and cell types.

genomics↗

bioRxiv2019

Integrating enhancer RNA signatures with diverse omics data identifies characteristics of transcription initiation in pancreatic islets

Identifying the tissue-specific molecular signatures of active regulatory elements is critical to understand gene regulatory mechanisms. Here, we identify transcription start sites (TSS) using cap analysis of gene expression (CAGE) across 57 human pancreatic islet samples. We identify 9,954 reproducible CAGE tag clusters (TCs), ~20% of which are islet-specific and occur mostly distal to known gene TSSs. We integrated islet CAGE data with histone modification and chromatin accessibility profiles to identify epigenomic signatures of transcription initiation. Using a massively parallel reporter assay, we validate transcriptional enhancer activity (5% FDR) for 2,279 of 3,378 (~68%) tested islet CAGE elements. TCs within accessible enhancers show higher enrichment to overlap type 2 diabetes genome-wide association study (GWAS) signals than existing islet annotations, which emphasizes the utility of mapping CAGE profiles in disease-relevant tissue. This work provides a high-resolution map of transcriptional initiation in human pancreatic islets with utility for dissecting functional enhancers at GWAS loci.

genomics↗