Pancreatic islets consist of multiple cell types that produce hormones required for glucose homeostasis, and islet dysfunction is a major factor in type 1 and type 2 diabetes. Numerous studies have assessed transcription across individual cell types using single-cell assays; however, there is no canonical reference of gene expression in islet cell types that is also easily accessible for researchers to query and use in bioinformatics pipelines. Here we present an integrated map of islet cell type-specific gene expression from 192,203 cells from single-cell RNA sequencing of 65 donors without diabetes, donors who were type 1 diabetes autoantibody positive, donors with type 1 diabetes, and donors with type 2 diabetes from the Human Pancreas Analysis Program. We identified 10 distinct cell types, annotated subpopulations of several cell types, and defined cell type-specific marker genes. We tested differential expression within each cell type across disease states and identified 1,701 genes with significant changes in expression, with most changes observed in β-cells from donors with type 1 diabetes. To facilitate user interaction, we provide several single-cell visualization and reference mapping tools, as well as the open-access analytical pipelines used to create this reference. The results will serve as a valuable resource to investigators studying islet biology.

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Glucocorticoids are key regulators of glucose homeostasis and pancreatic islet function, but the gene regulatory programs driving responses to glucocorticoid signaling in islets and the contribution of these programs to diabetes risk are unknown. In this study we used ATAC-seq and RNA-seq to map chromatin accessibility and gene expression from eleven primary human islet samples cultured in vitro with the glucocorticoid dexamethasone at multiple doses and durations. We identified thousands of accessible chromatin sites and genes with significant changes in activity in response to glucocorticoids. Chromatin sites up-regulated in glucocorticoid signaling were prominently enriched for glucocorticoid receptor binding sites and up-regulated genes were enriched for ion transport and lipid metabolism, whereas down-regulated chromatin sites and genes were enriched for inflammatory, stress response and proliferative processes. Genetic variants associated with glucose levels and T2D risk were enriched in glucocorticoid-responsive chromatin sites, including fine-mapped variants at 51 known signals. Among fine-mapped variants in glucocorticoid-responsive chromatin, a likely casual variant at the 2p21 locus had glucocorticoid-dependent allelic effects on beta cell enhancer activity and affected SIX2 and SIX3 expression. Our results provide a comprehensive map of islet regulatory programs in response to glucocorticoids through which we uncover a role for islet glucocorticoid signaling in mediating genetic risk of T2D.

Genetic risk variants that have been identified in genome-wide association studies of complex diseases are primarily non-coding¹. Translating these risk variants into mechanistic insights requires detailed maps of gene regulation in disease-relevant cell types². Here we combined two approaches: a genome-wide association study of type 1 diabetes (T1D) using 520,580 samples, and the identification of candidate cis-regulatory elements (cCREs) in pancreas and peripheral blood mononuclear cells using single-nucleus assay for transposase-accessible chromatin with sequencing (snATAC-seq) of 131,554 nuclei. Risk variants for T1D were enriched in cCREs that were active in T cells and other cell types, including acinar and ductal cells of the exocrine pancreas. Risk variants at multiple T1D signals overlapped with exocrine-specific cCREs that were linked to genes with exocrine-specific expression. At the CFTR locus, the T1D risk variant rs7795896 mapped to a ductal-specific cCRE that regulated CFTR; the risk allele reduced transcription factor binding, enhancer activity and CFTR expression in ductal cells. These findings support a role for the exocrine pancreas in the pathogenesis of T1D and highlight the power of large-scale genome-wide association studies and single-cell epigenomics for understanding the cellular origins of complex disease.

Gene regulation is highly cell type-specific and understanding the function of non-coding genetic variants associated with complex traits requires molecular phenotyping at cell type resolution. In this study we performed single nucleus ATAC-seq (snATAC-seq) and genotyping in peripheral blood mononuclear cells from 13 individuals. Clustering chromatin accessibility profiles of 96,002 total nuclei identified 17 immune cell types and sub-types. We mapped chromatin accessibility QTLs (caQTLs) in each immune cell type and sub-type using individuals of European ancestry which identified 6,901 caQTLs at FDR < .10 and 4,220 caQTLs at FDR < .05, including those obscured from assays of bulk tissue such as with divergent effects on different cell types. For 3,941 caQTLs we further annotated putative target genes of variant activity using single cell co-accessibility, and caQTL variants were significantly correlated with the accessibility level of linked gene promoters. We fine-mapped loci associated with 16 complex immune traits and identified immune cell caQTLs at 622 candidate causal variants, including those with cell type-specific effects. At the 6q15 locus associated with type 1 diabetes, in line with previous reports, variant rs72928038 was a naïve CD4+ T cell caQTL linked to BACH2 and we validated the allelic effects of this variant on regulatory activity in Jurkat T cells. These results highlight the utility of snATAC-seq for mapping genetic effects on accessible chromatin in specific cell types.

Dysfunctional pancreatic islet beta cells are a hallmark of type 2 diabetes (T2D), but a comprehensive understanding of the underlying mechanisms, including gene dysregulation, is lacking. Here we integrate information from measurements of chromatin accessibility, gene expression and function in single beta cells with genetic association data to nominate disease-causal gene regulatory changes in T2D. Using machine learning on chromatin accessibility data from 34 nondiabetic, pre-T2D and T2D donors, we identify two transcriptionally and functionally distinct beta cell subtypes that undergo an abundance shift during T2D progression. Subtype-defining accessible chromatin is enriched for T2D risk variants, suggesting a causal contribution of subtype identity to T2D. Both beta cell subtypes exhibit activation of a stress-response transcriptional program and functional impairment in T2D, which is probably induced by the T2D-associated metabolic environment. Our findings demonstrate the power of multimodal single-cell measurements combined with machine learning for characterizing mechanisms of complex diseases.

Genetic variants affecting pancreatic islet enhancers are central to T2D risk, but the gene targets of islet enhancer activity are largely unknown. We generate a high-resolution map of islet chromatin loops using Hi-C assays in three islet samples and use loops to annotate target genes of islet enhancers defined using ATAC-seq and published ChIP-seq data. We identify candidate target genes for thousands of islet enhancers, and find that enhancer looping is correlated with islet-specific gene expression. We fine-map T2D risk variants affecting islet enhancers, and find that candidate target genes of these variants defined using chromatin looping and eQTL mapping are enriched in protein transport and secretion pathways. At IGF2BP2, a fine-mapped T2D variant reduces islet enhancer activity and IGF2BP2 expression, and conditional inactivation of IGF2BP2 in mouse islets impairs glucose-stimulated insulin secretion. Our findings provide a resource for studying islet enhancer function and identifying genes involved in T2D risk.

Single-nucleus assay for transposase-accessible chromatin using sequencing (snATAC-seq) creates new opportunities to dissect cell type-specific mechanisms of complex diseases. Since pancreatic islets are central to type 2 diabetes (T2D), we profiled 15,298 islet cells by using combinatorial barcoding snATAC-seq and identified 12 clusters, including multiple alpha, beta and delta cell states. We cataloged 228,873 accessible chromatin sites and identified transcription factors underlying lineage- and state-specific regulation. We observed state-specific enrichment of fasting glucose and T2D genome-wide association studies for beta cells and enrichment for other endocrine cell types. At T2D signals localized to islet-accessible chromatin, we prioritized variants with predicted regulatory function and co-accessibility with target genes. A causal T2D variant rs231361 at the KCNQ1 locus had predicted effects on a beta cell enhancer co-accessible with INS and genome editing in embryonic stem cell-derived beta cells affected INS levels. Together our findings demonstrate the power of single-cell epigenomics for interpreting complex disease genetics.

We combined functional genomics and human genetics to investigate processes that affect type 1 diabetes (T1D) risk by mediating beta cell survival in response to proinflammatory cytokines. We mapped 38,931 cytokine-responsive candidate cis-regulatory elements (cCREs) in beta cells using ATAC-seq and snATAC-seq and linked them to target genes using co-accessibility and HiChIP. Using a genome-wide CRISPR screen in EndoC-βH1 cells, we identified 867 genes affecting cytokine-induced survival, and genes promoting survival and up-regulated in cytokines were enriched at T1D risk loci. Using SNP-SELEX, we identified 2,229 variants in cytokine-responsive cCREs altering transcription factor (TF) binding, and variants altering binding of TFs regulating stress, inflammation, and apoptosis were enriched for T1D risk. At the 16p13 locus, a fine-mapped T1D variant altering TF binding in a cytokine-induced cCRE interacted with SOCS1, which promoted survival in cytokine exposure. Our findings reveal processes and genes acting in beta cells during inflammation that modulate T1D risk.