Journal
MOLECULAR METABOLISM
Volume 32, Issue -, Pages 109-121Publisher
ELSEVIER
DOI: 10.1016/j.molmet.2019.12.006
Keywords
Islet; Epigenomics; Deep learning; Single cell; Chromatin; Type 2 diabetes
Categories
Funding
- American Diabetes Association Fellowship
- National Institute of Diabetes and Digestive and Kidney Diseases [R01 DK117960]
- National Heart, Lung, and Blood Institute [U01 HL137182]
- ADA Pathway to Stop Diabetes grant [1-14-INI-07]
- National Institutes of Health [1-ZIA-HG000024]
- NATIONAL HUMAN GENOME RESEARCH INSTITUTE [ZIAHG000024] Funding Source: NIH RePORTER
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Objective: Type 2 diabetes (T2D) is a complex disease characterized by pancreatic islet dysfunction, insulin resistance, and disruption of blood glucose levels. Genome-wide association studies (GWAS) have identified > 400 independent signals that encode genetic predisposition. More than 90% of associated single-nucleotide polymorphisms (SNPs) localize to non-coding regions and are enriched in chromatin-defined islet enhancer elements, indicating a strong transcriptional regulatory component to disease susceptibility. Pancreatic islets are a mixture of cell types that express distinct hormonal programs, so each cell type may contribute differentially to the underlying regulatory processes that modulate T2D-associated transcriptional circuits. Existing chromatin profiling methods such as ATAC-seq and DNase-seq, applied to islets in bulk, produce aggregate profiles that mask important cellular and regulatory heterogeneity. Methods: We present genome-wide single-cell chromatin accessibility profiles in >1,600 cells derived from a human pancreatic islet sample using single-cell combinatorial indexing ATAC-seq (sci-ATAC-seq). We also developed a deep learning model based on U-Net architecture to accurately predict open chromatin peak calls in rare cell populations. Results: We show that sci-ATAC-seq profiles allow us to deconvolve alpha, beta, and delta cell populations and identify cell-type-specific regulatory signatures underlying T2D. Particularly, T2D GWAS SNPs are significantly enriched in beta cell-specific and across cell-type shared islet open chromatin, but not in alpha or delta cell-specific open chromatin. We also demonstrate, using less abundant delta cells, that deep learning models can improve signal recovery and feature reconstruction of rarer cell populations. Finally, we use co-accessibility measures to nominate the cell-specific target genes at 104 non-coding T2D GWAS signals. Conclusions: Collectively, we identify the islet cell type of action across genetic signals of T2D predisposition and provide higher-resolution mechanistic insights into genetically encoded risk pathways. Published by Elsevier GmbH.
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