4.8 Article

Single-cell chromatin accessibility identifies pancreatic islet cell type- and state-specific regulatory programs of diabetes risk

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NATURE GENETICS
卷 53, 期 4, 页码 455-+

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NATURE PORTFOLIO
DOI: 10.1038/s41588-021-00823-0

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  1. NIH [R01DK114650, U01DK105554, R01DK068471, U01DK105541, U01DK120429]
  2. University of California, San Diego School of Medicine

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By analyzing human pancreatic islet cells using single-cell ATAC-seq, different cell clusters and transcription factors that underlie lineage- and state-specific regulation have been identified, which helps prioritize type 2 diabetes risk variants. This study demonstrates the power of single-cell epigenomics in interpreting complex disease genetics and sheds light on potential mechanisms underlying T2D.
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. Single-cell ATAC-seq analysis of human pancreatic islet cells identifies different cell clusters and transcription factors that underlie lineage- and state-specific regulation and helps prioritize type 2 diabetes risk variants.

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