Single-cell chromatin accessibility identifies pancreatic islet cell type– and state-specific regulatory programs of diabetes 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 barcodin...
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Published in | Nature genetics Vol. 53; no. 4; pp. 455 - 466 |
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Main Authors | , , , , , , , , , , , , , , , , , , , |
Format | Journal Article |
Language | English |
Published |
New York
Nature Publishing Group US
01.04.2021
Nature Publishing Group |
Subjects | |
Online Access | Get full text |
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Summary: | 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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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 Authors contributed equally to this work K.J.G., D.U.G, and M.Sander conceived of and supervised the research in the study; K.J.G., D.U.G., M.Sander, J.C., C.Z, and Z.C. wrote the manuscript; J.C. performed analyses of single cell and genetic data; C.Z., M.Schlichting and J.W. performed hESC experiments; Z.C. performed analyses of single cell and Hi-C data; J.Y.H. performed combinatorial barcoding single cell assays and genotyping; M.M performed 10x single cell assays; R.M. performed Hi-C experiments; S.H., A.D. and M.O. performed reporter experiments; Y.Q. performed analyses of 4C data; Y.Sui performed analyses of hESC data; Y.Sun and P.K. developed and processed data for the epigenome database; R.F. contributed to analyses of single cell data; S.P. contributed to the development of single cell assays. AUTHOR CONTRIBUTIONS Authors jointly supervised this work |
ISSN: | 1061-4036 1546-1718 |
DOI: | 10.1038/s41588-021-00823-0 |