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 inNature genetics Vol. 53; no. 4; pp. 455 - 466
Main Authors Chiou, Joshua, Zeng, Chun, Cheng, Zhang, Han, Jee Yun, Schlichting, Michael, Miller, Michael, Mendez, Robert, Huang, Serina, Wang, Jinzhao, Sui, Yinghui, Deogaygay, Allison, Okino, Mei-Lin, Qiu, Yunjiang, Sun, Ying, Kudtarkar, Parul, Fang, Rongxin, Preissl, Sebastian, Sander, Maike, Gorkin, David U., Gaulton, Kyle J.
Format Journal Article
LanguageEnglish
Published New York Nature Publishing Group US 01.04.2021
Nature Publishing Group
Subjects
45/23
631/114/1314
631/208/177
692/699/2743/137/773
Accessibility
Agriculture
Animal Genetics and Genomics
Beta cells
Biomedical and Life Sciences
Biomedicine
Blood Glucose - metabolism
Cancer Research
Cell Differentiation
Chromatin
Chromatin - chemistry
Chromatin - metabolism
Combinatorial analysis
Diabetes
Diabetes mellitus (non-insulin dependent)
Diabetes Mellitus, Type 2 - genetics
Diabetes Mellitus, Type 2 - metabolism
Diabetes Mellitus, Type 2 - pathology
Disease
Epigenomics
Fasting
Gene expression
Gene Expression Profiling
Gene Function
Genetic aspects
Genetic regulation
Genetic research
Genetics
Genome editing
Genome-wide association studies
Genome-Wide Association Study
Genomes
Glucagon-Secreting Cells - metabolism
Glucagon-Secreting Cells - pathology
Health risk assessment
Health risks
High-Throughput Nucleotide Sequencing
Human Embryonic Stem Cells - cytology
Human Genetics
Humans
Identification and classification
Insulin-Secreting Cells - metabolism
Insulin-Secreting Cells - pathology
Islet cells
KCNQ1 Potassium Channel - genetics
KCNQ1 Potassium Channel - metabolism
KCNQ1 protein
Multigene Family
Pancreas
Pancreatic beta cells
Pancreatic Polypeptide-Secreting Cells - metabolism
Pancreatic Polypeptide-Secreting Cells - pathology
Polymorphism, Genetic
Potassium channels (voltage-gated)
Risk factors
Single-Cell Analysis
Somatostatin-Secreting Cells - metabolism
Somatostatin-Secreting Cells - pathology
Structure
Transcription factors
Transcription Factors - classification
Transcription Factors - genetics
Transcription Factors - metabolism
Transposase
Type 2 diabetes
United States
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Abstract 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.
AbstractList Single nucleus ATAC-seq (snATAC-seq) creates new opportunities to dissect cell type-specific mechanisms of complex diseases. As pancreatic islets are central to type 2 diabetes (T2D), we profiled 15.3k islet cells 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 GWAS for beta cells as well as 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-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-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 cellderived beta cells affected INS levels. Together our findings demonstrate the power of single-cell epigenomics for interpreting complex disease genetics.
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.
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-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-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.
Audience Academic
Author Sun, Ying
Mendez, Robert
Sui, Yinghui
Wang, Jinzhao
Kudtarkar, Parul
Preissl, Sebastian
Han, Jee Yun
Cheng, Zhang
Gaulton, Kyle J.
Deogaygay, Allison
Chiou, Joshua
Schlichting, Michael
Gorkin, David U.
Huang, Serina
Sander, Maike
Qiu, Yunjiang
Zeng, Chun
Fang, Rongxin
Miller, Michael
Okino, Mei-Lin
AuthorAffiliation 1. Biomedical Graduate Studies Program, University of California San Diego, La Jolla CA
5. Institute for Genomic Medicine, University of California San Diego, La Jolla CA
2. Department of Pediatrics, Pediatric Diabetes Research Center, University of California San Diego, La Jolla CA
3. Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla CA
4. Center for Epigenomics, University of California San Diego, La Jolla CA
AuthorAffiliation_xml – name: 4. Center for Epigenomics, University of California San Diego, La Jolla CA
– name: 5. Institute for Genomic Medicine, University of California San Diego, La Jolla CA
– name: 3. Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla CA
– name: 1. Biomedical Graduate Studies Program, University of California San Diego, La Jolla CA
– name: 2. Department of Pediatrics, Pediatric Diabetes Research Center, University of California San Diego, La Jolla CA
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  orcidid: 0000-0002-4618-0647
  surname: Chiou
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  organization: Department of Pediatrics, Pediatric Diabetes Research Center, University of California, San Diego, Department of Cellular and Molecular Medicine, University of California, San Diego
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  givenname: Zhang
  orcidid: 0000-0003-3863-2866
  surname: Cheng
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  organization: Center for Epigenomics, University of California, San Diego
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  givenname: Jee Yun
  surname: Han
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  organization: Center for Epigenomics, University of California, San Diego
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  surname: Schlichting
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  organization: Center for Epigenomics, University of California, San Diego
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  fullname: Huang, Serina
  organization: Department of Pediatrics, Pediatric Diabetes Research Center, University of California, San Diego
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  givenname: Jinzhao
  orcidid: 0000-0003-1595-9460
  surname: Wang
  fullname: Wang, Jinzhao
  organization: Department of Pediatrics, Pediatric Diabetes Research Center, University of California, San Diego, Department of Cellular and Molecular Medicine, University of California, San Diego
– sequence: 10
  givenname: Yinghui
  surname: Sui
  fullname: Sui, Yinghui
  organization: Department of Pediatrics, Pediatric Diabetes Research Center, University of California, San Diego, Department of Cellular and Molecular Medicine, University of California, San Diego
– sequence: 11
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  givenname: Yunjiang
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  surname: Qiu
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  organization: Department of Pediatrics, Pediatric Diabetes Research Center, University of California, San Diego
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  givenname: Rongxin
  surname: Fang
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  organization: Department of Cellular and Molecular Medicine, University of California, San Diego
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  givenname: Sebastian
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  surname: Preissl
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  givenname: Maike
  orcidid: 0000-0001-5308-7785
  surname: Sander
  fullname: Sander, Maike
  email: masander@ucsd.edu
  organization: Department of Pediatrics, Pediatric Diabetes Research Center, University of California, San Diego, Department of Cellular and Molecular Medicine, University of California, San Diego, Institute for Genomic Medicine, University of California, San Diego
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  organization: Department of Pediatrics, Pediatric Diabetes Research Center, University of California, San Diego, Institute for Genomic Medicine, University of California, San Diego
BackLink https://www.ncbi.nlm.nih.gov/pubmed/33795864$$D View this record in MEDLINE/PubMed
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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
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Snippet Single-nucleus assay for transposase-accessible chromatin using sequencing (snATAC-seq) creates new opportunities to dissect cell type–specific mechanisms of...
Single-nucleus assay for transposase-accessible chromatin using sequencing (snATAC-seq) creates new opportunities to dissect cell type-specific mechanisms of...
Single nucleus ATAC-seq (snATAC-seq) creates new opportunities to dissect cell type-specific mechanisms of complex diseases. As pancreatic islets are central...
SourceID pubmedcentral
proquest
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pubmed
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springer
SourceType Open Access Repository
Aggregation Database
Index Database
Enrichment Source
Publisher
StartPage 455
SubjectTerms 45/23
631/114/1314
631/208/177
692/699/2743/137/773
Accessibility
Agriculture
Animal Genetics and Genomics
Beta cells
Biomedical and Life Sciences
Biomedicine
Blood Glucose - metabolism
Cancer Research
Cell Differentiation
Chromatin
Chromatin - chemistry
Chromatin - metabolism
Combinatorial analysis
Diabetes
Diabetes mellitus (non-insulin dependent)
Diabetes Mellitus, Type 2 - genetics
Diabetes Mellitus, Type 2 - metabolism
Diabetes Mellitus, Type 2 - pathology
Disease
Epigenomics
Fasting
Gene expression
Gene Expression Profiling
Gene Function
Genetic aspects
Genetic regulation
Genetic research
Genetics
Genome editing
Genome-wide association studies
Genome-Wide Association Study
Genomes
Glucagon-Secreting Cells - metabolism
Glucagon-Secreting Cells - pathology
Health risk assessment
Health risks
High-Throughput Nucleotide Sequencing
Human Embryonic Stem Cells - cytology
Human Genetics
Humans
Identification and classification
Insulin-Secreting Cells - metabolism
Insulin-Secreting Cells - pathology
Islet cells
KCNQ1 Potassium Channel - genetics
KCNQ1 Potassium Channel - metabolism
KCNQ1 protein
Multigene Family
Pancreas
Pancreatic beta cells
Pancreatic Polypeptide-Secreting Cells - metabolism
Pancreatic Polypeptide-Secreting Cells - pathology
Polymorphism, Genetic
Potassium channels (voltage-gated)
Risk factors
Single-Cell Analysis
Somatostatin-Secreting Cells - metabolism
Somatostatin-Secreting Cells - pathology
Structure
Transcription factors
Transcription Factors - classification
Transcription Factors - genetics
Transcription Factors - metabolism
Transposase
Type 2 diabetes
Title Single-cell chromatin accessibility identifies pancreatic islet cell type– and state-specific regulatory programs of diabetes risk
URI https://link.springer.com/article/10.1038/s41588-021-00823-0
https://www.ncbi.nlm.nih.gov/pubmed/33795864
https://www.proquest.com/docview/2512166998
https://www.proquest.com/docview/2508576319
https://pubmed.ncbi.nlm.nih.gov/PMC9037575
Volume 53
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