Integrating genetics with single-cell multiomic measurements across disease states identifies mechanisms of beta cell dysfunction in type 2 diabetes
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 sin...
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Veröffentlicht in: | Nature genetics 2023-06, Vol.55 (6), p.984-994 |
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creator | Wang, Gaowei Chiou, Joshua Zeng, Chun Miller, Michael Matta, Ileana Han, Jee Yun Kadakia, Nikita Okino, Mei-Lin Beebe, Elisha Mallick, Medhavi Camunas-Soler, Joan dos Santos, Theodore Dai, Xiao-Qing Ellis, Cara Hang, Yan Kim, Seung K. MacDonald, Patrick E. Kandeel, Fouad R. Preissl, Sebastian Gaulton, Kyle J. Sander, Maike |
description | 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.
Single-cell multiomic and functional characterization of human pancreatic islets identifies two beta cell subtypes correlated with type 2 diabetes progression that exhibit distinct gene regulatory programs and electrophysiological phenotypes. |
doi_str_mv | 10.1038/s41588-023-01397-9 |
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Single-cell multiomic and functional characterization of human pancreatic islets identifies two beta cell subtypes correlated with type 2 diabetes progression that exhibit distinct gene regulatory programs and electrophysiological phenotypes.</description><identifier>ISSN: 1061-4036</identifier><identifier>EISSN: 1546-1718</identifier><identifier>DOI: 10.1038/s41588-023-01397-9</identifier><identifier>PMID: 37231096</identifier><language>eng</language><publisher>New York: Nature Publishing Group US</publisher><subject>38/23 ; 45/91 ; 631/208/177 ; 631/553/1833 ; 692/699/2743/137/773 ; Accessibility ; Accuracy ; Agriculture ; Animal Genetics and Genomics ; Beta cells ; Biomedical and Life Sciences ; Biomedicine ; Cancer Research ; Cell activation ; Cells ; Chromatin ; Chromatin - metabolism ; Diabetes ; Diabetes mellitus (non-insulin dependent) ; Diabetes Mellitus, Type 2 - genetics ; Disease ; Gene expression ; Gene Expression Regulation ; Gene Function ; Genetics ; Human Genetics ; Humans ; Information processing ; Insulin-Secreting Cells - metabolism ; Learning algorithms ; Machine learning ; Multiomics ; Transcription activation ; Variance analysis</subject><ispartof>Nature genetics, 2023-06, Vol.55 (6), p.984-994</ispartof><rights>The Author(s), under exclusive licence to Springer Nature America, Inc. 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</rights><rights>2023. The Author(s), under exclusive licence to Springer Nature America, Inc.</rights><rights>Copyright Nature Publishing Group Jun 2023</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c475t-8ac4fa7460847c2314858eb99a94c69e510970e1b617b2f20fba39886a1dc9903</citedby><cites>FETCH-LOGICAL-c475t-8ac4fa7460847c2314858eb99a94c69e510970e1b617b2f20fba39886a1dc9903</cites><orcidid>0000-0002-4035-7915 ; 0000-0002-1884-1671 ; 0000-0002-9761-3609 ; 0000-0001-6148-8132 ; 0000-0001-8971-5616 ; 0000-0001-6135-7810 ; 0000-0003-1318-7161 ; 0000-0002-4618-0647</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>230,314,780,784,885,27924,27925</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/37231096$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Wang, Gaowei</creatorcontrib><creatorcontrib>Chiou, Joshua</creatorcontrib><creatorcontrib>Zeng, Chun</creatorcontrib><creatorcontrib>Miller, Michael</creatorcontrib><creatorcontrib>Matta, Ileana</creatorcontrib><creatorcontrib>Han, Jee Yun</creatorcontrib><creatorcontrib>Kadakia, Nikita</creatorcontrib><creatorcontrib>Okino, Mei-Lin</creatorcontrib><creatorcontrib>Beebe, Elisha</creatorcontrib><creatorcontrib>Mallick, Medhavi</creatorcontrib><creatorcontrib>Camunas-Soler, Joan</creatorcontrib><creatorcontrib>dos Santos, Theodore</creatorcontrib><creatorcontrib>Dai, Xiao-Qing</creatorcontrib><creatorcontrib>Ellis, Cara</creatorcontrib><creatorcontrib>Hang, Yan</creatorcontrib><creatorcontrib>Kim, Seung K.</creatorcontrib><creatorcontrib>MacDonald, Patrick E.</creatorcontrib><creatorcontrib>Kandeel, Fouad R.</creatorcontrib><creatorcontrib>Preissl, Sebastian</creatorcontrib><creatorcontrib>Gaulton, Kyle J.</creatorcontrib><creatorcontrib>Sander, Maike</creatorcontrib><title>Integrating genetics with single-cell multiomic measurements across disease states identifies mechanisms of beta cell dysfunction in type 2 diabetes</title><title>Nature genetics</title><addtitle>Nat Genet</addtitle><addtitle>Nat Genet</addtitle><description>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.
Single-cell multiomic and functional characterization of human pancreatic islets identifies two beta cell subtypes correlated with type 2 diabetes progression that exhibit distinct gene regulatory programs and electrophysiological phenotypes.</description><subject>38/23</subject><subject>45/91</subject><subject>631/208/177</subject><subject>631/553/1833</subject><subject>692/699/2743/137/773</subject><subject>Accessibility</subject><subject>Accuracy</subject><subject>Agriculture</subject><subject>Animal Genetics and Genomics</subject><subject>Beta cells</subject><subject>Biomedical and Life Sciences</subject><subject>Biomedicine</subject><subject>Cancer Research</subject><subject>Cell activation</subject><subject>Cells</subject><subject>Chromatin</subject><subject>Chromatin - metabolism</subject><subject>Diabetes</subject><subject>Diabetes mellitus (non-insulin dependent)</subject><subject>Diabetes Mellitus, Type 2 - genetics</subject><subject>Disease</subject><subject>Gene 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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.
Single-cell multiomic and functional characterization of human pancreatic islets identifies two beta cell subtypes correlated with type 2 diabetes progression that exhibit distinct gene regulatory programs and electrophysiological phenotypes.</abstract><cop>New York</cop><pub>Nature Publishing Group US</pub><pmid>37231096</pmid><doi>10.1038/s41588-023-01397-9</doi><tpages>11</tpages><orcidid>https://orcid.org/0000-0002-4035-7915</orcidid><orcidid>https://orcid.org/0000-0002-1884-1671</orcidid><orcidid>https://orcid.org/0000-0002-9761-3609</orcidid><orcidid>https://orcid.org/0000-0001-6148-8132</orcidid><orcidid>https://orcid.org/0000-0001-8971-5616</orcidid><orcidid>https://orcid.org/0000-0001-6135-7810</orcidid><orcidid>https://orcid.org/0000-0003-1318-7161</orcidid><orcidid>https://orcid.org/0000-0002-4618-0647</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | 38/23 45/91 631/208/177 631/553/1833 692/699/2743/137/773 Accessibility Accuracy Agriculture Animal Genetics and Genomics Beta cells Biomedical and Life Sciences Biomedicine Cancer Research Cell activation Cells Chromatin Chromatin - metabolism Diabetes Diabetes mellitus (non-insulin dependent) Diabetes Mellitus, Type 2 - genetics Disease Gene expression Gene Expression Regulation Gene Function Genetics Human Genetics Humans Information processing Insulin-Secreting Cells - metabolism Learning algorithms Machine learning Multiomics Transcription activation Variance analysis |
title | Integrating genetics with single-cell multiomic measurements across disease states identifies mechanisms of beta cell dysfunction in type 2 diabetes |
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