Single-cell RNA sequencing combined with single-cell proteomics identifies the metabolic adaptation of islet cell subpopulations to high-fat diet in mice.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36538064", "kind": "science-record", "revision": 2, "content_hash": "sha256:f83e63674785b5b2420726d261bf4ad578b2b2bd5086666426f0041ba9bdb83a", "created_at": "2026-11-01T19:01:03Z", "updated_at": "2026-11-01T21:29:44Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Single-cell RNA sequencing combined with single-cell proteomics identifies the metabolic adaptation of islet cell subpopulations to high-fat diet in mice.", "doi": "10.1007/s00125-022-05849-5", "pmid": "36538064", "authors": ["Qi Fu", "Hemin Jiang", "Yu Qian", "Hui Lv", "Hao Dai", "Yuncai Zhou", "Yang Chen", "Yunqiang He", "Rui Gao", "Shuai Zheng", "Yucheng Liang", "Siqi Li", "Xinyu Xu", "Kuanfeng Xu", "Tao Yang"], "journal": "Diabetologia", "year": 2023, "abstract": "AIMS/HYPOTHESIS: Islets have complex heterogeneity and subpopulations. Cell surface markers representing alpha, beta and delta cell subpopulations are urgently needed for investigations to explore the compositional changes of each subpopulation in obesity progress and diabetes onset, and the adaptation mechanism of islet metabolism induced by a high-fat diet (HFD).\nMETHODS: Single-cell RNA sequencing (scRNA-seq) was applied to identify alpha, beta and delta cell subpopulation markers in an HFD-induced mouse model of glucose intolerance. Flow cytometry and immunostaining were used to sort and assess the proportion of each subpopulation. Single-cell proteomics was performed on sorted cells, and the functional status of each alpha, beta and delta cell subpopulation in glucose intolerance was deeply elucidated based on protein expression.\nRESULTS: A total of 33,999 cells were analysed by scRNA-seq and clustered into eight populations, including alpha, beta and delta cells. For alpha cells, scRNA-seq revealed that the Ace2low subpopulation had downregulated expression of genes related to alpha cell function and upregulated expression of genes associated with beta cell characteristics in comparison with the Ace2high subpopulation. The impaired function and increased fragility of ACE2low alpha cells exposure to HFD was further suggested by single-cell proteomics. As for beta cells, the CD81high subpopulation may indicate an immature signature of beta cells compared with the CD81low subpopulation, which had robust function. We also found differential expression of Slc2a2 in delta cells and a potentially stronger cellular function and metabolism in GLUT2low delta cells than GLUT2high delta cells. Moreover, an increased proportion of ACE2low alpha cells and CD81low beta cells, with a constant proportion of GLUT2low delta cells, were observed in HFD-induced glucose intolerance.\nCONCLUSIONS/INTERPRETATION: We identified ACE2, CD81 and GLUT2 as surface markers to distinguish, respectively, alpha, beta and delta cell subpopulations with heterogeneous maturation and function. The changes in the proportion and functional status of islet endocrine subpopulations reflect the metabolic adaptation of islets to high-fat stress, which weakened the function of alpha cells and enhanced the function of beta and delta cells to bring about glycaemic homeostasis. Our findings provide a fundamental resource for exploring the mechanisms maintaining each islet endocrine subpopulation's fate and function in health and disease.\nDATA AVAILABILITY: The scRNA-seq analysis datasets from the current study are available in the Gene Expression Omnibus (GEO) repository under the accession number GSE203376.", "claims": [{"id": "claim-method", "statement": "Comparisons of data between two groups were carried out using two-tailed t tests.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36538064", "locator": "sections[15] \"Statistical analysis\" paragraph 2", "quote": "Comparisons of data between two groups were carried out using two-tailed t tests."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The scRNA-seq analysis datasets during the current study are available in the Gene Expression Omnibus (GEO) repository under the accession number GSE203376 (www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE203376). Other datasets are available from the corresponding authors upon reasonable request.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36538064", "locator": "sections[30] \"notes\" paragraph 1", "quote": "The scRNA-seq analysis datasets during the current study are available in the Gene Expression Omnibus (GEO) repository under the accession number GSE203376 (www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE203376). 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