Characterisation of Ppy-lineage cells clarifies the functional heterogeneity of pancreatic beta cells in mice.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:34498099", "kind": "science-record", "revision": 8, "content_hash": "sha256:dfb5fc700f2390470bcac860b92f68e306b88af502f48f30d29d3b4f846e5be9", "created_at": "2026-11-01T15:41:51Z", "updated_at": "2026-11-01T22:47:28Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Characterisation of Ppy-lineage cells clarifies the functional heterogeneity of pancreatic beta cells in mice.", "doi": "10.1007/s00125-021-05560-x", "pmid": "34498099", "authors": ["Takahiro Fukaishi", "Yuko Nakagawa", "Ayako Fukunaka", "Takashi Sato", "Akemi Hara", "Keiko Nakao", "Michiko Saito", "Kenji Kohno", "Takeshi Miyatsuka", "Motoyuki Tamaki", "Munehide Matsuhisa", "Taka-Aki Matsuoka", "Tetsuya Yamada", "Hirotaka Watada", "Yoshio Fujitani"], "journal": "Diabetologia", "year": 2021, "abstract": "AIMS/HYPOTHESIS: Pancreatic polypeptide (PP) cells, which secrete PP (encoded by the Ppy gene), are a minor population of pancreatic endocrine cells. Although it has been reported that the loss of beta cell identity might be associated with beta-to-PP cell-fate conversion, at present, little is known regarding the characteristics of Ppy-lineage cells.\nMETHODS: We used Ppy-Cre driver mice and a PP-specific monoclonal antibody to investigate the association between Ppy-lineage cells and beta cells. The molecular profiles of endocrine cells were investigated by single-cell transcriptome analysis and the glucose responsiveness of beta cells was assessed by Ca2+ imaging. Diabetic conditions were experimentally induced in mice by either streptozotocin or diphtheria toxin.\nRESULTS: Ppy-lineage cells were found to contribute to the four major types of endocrine cells, including beta cells. Ppy-lineage beta cells are a minor subpopulation, accounting for 12-15% of total beta cells, and are mostly (81.2%) localised at the islet periphery. Unbiased single-cell analysis with a Ppy-lineage tracer demonstrated that beta cells are composed of seven clusters, which are categorised into two groups (i.e. Ppy-lineage and non-Ppy-lineage beta cells). These subpopulations of beta cells demonstrated distinct characteristics regarding their functionality and gene expression profiles. Ppy-lineage beta cells had a reduced glucose-stimulated Ca2+ signalling response and were increased in number in experimental diabetes models.\nCONCLUSIONS/INTERPRETATION: Our results indicate that an unexpected degree of beta cell heterogeneity is defined by Ppy gene activation, providing valuable insight into the homeostatic regulation of pancreatic islets and future therapeutic strategies against diabetes.\nDATA AVAILABILITY: The single-cell RNA sequence (scRNA-seq) analysis datasets generated in this study have been deposited in the Gene Expression Omnibus (GEO) under the accession number GSE166164 ( www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE166164 ).", "claims": [{"id": "claim-method", "statement": "Statistical comparisons between two groups were performed by the two-tailed Student’s t test, and one-way ANOVA followed by the Bonferroni post hoc test was used for comparisons between groups. For the analysis of statistical significance, the likelihood ratio test was performed by modelling as a two-part generalised regression model using model-based analysis of single-cell transcriptomics [24], and the Bonferroni method was used for multiple comparison adjustment. The scRNA-seq analysis datasets generated in this study have been deposited in the Gene Expression Omnibus (GEO) under the accession number GSE166164 (www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE166164).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34498099", "locator": "sections[7] \"Statistical analysis (other than for scRNA-seq analysis data)\" paragraph 2", "quote": "Statistical comparisons between two groups were performed by the two-tailed Student’s t test, and one-way ANOVA followed by the Bonferroni post hoc test was used for comparisons between groups."}, {"asset": "source:pmid:34498099", "locator": "sections[9] \"Analysis of scRNA-seq data\" paragraph 6", "quote": "For the analysis of statistical significance, the likelihood ratio test was performed by modelling as a two-part generalised regression model using model-based analysis of single-cell transcriptomics [24], and the Bonferroni method was used for multiple comparison adjustment."}, {"asset": "source:pmid:34498099", "locator": "sections[25] \"Data availability\" paragraph 2", "quote": "The scRNA-seq analysis datasets generated in this study have been deposited in the Gene Expression Omnibus (GEO) under the accession number GSE166164 (www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE166164)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The scRNA-seq analysis datasets generated in this study have been deposited in the Gene Expression Omnibus (GEO) under the accession number GSE166164 (www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE166164). 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