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Kahn, S. E., Hull, R. L. & Utzschneider, K. M. Mechanisms linking obesity to insulin resistance and type 2 diabetes. Nature 444, 840–846 (2006).
Halban, P. A. et al. β-cell failure in type 2 diabetes: postulated mechanisms and prospects for prevention and treatment. Diabetes Care 37, 1751–1758 (2014).
Mahajan, A. et al. Fine-mapping type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps. Nat. Genet. 50, 1505–1513 (2018).
Rai, V. et al. Single-cell ATAC-seq in human pancreatic islets and deep learning upscaling of rare cells reveals cell-specific type 2 diabetes regulatory signatures. Mol. Metab. 32, 109–121 (2019).
Chiou, J. et al. Single-cell chromatin accessibility identifies pancreatic islet cell type- and state-specific regulatory programs of diabetes risk. Nat. Genet. 53, 455–466 (2021).
Ahlqvist, E., Prasad, R. B. & Groop, L. Subtypes of type 2 diabetes determined from clinical parameters. Diabetes 69, 2086–2093 (2020).
Redondo, M. J. et al. The clinical consequences of heterogeneity within and between different diabetes types. Diabetologia 63, 2040–2048 (2020).
Weitz, J., Menegaz, D. & Caicedo, A. Deciphering the complex communication networks that orchestrate pancreatic islet function. Diabetes 70, 17–26 (2020).
Vujkovic, M. et al. Discovery of 318 new risk loci for type 2 diabetes and related vascular outcomes among 1.4 million participants in a multi-ancestry meta-analysis. Nat. Genet. 52, 680–691 (2020).
Mahajan, A. et al. Multi-ancestry genetic study of type 2 diabetes highlights the power of diverse populations for discovery and translation. Nat. Genet. 54, 560–572 (2022).
Parker, S. C. J. et al. Chromatin stretch enhancer states drive cell-specific gene regulation and harbor human disease risk variants. Proc. Natl Acad. Sci. USA 110, 17921–17926 (2013).
Trynka, G. et al. Chromatin marks identify critical cell types for fine mapping complex trait variants. Nat. Genet. 45, 124–130 (2013).
Pasquali, L. et al. Pancreatic islet enhancer clusters enriched in type 2 diabetes risk-associated variants. Nat. Genet. 46, 136–43 (2014).
Walker, J. T., Saunders, D. C., Brissova, M. & Powers, A. C. The human islet: mini-organ with mega-impact. Endocr. Rev. 42, bnab010 (2021).
Brissova, M. et al. Assessment of human pancreatic islet architecture and composition by laser scanning confocal microscopy. J. Histochem. Cytochem. 53, 1087–1097 (2005).
Dai, C. et al. Stress-impaired transcription factor expression and insulin secretion in transplanted human islets. J. Clin. Invest. 126, 1857–1870 (2016).
Wigger, L. et al. Multi-omics profiling of living human pancreatic islet donors reveals heterogeneous beta cell trajectories towards type 2 diabetes. Nat. Metab. 3, 1017–1031 (2021).
Camunas-Soler, J. et al. Patch-seq links single-cell transcriptomes to human islet dysfunction in diabetes. Cell Metab. 31, 1017–1031.e4 (2020).
Shapira, S. N., Naji, A., Atkinson, M. A., Powers, A. C. & Kaestner, K. H. Understanding islet dysfunction in type 2 diabetes through multidimensional pancreatic phenotyping: The Human Pancreas Analysis Program. Cell Metab. 34, 1906–1913 (2022).
Albrechtsen, N. J. W. et al. The liver–α-cell axis and type 2 diabetes. Endocr. Rev. 40, 1353–1366 (2019).
Wu, M. et al. Single-cell analysis of the human pancreas in type 2 diabetes using multi-spectral imaging mass cytometry. Cell Rep. 37, 109919 (2021).
Dam, T. J. Pvan et al. CiliaCarta: an integrated and validated compendium of ciliary genes. PLoS ONE 14, e0216705 (2019).
Smith, S. B. et al. Rfx6 directs islet formation and insulin production in mice and humans. Nature 463, 775–780 (2010).
Patel, K. A. et al. Heterozygous RFX6 protein truncating variants are associated with MODY with reduced penetrance. Nat. Commun. 8, 888 (2017).
Varshney, A. et al. Genetic regulatory signatures underlying islet gene expression and type 2 diabetes. Proc. Natl Acad. Sci. USA 114, 2301–2306 (2017).
Walker, J. T. et al. Integrated human pseudoislet system and microfluidic platform demonstrates differences in G-protein-coupled-receptor signaling in islet cells. JCI Insight 5, e137017 (2020).
Viñuela, A. et al. Genetic variant effects on gene expression in human pancreatic islets and their implications for T2D. Nat. Commun. 11, 4912 (2020).
Kahn, S. E., Zraika, S., Utzschneider, K. M. & Hull, R. L. The beta cell lesion in type 2 diabetes: there has to be a primary functional abnormality. Diabetologia 52, 1003–1012 (2009).
Meier, J. J. & Bonadonna, R. C. Role of reduced β-cell mass versus impaired β-cell function in the pathogenesis of type 2 diabetes. Diabetes Care 36, S113–S119 (2013).
Cohrs, C. M. et al. Dysfunction of persisting β cells is a key feature of early type 2 diabetes pathogenesis. Cell Rep. 31, 107469 (2020).
McCarthy, M. I. Painting a new picture of personalised medicine for diabetes. Diabetologia 60, 793–799 (2017).
Chandra, V. et al. RFX6 regulates insulin secretion by modulating Ca2+ homeostasis in human β cells. Cell Rep. 9, 2206–2218 (2014).
Piccand, J. et al. Rfx6 maintains the functional identity of adult pancreatic β cells. Cell Rep. 9, 2219–2232 (2014).
Choksi, S. P., Lauter, G., Swoboda, P. & Roy, S. Switching on cilia: transcriptional networks regulating ciliogenesis. Development 141, 1427–1441 (2014).
Piasecki, B. P., Burghoorn, J. & Swoboda, P. Regulatory factor X (RFX)-mediated transcriptional rewiring of ciliary genes in animals. Proc. Natl Acad. Sci. USA 107, 12969–12974 (2010).
Kurki, M. I. et al. FinnGen provides genetic insights from a well-phenotyped isolated population. Nature 613, 508–518 (2023).
Iotchkova, V. et al. GARFIELD classifies disease-relevant genomic features through integration of functional annotations with association signals. Nat. Genet. 51, 343–353 (2019).
Gloyn, A. L. et al. Every islet matters: improving the impact of human islet research. Nat. Metab. 4, 970–977 (2022).
Balamurugan, A. N., Chang, Y., Fung, J. J., Trucco, M. & Bottino, R. Flexible management of enzymatic digestion improves human islet isolation outcome from sub‐optimal donor pancreata. Am. J. Transplant. 3, 1135–1142 (2003).
Dai, C. et al. Age-dependent human β cell proliferation induced by glucagon-like peptide 1 and calcineurin signaling. J. Clin. Invest. 127, 3835–3844 (2017).
Brissova, M. et al. α cell function and gene expression are compromised in type 1 diabetes. Cell Rep. 22, 2667–2676 (2018).
Brissova, M. et al. Islet microenvironment, modulated by vascular endothelial growth factor-A signaling, promotes β cell regeneration. Cell Metab. 19, 498–511 (2014).
Brissova, M. et al. The Integrated Islet Distribution Program answers the call for improved human islet phenotyping and reporting of human islet characteristics in research articles. Diabetologia 62, 1312–1314 (2019).
Kayton, N. S. et al. Human islet preparations distributed for research exhibit a variety of insulin-secretory profiles. Am. J. Physiol. Endocrinol. Metab. 308, E592–E602 (2015).
Fitzmaurice, G. M., Laird, N. M. & Ware, J. H. Applied Longitudinal Analysis (Wiley, 2011).
Shultz, L. D. et al. Human lymphoid and myeloid cell development in NOD/LtSz-scid IL2Rγnull mice engrafted with mobilized human hemopoietic stem cells. J. Immunol. 174, 6477–6489 (2005).
Dai, C. et al. Tacrolimus- and sirolimus-induced human β cell dysfunction is reversible and preventable. JCI Insight 5, e130770 (2020).
Dorrell, C. et al. Transcriptomes of the major human pancreatic cell types. Diabetologia 54, 2832 (2011).
Saunders, D. C. et al. Ectonucleoside triphosphate diphosphohydrolase-3 antibody targets adult human pancreatic β cells for in vitro and in vivo analysis. Cell Metab. 29, 745–754.e4 (2019).
Dorrell, C. et al. Human islets contain four distinct subtypes of β cells. Nat. Commun. 7, 11756 (2016).
Haliyur, R. et al. Human islets expressing HNF1A variant have defective β cell transcriptional regulatory networks. J. Clin. Invest. 129, 246–251 (2018).
Marzban, L., Park, K. & Verchere, C. B. Islet amyloid polypeptide and type 2 diabetes. Exp. Gerontol. 38, 347–351 (2003).
Westermark, P., Andersson, A. & Westermark, G. T. Islet amyloid polypeptide, islet amyloid, and diabetes mellitus. Physiol. Rev. 91, 795–826 (2011).
Hart, N. J. et al. Cystic fibrosis–related diabetes is caused by islet loss and inflammation. JCI Insight 3, e98240 (2018).
Noguchi, G. M. & Huising, M. O. Integrating the inputs that shape pancreatic islet hormone release. Nat. Metab. 1, 1189–1201 (2019).
Black, S. et al. CODEX multiplexed tissue imaging with DNA-conjugated antibodies. Nat. Protoc. 16, 3802–3835 (2021).
Blondel, V. D., Guillaume, J.-L., Lambiotte, R. & Lefebvre, E. Fast unfolding of communities in large networks. J. Stat. Mech. Theory Exp. 2008, P10008 (2008).
Luhn, H. P. The automatic creation of literature abstracts. IBM J. Res. Dev. 2, 159–165 (1958).
Schürch, C. M. et al. Coordinated cellular neighborhoods orchestrate antitumoral immunity at the colorectal cancer invasive front. Cell 182, 1341–1359.e19 (2020).
Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).
Liao, Y., Smyth, G. K. & Shi, W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30, 923–930 (2014).
Hartley, S. W. & Mullikin, J. C. QoRTs: a comprehensive toolset for quality control and data processing of RNA-Seq experiments. BMC Bioinformatics 16, 224 (2015).
Wang, L. et al. Measure transcript integrity using RNA-seq data. BMC Bioinformatics 17, 58 (2016).
Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).
Risso, D., Ngai, J., Speed, T. P. & Dudoit, S. Normalization of RNA-seq data using factor analysis of control genes or samples. Nat. Biotechnol. 32, 896–902 (2014).
Lee, C., Patil, S. & Sartor, M. A. RNA-Enrich: a cut-off free functional enrichment testing method for RNA-seq with improved detection power. Bioinformatics 32, 1100–1102 (2016).
Supek, F., Bošnjak, M., Škunca, N. & Šmuc, T. REVIGO summarizes and visualizes long lists of Gene Ontology terms. PLoS ONE 6, e21800 (2011).
Shannon, P. et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498–2504 (2003).
Zhou, Y. et al. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat. Commun. 10, 1523 (2019).
Langfelder, P. & Horvath, S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 9, 559 (2008).
Saeedi, P. et al. Global and regional diabetes prevalence estimates for 2019 and projections for 2030 and 2045: results from the International Diabetes Federation Diabetes Atlas, 9th edition. Diabetes Res. Clin. Pract. 157, 107843 (2019).
Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015).
Naba, A. et al. The matrisome: in silico definition and in vivo characterization by proteomics of normal and tumor extracellular matrices. Mol. Cell Proteomics 11, M111.014647 (2012).
Breuer, K. et al. InnateDB: systems biology of innate immunity and beyond—recent updates and continuing curation. Nucleic Acids Res. 41, D1228–D1233 (2013).
Kolberg, L., Raudvere, U., Kuzmin, I., Vilo, J. & Peterson, H. gprofiler2–an R package for gene list functional enrichment analysis and namespace conversion toolset g:Profiler. F1000research 9, ELIXIR–709 (2020).
Chen, J. et al. The trans-ancestral genomic architecture of glycemic traits. Nat. Genet. 53, 840–860 (2021).
Bailey, T. L. et al. MEME Suite: tools for motif discovery and searching. Nucleic Acids Res. 37, W202–W208 (2009).
Weirauch, M. T. et al. Determination and inference of eukaryotic transcription factor sequence specificity. Cell 158, 1431–1443 (2014).
Das, S. et al. Next-generation genotype imputation service and methods. Nat. Genet. 48, 1284–1287 (2016).
Loh, P.-R. et al. Reference-based phasing using the Haplotype Reference Consortium panel. Nat. Genet. 48, 1443–1448 (2016).
Auton, A. et al. A global reference for human genetic variation. Nature 526, 68–74 (2015).
Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25, 1754–1760 (2009).
Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).
Orchard, P., Kyono, Y., Hensley, J., Kitzman, J. O. & Parker, S. C. J. Quantification, dynamic visualization, and validation of bias in ATAC-seq data with ataqv. Cell Syst. 10, 298–306.e4 (2020).
Lun, A. T. L. et al. EmptyDrops: distinguishing cells from empty droplets in droplet-based single-cell RNA sequencing data. Genome Biol. 20, 63 (2019).
Kang, H. M. et al. Multiplexed droplet single-cell RNA-sequencing using natural genetic variation. Nat. Biotechnol. 36, 89–94 (2018).
Yang, S. et al. Decontamination of ambient RNA in single-cell RNA-seq with DecontX. Genome Biol. 21, 57 (2020).
R Core Team. R: A Language and Environment for Statistical Computing. http://www.R-project.org/ (R Foundation for Statistical Computing, 2020).
Butler, A., Hoffman, P., Smibert, P., Papalexi, E. & Satija, R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat. Biotechnol. 36, 411 (2018).
Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902.e21 (2019).
Hao, Y. et al. Integrated analysis of multimodal single-cell data. Cell 184, 3573–3587.e29 (2021).
Thibodeau, A. et al. AMULET: a novel read count-based method for effective multiplet detection from single nucleus ATAC-seq data. Genome Biol. 22, 252 (2021).
Speir, M. L. et al. UCSC Cell Browser: visualize your single-cell data. Bioinformatics 37, 4578–4580 (2021).
Sande, B. Vde et al. A scalable SCENIC workflow for single-cell gene regulatory network analysis. Nat. Protoc. 15, 2247–2276 (2020).
Quinlan, A. R. BEDTools: the Swiss‐army tool for genome feature analysis. Curr. Protoc. Bioinform. 47, 11.12.1–11.12.34 (2014).
Zhang, Y. et al. Model-based analysis of ChIP-seq (MACS). Genome Biol. 9, R137–R137 (2008).
Kent, W. J., Zweig, A. S., Barber, G., Hinrichs, A. S. & Karolchik, D. BigWig and BigBed: enabling browsing of large distributed datasets. Bioinformatics 26, 2204–2207 (2010).
Grant, C. E., Bailey, T. L. & Noble, W. S. FIMO: scanning for occurrences of a given motif. Bioinformatics 27, 1017–1018 (2011).
Kheradpour, P. & Kellis, M. Systematic discovery and characterization of regulatory motifs in ENCODE TF binding experiments. Nucleic Acids Res. 42, 2976–2987 (2014).
Jolma, A. et al. DNA-binding specificities of human transcription factors. Cell 152, 327–339 (2013).
Chinwalla, A. T. et al. Initial sequencing and comparative analysis of the mouse genome. Nature 420, 520–562 (2002).
Bailey, T. L. & Elkan, C. Fitting a mixture model by expectation maximization to discover motifs in biopolymers. Proc. Int. Conf. Intell. Syst. Mol. Biol. 2, 28–36 (1994).
Bailey, T. L. DREME: motif discovery in transcription factor ChIP–seq data. Bioinformatics 27, 1653–1659 (2011).
Bailey, T. L., Johnson, J., Grant, C. E. & Noble, W. S. The MEME suite. Nucleic Acids Res. 43, W39–W49 (2015).
Bowden, J., Smith, G. D. & Burgess, S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int. J. Epidemiol. 44, 512–525 (2015).
Bowden, J., Smith, G. D., Haycock, P. C. & Burgess, S. Consistent estimation in Mendelian randomization with some invalid instruments using a weighted median estimator. Genet. Epidemiol. 40, 304–314 (2016).
Ye, T., Shao, J. & Kang, H. Debiased inverse-variance weighted estimator in two-sample summary-data Mendelian randomization. Ann. Stat. 49, 2079–2100 (2021).
Verbanck, M., Chen, C.-Y., Neale, B. & Do, R. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat. Genet. 50, 693 (2018).
Yavorska, O. O. & Burgess, S. MendelianRandomization: an R package for performing Mendelian randomization analyses using summarized data. Int. J. Epidemiol. 46, 1734–1739 (2017).
Sudlow, C. et al. UK Biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med. 12, e1001779 (2015).
McCarthy, S. et al. A reference panel of 64,976 haplotypes for genotype imputation. Nat. Genet. 48, 1279–1283 (2016).
Loh, P.-R. et al. Efficient Bayesian mixed model analysis increases association power in large cohorts. Nat. Genet. 47, 284–290 (2015).
Bonner-Weir, S. & O’Brien, T. D. Islets in type 2 diabetes: in honor of Dr. Robert C. Turner. Diabetes 57, 2899–2904 (2008).
Sakuraba, H. et al. Reduced beta-cell mass and expression of oxidative stress-related DNA damage in the islet of Japanese type II diabetic patients. Diabetologia 45, 85–96 (2002).
Butler, A. E. et al. β-cell deficit and increased β-cell apoptosis in humans with type 2 diabetes. Diabetes 52, 102–110 (2003).
Rahier, J., Guiot, Y., Goebbels, R. M., Sempoux, C. & Henquin, J. C. Pancreatic β‐cell mass in European subjects with type 2 diabetes. Diabetes Obes. Metab. 10, 32–42 (2008).
Talchai, C., Xuan, S., Lin, H. V., Sussel, L. & Accili, D. Pancreatic β cell dedifferentiation as a mechanism of diabetic β cell failure. Cell 150, 1223–1234 (2012).
Masters, S. L. et al. Activation of the NLRP3 inflammasome by islet amyloid polypeptide provides a mechanism for enhanced IL-1β in type 2 diabetes. Nat. Immunol. 11, 897–904 (2010).
Westwell-Roper, C. Y., Ehses, J. A. & Verchere, C. B. Resident macrophages mediate islet amyloid polypeptide–induced islet IL-1β production and β-cell dysfunction. Diabetes 63, 1698–1711 (2014).
Nair, G. & Hebrok, M. Islet formation in mice and men: lessons for the generation of functional insulin-producing β-cells from human pluripotent stem cells. Curr. Opin. Genet. Dev. 32, 171–180 (2015).
Arrojo e Drigo, R. et al. New insights into the architecture of the islet of Langerhans: a focused cross-species assessment. Diabetologia 58, 2218–2228 (2015).
Unger, R. H. & Cherrington, A. D. Glucagonocentric restructuring of diabetes: a pathophysiologic and therapeutic makeover. J. Clin. Invest. 122, 4–12 (2012).
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