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Advances in single-cell multi-omics\u2014transcriptomics, chromatin accessibility profiling, and integrative analyses\u2014have offered unprecedented insights into the cellular heterogeneity and regulatory networks of pancreatic islets. We highlight recent discoveries in islet cell heterogeneity and \u03b2-cell pathophysiology, with a particular focus on dysfunction and dedifferentiation. We further underscore the computational frameworks that enable these discoveries, spanning data preprocessing, multi-omics integration, and machine learning-driven analyses, which collectively enable the dissection of disease-relevant cell subpopulations and the reconstruction of developmental and regulatory trajectories. We also examine how impaired signaling within islets and chronic adipose inflammation contribute to T2DM pathogenesis. Finally, we discuss key challenges in clinical translation\u2014including limited population diversity in single-cell atlases and the interpretability of computational models\u2014and propose future directions toward precision diagnostics and therapeutic innovation in T2DM.<\/jats:p>","DOI":"10.3390\/ijms262211005","type":"journal-article","created":{"date-parts":[[2025,11,14]],"date-time":"2025-11-14T09:06:36Z","timestamp":1763111196000},"page":"11005","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Single-Cell Multi-Omics in Type 2 Diabetes Mellitus: Revealing Cellular Heterogeneity and Mechanistic Insights"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-3000-3205","authenticated-orcid":false,"given":"Yijie","family":"Wei","sequence":"first","affiliation":[{"name":"Department of Clinical Laboratory, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & 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Metab. TEM"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"973","DOI":"10.1016\/j.cell.2016.10.025","article-title":"Hematopoietic-Derived Galectin-3 Causes Cellular and Systemic Insulin Resistance","volume":"167","author":"Li","year":"2016","journal-title":"Cell"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1016\/j.cell.2013.12.012","article-title":"What We Talk About When We Talk About Fat","volume":"156","author":"Rosen","year":"2014","journal-title":"Cell"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"5567","DOI":"10.1038\/s41467-024-49724-w","article-title":"Identification of unique cell type responses in pancreatic islets to stress","volume":"15","author":"Maestas","year":"2024","journal-title":"Nat. Commun."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"5400","DOI":"10.1038\/s41467-023-41228-3","article-title":"Single cell multiomic analysis reveals diabetes-associated \u03b2-cell heterogeneity driven by HNF1A","volume":"14","author":"Weng","year":"2023","journal-title":"Nat. Commun."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"723","DOI":"10.1038\/s41592-021-01171-x","article-title":"The triumphs and limitations of computational methods for scRNA-seq","volume":"18","author":"Kharchenko","year":"2021","journal-title":"Nat. Methods"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"571","DOI":"10.2174\/0113894501347158250305074908","article-title":"The Role of Glycolipids and their Toxicity in the Context of Nanomaterials and Nanoparticles: A Review of the Literature","volume":"26","author":"Li","year":"2025","journal-title":"Curr. 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Pharmacother., 173.","DOI":"10.1016\/j.biopha.2024.116292"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Leenders, F., de Koning, E.J.P., and Carlotti, F. (2024). Pancreatic \u03b2-Cell Identity Change through the Lens of Single-Cell Omics Research. Int. J. Mol. Sci., 25.","DOI":"10.3390\/ijms25094720"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"455","DOI":"10.1038\/s41588-021-00823-0","article-title":"Single cell chromatin accessibility identifies pancreatic islet cell type- and state-specific regulatory programs of diabetes risk","volume":"53","author":"Chiou","year":"2021","journal-title":"Nat. Genet."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1783","DOI":"10.2337\/db18-0365","article-title":"Pseudotime Ordering of Single Human \u03b2-Cells Reveals States of Insulin Production and Unfolded Protein Response","volume":"67","author":"Xin","year":"2018","journal-title":"Diabetes"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1823","DOI":"10.1038\/ijo.2016.173","article-title":"Relevance of omental pericellular adipose tissue collagen in the pathophysiology of human abdominal obesity and related cardiometabolic risk","volume":"40","author":"Michaud","year":"2016","journal-title":"Int. J. Obes."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"458","DOI":"10.1016\/j.cmet.2021.11.014","article-title":"Distinct properties of adipose stem cell subpopulations determine fat depot-specific characteristics","volume":"34","author":"Nahmgoong","year":"2022","journal-title":"Cell Metab."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"eaav2501","DOI":"10.1126\/science.aav2501","article-title":"Identification of a mesenchymal progenitor cell hierarchy in adipose tissue","volume":"364","author":"Merrick","year":"2019","journal-title":"Science"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"4827","DOI":"10.1038\/s41467-024-48914-w","article-title":"Single-cell analysis reveals a subpopulation of adipose progenitor cells that impairs glucose homeostasis","volume":"15","author":"Wang","year":"2024","journal-title":"Nat. Commun."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Khin, P.-P., Lee, J.-H., and Jun, H.-S. (2021). A Brief Review of the Mechanisms of \u03b2-Cell Dedifferentiation in Type 2 Diabetes. Nutrients, 13.","DOI":"10.3390\/nu13051593"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1007\/s11892-019-1194-6","article-title":"Alterations in Beta Cell Identity in Type 1 and Type 2 Diabetes","volume":"19","author":"Moin","year":"2019","journal-title":"Curr. Diabetes Rep."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1044","DOI":"10.1210\/jc.2015-2860","article-title":"Evidence of \u03b2-Cell Dedifferentiation in Human Type 2 Diabetes","volume":"101","author":"Cinti","year":"2016","journal-title":"J. Clin. Endocrinol. Metab."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"101057","DOI":"10.1016\/j.molmet.2020.101057","article-title":"Single-cell transcriptomics of human islet ontogeny defines the molecular basis of \u03b2-cell dedifferentiation in T2D","volume":"42","author":"Avrahami","year":"2020","journal-title":"Mol. Metab."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2175","DOI":"10.2337\/db16-1355","article-title":"Chronic \u03b2-Cell Depolarization Impairs \u03b2-Cell Identity by Disrupting a Network of Ca2+-Regulated Genes","volume":"66","author":"Stancill","year":"2017","journal-title":"Diabetes"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1017","DOI":"10.21037\/atm-22-4303","article-title":"Exploring biomarkers and transcriptional factors in type 2 diabetes by comprehensive bioinformatics analysis on RNA-Seq and scRNA-Seq data","volume":"10","author":"Huang","year":"2022","journal-title":"Ann. Transl. Med."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1428","DOI":"10.1038\/s12276-020-0420-2","article-title":"Single-cell multiomics: Technologies and data analysis methods","volume":"52","author":"Lee","year":"2020","journal-title":"Exp. Mol. Med."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"551","DOI":"10.2174\/0113894501351461250301072444","article-title":"Fibroblast Growth Factors: Roles and Emerging Therapeutic Applications","volume":"26","author":"Ternier","year":"2025","journal-title":"Curr. Drug Targets"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1860","DOI":"10.3390\/cimb45030120","article-title":"Revealing the History and Mystery of RNA-Seq","volume":"45","author":"Gondane","year":"2023","journal-title":"Curr. Issues Mol. Biol."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1038\/s12276-018-0071-8","article-title":"Single-cell RNA sequencing technologies and bioinformatics pipelines","volume":"50","author":"Hwang","year":"2018","journal-title":"Exp. Mol. Med."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Li, H., Zou, L., Long, Z., and Zhan, J. (2024). Immunometabolic alterations in type 2 diabetes mellitus revealed by single-cell RNA sequencing: Insights into subtypes and therapeutic targets. Front. Immunol., 15.","DOI":"10.3389\/fimmu.2024.1537909"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"496","DOI":"10.1038\/s41586-019-0969-x","article-title":"The single cell transcriptional landscape of mammalian organogenesis","volume":"566","author":"Cao","year":"2019","journal-title":"Nature"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Street, K., Risso, D., Fletcher, R.B., Das, D., Ngai, J., Yosef, N., Purdom, E., and Dudoit, S. (2018). Slingshot: Cell lineage and pseudotime inference for single-cell transcriptomics. BMC Genom., 19.","DOI":"10.1186\/s12864-018-4772-0"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1888","DOI":"10.1016\/j.cell.2019.05.031","article-title":"Comprehensive integration of single-cell data","volume":"177","author":"Stuart","year":"2019","journal-title":"Cell"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1007\/978-1-0716-2756-3_15","article-title":"Integration of scATAC-Seq with scRNA-Seq Data","volume":"2584","author":"Berest","year":"2023","journal-title":"Methods Mol. Biol."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Kim, D., Tran, A., Kim, H.J., Lin, Y., Yang, J.Y.H., and Yang, P. (2023). Gene regulatory network reconstruction: Harnessing the power of single-cell multi-omic data. npj Syst. Biol. Appl., 9.","DOI":"10.1038\/s41540-023-00312-6"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"403","DOI":"10.1038\/s41588-021-00790-6","article-title":"ArchR is a scalable software package for integrative single-cell chromatin accessibility analysis","volume":"53","author":"Granja","year":"2021","journal-title":"Nat. Genet."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1333","DOI":"10.1038\/s41592-021-01282-5","article-title":"Single-cell chromatin state analysis with Signac","volume":"18","author":"Stuart","year":"2021","journal-title":"Nat. Methods"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"363","DOI":"10.1038\/s41592-023-01791-5","article-title":"Single-cell proteomics enabled by next-generation sequencing or mass spectrometry","volume":"20","author":"Bennett","year":"2023","journal-title":"Nat. Methods"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"e144392","DOI":"10.1172\/jci.insight.144392","article-title":"Proteomic profiling reveals biomarkers and pathways in type 2 diabetes risk","volume":"6","author":"Ngo","year":"2021","journal-title":"JCI Insight"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Deshmukh, A.S. (2016). Proteomics of Skeletal Muscle: Focus on Insulin Resistance and Exercise Biology. Proteomes, 4.","DOI":"10.3390\/proteomes4010006"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Iyer, A., Hamers, A.A.J., and Pillai, A.B. (2022). CyTOF\u00ae for the Masses. Front. Immunol., 13.","DOI":"10.3389\/fimmu.2022.815828"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Song, H.-W., Martin, J., Shi, X., and Tyznik, A.J. (2025). Key Considerations on CITE-Seq for Single-Cell Multiomics. Proteomics, e202400011.","DOI":"10.1002\/pmic.202400011"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"661","DOI":"10.1016\/j.tibs.2021.01.013","article-title":"Single-Cell Proteomics","volume":"46","author":"Vistain","year":"2021","journal-title":"Trends Biochem. Sci."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1038\/nrg3833","article-title":"Computational and analytical challenges in single-cell transcriptomics","volume":"16","author":"Stegle","year":"2015","journal-title":"Nat. Rev. Genet."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Chhangawala, S., Rudy, G., Mason, C.E., and Rosenfeld, J.A. (2015). The impact of read length on quantification of differentially expressed genes and splice junction detection. Genome Biol., 16.","DOI":"10.1186\/s13059-015-0697-y"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Love, M.I., Huber, W., and Anders, S. (2014). Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol., 15.","DOI":"10.1186\/s13059-014-0550-8"},{"key":"ref_46","first-page":"2122","article-title":"A step-by-step workflow for low-level analysis of single-cell RNA-seq data with Bioconductor","volume":"5","author":"Lun","year":"2016","journal-title":"F1000Research"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Hafemeister, C., and Satija, R. (2019). Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression. Genome Biol., 20.","DOI":"10.1186\/s13059-019-1874-1"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"2468","DOI":"10.1016\/j.cmet.2024.09.006","article-title":"Multi-omic human pancreatic islet endoplasmic reticulum and cytokine stress response mapping provides type 2 diabetes genetic insights","volume":"36","author":"Sokolowski","year":"2024","journal-title":"Cell Metab."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Lin, P., Troup, M., and Ho, J.W.K. (2017). CIDR: Ultrafast and accurate clustering through imputation for single-cell RNA-seq data. Genome Biol., 18.","DOI":"10.1186\/s13059-017-1188-0"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"390","DOI":"10.1038\/s41467-018-07931-2","article-title":"Single-cell RNA-seq denoising using a deep count autoencoder","volume":"10","author":"Eraslan","year":"2019","journal-title":"Nat. Commun."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"716","DOI":"10.1016\/j.cell.2018.05.061","article-title":"Recovering gene interactions from single-cell data using data diffusion","volume":"174","author":"Dijk","year":"2018","journal-title":"Cell"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1029","DOI":"10.1016\/j.tibtech.2022.02.005","article-title":"Are batch effects still relevant in the age of big data?","volume":"40","author":"Goh","year":"2022","journal-title":"Trends Biotechnol."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Yu, Y., Mai, Y., Zheng, Y., and Shi, L. (2024). Assessing and mitigating batch effects in large-scale omics studies. Genome Biol., 25.","DOI":"10.1186\/s13059-024-03401-9"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Tran, H.T.N., Ang, K.S., Chevrier, M., Zhang, X., Lee, N.Y.S., Goh, M., and Chen, J. (2020). A benchmark of batch-effect correction methods for single-cell RNA sequencing data. Genome Biol., 21.","DOI":"10.1186\/s13059-019-1850-9"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"1594","DOI":"10.1038\/s41587-023-02040-y","article-title":"Mosaic integration and knowledge transfer of single-cell multimodal data with MIDAS","volume":"42","author":"He","year":"2024","journal-title":"Nat. Biotechnol."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"421","DOI":"10.1038\/nbt.4091","article-title":"Batch effects in single-cell RNA sequencing data are corrected by matching mutual nearest neighbours","volume":"36","author":"Haghverdi","year":"2018","journal-title":"Nat. Biotechnol."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"1289","DOI":"10.1038\/s41592-019-0619-0","article-title":"Fast, sensitive, and accurate integration of single cell data with Harmony","volume":"16","author":"Korsunsky","year":"2019","journal-title":"Nat. Methods"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"e42","DOI":"10.1093\/nar\/gkab004","article-title":"Flexible comparison of batch correction methods for single-cell RNA-se qusing BatchBench","volume":"49","author":"Kiselev","year":"2021","journal-title":"Nucleic Acids Res."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1038\/s41587-023-01767-y","article-title":"Dictionary learning for integrative, multimodal and scalable single-cell analysis","volume":"42","author":"Hao","year":"2024","journal-title":"Nat. Biotechnol."},{"key":"ref_60","first-page":"293","article-title":"Best practices for single-cell analysis across modalities","volume":"42","author":"Heumos","year":"2023","journal-title":"Nat. Rev. Genet."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"1615","DOI":"10.1038\/s42255-023-00876-x","article-title":"Delineating mouse \u03b2-cell identity during lifetime and in diabetes with a single cell atlas","volume":"5","author":"Hrovatin","year":"2023","journal-title":"Nat. Metab."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"2225","DOI":"10.1093\/bioinformatics\/btv122","article-title":"Normalization and noise reduction for single cell RNA-seq experiments","volume":"31","author":"Ding","year":"2015","journal-title":"Bioinformatics"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Vallejos, C.A., Marioni, J.C., and Richardson, S. (2015). BASiCS: Bayesian Analysis of Single-Cell Sequencing Data. PLoS Comput. Biol., 11.","DOI":"10.1371\/journal.pcbi.1004333"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"584","DOI":"10.1038\/nmeth.4263","article-title":"SCnorm: Robust normalization of single-cell RNA-seq data","volume":"14","author":"Bacher","year":"2017","journal-title":"Nat. Methods"},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Townes, F.W., and Irizarry, R.A. (2020). Quantile normalization of single-cell RNA-seq read counts without unique molecular identifiers. Genome Biol., 21.","DOI":"10.1186\/s13059-020-02078-0"},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Pierson, E., and Yau, C. (2015). ZIFA: Dimensionality reduction for zero-inflated single-cell gene expression analysis. Genome Biol., 16.","DOI":"10.1186\/s13059-015-0805-z"},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Arisdakessian, C., Poirion, O., Yunits, B., Zhu, X., and Garmire, L.X. (2019). DeepImpute: An accurate, fast, and scalable deep neural network method to impute single-cell RNA-seq data. Genome Biol., 20.","DOI":"10.1186\/s13059-019-1837-6"},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Wang, T., Zhao, H., Xu, Y., Wang, Y., Shang, X., Peng, J., and Xiao, B. (2023). scMultiGAN: Cell-specific imputation for single-cell transcriptomes with multiple deep generative adversarial networks. Brief. Bioinform., 24.","DOI":"10.1093\/bib\/bbad384"},{"key":"ref_69","first-page":"964","article-title":"BBKNN: Fast batch alignment of single cell transcriptomes","volume":"36","author":"Young","year":"2019","journal-title":"Bioinformatics"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"685","DOI":"10.1038\/s41587-019-0113-3","article-title":"Efficient integration of heterogeneous single-cell transcriptomes using Scanorama","volume":"37","author":"Hie","year":"2019","journal-title":"Nat. Biotechnol."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"2338","DOI":"10.1038\/s41467-020-15851-3","article-title":"Deep learning enables accurate clustering with batch effect removal in single-cell RNA-seq analysis","volume":"11","author":"Li","year":"2020","journal-title":"Nat. Commun."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"960","DOI":"10.1038\/s41467-023-36635-5","article-title":"Batch alignment of single-cell transcriptomics data using deep metric learning","volume":"14","author":"Yu","year":"2023","journal-title":"Nat. Commun."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1038\/s41592-021-01336-8","article-title":"Benchmarking atlas-level data integration in single-cell genomics","volume":"19","author":"Luecken","year":"2022","journal-title":"Nat. Methods"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"1202","DOI":"10.1038\/s41587-021-00895-7","article-title":"Computational principles and challenges in single-cell data integration","volume":"39","author":"Argelaguet","year":"2021","journal-title":"Nat. Biotechnol."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1007\/s11427-023-2561-0","article-title":"Single-cell omics: Experimental workflow, data analyses and applications","volume":"68","author":"Sun","year":"2024","journal-title":"Sci. China Life Sci."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1038\/s41576-019-0093-7","article-title":"Integrative single-cell analysis","volume":"20","author":"Stuart","year":"2019","journal-title":"Nat. Rev. Genet."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"3573","DOI":"10.1016\/j.cell.2021.04.048","article-title":"Integrated analysis of multimodal single-cell data","volume":"184","author":"Hao","year":"2021","journal-title":"Cell"},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"272","DOI":"10.1038\/s41592-020-01050-x","article-title":"Joint probabilistic modeling of single-cell multi-omic data with total VI","volume":"18","author":"Gayoso","year":"2021","journal-title":"Nat. Methods"},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"7762","DOI":"10.1038\/s41467-024-51382-x","article-title":"scConfluence: Single-cell diagonal integration with regularized Inverse Optimal Transport on weakly connected features","volume":"15","author":"Samaran","year":"2024","journal-title":"Nat. Commun."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"1096","DOI":"10.1038\/s41587-023-01935-0","article-title":"Integration of spatial and single-cell data across modalities with weakly linked features","volume":"42","author":"Chen","year":"2024","journal-title":"Nat. Biotechnol."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"e2214414119","DOI":"10.1073\/pnas.2214414119","article-title":"Robust probabilistic modeling for single-cell multimodal mosaic integration and imputation via scVAEIT","volume":"119","author":"Du","year":"2022","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"875","DOI":"10.1038\/s41592-019-0537-1","article-title":"Data Denoising with transfer learning in single-cell transcriptomics","volume":"16","author":"Wang","year":"2019","journal-title":"Nat. Methods"},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"384","DOI":"10.1038\/s41467-023-36066-2","article-title":"scMoMaT jointly performs single cell mosaic integration and multi-modal bio-marker detection","volume":"14","author":"Zhang","year":"2023","journal-title":"Nat. Commun."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"780","DOI":"10.1038\/s41467-022-28431-4","article-title":"UINMF performs mosaic integration of single-cell multi-omic datasets using nonnegative matrix factorization","volume":"13","author":"Kriebel","year":"2022","journal-title":"Nat. Commun."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"949","DOI":"10.1093\/stcltm\/szae059","article-title":"Recent progress in modeling and treating diabetes using stem cell-derived islets","volume":"13","author":"Maestas","year":"2024","journal-title":"Stem Cells Transl. Med."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"284","DOI":"10.1038\/s41587-023-01766-z","article-title":"Stabilized mosaic single-cell data integration using unshared features","volume":"42","author":"Ghazanfar","year":"2024","journal-title":"Nat. Biotechnol."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"487","DOI":"10.1038\/s10038-024-01231-y","article-title":"Advances in AI and machine learning for predictive medicine","volume":"69","author":"Sharma","year":"2024","journal-title":"J. Hum. Genet."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1016\/j.cell.2020.03.022","article-title":"How Machine Learning Will Transform Biomedicine","volume":"181","author":"Goecks","year":"2020","journal-title":"Cell"},{"key":"ref_89","doi-asserted-by":"crossref","unstructured":"Mirza, B., Wang, W., Wang, J., Choi, H., Chung, N.C., and Ping, P. (2019). Machine Learning and Integrative Analysis of Biomedical Big Data. Genes, 10.","DOI":"10.3390\/genes10020087"},{"key":"ref_90","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015). U-Net: Convolutional Networks for Biomedical Image Segmentation, Springer.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/j.molmet.2019.12.006","article-title":"Single-cell ATAC-Seq in human pancreatic islets and deep learning upscaling of rare cells reveals cell-specific type 2 diabetes regulatory signatures","volume":"32","author":"Rai","year":"2019","journal-title":"Mol. Metab."},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"1284","DOI":"10.1038\/s41467-025-56475-9","article-title":"A mechanism-informed deep neural network enables prioritization of regulators that drive cell state transitions","volume":"16","author":"Xi","year":"2025","journal-title":"Nat. Commun."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"984","DOI":"10.1038\/s41588-023-01397-9","article-title":"Integrating genetics with single-cell multiomic measurements across disease states identifies mechanisms of beta cell dysfunction in type 2 diabetes","volume":"55","author":"Wang","year":"2023","journal-title":"Nat. Genet."},{"key":"ref_94","doi-asserted-by":"crossref","unstructured":"Asada, K., Takasawa, K., Machino, H., Takahashi, S., Shinkai, N., Bolatkan, A., Kobayashi, K., Komatsu, M., Kaneko, S., and Okamoto, K. (2021). Single-Cell Analysis Using Machine Learning Techniques and Its Application to Medical Research. Biomedicines, 9.","DOI":"10.3390\/biomedicines9111513"},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"428","DOI":"10.1016\/j.cell.2014.09.040","article-title":"Generation of Functional Human Pancreatic \u03b2 Cells In Vitro","volume":"159","author":"Pagliuca","year":"2014","journal-title":"Cell"},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"bqac193","DOI":"10.1210\/endocr\/bqac193","article-title":"Therapeutic Strategies Targeting Pancreatic Islet \u03b2-Cell Proliferation, Regeneration, and Replacement","volume":"164","author":"Goode","year":"2022","journal-title":"Endocrinology"},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"454","DOI":"10.2174\/0113894501352925250225045555","article-title":"Targets Involved in the Pharmacology of Bothrops Snakebite: Statu Quo and Future Perspectives","volume":"26","author":"Alves","year":"2025","journal-title":"Curr. Drug Targets"},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"1443","DOI":"10.1038\/s42255-020-00314-2","article-title":"Single-cell lineage analysis reveals extensive multimodal transcriptional control during directed beta-cell differentiation","volume":"2","author":"Weng","year":"2020","journal-title":"Nat. Metab."},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"459","DOI":"10.1186\/s13287-020-01977-0","article-title":"NKX6.1 transcription factor: A crucial regulator of pancreatic \u03b2 cell development, identity, and proliferation","volume":"11","author":"Aigha","year":"2020","journal-title":"Stem Cell Res. Ther."},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"2497","DOI":"10.2337\/db14-1412","article-title":"The Basic Helix-Loop-Helix Transcription Factor NEUROG3 Is Required for Development of the Human Endocrine Pancreas","volume":"64","author":"McGrath","year":"2015","journal-title":"Diabetes"},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"177","DOI":"10.4161\/isl.1.3.9877","article-title":"Neurogenin3: A master regulator of pancreatic islet differentiation an dregeneration","volume":"1","author":"Rukstalis","year":"2009","journal-title":"Islets"},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"727","DOI":"10.1016\/j.devcel.2023.03.011","article-title":"Understanding cell fate acquisition in stem cell-derived pancreatic is lets using single-cell multiome-inferred regulomes","volume":"58","author":"Zhu","year":"2023","journal-title":"Dev. Cell"},{"key":"ref_103","first-page":"874","article-title":"PAX4 promotes PDX1-induced differentiation of mesenchymal stem cells into insulin-secreting cells","volume":"9","author":"Xu","year":"2017","journal-title":"Am. J. Transl. Res."},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"2918","DOI":"10.1128\/MCB.00103-16","article-title":"A Pdx-1-Regulated Soluble Factor Activates Rat and Human Islet Cell Proliferation","volume":"36","author":"Hayes","year":"2016","journal-title":"Mol. Cell. Biol."},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"1004","DOI":"10.1016\/j.cmet.2020.04.004","article-title":"A nutrient sensing transition at birth triggers glucose-responsive ins ulin secretion","volume":"31","author":"Helman","year":"2020","journal-title":"Cell Metab."},{"key":"ref_106","doi-asserted-by":"crossref","first-page":"535","DOI":"10.1016\/j.devcel.2015.01.002","article-title":"Weaning triggers a maturation step of pancreatic \u03b2 cells","volume":"32","author":"Enk","year":"2015","journal-title":"Dev. Cell"},{"key":"ref_107","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1016\/j.stem.2019.11.011","article-title":"Circadian Entrainment Triggers Maturation of Human In Vitro Islets","volume":"26","author":"Donaghey","year":"2020","journal-title":"Cell Stem Cell"},{"key":"ref_108","doi-asserted-by":"crossref","first-page":"1042","DOI":"10.1038\/s41587-022-01219-z","article-title":"Functional, metabolic and transcriptional maturation of human pancreatic islets derived from stem cells","volume":"40","author":"Balboa","year":"2022","journal-title":"Nat. Biotechnol."},{"key":"ref_109","doi-asserted-by":"crossref","first-page":"108067","DOI":"10.1016\/j.celrep.2020.108067","article-title":"Single-Cell Transcriptome Profiling Reveals \u03b2 Cell Maturation in Stem Cell-Derived Islets after Transplantation","volume":"32","author":"Augsornworawat","year":"2020","journal-title":"Cell Rep."},{"key":"ref_110","doi-asserted-by":"crossref","unstructured":"Hiyoshi, H., Sakuma, K., Tsubooka-Yamazoe, N., Asano, S., Mochida, T., Yamaura, J., Konagaya, S., Fujii, R., Matsumoto, H., and Ito, R. (2022). Characterization and reduction of non-endocrine cells accompanying islet-like endocrine cells differentiated from human iPSC. Sci. Rep., 12.","DOI":"10.1038\/s41598-022-08753-5"},{"key":"ref_111","doi-asserted-by":"crossref","first-page":"621","DOI":"10.1038\/s41586-023-06693-2","article-title":"Genetic risk converges on regulatory networks mediating early type 2 diabetes","volume":"624","author":"Walker","year":"2023","journal-title":"Nature"},{"key":"ref_112","doi-asserted-by":"crossref","first-page":"604","DOI":"10.1186\/s12967-022-03826-5","article-title":"Uncovering the gene regulatory network of type 2 diabetes through multi-omic data integration","volume":"20","author":"Liu","year":"2022","journal-title":"J. Transl. Med."},{"key":"ref_113","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1038\/s41392-024-01888-z","article-title":"Cell-cell communication: New insights and clinical implications","volume":"9","author":"Su","year":"2024","journal-title":"Signal Transduct. Target. Ther."},{"key":"ref_114","doi-asserted-by":"crossref","first-page":"17","DOI":"10.2337\/dbi19-0033","article-title":"Deciphering the Complex Communication Networks That Orchestrate Pancreatic Islet Function","volume":"70","author":"Weitz","year":"2021","journal-title":"Diabetes"},{"key":"ref_115","doi-asserted-by":"crossref","first-page":"451","DOI":"10.1007\/s00109-020-01887-x","article-title":"Paracrine signaling in islet function and survival","volume":"98","author":"Hartig","year":"2020","journal-title":"J. Mol. Med."},{"key":"ref_116","doi-asserted-by":"crossref","unstructured":"Gu, W., Anker, C.C.B., Christiansen, C.B., Moede, T., Berggren, P.-O., Hermansen, K., Gregersen, S., and Jeppesen, P.B. (2021). Pancreatic \u03b2 Cells Inhibit Glucagon Secretion from \u03b1 Cells: An In Vitro Demonstration of \u03b1-\u03b2 Cell Interaction. Nutrients, 13.","DOI":"10.3390\/nu13072281"},{"key":"ref_117","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1113\/JP274581","article-title":"\u03b4-cells and \u03b2-cells are electrically coupled and regulate \u03b1-cell activity via somatostatin","volume":"596","author":"Briant","year":"2018","journal-title":"J. Physiol."},{"key":"ref_118","doi-asserted-by":"crossref","first-page":"960","DOI":"10.2337\/db16-1291","article-title":"Reestablishment of Glucose Inhibition of Glucagon Secretion in Small P seudoislets","volume":"66","author":"Reissaus","year":"2017","journal-title":"Diabetes"},{"key":"ref_119","doi-asserted-by":"crossref","first-page":"2191","DOI":"10.1002\/j.2040-4603.2021.tb00177.x","article-title":"Intercellular Communication in the Islet of Langerhans in Health and D isease","volume":"11","author":"Ng","year":"2021","journal-title":"Compr. Physiol."},{"key":"ref_120","doi-asserted-by":"crossref","unstructured":"Bosi, E., Marselli, L., Suleiman, M., Tesi, M., De Luca, C., Del Guerra, S., Cnop, M., Eizirik, D.L., and Marchetti, P. (2022). A single-cell human islet interactome atlas identifies disrupted autocrine and paracrine communications in type 2 diabetes. NAR Genom. Bioinform., 4.","DOI":"10.1093\/nargab\/lqac084"},{"key":"ref_121","doi-asserted-by":"crossref","first-page":"715","DOI":"10.1007\/s00018-017-2655-1","article-title":"C3aR and C5aR1 act as key regulators of human and mouse \u03b2-cell function","volume":"75","author":"Atanes","year":"2017","journal-title":"Cell. Mol. Life Sci. CMLS"},{"key":"ref_122","doi-asserted-by":"crossref","unstructured":"Zatterale, F., Longo, M., Naderi, J., Raciti, G.A., Desiderio, A., Miele, C., and Beguinot, F. (2019). Chronic Adipose Tissue Inflammation Linking Obesity to Insulin Resistance and Type 2 Diabetes. Front. Physiol., 10.","DOI":"10.3389\/fphys.2019.01607"},{"key":"ref_123","doi-asserted-by":"crossref","first-page":"957","DOI":"10.1016\/j.immuni.2020.05.014","article-title":"Determinants of Resident Tissue Macrophage Identity and Function","volume":"52","author":"Chakarov","year":"2020","journal-title":"Immunity"},{"key":"ref_124","doi-asserted-by":"crossref","first-page":"13","DOI":"10.12703\/P6-13","article-title":"The M1 and M2 paradigm of macrophage activation: Time for reassessment","volume":"6","author":"Martinez","year":"2014","journal-title":"F1000Prime Rep."},{"key":"ref_125","doi-asserted-by":"crossref","unstructured":"Li, H., Meng, Y., He, S., Tan, X., Zhang, Y., Zhang, X., Wang, L., and Zheng, W. (2022). Macrophages, Chronic Inflammation, and Insulin Resistance. Cells, 11.","DOI":"10.3390\/cells11193001"},{"key":"ref_126","doi-asserted-by":"crossref","first-page":"2262","DOI":"10.1016\/j.cmet.2024.08.009","article-title":"PDIA3 defines a novel subset of adipose macrophages to exacerbate the development of obesity and metabolic disorders","volume":"36","author":"Luo","year":"2024","journal-title":"Cell Metab."},{"key":"ref_127","doi-asserted-by":"crossref","first-page":"1068","DOI":"10.2337\/dc08-2280","article-title":"Abdominal subcutaneous adipose tissue: A protective fat depot?","volume":"32","author":"Porter","year":"2009","journal-title":"Diabetes Care"},{"key":"ref_128","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1152\/physrev.00033.2011","article-title":"Pathophysiology of human visceral obesity: An update","volume":"93","author":"Tchernof","year":"2013","journal-title":"Physiol. Rev."},{"key":"ref_129","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1038\/s42255-019-0152-6","article-title":"Single-cell analysis of human adipose tissue identifies depot and disease specific cell types","volume":"2","author":"Vijay","year":"2020","journal-title":"Nat. Metab."},{"key":"ref_130","doi-asserted-by":"crossref","first-page":"366","DOI":"10.1016\/j.cell.2012.05.016","article-title":"Beige adipocytes are a distinct type of thermogenic fat cell in mouse and human","volume":"150","author":"Wu","year":"2012","journal-title":"Cell"},{"key":"ref_131","doi-asserted-by":"crossref","first-page":"1500","DOI":"10.1016\/j.cell.2016.08.052","article-title":"A Distinct Gene Module for Dysfunction Uncoupled from Activation in Tumor-Infiltrating T Cells","volume":"166","author":"Singer","year":"2016","journal-title":"Cell"},{"key":"ref_132","doi-asserted-by":"crossref","first-page":"1029","DOI":"10.1161\/CIRCULATIONAHA.106.638379","article-title":"T-cell accumulation and regulated on activation, normal T cell expressed and secreted upregulation in adipose tissue in obesity","volume":"115","author":"Wu","year":"2007","journal-title":"Circulation"},{"key":"ref_133","doi-asserted-by":"crossref","first-page":"3149","DOI":"10.1016\/j.celrep.2017.08.096","article-title":"Metabolically activated adipose tissue macrophages perform detrimental and beneficial functions during diet-induced obesity","volume":"20","author":"Coats","year":"2017","journal-title":"Cell Rep."},{"key":"ref_134","doi-asserted-by":"crossref","first-page":"970","DOI":"10.1038\/s42255-022-00607-8","article-title":"Every islet matters: Improving the impact of human islet research","volume":"4","author":"Gloyn","year":"2022","journal-title":"Nat. Metab."},{"key":"ref_135","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1016\/j.medj.2023.03.002","article-title":"Lack of ethnic diversity in single-cell transcriptomics hinders cell type detection and precision medicine inclusivity","volume":"4","author":"Kosaji","year":"2023","journal-title":"Medicine"},{"key":"ref_136","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1097\/MOP.0000000000000356","article-title":"Genetic and epigenetic factors in the regulation of the immune response","volume":"28","author":"Dahmer","year":"2016","journal-title":"Curr. Opin. Pediatr."},{"key":"ref_137","doi-asserted-by":"crossref","first-page":"411","DOI":"10.1038\/nbt.4096","article-title":"Integrating single-cell transcriptomic data across different conditions, technologies, and species","volume":"36","author":"Butler","year":"2018","journal-title":"Nat. Biotechnol."},{"key":"ref_138","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Mathew, D., Lim, T.L., Mason, K., Martinez, C.M., Huang, S., Wherry, E.J., Susztak, K., Minn, A.J., and Ma, Z. (Nat. Biotechnol., 2024). Recovery of biological signals lost in single-cell batch integration with CellANOVA, Nat. Biotechnol., epub ahead of printing.","DOI":"10.1038\/s41587-024-02463-1"},{"key":"ref_139","doi-asserted-by":"crossref","unstructured":"Hu, X., Li, H., Chen, M., Qian, J., and Jiang, H. (2025). Reference-informed evaluation of batch correction for single-cell omics data with overcorrection awareness. Commun. Biol., 8.","DOI":"10.1038\/s42003-025-07947-7"},{"key":"ref_140","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1093\/bib\/bbaa042","article-title":"Computational methods for the integrative analysis of single-cell data","volume":"22","author":"Forcato","year":"2021","journal-title":"Brief. Bioinform."},{"key":"ref_141","doi-asserted-by":"crossref","unstructured":"Cheng, Y., Ma, X., Yuan, L., Sun, Z., and Wang, P. (2023). Evaluating imputation methods for single-cell RNA-seq data. BMC Bioinform., 24.","DOI":"10.1186\/s12859-023-05417-7"},{"key":"ref_142","doi-asserted-by":"crossref","first-page":"725","DOI":"10.1016\/j.devcel.2023.04.008","article-title":"Improving stem cell-derived pancreatic islets using single-cell multi-inferred regulomes","volume":"58","year":"2023","journal-title":"Dev. Cell"},{"key":"ref_143","doi-asserted-by":"crossref","unstructured":"Wu, X., Yang, X., Dai, Y., Zhao, Z., Zhu, J., Guo, H., and Yang, R. (2024). Single-cell sequencing to multi-omics: Technologies and applications. Biomark. Res., 12.","DOI":"10.1186\/s40364-024-00643-4"},{"key":"ref_144","doi-asserted-by":"crossref","first-page":"1206","DOI":"10.1038\/s41431-024-01564-4","article-title":"Overcoming barriers to single-cell RNA sequencing adoption in low- and middle-income countries","volume":"32","author":"Cribbs","year":"2024","journal-title":"Eur. J. Hum. Genet. EJHG"},{"key":"ref_145","doi-asserted-by":"crossref","first-page":"1906","DOI":"10.1016\/j.cmet.2022.09.013","article-title":"Understanding islet dysfunction in type 2 diabetes through multidimensional pancreatic phenotyping: The Human Pancreas Analysis Program","volume":"34","author":"Shapira","year":"2022","journal-title":"Cell Metab."},{"key":"ref_146","doi-asserted-by":"crossref","first-page":"1338","DOI":"10.1038\/s41467-024-55415-3","article-title":"Single-cell atlas of human pancreatic islet and acinar endothelial cells in health and diabetes","volume":"16","author":"Li","year":"2025","journal-title":"Nat. Commun."},{"key":"ref_147","doi-asserted-by":"crossref","unstructured":"Toussaint, P.A., Leiser, F., Thiebes, S., Schlesner, M., Brors, B., and Sunyaev, A. (2023). Explainable artificial intelligence for omics data: A systematic mapping study. Brief. Bioinform., 25.","DOI":"10.1093\/bib\/bbad453"},{"key":"ref_148","doi-asserted-by":"crossref","first-page":"1853","DOI":"10.1016\/j.cell.2016.11.038","article-title":"Perturb-Seq: Dissecting Molecular Circuits with Scalable Single-Cell RNA Profiling of Pooled Genetic Screens","volume":"167","author":"Dixit","year":"2016","journal-title":"Cell"},{"key":"ref_149","doi-asserted-by":"crossref","unstructured":"Lee, Y., Lee, M., Shin, Y., Kim, K., and Kim, T. (2025). Spatial Omics in Clinical Research: A Comprehensive Review of Technologies and Guidelines for Applications. Int. J. Mol. Sci., 26.","DOI":"10.3390\/ijms26093949"},{"key":"ref_150","doi-asserted-by":"crossref","first-page":"9901","DOI":"10.1038\/s41467-024-54300-3","article-title":"Data sharing ethics toolkit: The Human Cell Atlas","volume":"15","author":"Kirby","year":"2024","journal-title":"Nat. Commun."}],"container-title":["International Journal of Molecular Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1422-0067\/26\/22\/11005\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,15]],"date-time":"2025-11-15T05:24:11Z","timestamp":1763184251000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1422-0067\/26\/22\/11005"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,13]]},"references-count":150,"journal-issue":{"issue":"22","published-online":{"date-parts":[[2025,11]]}},"alternative-id":["ijms262211005"],"URL":"https:\/\/doi.org\/10.3390\/ijms262211005","relation":{},"ISSN":["1422-0067"],"issn-type":[{"value":"1422-0067","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,13]]}}}