scRiskCell: A single‐cell framework for quantifying islet risk cells and their adaptive dynamics in type 2 diabetes
Open accessAbstract
scRiskCell is an interpretable intelligent computational framework that leverages nearly 500,000 islet cell expression profiles from 106 donors across different continuous disease states. By calculating the intrinsic relationship between donor disease states and cell expression profiles, it assigns a pseudo-cell state index to each cell. Sorting the pseudo-indexes of cells enables the identification of risk cells truly disrupted by the disease. Importantly, scRiskCell reveals the dynamic aggregation pattern of risk cells during disease progression, providing mechanistic insights for early disease prediction and clinical dynamic monitoring of disease progression.
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Traditional ML algorithms have been the cornerstone of early AI-assisted diagnostic models, particularly demonstrating robust performance in scenarios where features are well defined and sample sizes are moderate.
“Traditional ML algorithms have been the cornerstone of early AI-assisted diagnostic models [106–110], particularly demonstrating robust performance in scenarios where features are well defined and sample sizes are moderate.”
From Artificial intelligence-powered prediction of diabetic complications: from clinical data to molecular omics (2026) · cites this paper as [108]
The Results and Discussion section reports that a support vector machine using a single defined feature—the beta risk cell proportion—predicted diabetes states in 106 individuals, with AUCs of at least 0.837; it also reports strong performance in external datasets. This supports an example of robust ML performance with a defined feature and a moderate donor sample, but the paper does not establish that traditional ML algorithms were the cornerstone of early AI-assisted diagnostic models generally. (LLM, conf=0.82)
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References 20 · Cited by 22
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- [1]Kalyani, Rita R., Sherita H. Golden, and William T. Cefalu. 2017. “ Diabetes and Aging: Unique Considerations and Goals of Care.” Diabetes Care 40: 440– 443. 10.2337/dci17-0005 28325794 PMC5360288 · DOI · Paper page
- [4]Wang, Gaowei, Joshua Chiou, Chun Zeng, Michael Miller, Ileana Matta, Jee Yun Han, Nikita Kadakia, et al. 2023. “ Integrating Genetics With Single‐Cell Multiomic Measurements Across Disease States Identifies Mechanisms of Beta Cell Dysfunction in Type 2 Diabetes.” Nature Genetics 55: 984– 994. 10.1038/s41588-023-01397-9 37231096 PMC10550816 · DOI
- [5]Cohrs, Christian M., Julia K. Panzer, Denise M. Drotar, Stephen J. Enos, Nicole Kipke, Chunguang Chen, Robert Bozsak, et al. 2020. “ Dysfunction of Persisting β Cells Is a Key Feature of Early Type 2 Diabetes Pathogenesis.” Cell Reports 31: 107469. 10.1016/j.celrep.2020.03.033 32268101 · DOI
- [6]Chen, Chunguang, Christian M. Cohrs, Julia Stertmann, Robert Bozsak, and Stephan Speier. 2017. “ Human Beta Cell Mass and Function in Diabetes: Recent Advances in Knowledge and Technologies to Understand Disease Pathogenesis.” Molecular Metabolism 6: 943– 957. 10.1016/j.molmet.2017.06.019 28951820 PMC5605733 · DOI
- [7]Avrahami, Dana, Yue J. Wang, Jonathan Schug, Eseye Feleke, Long Gao, Chengyang Liu, Ali Naji, Benjamin Glaser, and Klaus H. Kaestner. 2020. “ Single‐Cell Transcriptomics of Human Islet Ontogeny Defines the Molecular Basis of β‐Cell Dedifferentiation in T2D.” Molecular Metabolism 42: 101057. 10.1016/j.molmet.2020.101057 32739450 PMC7471622 · DOI
- [8]Xin, Yurong, Giselle Dominguez Gutierrez, Haruka Okamoto, Jinrang Kim, Ann‐Hwee Lee, Christina Adler, Min Ni, George D. Yancopoulos, Andrew J. Murphy, and Jesper Gromada. 2018. “ Pseudotime Ordering of Single Human β‐Cells Reveals States of Insulin Production and Unfolded Protein Response.” Diabetes 67: 1783– 1794. 10.2337/db18-0365 29950394 · DOI · Paper page
- [9]Segerstolpe, Åsa, Athanasia Palasantza, Pernilla Eliasson, Eva‐Marie Andersson, Anne‐Christine Andréasson, Xiaoyan Sun, Simone Picelli, et al. 2016. “ Single‐Cell Transcriptome Profiling of Human Pancreatic Islets in Health and Type 2 Diabetes.” Cell Metabolism 24: 593– 607. 10.1016/j.cmet.2016.08.020 27667667 PMC5069352 · DOI · Paper page
- [10]Rubio‐Navarro, Alfonso, Nicolás Gómez‐Banoy, Lisa Stoll, Friederike Dündar, Alex M. Mawla, Lunkun Ma, Eric Cortada, et al. 2023. “ A Beta Cell Subset with Enhanced Insulin Secretion and Glucose Metabolism Is Reduced in Type 2 Diabetes.” Nature Cell Biology 25: 565– 578. 10.1038/s41556-023-01103-1 36928765 PMC10449536 · DOI
- [11]Kang, Randy B., Yansui Li, Carolina Rosselot, Tuo Zhang, Mustafa Siddiq, Prashant Rajbhandari, Andrew F. Stewart, Donald K. Scott, Adolfo Garcia‐Ocana, and Geming Lu. 2023. “ Single‐Nucleus RNA Sequencing of Human Pancreatic Islets Identifies Novel Gene Sets and Distinguishes β‐Cell Subpopulations With Dynamic Transcriptome Profiles.” Genome Medicine 15: 30. 10.1186/s13073-023-01179-2 37127706 PMC10150516 · DOI
- [12]Baron, Maayan, Adrian Veres, Samuel L. Wolock, Aubrey L. Faust, Renaud Gaujoux, Amedeo Vetere, Jennifer Hyoje Ryu, et al. 2016. “ A Single‐Cell Transcriptomic Map of the Human and Mouse Pancreas Reveals Inter‐ and Intra‐Cell Population Structure.” Cell Systems 3: 346– 360.e4. 10.1016/j.cels.2016.08.011 27667365 PMC5228327 · DOI · Paper page
- [13]Weng, Chen, Anniya Gu, Shanshan Zhang, Leina Lu, Luxin Ke, Peidong Gao, Xiaoxiao Liu, et al. 2023. “ Single Cell Multiomic Analysis Reveals Diabetes‐Associated β‐Cell Heterogeneity Driven by HNF1A.” Nature Communications 14: 5400. 10.1038/s41467-023-41228-3 PMC10480445 37669939 · DOI
- [14]Shapira, Suzanne N., Ali Naji, Mark A. Atkinson, Alvin C. Powers, and Klaus H. Kaestner. 2022. “ Understanding Islet Dysfunction in Type 2 Diabetes Through Multidimensional Pancreatic Phenotyping: The Human Pancreas Analysis Program.” Cell Metabolism 34: 1906– 1913. 10.1016/j.cmet.2022.09.013 36206763 PMC9742126 · DOI
- [15]Fasolino, Maria, Gregory W. Schwartz, Abhijeet R. Patil, Aanchal Mongia, Maria L. Golson, Yue J. Wang, Ashleigh Morgan, et al. 2022. “ Single‐Cell Multi‐Omics Analysis of Human Pancreatic Islets Reveals Novel Cellular States in Type 1 Diabetes.” Nature Metabolism 4: 284– 299. 10.1038/s42255-022-00531-x PMC8938904 35228745 · DOI
- [16]Xie, Xueqin, Changchun Wu, Yuduo Hao, Tianyu Wang, Yuhe Yang, Peiling Cai, Yang Zhang, et al. 2023. “ Benefits and Risks of Drug Combination Therapy for Diabetes Mellitus and Its Complications: A Comprehensive Review.” Frontiers in Endocrinology 14: 1301093. 10.3389/fendo.2023.1301093 38179301 PMC10766371 · DOI
- [18]Taneera, Jalal, Abdul Khader Mohammed, Anila Khalique, Bashair M. Mussa, Nabil Sulaiman, Yasser Bustanji, Mohamed A. Saleh, Mohamed Madkour, Eman Abu‐Gharbieh, and Waseem El‐Huneidi. 2024. “ Unraveling the Significance of PPP1R1A Gene in Pancreatic β‐Cell Function: A Study in INS‐1 Cells and Human Pancreatic Islets.” Life Sciences 345: 122608. 10.1016/j.lfs.2024.122608 38574885 · DOI
- [19]Fang, Zhou, Chen Weng, Haiyan Li, Ran Tao, Weihua Mai, Xiaoxiao Liu, Leina Lu, et al. 2019. “ Single‐Cell Heterogeneity Analysis and CRISPR Screen Identify Key β‐Cell‐Specific Disease Genes.” Cell Reports 26: 3132– 3144.e7. 10.1016/j.celrep.2019.02.043 30865899 PMC6573026 · DOI
- [20]Chiou, Joshua, Chun Zeng, Zhang Cheng, Jee Yun Han, Michael Schlichting, Michael Miller, Robert Mendez, et al. 2021. “ Single‐Cell Chromatin Accessibility Identifies Pancreatic Islet Cell Type– and State‐Specific Regulatory Programs of Diabetes Risk.” Nature Genetics 53: 455– 466. 10.1038/s41588-021-00823-0 33795864 PMC9037575 · DOI
- Authors
- Xie, X., Wu, C., Dao, F., Deng, K., Yan, D., Huang, J., Lyu, H., & Lin, H.
- Affiliations
- University of Electronic Science and Technology of China (CN)
- Nanyang Technological University (SG)
- Capital Medical University (CN)
- Department of Clinical Laboratory, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Life Science and Technology University of Electronic Science and Technology of China Chengdu China
- Corresponding author
- Huang, Jian
- Lin, Hao
- Venue
- iMeta
- Publisher
- Wiley
- Published
- 2025-06-24
- Volume
- 4
- Issue
- 4
- Pages
- e70060-e70060
- ISSN
- 2770-596X (ISSN-L)
- 2770-5986
- PMC
- PMC12371254
- OpenAlex
- W4411662378
- Cited by
- 22
- References
- 20
- Citations by year
- 2026: 16 · 2025: 6
- FWCI
- 20.70
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- iMeta (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Sichuan Province Science and Technology Support Program (2025ZNSFSC1465)
- China Postdoctoral Science Foundation (2023TQ0047, GZC20230380)
- National Natural Science Foundation of China (62402089, 82130112, U24A20789)
- Topics
- Health Sciences › Medicine › Surgery › Pancreatic function and diabetes
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Diabetes and associated disorders
- Keywords
- type 2 diabetes, pancreatic islets, single-cell transcriptomics, disease progression, early disease prediction, disease state classification, patient monitoring
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