Interpretable machine learning-guided single-cell mapping deciphers multi-lineage pancreatic dysregulation in type 2 diabetes
Open accessAbstract
BACKGROUND: Pancreatic cellular heterogeneity is fundamental to systemic metabolic regulation, yet its pathological remodeling in diabetes remains poorly characterized. METHODS: We integrated single-cell RNA sequencing with machine learning frameworks to decode pancreatic heterogeneity. Novel tools included PanSubPred (two-stage feature selection/XGBoost classifier) for multi-lineage annotation and PSC-Stat (XGBoost/Gini optimization) for stellate cell activation analysis. RESULTS: By establishing PanSubPred, we systematically decoded pancreatic cellular diversity, identifying 64 cell-type-specific markers (38 novel) that maintained cross-dataset accuracy (AUC > 0.970) even after excluding known canonical markers. Building on this annotation precision, we developed PSC-Stat to quantify stellate cell activation dynamics, revealing their progressive activation from diabetes to pancreatic cancer (activated/quiescent ratio: control: 1.44 ± 1.02, diabetes: 4.72 ± 4.01, pancreatic cancer: 18.67 ± 18.70). Diabetes reorganized intercellular communication into ductal-centric hubs via FGF7-FGFR2/3, EFNB3-EPHB2/4/6 and EFNA5-EPHA2 axes, from which we derived a 15-gene signature for diabetic ductal cells (AUC = 0.846). Beta cell heterogeneity analysis uncovered diabetes-associated depletion of mature insulin-secretory clusters (INS + NKX6-1+), expansion of immature (CD81 + RBP4+) and endoplasmic reticulum stress-adapted subtypes (DDIT3 + HSPA5+). Moreover, non-beta lineages exhibited parallel dysfunction: acinar cells shifted toward inflammatory states (CCL2 + CXCL17+), while ductal cells adopted secretory phenotypes (MUC1 + CFTR+). CONCLUSIONS: This study presents a machine learning-based single-cell framework that systematically maps pancreatic cellular alterations in diabetes. The identified novel signatures, stellate activation dynamics, and beta cell maturation trajectories may serve as potential targets for diabetic management and pancreatic cancer risk stratification.
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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 [110]
The Results section on the 15-gene diagnostic signature reports that a logistic-regression model performed best among the algorithms tested and showed good discrimination, including in external validation (AUC 0.846). This supports an example of a traditional ML algorithm performing well with selected features, but the paper does not establish that such algorithms were the cornerstone of early AI-assisted diagnostics or that their performance is generally robust when sample sizes are moderate. (LLM, conf=0.83)
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- Authors
- Xie, X., Wu, C., Yang, Y., Su, W., Dao, F., Huang, J., Shi, Z., Lyu, H., & Lin, H.
- Affiliations
- University of Electronic Science and Technology of China (CN)
- Nanyang Technological University (SG)
- Chengdu University (CN)
- Corresponding author
- Lin, Hao
- Venue
- Cardiovascular Diabetology
- Published
- 2025-07-24
- Volume
- 24
- Issue
- 1
- Pages
- 300-300
- ISSN
- 1475-2840 (ISSN-L)
- PMC
- PMC12291316
- OpenAlex
- W4412630432
- Cited by
- 10
- References
- 74
- Citations by year
- 2026: 8 · 2025: 2
- FWCI
- 9.41
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Cardiovascular Diabetology (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- DR-NTU (Nanyang Technological University) (publishedVersion, cc-by-nc-nd)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- China Postdoctoral Science Foundation (2023TQ0047)
- Sichuan Science and Technology Program (2025ZNSFSC1465)
- National Natural Science Foundation of China (62402089, 82130112)
- 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, cell type annotation, stellate cell activation, beta-cell heterogeneity, beta cell maturation, intercellular communication, ductal cells, acinar cells, pancreatic cancer, endoplasmic reticulum stress, XGBoost, model interpretability, single-cell RNA sequencing
- MeSH terms
- Machine Learning, Insulin Secretion, RNA-Seq, Animals, Diabetes Mellitus, Type 2, Humans, Male, Mitochondria, Pancreas, Pancreatic Neoplasms, Phenotype, Software Validation, Signal Transduction, Sequence Analysis, RNA, Disease Progression, Cell Lineage, Pancreatic Stellate Cells, Single-Cell Analysis, Transcriptome
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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