Interpretable machine learning-guided single-cell mapping deciphers multi-lineage pancreatic dysregulation in type 2 diabetes

作者：Xueqin Xie, Changchun Wu, Yuhe R. Yang, Wei Su, Fanny Dao, Jian Huang, Zheng Shi, Hao Lv, Hao Lin · 发表于：Cardiovascular Diabetology · 年份：2025 · DOI：10.1186/s12933-025-02865-8 · 被引用次数：7 · 研究领域：Single-cell and spatial transcriptomics、Pancreatic function and diabetes、Pancreatic and Hepatic Oncology Research

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 par...