Interpretable machine learning-guided single-cell mapping deciphers multi-lineage pancreatic dysregulation in type 2 diabetes
Abstract 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...
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BMC
2025-07-01
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| Series: | Cardiovascular Diabetology |
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| Online Access: | https://doi.org/10.1186/s12933-025-02865-8 |
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| author | Xueqin Xie Changchun Wu Yuhe Yang Wei Su Fuying Dao Jian Huang Zheng Shi Hao Lyu Hao Lin |
| author_facet | Xueqin Xie Changchun Wu Yuhe Yang Wei Su Fuying Dao Jian Huang Zheng Shi Hao Lyu Hao Lin |
| author_sort | Xueqin Xie |
| collection | DOAJ |
| description | Abstract 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. Graphical abstract |
| format | Article |
| id | doaj-art-2c5ab9b452cd414da7248b8611f310ef |
| institution | DOAJ |
| issn | 1475-2840 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | BMC |
| record_format | Article |
| series | Cardiovascular Diabetology |
| spelling | doaj-art-2c5ab9b452cd414da7248b8611f310ef2025-08-20T03:04:22ZengBMCCardiovascular Diabetology1475-28402025-07-0124111810.1186/s12933-025-02865-8Interpretable machine learning-guided single-cell mapping deciphers multi-lineage pancreatic dysregulation in type 2 diabetesXueqin Xie0Changchun Wu1Yuhe Yang2Wei Su3Fuying Dao4Jian Huang5Zheng Shi6Hao Lyu7Hao Lin8Department 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 ChinaDepartment 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 ChinaDepartment 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 ChinaDepartment 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 ChinaSchool of Biological Sciences, Nanyang Technological UniversityDepartment 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 ChinaClinical Genetics Laboratory, Clinical Medical College & Affiliated Hospital, Chengdu UniversityDepartment 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 ChinaDepartment 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 ChinaAbstract 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. Graphical abstracthttps://doi.org/10.1186/s12933-025-02865-8Pancreatic cellular heterogeneitySingle-cell transcriptomicsMachine learningType 2 diabetesStellate cell activationBeta cell dysfunction |
| spellingShingle | Xueqin Xie Changchun Wu Yuhe Yang Wei Su Fuying Dao Jian Huang Zheng Shi Hao Lyu Hao Lin Interpretable machine learning-guided single-cell mapping deciphers multi-lineage pancreatic dysregulation in type 2 diabetes Cardiovascular Diabetology Pancreatic cellular heterogeneity Single-cell transcriptomics Machine learning Type 2 diabetes Stellate cell activation Beta cell dysfunction |
| title | Interpretable machine learning-guided single-cell mapping deciphers multi-lineage pancreatic dysregulation in type 2 diabetes |
| title_full | Interpretable machine learning-guided single-cell mapping deciphers multi-lineage pancreatic dysregulation in type 2 diabetes |
| title_fullStr | Interpretable machine learning-guided single-cell mapping deciphers multi-lineage pancreatic dysregulation in type 2 diabetes |
| title_full_unstemmed | Interpretable machine learning-guided single-cell mapping deciphers multi-lineage pancreatic dysregulation in type 2 diabetes |
| title_short | Interpretable machine learning-guided single-cell mapping deciphers multi-lineage pancreatic dysregulation in type 2 diabetes |
| title_sort | interpretable machine learning guided single cell mapping deciphers multi lineage pancreatic dysregulation in type 2 diabetes |
| topic | Pancreatic cellular heterogeneity Single-cell transcriptomics Machine learning Type 2 diabetes Stellate cell activation Beta cell dysfunction |
| url | https://doi.org/10.1186/s12933-025-02865-8 |
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