Single-Islet Proteomics Maps Pseudo-Temporal Islet Immune Responses and Dysfunction in Presymptomatic Type 1 Diabetes

Aims/hypothesis: Progressive β-cell dysfunction precedes the onset of type 1 diabetes (T1D), yet the molecular mechanisms driving early pathogenesis remain poorly understood. Functional and multiplexed imaging studies have reported lobular heterogeneity in the pancreas with respect to immune and β-cell dysfunction signatures. Although single-cell transcriptomics has identified cellular changes, it provides limited insight into the heterogeneity of distinct islet microenvironments. In this exploratory study, we employ single-islet proteomics to profile intra-donor islet heterogeneity across three multiple autoantibody-positive donors (mAAb+), representing the transition from Stage 1 to Stage 2 T1D, alongside matched non-diabetic controls, to resolve early T1D pathogenesis and identify cellular processes coupled to the islet immune response. Methods: Laser capture microdissection was used to isolate 439 individual pancreatic islets from presymptomatic mAAb+ (n = 3 donors, ~100 islets/donor) and non-diabetic control (n = 3 donors, ~50 islets/donor) organ donors obtained through the Network for Pancreatic Organ Donors with Diabetes. Islet identification and T-cell infiltration were evaluated using 3 multiplex immunohistochemistry assays for insulin, glucagon, and cell differentiation 3 proteins. Adjacent serial sections were used for islet laser capture microdissection and proteomic analysis using the Nanodroplet Processing in One pot for Trace Samples. Downstream proteomics analysis combined, weighted gene co-expression network analysis, random forest-based feature selection, linear modeling with empirical Bayes moderation, and gene set enrichment accounting for inter-gene correlation. Results: The single-islet proteomics workflow demonstrated high analytical reproducibility, with Pearson correlation coefficients exceeding 0.96 and an average of approximately 5,800 proteins quantified per donor. By combining weighted gene co-expression network analysis with random forest-based feature selection, we identified a 40-protein panel, defined as the Islet Immune Response Signature (IIRS), was identified that tracks activation and reflects a pseudo-temporal progression of the islet immune response. Additionally, functionally clustered protein modules across mAAb+ donors revealed significant intra-donor heterogeneity in β-cell-specific markers, pointing to β-cell dysfunction. Along the same axis, we established a panel of 42 proteins, defined as β-cell profile (BCP), with the highest correlation to insulin and Ectonucleoside triphosphate diphosphohydrolase 3, both β-cell markers. IIRS and BCP were found to correlate only weakly (Pearson r = 0.13), indicating that immune activation and β-cell function follow partially decoupled trajectories. Pathway analysis highlighted extracellular matrix remodeling associated with both the signature panels. Strong dysregulation of extracellular matrix organization in mAAb+ donors relative to their non-diabetic counterparts. Specifically, integrin-mediated signaling, cell-matrix adhesion, and collagen fibril organization show low association, while hyaluronan metabolic process shows strong association with IIRS. In contrast, ECM-modifying and ECM-degrading proteins (QSOX1, FBLN7, FAP, DPP4) consistently correlated negatively with BCP. Further, unique to β-cell function are pathways regarding mRNA processing and splicing, particularly in donors with insulin-depleted islets. Conclusion/interpretation: Our results reveal highly consistent proteomic patterns that reflect pseudo-time progression in the islet immune response and β-cell dysfunction. Pathways, including extracellular matrix remodeling and mRNA processing, were identified at the proteomic level as closely associated with progressive islet immune activation and loss of β-cell function. These findings provide evidence of early islet dysfunction, offer a valuable resource for investigating T1D pathogenesis, including novel candidates for functional studies, and underscore the utility of single-islet spatial proteomics for examining islet heterogeneity in T1D.