Deep learning-powered quantification of endocrine cells and CD3+ T cells in the natural history of type 1 diabetes

AIMS/HYPOTHESIS: Histopathological analysis in type 1 diabetes presents challenges in achieving precise characterisation with cellular quantification from whole-slide images (WSIs). To ensure a comprehensive understanding of changes in endocrine cells, this study leveraged a pre-trained deep learning-assisted analysis workflow to enhance the understanding of histopathological features of type 1 diabetes development across pancreases from control, autoantibody-positive and type 1 diabetes organ donors. METHODS: cells located within a 20 µm periphery of each endocrine cell group were quantified. Ordinal regression was applied to assess disease stage-associated patterns in quantified predictors. The Kruskal-Wallis test was used to compare across the five donor groups. For pairwise comparisons, Wilcoxon rank-sum tests with Bonferroni correction were conducted. RESULTS: immune cell infiltration in islets demonstrated a high association with type 1 diabetes progression. Infiltration in both islets and clusters peaked at disease onset before declining, suggesting that these structures are synchronised targets within the autoimmune process. Insulitic clusters were evident even prior to the onset of type 1 diabetes, underscoring the early involvement of these structures in the autoimmune process. CONCLUSIONS/INTERPRETATION: The deep learning-powered approach enabled our study to include clusters of endocrine cells scattered throughout WSIs, providing precise quantitative evidence of cluster-level infiltration. The identification of autoimmune patterns in both islets and clusters, alongside the quantification of beta and alpha cells across donor groups and pancreatic regions, offers a more detailed understanding of type 1 diabetes pathogenesis. Our findings provide robust evidence of cluster-level infiltration even before type 1 diabetes onset, supporting early intervention efforts to preserve beta cells.