Autofluorescence intensity patterns encode α/β cell identity in human islets

Understanding the behavior of - and {beta}-cells within intact human islets is essential for elucidating mechanisms of metabolic control in diabetes. Current cell-type identification strategies rely on destructive labeling or on advanced imaging modalities such as Fluorescence Lifetime Imaging Microscopy (FLIM), which provide rich metabolic information but require specialized instrumentation and acquisition protocols. Here we show that structured intracellular intensity patterns derived from endogenous autofluorescence are sufficient to discriminate and {beta} cells in living human islets. Using rotation-invariant Local Ternary Pattern (LTP) descriptors combined with morphological features, we achieve highly accurate classification (AUC = 0.92), improving upon previously reported benchmarks. The resulting framework is lightweight, interpretable, and compatible with standard imaging configurations, enabling accessible and scalable analysis of label-free microscopy data. Interpretability analyses demonstrate that discrimination is driven predominantly by fine-scale intracellular intensity organization rather than global morphology. In the spectral window employed, cytoplasmic autofluorescence is prominently shaped by lipofuscin-rich granules. Consistent with prior reports of higher lipofuscin accumulation in {beta}-cells, the dominant features identified here likely reflect differences in granule abundance and spatial organization between endocrine cell types. These findings indicate that endogenous intensity patterns encode sufficient structural information for reliable /{beta} discrimination, providing a biologically grounded and fully non-destructive framework for the identification of pancreatic islet cell types.