Multiplexed Deep Visual Proteomics Unveils Spatial Heterogeneity and Rare Endocrine States in Human Pancreatic Islets

Abstract Understanding tissue function requires connecting spatial context with molecular depth, yet such integration remains challenging in complex organs like the pancreas. We developed multiplexed Deep Visual Proteomics (mxDVP), an end-to-end workflow combining high-plex imaging, automated computational analysis, and spatially guided ultra-sensitive mass spectrometry. Powered by PIPΣX, an open-source image analysis framework enabling whole-slide membrane-aware segmentation, annotation, and laser microdissection export without programming expertise, mxDVP achieves >6,000 protein identifications from as few as 100 small islet cells. Applied to human pancreatic islets, mxDVP allowed us to segment over 860,000 cells and revealed 12 endocrine subtypes, including rare polyhormonal and progenitor-like populations that exhibit spatial self-organization, co-expression of INSM1 and SCG3, and hybrid α/β/δ signatures. These findings uncover a previously hidden spectrum of endocrine plasticity and spatial organization. By integrating imaging and deep proteomics in an accessible framework, mxDVP enables discovery of rare cell states whose biological roles depend on tissue architecture.