Optical imaging and single-cell RNA sequencing reveal individual heterogeneity in pancreatic cells that supports global function

Our study investigated the heterogeneity in glucose-induced excitability within islet cells, providing a cellular basis for their roles in glucose-responsive hormone secretion. To comprehensively map glucose-induced excitability profiles, we developed SHIMMER, a high-throughput, multimodal data acquisition platform that integrates electrical signals, Ca2+ fluxes, and single-cell sequencing data. By quantifying the non-stationary electrical and calcium signal dynamics, we formulated a machine learning-based electro-calcium model that shows how variations in membrane conductance and Ca2+ clearance contribute to increased heterogeneity in cytosolic Ca2+ accumulation. Using SHIMMER for single-cell sequencing on representative cells, we identified several genes that associated with electro-calcium characteristics, and found a potential marker for a highly excitable cell subpopulation. These findings indicate that islet cells tend to exhibit a large heterogeneity in glucose-induced excitability, being essential for their normal functioning.

This dataset include all processed data we mentioned in our paper, the code were uploaded on https://github.com/CullinanLi/heterogeneity-in-pancreatic-islet-cells, with the same folder name, if we missed anything, please feel free to contact the lead contact.