Glucose-dependent activation, activity, and deactivation of beta cell networks in acute mouse pancreas tissue slices

Glucose progressively stimulates insulin release over a wide range of concentrations. However, the nutrient coding underlying activation, activity, and deactivation of beta cells affecting insulin release remains only partially described. Experimental data indicate that nutrient sensing in coupled beta cells in islets is predominantly a collective trait, overriding to a large extent functional differences between cells. However, some degree of heterogeneity between coupled beta cells may play important roles. To further elucidate glucose-dependent modalities in coupled beta cells, the degree of functional heterogeneity, and uncover the emergent collective operations, we combined acute mouse pancreas tissue slices with functional multicellular calcium imaging. We recorded beta cell calcium responses from threshold (7 mM) to supraphysiological (16 mM) glucose concentrations with high spatial and temporal resolution. This enabled the analysis of both classical physiological parameters and complex network parameters, as well as their comparison at the level of individual cells. The activation profile displayed two major glucose concentration-dependent features, shortening of delays to initial activation, and shortening of delays until half activation with increasing glucose concentration. Inversely, during deactivation both delays to initial deactivation and until half deactivation were progressively longer with increasing glucose concentration. The plateau activity with fast calcium oscillations expressed two types of glucose-dependence. Physiological concentrations mostly affected the frequency of oscillations, whereas supraphysiological concentrations progressively prolonged the duration of oscillations. Most of the measured functional network parameters also showed clear glucose-dependence. In conclusion, we propose novel understanding for glucose-dependent coding properties in beta cell networks, and its deciphering may have repercussions for our understanding of the normal physiology of glucose homeostasis as well as of disturbances of metabolic homeostasis, such as diabetes mellitus.