A coordinated functioning of beta cells within pancreatic islets is mediated by oscillatory membrane depolarization and subsequent changes in cytoplasmic calcium concentration. While gap junctions allow for intraislet information exchange, beta cells within islets form complex syncytia that are intrinsically nonlinear and highly heterogeneous. To study spatiotemporal calcium dynamics within these syncytia, we make use of computational modeling and confocal high-speed functional multicellular imaging. We show that model predictions are in good agreement with experimental data, especially if a high degree of heterogeneity in the intercellular coupling term is assumed. In particular, during the first few minutes after stimulation, the probability distribution of calcium wave sizes is characterized by a power law, thus indicating critical behavior. After this period, the dynamics changes qualitatively such that the number of global intercellular calcium events increases to the point where the behavior becomes supercritical. To better mimic normal in vivo conditions, we compare the described behavior during supraphysiological non-oscillatory stimulation with the behavior during exposure to a slightly lower and oscillatory glucose challenge. In the case of this protocol, we observe only critical behavior in both experiment and model. Our results indicate that the loss of oscillatory changes, along with the rise in plasma glucose observed in diabetes, could be associated with a switch to supercritical calcium dynamics and loss of beta cell functionality.
COBISS.SI-ID: 512760376
The paper provides a review of the recent advances in the study of complex biological systems that were inspired and enabled by methods of network science. Specific focus is given to the extraction of functional connectivity patterns in multicellular systems, with emphasis on insulin secreting beta cell populations in islets of Langerhans. Describing the intercellular activity patterns by means of network language has not only revealed that beta cell networks share many architectural similarities with several other real-life networks, such as small-worldness, heterogeneity, and modularity, but also leads to important new insights into the relationship between cellular metabolic activity and the orchestration of collective behavior. Most importantly, the paper describes the emerging field of multilayer networks and highlights the potential offered by this framework in exploring the complex organization of tissues in both health and disease.
COBISS.SI-ID: 512746040
Major part of a pancreatic islet is composed of ß-cells that secrete insulin, a key hormone regulating influx of nutrients into all cells in a vertebrate organism to support nutrition, housekeeping or energy storage. ß-cells constantly communicate with each other using both direct, short-range interactions through gap junctions, and paracrine long-range signaling. However, how these cell interactions shape collective sensing and cell behavior in islets that leads to insulin release is unknown. When stimulated by specific ligands, primarily glucose, ß-cells collectively respond with expression of a series of transient Ca2+ changes on several temporal scales. Here we reanalyze a set of Ca2+ spike trains recorded in acute rodent pancreatic tissue slice under physiological conditions. We found strongly correlated states of co-spiking cells coexisting with mostly weak pairwise correlations widespread across the islet. Furthermore, the collective Ca2+ spiking activity in islet shows on-off intermittency with scaling of spiking amplitudes, and stimulus dependent autoassociative memory features. We use a simple spin glass-like model for the functional network of a ß-cell collective to describe these findings and argue that Ca2+ spike trains produced by collective sensing of ß-cells constitute part of the islet metabolic code that regulates insulin release and limits the islet size.
COBISS.SI-ID: 21119766