The islets of Langerhans exist as multicellular networks that regulate blood glucose levels. The majority of cells in the islet are excitable, insulin-producing β-cells that are electrically coupled via gap junction channels. β-cells are known to display heterogeneous functionality. However, due to gap junction coupling, β-cells show coordinated [Ca2+] oscillations when stimulated with glucose, and global quiescence when unstimulated. Small subpopulations of highly functional β-cells have been suggested to control [Ca2+] dynamics across the islet. When these populations were targeted by optogenetic silencing or photoablation, [Ca2+] dynamics across the islet were largely disrupted. In this study, we investigated the theoretical basis of these experiments and how small populations can disproportionality control islet [Ca2+] dynamics. Using a multicellular islet model, we generated normal, skewed or bimodal distributions of β-cell heterogeneity. We examined how islet [Ca2+] dynamics were disrupted when cells were targeted via hyperpolarization or populations were removed; to mimic optogenetic silencing or photoablation, respectively. Targeted cell populations were chosen based on characteristics linked to functional subpopulation, including metabolic rate of glucose oxidation or [Ca2+] oscillation frequency. Islets were susceptible to marked suppression of [Ca2+] when ~10% of cells with high metabolic activity were hyperpolarized; where hyperpolarizing cells with normal metabolic activity had little effect. However, when highly metabolic cells were removed from the model, [Ca2+] oscillations remained. Similarly, when ~10% of cells with either the highest frequency or earliest elevations in [Ca2+] were removed from the islet, the [Ca2+] oscillation frequency remained largely unchanged. Overall, these results indicate small populations of β-cells with either increased metabolic activity or increased frequency are unable to disproportionately control islet-wide [Ca2+] via gap junction coupling. Therefore, we need to reconsider the physiological basis for such small β-cell populations or the mechanism by which they may be acting to control normal islet function.

The spatial architecture of the islets of Langerhans is hypothesized to facilitate synchronized insulin secretion among β cells yet testing this in vivo in the intact pancreas is challenging. Robo βKO mice, in which the genes Robo1 and Robo2 are deleted selectively in β cells, provide a unique model of altered islet spatial architecture without loss of β cell differentiation or islet damage from diabetes. Combining Robo βKO mice with intravital microscopy, we show here that Robo βKO islets have reduced synchronized intra-islet Ca2+ oscillations among β cells in vivo. We provide evidence that this loss is not due to a β cell-intrinsic function of Robo, mis-expression or mis-localization of Cx36 gap junctions, or changes in islet vascularization or innervation, suggesting that the islet architecture itself is required for synchronized Ca2+ oscillations. These results have implications for understanding structure-function relationships in the islets during progression to diabetes as well as engineering islets from stem cells.

The islets of Langerhans exist as a multicellular network that is important for the regulation of blood glucose levels. The majority of cells in the islet are insulin-producing β-cells, which are excitable cells that are electrically coupled via gap junction channels. β-cells have long been known to display heterogeneous functionality. However, due to gap junction electrical coupling, β-cells show coordinated [Ca2+] oscillations when stimulated with glucose, and global quiescence when unstimulated. Small subpopulations of highly functional β-cells have been suggested to control the dynamics of [Ca2+] and insulin release across the islet. In this study, we investigated the theoretical basis of whether small subpopulations of β-cells can disproportionality control islet [Ca2+] dynamics. Using a multicellular model of the islet, we generated continuous or bimodal distributions of β-cell heterogeneity and examined how islet [Ca2+] dynamics depended on the presence of cells with increased excitability or increased oscillation frequency. We found that the islet was susceptible to marked suppression of [Ca2+] when a ~10% population of cells with high metabolic activity was hyperpolarized; where hyperpolarizing cells with normal metabolic activity had little effect. However, when these highly metabolic cells were removed from the islet model, near normal [Ca2+] remained. Similarly, when ~10% of cells with either the highest frequency or earliest elevations in [Ca2+] were removed from the islet, the [Ca2+] oscillation frequency remained largely unchanged. Overall these results indicate that small populations of β-cells with either increased excitability or increased frequency, or signatures of [Ca2+] dynamics that suggest such properties, are unable to disproportionately control islet-wide [Ca2+] via gap junction coupling. As such, we need to reconsider the physiological basis for such small β-cell populations or the mechanism by which they may be acting to control normal islet function.

Network theory provides tools for quantifying communication within heterogeneous biological systems. Calcium-based imaging is a common technique used to analyze cellular dynamics and create functional networks. However, the use of functional networks in understanding the underlying biological structure, formed by physical connections, has not been well investigated. The islet of Langerhans is a biological system with relatively simple architecture that affords the opportunity for high-fidelity experimental and computation analysis, making it an ideal system to answer biological network theory-based questions. Here, we analyze both experimental and computational models of the islet to quantify the relationship between the gap junction structural network and functional synchronization networks. We show that functional networks poorly reflect gap junction structure. Instead, they indicate intrinsic factors driving excitability, such as cellular metabolism. This surprising finding provides a new interpretation of functional networks within biological systems.