Laboratory of Biological Modeling, NIDDK, National Institutes of Health, Bethesda, Maryland, United States of America.
Section of Endocrinology, Diabetes and Metabolism, Department of Medicine, The University of Chicago, Chicago, Illinois, United States of America.
PLoS Comput Biol. 2023 Nov 9;19(11):e1011617. doi: 10.1371/journal.pcbi.1011617. eCollection 2023 Nov.
The islets of Langerhans are critical endocrine micro-organs that secrete hormones regulating energy metabolism in animals. Insulin and glucagon, secreted by beta and alpha cells, respectively, are responsible for metabolic switching between fat and glucose utilization. Dysfunction in their secretion and/or counter-regulatory influence leads to diabetes. Debate in the field centers on the cytoarchitecture of islets, as the signaling that governs hormonal secretion depends on structural and functional factors, including electrical connectivity, innervation, vascularization, and physical proximity. Much effort has therefore been devoted to elucidating which architectural features are significant for function and how derangements in these features are correlated or causative for dysfunction, especially using quantitative network science or graph theory characterizations. Here, we ask if there are non-local features in islet cytoarchitecture, going beyond standard network statistics, that are relevant to islet function. An example is ring structures, or cycles, of α and δ cells surrounding β cell clusters or the opposite, β cells surrounding α and δ cells. These could appear in two-dimensional islet section images if a sphere consisting of one cell type surrounds a cluster of another cell type. To address these issues, we developed two independent computational approaches, geometric and topological, for such characterizations. For the latter, we introduce an application of topological data analysis to determine locations of topological features that are biologically significant. We show that both approaches, applied to a large collection of islet sections, are in complete agreement in the context both of developmental and diabetes-related changes in islet characteristics. The topological approach can be applied to three-dimensional imaging data for islets as well.
胰岛是重要的内分泌微器官,分泌激素调节动物的能量代谢。分别由β细胞和α细胞分泌的胰岛素和胰高血糖素负责脂肪和葡萄糖利用之间的代谢转换。它们的分泌功能障碍和/或反馈调节影响会导致糖尿病。该领域的争论集中在胰岛的细胞结构上,因为调节激素分泌的信号取决于结构和功能因素,包括电连接、神经支配、血管化和物理接近度。因此,人们付出了很大的努力来阐明哪些结构特征对功能很重要,以及这些特征的失调与功能障碍之间是如何相关或因果相关的,特别是使用定量网络科学或图论特征描述。在这里,我们询问胰岛细胞结构中是否存在非局部特征,这些特征超出了标准网络统计,与胰岛功能相关。一个例子是α和δ细胞围绕β细胞簇的环结构或循环,或者相反,β细胞围绕α和δ细胞。如果一个细胞类型的球体围绕另一个细胞类型的簇,则这些结构可能出现在二维胰岛切片图像中。为了解决这些问题,我们开发了两种独立的计算方法,几何方法和拓扑方法,用于此类特征描述。对于后者,我们引入了拓扑数据分析的应用,以确定生物学上有意义的拓扑特征的位置。我们表明,这两种方法,应用于大量胰岛切片,在胰岛特征的发育和糖尿病相关变化的背景下是完全一致的。拓扑方法也可以应用于胰岛的三维成像数据。