Early experiences in 4D quantitative analysis of insulin granules in living beta-cells

Pancreatic beta cells biosynthesize and package insulin in insulin granules, whose secretion is regulated to maintain blood glucose homeostasis. The detailed knowledge of the dynamics of insulin granules could reveal defects in the intracellular handling and secretion of these granules, leading to impaired insulin secretion and consequently to the development of several metabolic diseases, including type-2 diabetes and the metabolic syndrome. The use of spinning disk confocal and light sheet microscopy with fast sequential scanning that enable rapid volumetric imaging, allows to monitor at high spatial and temporal resolution the dynamics of insulin granules. However, obtaining all the information for accurate 3D imaging and particle tracking within a single cell is complex and challenging, and extracting information from the particle tracking data requires to analyse the segments of motion trajectories. To this aim, we present in this study a quantitative analysis of the 4D motion of insulin granules in glucose-stimulated INS- 1E beta cells. First, we tracked each granule inside the cells via a computer-based automatic approach relying on a two-step iterative process. Next, we removed the artifacts and introduced a set of quantitative cinematic features describing granule dynamics. Finally, we implemented an unsupervised machine learning based exploratory data analysis, which allows to distinguish two sets of granules marked by distinct dynamics: a first pool is characterized by a diffusive dynamic behavior, and a second pool that is characterized by a more directed and targeted movement. These pools may have distinct functional roles and/or interactions with other structures and organelles in beta cells that could be selectively impaired in pathological settings.