Algorithm for semi-automatic detection of insulin granule exocytosis in human pancreatic β-cells.

Image processing and analysis are two significant areas that are highly important for interpreting enormous amounts of data obtained from microscopy-based experiments. Several image analysis tools exist for the general detection of fundamental cellular processes, but tools to detect highly distinct cellular functions are few. One such process is exocytosis, which involves the release of vesicular content out of the cell. The size of the vesicles and the inherent differences in the imaging parameters demand specific analysis platforms for detecting exocytosis. In this direction, we have developed an image-processing algorithm based on Lagrangian particle tracking. The tool was developed to ensure that there is efficient detection of punctate structures initially developed by mathematical equations, fluorescent beads and cellular images with fluorescently labelled vesicles that can exocytose. The detection of these punctate structures using the tool was compared with other existing tools, such as find maxima in ImageJ and manual detection. The tool not only met the precision of existing solutions but also expedited the process, resulting in a more time-efficient solution. During exocytosis, there is a sudden dip in the intensity of the fluorescently labelled vesicles that look like punctate structures. The algorithm precisely locates the vesicles' coordinates and quantifies the variations in their respective intensities. Subsequently, the algorithm processes and retrieves pertinent information from large datasets surpassing that of conventional methods under our evaluation, affirming its efficacy. Furthermore, the tool exhibits adaptability for the image analysis of diverse cellular processes, requiring only minimal modifications to ensure accurate detection of exocytosis.