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bioRxiv · 10.1101/2023.04.06.535890

BetaBuddy: An end-to-end computer vision pipeline for the automated analysis of insulin secreting β-cells

Alsup, A. M.·Fowlds, K.·Cho, M.·Luber, J. M.
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Abstract

Insulin secretion from pancreatic {beta}-cells is integral in maintaining the delicate equilibrium of blood glucose levels. Calcium is known to be a key regulator and triggers the release of insulin. This sub-cellular process can be monitored and tracked through live-cell imaging and subsequent cell segmentation, registration, tracking, and analysis of the calcium level in each cell. Current methods of analysis typically require the manual outlining of {beta}-cells, involve multiple software packages, and necessitate multiple researchers - all of which tend to introduce biases. Utilizing deep learning algorithms, we have therefore created a pipeline to automatically segment and track thousands of cells, which greatly reduces the time required to gather and analyze a large number of sub-cellular images and improve accuracy. Tracking cells over a time-series image stack also allows researchers to isolate specific calcium spiking patterns and spatially identify those of interest, creating an efficient and user-friendly analysis tool. Using our automated pipeline, a previous dataset used to evaluate changes in calcium spiking activity in {beta}-cells post-electric field stimulation was reanalyzed. Changes in spiking activity were found to be underestimated previously with manual segmentation. Moreover, the machine learning pipeline provides a powerful and rapid computational approach to examine, for example, how calcium signaling is regulated by intracellular interactions in a cluster of {beta}-cells.

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Alsup, A. M., Fowlds, K., Cho, M., Luber, J. M.. 2023-04-06. BetaBuddy: An end-to-end computer vision pipeline for the automated analysis of insulin secreting β-cells. https://doi.org/10.1101/2023.04.06.535890

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