SNAzzy: an image processing pipeline for investigating global Synchronous Network Activity

SNAzzy is a Python pipeline that enables high-throughput analysis of synchronous network activity in Drosophila embryos from widefield time-lapse datasets with dozens of specimens and thousands of time points.

• Why it matters: Large, multidimensional recordings from many embryos create datasets that are difficult to process and analyze, limiting efficient study of synchronous network activity during nervous system development.
• What they did: The package processes and analyzes fluorescent widefield microscopy time-lapse data by cropping individual specimens, using adaptive regions of interest, applying a custom peak detection algorithm, and providing a GUI for visualization and curation.
• The result: By combining these steps, SNAzzy supports quantification of fluorescence and morphology changes over time and enables dataset comparison, with the code released at https://github.com/ACRLab/snazzy.

AI summary of the abstract · may contain errors · not medical advice · read the paper.