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

Genetically encoded fluorescent indicators are powerful tools for monitoring biological processes in live samples (Lin and Schnitzer 2016; Nakai, Ohkura, and Imoto 2001). When combined with a large field of view, a single time-lapse recording has the potential to capture many specimens, facilitating high-throughput data collection. However, this approach generates large, multidimensional datasets that are challenging to process and analyze. We present SNAzzy, a Python package for studying synchronous network activity (SNA) in Drosophila embryos via high-throughput microscopy. SNA is a hallmark of developing nervous systems (Wu, Kourdougli, and Portera-Cailliau 2024; Blankenship and Feller 2009; Akin and Zipursky 2020), often studied using genetically encoded calcium indicators to monitor neural activity in vivo. SNAzzy processes and analyzes time-lapse datasets taken from live samples using fluorescent widefield microscopy. Each dataset contains dozens of individual specimens in the same field of view and thousands of time points. The software offers individual specimen cropping for optimization of storage and processing, adaptive regions of interest for quantification of fluorescence and changes in morphology over time, a custom peak detection algorithm, and a graphical user interface for data visualization, curation, and dataset comparison. The code is available at https://github.com/ACRLab/snazzy.