MCA: A Multicellular analysis Calcium Imaging toolbox for ImageJ

Functional imaging using genetically encoded indicators, such as GCaMP, has become a foundational tool for in vivo experiments and allows for the analysis of cellular dynamics, sensory processing, and cellular communication. However, large scale or complex functional imaging experiments pose analytical challenges. Many programs have worked to create pipelines to address these challenges, however, most platforms require proprietary software, impose operational restrictions, offer limited outputs, or require significant knowledge of various programming languages, which collectively can limit utility. To address this, we designed MCA (a Multicellular Analysis toolkit) to work with ImageJ, a widely used open-source software which has been the standard image analysis platform for the last 30 years. We developed MCA to be visually intuitive, utilizing ImageJs platform to generate new images based on completed tasks so users can visually see each step in the analysis pipeline. In addition, MCA implements a user-friendly GUI providing a simple interface which resembles other native ImageJ plugins. We incorporated functionality for rigid registration to correct motion artifacts, algorithms for cell body prediction, and methods for annotating cells and exporting data. For cell prediction, we trained a custom model in Cellpose 2.0 for segmentation of nuclei expressing pan-neuronal nuclear localized GCaMP in zebrafish. We validated the accuracy of MCA output to previously published zebrafish calcium imaging data which elicited visually evoked neuronal responses. To show the versatility of MCA, we also show that our software can be utilized for multiple sensory modalities, brain regions, and multiple model organisms including Drosophila and mouse. Together these data show that MCA is viable for extracting calcium dynamics in a user-friendly environment for multiple forms of functional imaging. MotivationCalcium imaging has become one of the most common methods for investigating neural activity, however analytical methods are limited to a few software platforms or are custom made. This limits replicability and imposes restrictions on incorporating additional tools to support analysis. To address these challenges, we developed a modular, graphical based, open-source toolbox, based in the ImageJ application, for performing functional imaging analysis in diverse models and datasets. HighlightsO_LIDeveloped MCA, an ImageJ based plugin for analyzing functional imaging datasets. C_LIO_LIValidated accuracy of MCA functions C_LIO_LIUtilized MCA across multiple sensory modalities and model organisms C_LI