Functional imaging using genetically encoded indicators has become a foundational tool for cellular dynamics and communication analysis. However, large or complex experiments pose analytical challenges. Many programs address these challenges; however, most require proprietary software, impose restrictions, or require programming knowledge, which limits their utility. To address this, we designed MCA (Multicellular Analysis toolkit) to work with ImageJ, a widely used open-source software. MCA utilizes ImageJ to generate new images based on completed tasks, allowing visualization of the analysis pipeline. MCA also implements a user-friendly graphical user interface (GUI) resembling native ImageJ plugins. We incorporated rigid registration for motion correction, cell prediction algorithms, and data annotation and exporting features. We validated MCA using previously published zebrafish visual response calcium imaging data. To further show MCA's versatility, we also tested multiple sensory responses, brain regions, and model organisms, including Drosophila and mouse. Altogether, MCA is a user-friendly environment viable for multiple forms of functional imaging analysis.
Hageter et al. (Thu,) studied this question.
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