Quantifying Calcium Dynamics in T Cell Populations: An Automated Analysis Framework for Antigen Fluorescence Applying Functional Anova

Calcium plays a pivotal role in a wide array of physiological processes, serving as a key indicator of cellular activity. This study underscores the importance of understanding calcium patterns in response to stimuli, highlighting the necessity of automated segmentation in fluorescence movies to analyze large cell populations exposed to diverse stimuli. We present a framework for analyzing videos capturing calcium fluorescence in extensive T cell populations exposed to multiple antigens, including UCHT1, 9V:MHC, OKT3, 4D:MHC, and a negative antigen-free control. We utilized Fiji software for video preprocessing and CaImAn for region of interest segmentation and temporal calcium transient extraction. Subsequently, we conducted statistical analyses using empirical models including linear and polynomial regression, spline regression, and functional additive models. Our findings indicate that the functional ANOVA model was the most appropriate, revealing significant impacts of calcium signaling for the aforementioned antigens and the negative control across distinct time intervals. This study developed an interactive Shiny application called SignalPredict where the functional ANOVA model is available, thus facilitating analysis for scientists without programming expertise. These findings support the concept of unique effects of each antigen on calcium signaling in T cells, enhancing our understanding of cellular activity and its implications in biomedical and immunological research. This methodology provides a valuable tool for further exploration of cellular response in physiological and pathological contexts.