An open-source method for analysis of confocal calcium imaging with sparse cells

Background: The research in the neuroscience field has evolved to use complex imaging and computational tools to extract comprehensive information from data sets. Calcium imaging is a widely used technique that requires sophisticated software to get precise and reproducible results. Many laboratories struggle to adopt computational methods due to the lack of computational knowledge and paywalls for software. New Method: Here we propose a calcium imaging analysis method using TrackMate, an open-source Fiji plugin, to track neurons at single-cell resolution, detect regions of interest (ROIs), and extract fluorescence intensities. For confocal images, this method uses the maximal value to represent the cell intensity for each z stack. This method can be done without coding or be combined with Python or Jupyter Notebook scripts to accelerate the analysis. Results: This method is validated in fly larval cool neurons, whose calcium changes in these neurons respond to temperature fluctuation. It also identifies potential problems in approaches that extract signals from maximal projection images. Comparison with existing methods: This method does not depend on programming knowledge and/or commercial software but uses open-source software and requires no coding abilities. Since TrackMate automatically defines ROIs, this method greatly avoids human error and increases reproducibility. In addition, practice images and a step-by-step guide are provided to help users adopt this method in their experiments. Conclusions: Therefore, this open-source method allows for the analysis of calcium imaging data at single-cell resolution with high reproducibility and has the potential to be applied in various cell types and animal models by researchers with different programming abilities.