CALIMA: The semi-automated open-source calcium imaging analyzer. (October 2019) - Radstake, F.D.W.; Raaijmakers, E.A.L.; Luttge, R.; Zinger, S.; Frimat, J.P.
Highlights: Although calcium imaging is widely used in biological research, there are relatively few tools available for its analysis. These tools often enable only part of the analysis or require expensive software packages to run. Here we proposed the semi-automated standalone open-source software tool CALIMA, and we show that it is a valuable addition to the field of calcium imaging. As demonstrated with three highly different real-life datasets, CALIMA can detect cells and their activity with high sensitivity. CALIMA furthermore enables the user to reconstruct a network, and allows the user to limit the length of neuronal connections in said network. As some cell types grow neurites of limited length, the latter option can help to study the communication in groups of such cells. Abstract: Background and objective: Ever since its discovery, calcium imaging has proven its worth in discovering new insights into the mechanisms of cellular communication. Yet, the analysis of the data generated by calcium imaging experiments demands a large amount of time from researchers. Tools enabling automated and semi-automated analysis are available, but often they allow automating only a part of the data analysis process. Therefore, we developed CALIMA (https://aethelraed.nl/calima ), a free and open-source standalone software tool that provides an opportunity to quickly detect cells, to obtain the calcium spikes, and to determine the underlying network structure of neuronal cell cultures. Methods: Owing to the difference of Gaussians algorithm applied for the cell detection, CALIMA is able to detect regions of interest (ROIs) quickly. The z-scoring algorithm provides a means to set the requirements for spike detection, and the neuronal connections can be reconstructed by analyzing the cross-correlation between the cellular activity. We evaluated CALIMA's reliability, speed, and functionality with a special focus on neuronal cell detection and network reconstruction. The evaluation was performed by using real-life data such as a known example dataset (cultured primary rat cortical neurons, University of Pennsylvania) and by analyzing video graphic footage of in vitro brain cell samples (SH-SY5Y neuroblastoma cultures, one sample with synchronous neuron firing). The obtained results were compared to the corresponding outcomes observed on same datasets for other similar software solutions. Moreover, we compared the results of segmentation and peak detection analysis, the ones obtained using CALIMA and those acquired manually. Results: CALIMA was able to detect the cells in the cultures within seconds. The average sensitivity was 82% across the datasets checked, comparing favorably with the alternative software solutions. Using the correct parameters, CALIMA's Ca-spikes detection sensitivity reached 96%. Lastly, neuronal networks were reconstructed by combining the data on the ROI's activity and the cell's positions, finding the most likely inter-cell connections. Conclusions: We found that CALIMA proved to be a robust and fast tool to analyze the data of experiments for the digital reconstruction of the neuronal cellular network while being able to process the analysis steps with minimal user input required and in a time efficient manner.