CALIMA: The semi-automated open-source calcium imaging analyzer

Area of Science:

• Neuroscience
• Cell Biology
• Bioinformatics

Background:

• Calcium imaging is crucial for studying cellular communication.
• Analyzing calcium imaging data is time-consuming for researchers.
• Existing automated tools offer only partial data analysis solutions.

Purpose of the Study:

• To develop CALIMA, a free, open-source software for automated calcium imaging data analysis.
• To enable rapid cell detection, calcium spike identification, and neuronal network reconstruction.
• To reduce the manual workload in analyzing neuronal cell culture experiments.

Main Methods:

• Utilized the difference of Gaussians algorithm for rapid region of interest (ROI) detection.
• Employed the z-scoring algorithm for setting calcium spike detection parameters.
• Reconstructed neuronal connections by analyzing cross-correlations in cellular activity.
• Evaluated performance using real-life datasets of primary rat cortical neurons and SH-SY5Y neuroblastoma cultures.

Main Results:

• CALIMA detected cells within seconds, achieving an average sensitivity of 82%.
• Calcium spike detection sensitivity reached 96% with optimized parameters.
• Neuronal networks were successfully reconstructed by integrating ROI activity and cell positions.
• Performance favorably compared to alternative software solutions and manual analysis.

Conclusions:

• CALIMA is a robust and fast tool for analyzing calcium imaging data.
• It facilitates the digital reconstruction of neuronal cellular networks.
• The software requires minimal user input and offers time-efficient analysis.