Versatile and automated workflow for the analysis of oligodendroglial calcium signals.

Although intracellular Ca2+ signals of oligodendroglia, the myelin-forming cells of the central nervous system, regulate vital cellular processes including myelination, few studies on oligodendroglia Ca2+ signal dynamics have been carried out and existing software solutions are not adapted to the analysis of the complex Ca2+ signal characteristics of these cells. Here, we provide a comprehensive solution to analyze oligodendroglia Ca2+ imaging data at the population and single-cell levels. We describe a new analytical pipeline containing two free, open source and cross-platform software programs, Occam and post-prOccam, that enable the fully automated analysis of one- and two-photon Ca2+ imaging datasets from oligodendroglia obtained by either ex vivo or in vivo Ca2+ imaging techniques. Easily configurable, our software solution is optimized to obtain unbiased results from large datasets acquired with different imaging techniques. Compared to other recent software, our solution proved to be fast, low memory demanding and faithful in the analysis of oligodendroglial Ca2+ signals in all tested imaging conditions. Our versatile and accessible Ca2+ imaging data analysis tool will facilitate the elucidation of Ca2+-mediated mechanisms in oligodendroglia. Its configurability should also ensure its suitability with new use cases such as other glial cell types or even cells outside the CNS.

Quantitative Analysis of Microscopy Data to Evaluate Bacterial Responses to Antibiotic Treatment.

Microscopy is a powerful method to evaluate the direct effects of antibiotic action on the single cell level. As with other methodologies, microscopy data is obtained through sufficient biological and technical replicate experiments, where evaluation of the sample is generally followed over time. Even if a single antibiotic is tested for a defined time, the most certain outcome is large amounts of raw data that requires systematic analysis. Although microscopy is a helpful qualitative method, the recorded information is stored as defined quantifiable units, the pixels. When this information is transferred to diverse bioinformatic tools, it is possible to analyze the microscopy data while avoiding the inherent bias associated to manual quantification. Here, we briefly describe methods for the analysis of microscopy images using open-source programs, with a special focus on bacteria exposed to antibiotics.