Nico Fricke

Computational tool for an automized single and high content vesicle image analysis –Identification of chemical modulators of membrane phases.

Giant Unilamellar Vesicles (GUVs), as well as Giant Plasma Membrane-derived Vesicles (GPMVs) are useful model systems to mimic and study biological membrane phase behavior. In order to analyze high content vesicle image data, as well as single vesicles,a fully automated and completely unbiased computational tool, VesA, has been developed. VesAdetects, selects, and grabs all obtainable vesicle parameters and statistically analyzes them, resulting in precise values and distributions. VesA has been developed in a very general way, suitable for many projects and experimental setups, and as an expandable platform to include other models or algorithms.Using VesA, automated analysis is successfully being carried outon high throughput epi-fluorescent imaging datasets to identify the impact of small chemical molecules on membrane phase behavior, in particular raft-like phases. Here, the analysis of millions of vesicles could validate known modulators, but also identify new compounds that alter membrane phases. In other ongoing projects the tool is being used to quantify the effect of chemicals on the partitioning of proteins. In addition,low throughput imaging of vesicles, such as temperature perturbations and confocal microscopy,can be analyzed.