Abstract
Giant unilamellar vesicles (GUVs) are cell-sized aqueous compartments enclosed by a phospholipid bilayer. Due to their cell-mimicking properties, GUVs have become a widespread experimental tool in synthetic biology to study membrane properties and cellular processes. In stark contrast to the experimental progress, quantitative analysis of GUV microscopy images has received much less attention. Currently, most analysis is performed either manually or with custom-made scripts, which makes analysis time-consuming and results difficult to compare across studies. To make quantitative GUV analysis accessible and fast, we present DisGUVery, an open-source, versatile software that encapsulates multiple algorithms for automated detection and analysis of GUVs in microscopy images. With a performance analysis, we demonstrate that DisGUVery's three vesicle detection modules successfully identify GUVs in images obtained with a wide range of imaging sources, in various typical GUV experiments. Multiple predefined analysis modules allow the user to extract properties such as membrane fluorescence, vesicle shape, and internal fluorescence from large populations. A new membrane segmentation algorithm facilitates spatial fluorescence analysis of nonspherical vesicles. Altogether, DisGUVery provides an accessible tool to enable high-throughput automated analysis of GUVs, and thereby to promote quantitative data analysis in synthetic cell research.
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