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.