DisGUVery: a versatile open-source software for high-throughput image analysis of Giant Unilamellar Vesicles

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 pre-defined 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 non-spherical vesicles. Altogether, DisGUVery provides an accessible tool to enable high-throughput automated analysis of GUVs, and thereby to promote quantitative data analysis in GUV research.

Reconstituted branched actin networks sense and generate micron-scale membrane curvature

“…Quantitative confocal microscopy images were analyzed in an automated procedure. First, we located GUVs in the membrane images using the Template Matching module in the open source DisGUVery toolbox (64) (See Fig. S3 C).…”

VISION — an open-source software for automated multi-dimensional image analysis of cellular biophysics

“…Single-object analysis from microscopy images usually requires two distinct steps: the image segmentation (especially when working with clustered objects such as adherent cells) followed by the object identification/labelling. Available software recur to either morphology-related (Buren, Koenderink and Martinez-Torres, 2022) or AI-based algorithms (Berg et al ., 2019; Körber, 2022) to perform object detection. In VISION, object detection is accomplished through a six-step algorithmic process (Fig.…”

“…3G), although at the cost of spatial resolution. Common methods for membrane profiling involve calculating the integrated signal from segments of the membrane by slicing it into portions of equal angular amplitude (Buren, Koenderink and Martinez-Torres, 2022). Although this method works for spherical objects, it might yield inaccurate results on cells with irregular shapes.…”

“…Several open-source imaging programs have been developed to analyse object properties such as morphology, overall fluorescence intensity, number of particles etc . These software are often applicable either to certain geometry such as spherical vesicles (Stirling et al ., 2021; Buren, Koenderink and Martinez-Torres, 2022) or specialized in analysing specific subcellular compartments, such as the PM or the cell nucleus (Barry et al ., 2022). The options decrease even further when one needs to analyse cellular biophysical properties which requires mathematical operations.…”