Quantification of Giant Unilamellar Vesicle Fusion Products by High-Throughput Image Analysis

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Abstract

Artificial cells are based on dynamic compartmentalized systems. Thus, remodeling of membrane-bound systems, such as giant unilamellar vesicles, is finding applications beyond biological studies, to engineer cell-mimicking structures. Giant unilamellar vesicle fusion is rapidly becoming an essential experimental step as artificial cells gain prominence in synthetic biology. Several techniques have been developed to accomplish this step, with varying efficiency and selectivity. To date, characterization of vesicle fusion has relied on small samples of giant vesicles, examined either manually or by fluorometric assays on suspensions of small and large unilamellar vesicles. Automation of the detection and characterization of fusion products is now necessary for the screening and optimization of these fusion protocols. To this end, we implemented a fusion assay based on fluorophore colocalization on the membranes and in the lumen of vesicles. Fluorescence colocalization was evaluated within single compartments by image segmentation with minimal user input, allowing the application of the technique to high-throughput screenings. After detection, statistical information on vesicle fluorescence and morphological properties can be summarized and visualized, assessing lipid and content transfer for each object by the correlation coefficient of different fluorescence channels. Using this tool, we report and characterize the unexpected fusogenic activity of sodium chloride on phosphatidylcholine giant vesicles. Lipid transfer in most of the vesicles could be detected after 20 h of incubation, while content exchange only occurred with additional stimuli in around 8% of vesicles.

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Caliari, A., Hanczyc, M. M., Imai, M., Xu, J., & Yomo, T. (2023). Quantification of Giant Unilamellar Vesicle Fusion Products by High-Throughput Image Analysis. International Journal of Molecular Sciences, 24(9). https://doi.org/10.3390/ijms24098241

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