Large-Scale Fluorescence Microscopy Analysis of Lipid Membrane Conformational Changes Optimized and Enabled by an AI-Guided Image Detection Method

Simplified and scalable models of physical systems are extremely valuable in a variety of different engineering fields to test and diagnose particular modes of failure and optimize build conditions. In this work, we develop a practical method to prepare and analyze giant unilamellar vesicles (GUVs) for detailed biophysical interrogations. The method is rapid, scalable, and versatile, where characterization of lipid membrane conformational changes can be performed on multiplexed samples using tissue culture plates and a convenient, high-throughput fluorescence microscopy setup. The simplicity of the setup is enabled by an AI image recognition model that, when trained on the appearance of GUVs in the images, outperforms other image segmentation methods such as the watershed algorithm or the Hough transform. The method allows for the rapid quantification of entire 96-well plates containing in total O (1,000,000) GUVs and provides a potential testbed for the development of drugs. We highlight the power of our system by including large-scale data on the screening of lipophilic analogs of the small molecule antimetabolite carmofur.