Automated quantification of vacuole fusion and lipophagy in < em > Saccharomyces cerevisiae < /em > from fluorescence and cryo-soft X-ray microscopy data using deep learning

Autophagy. 2023 Oct 31:1-21. doi: 10.1080/15548627.2023.2270378. Online ahead of print.ABSTRACTDuring starvation in the yeast Saccharomyces cerevisiae vacuolar vesicles fuse and lipid droplets (LDs) can become internalized into the vacuole in an autophagic process named lipophagy. There is a lack of tools to quantitatively assess starvation-induced vacuole fusion and lipophagy in intact cells with high resolution and throughput. Here, we combine soft X-ray tomography (SXT) with fluorescence microscopy and use a deep-learning computational approach to visualize and quantify these processes in yeast. We focus on yeast homologs of mammalian NPC1 (NPC intracellular cholesterol transporter 1; Ncr1 in yeast) and NPC2 proteins, whose dysfunction leads to Niemann Pick type C (NPC) disease in humans. We developed a convolutional neural network (CNN) model which classifies fully fused versus partially fused vacuoles based on fluorescence images of stained cells. This CNN, named Deep Yeast Fusion Network (DYFNet), revealed that cells lacking Ncr1 (ncr1∆ cells) or Npc2 (npc2∆ cells) have a reduced capacity for vacuole fusion. Using a second CNN model, we implemented a pipeline named LipoSeg to perform automated instance segmentation of LDs and vacuoles from high-resolution reconstructions of X-ray tomograms. From that, we obtained 3D renderings of LDs inside and outside of the vacuole in a fully automated manner and additionally measured droplet volume, number, and distribution. We f...

Source: Autophagy - November 1, 2023 Category: Cytology Authors: Jacob Marcus Egebjerg Maria Szomek Katja Thaysen Alice Dupont Juhl Suzana Kozakijevic Stephan Werner Christoph Pratsch Gerd Schneider Sergey Kapishnikov Axel Ekman Richard R öttger Daniel W üstner Source Type: research