Identification of novel lipid droplet factors that regulate autophagy and cholesterol efflux in macrophage foam cells

Robichaud, Sabrina; Fairman, Garrett; Vijithakumar, Viyashini; Mak, Esther; Cook, Donald N.; Pelletier, Alexander R.; Vanderhyden, Barbara C.; Figeys, Daniel; Lavallée‐Adam, Mathieu; Baetz, Kristin; Ouimet, Mireille

doi:10.1016/j.atherosclerosis.2021.06.011

Cited by 1 publication

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“…Lipophagy is often studied in yeast, Saccharomyces cerevisiae , because this model organism is straightforward to manipulate genetically and also because the yeast vacuole is large enough to be amenable to light microscopic investigation. This allows for counting the number of LDs inside vacuoles as a measure for lipophagy under various starvation conditions and genetic backgrounds [ 7 , 18 , 69 , 83 , 84 ]. Similarly, vacuole morphology is often assessed by visual inspection, and from the number of attached vesicles the fusion status is inferred [ 11 , 15 , 17 ].…”

Section: Discussionmentioning

confidence: 99%

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

Egebjerg,

Szomek,

Thaysen

et al. 2023

Autophagy

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During 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 find that ncr1∆ and npc2∆ cells could ingest LDs into vacuoles normally but showed compromised degradation of LDs and accumulation of lipid vesicles inside vacuoles. Our new method is versatile and allows for analysis of vacuole fusion, droplet size and lipophagy in intact cells. Abbreviations: BODIPY493/503: 4,4-difluoro-1,3,5,7,8-pentamethyl-4-bora-3a,4a-diaza- s -Indacene; BPS: bathophenanthrolinedisulfonic acid disodium salt hydrate; CNN: convolutional neural network; DHE; dehydroergosterol; npc2∆ , yeast deficient in Npc2; DSC, Dice similarity coefficient; EM, electron microscopy; EVs, extracellular vesicles; FIB-SEM, focused ion beam milling-scanning electron microscopy; FM 4-64, N -(3-triethylammoniumpropyl)-4-(6-[4-{diethylamino} phenyl] hexatrienyl)-pyridinium dibromide; LDs, lipid droplets; Ncr1, yeast homolog of human NPC1 protein; ncr1∆ , yeast deficient in Ncr1; NPC, Niemann Pick type C; NPC2, Niemann Pick type C homolog; OD 600 , optical density at 600 nm; ReLU, rectifier linear unit; PPV, positive predictive value; NPV, negative predictive value; MCC, Matthews correlation coefficient; SXT, soft X-ray tomography; UV, ultraviolet; YPD, yeast extract peptone dextrose

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