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

Abstract

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

Ergosterol mediates aggregation of natamycin in the yeast plasma membrane

“…To study the impact of natamycin on yeast cells at the ultrastructural level, we used soft X-ray tomography (SXT), a method that that uses the absorption of X-rays in the so-called water window by carbon-rich material as contrast principle 38 . SXT allows for imaging entire yeast cells in their near-native state upon cryo-fixation and without the need for cell fixation [39][40][41] . Using a synchrotron as source of X-rays in the appropriate energy range of about 510 eV and suitable zone plates to focus the incident X-rays to the sample, SXT can obtain 30-50 nm isotropic resolution with typical acquisition times of about 30 min per tomogram 42,43 .…”

Automated 3D cytoplasm segmentation in soft X-ray tomography

“…Furthermore, neural network-based algorithms, such as convolutional neural networks or U-Net, have been used to extract membranous organelles in the study by Dyhr et al., 9 Francis et al., 18 and Egebjerg et al. 19 A segmentation method based on the combination of 2D U-Nets was used to automatically segment whole β-cells anatomy. 20 Most of these methods are designed for a very specialized organelle type or pathological condition, and automatic segmentation of whole-cell anatomy requires complex pre- and post-processing steps.…”