YeastMate: neural network-assisted segmentation of mating and budding events in<i>Saccharomyces cerevisiae</i>

Bunk, David M.; Moriasy, Julian; Thoma, Felix; Jakubke, Christopher; Osman, Christof; Hörl, David

doi:10.1093/bioinformatics/btac107

Cited by 18 publications

(9 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Our classification CNN complements existing methods for automated, deep learning-based phenotype scoring in yeast cells. For example, multi-layer CNNs have been used for classifying subcellular localizations of green-fluorescent-protein-tagged proteins in yeast, and classification of such autophagy markers in the vacuole [9, 58, 61, 62].…”

Section: Discussionmentioning

confidence: 99%

“…Automated detection and segmentation of yeast cells is an important step for large-scale live-cell imaging experiments. It can be demanding, if cells tend to cluster, bud extensively or growth in heterogeneous environments, such as microfluidic devices, and a variety of advanced algorithms including CNNs have been developed recently to address this problem, e.g., [61, 73, 74, 75, 76, 77, 78, 79, 80]. Since our experimental setup is much simpler with well separated cells on a rather homogeneous background, we can rely on a morphometric segmentation procedure, the circular Hough transform, which detects objects based on their circularity.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

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

Egebjerg

Szomek

Thaysen

et al. 2023

Preprint

View full text Add to dashboard Cite

Lipophagy is a form of autophagy by which lipid droplets (LDs) become digested to provide nutrients as a cellular response to starvation. Lipophagy is often studied in yeast, Saccharomyces cerevisiae, in which LDs become internalized into the vacuole. There is a lack of tools to quantitatively assess 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 lipophagy in yeast. We focus on yeast homologs of mammalian Niemann Pick type C proteins, whose dysfunction leads to Niemann Pick type C disease in humans, i.e., NPC1 (named NCR1 in yeast) and NPC2. We developed a convolutional neural network (CNN) model which classifies ring-shaped versus lipid-filled or fragmented vacuoles containing ingested LDs in fluorescence images from wild-type yeast and from cells lacking NCR1 (delta ncr1 cells) or NPC2 (Δnpc2 cells). Using a second CNN model, which performs automated segmentation of LDs and vacuoles from high-resolution reconstructions of X-ray tomograms, we can obtain 3D renderings of LDs inside and outside of the vacuole in a fully automated manner and additionally measure droplet volume, number, and distribution. We find that cells lacking functional NPC proteins can ingest LDs into vacuoles normally but show compromised degradation of LDs and accumulation of lipid vesicles inside vacuoles. This phenotype is most severe in delta npc2 cells. Our new method is versatile and allows for automated high-throughput 3D visualization and quantification of lipophagy in intact cells.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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

Egebjerg

Szomek

Thaysen

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…This protocol is fast but may detect circles outside of cells and does not account for low signal or high cell density, which can render the ROI unusable. ROIs can also be extracted using cell segmentation tools based on CNNs e.g [ 106–111 ], but may miss some cells and usually take much longer. A CNN classifier was trained to filter out the unusable ROIs (no cell, low signal, too crowded).…”

Section: Methodsmentioning

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

View full text Add to dashboard Cite

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

show abstract

“…Brightness and contrast were adjusted for each channel equally in individual experiments for analysis. The ratio of 'LDs in/out' of the vacuole using BODIPY was calculated by automatically measuring Integrated Density (IntDen) 'inside of vacuoles' segmented with the Huang algorithm and divided by the IntDen of 'outside of vacuoles' (calculated by subtracting the IntDen 'inside the vacuoles' from the IntDen of the whole cell, segmented with YeastMate) (Bunk et al, 2022).…”

Section: Image Analysis and Quantificationmentioning

confidence: 99%

LDO proteins and Vac8 form a vacuole-lipid droplet contact site required for lipophagy in response to starvation

Álvarez-Guerra

Block

Broeskamp

et al. 2023

Preprint

View full text Add to dashboard Cite

Lipid droplets (LDs) are fat storage organelles critical for energy and lipid metabolism. Upon nutrient exhaustion, cells consume LDs via gradual lipolysis or via lipophagy, the en bloc uptake of LDs into the vacuole. Here, we show that LDs dock to the vacuolar membrane via a contact site that is required for lipophagy in yeast. The LD-localized LDO proteins carry an intrinsically disordered region that associates with vacuolar Vac8 to form vCLIP, the vacuolar-LD contact site. Nutrient limitation drives vCLIP formation, and its inactivation blocks lipophagy. Vac8 is sufficient to recruit LDs to cellular membranes. We establish a functional link between lipophagy and microautophagy of the nucleus, both requiring Vac8 to form respective contact sites upon metabolic stress. In sum, we unravel the molecular architecture of vCLIP, a contact site required for lipophagy, and find that Vac8 provides a platform for multiple and competing contact sites associated with autophagy.

show abstract

YeastMate: neural network-assisted segmentation of mating and budding events inSaccharomyces cerevisiae

Cited by 18 publications

References 14 publications

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

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

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

LDO proteins and Vac8 form a vacuole-lipid droplet contact site required for lipophagy in response to starvation

Contact Info

Product

Resources

About