Automated 3D cytoplasm segmentation in soft X-ray tomography

Erozan, Ayse; Lösel, Philipp D.; Heuveline, Vincent; Weinhardt, Venera

doi:10.1016/j.isci.2024.109856

Cited by 3 publications

References 39 publications

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Automated segmentation of soft X-ray tomography: native cellular structure with sub-micron resolution at high throughput for whole-cell quantitative imaging in yeast

Chen,

Mirvis,

Ekman

et al. 2024

Preprint

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Soft X-ray tomography (SXT) is an invaluable tool for quantitatively analyzing cellular structures at sub-optical isotropic resolution. However, it has traditionally depended on manual segmentation, limiting its scalability for large datasets. Here, we leverage a deep learning-based auto-segmentation pipeline to segment and label cellular structures in hundreds of cells across three Saccharomyces cerevisiae strains. This task-based pipeline employs manual iterative refinement to improve segmentation accuracy for key structures, including the cell body, nucleus, vacuole, and lipid droplets, enabling high-throughput and precise phenotypic analysis. Using this approach, we quantitatively compared the 3D whole-cell morphometric characteristics of wild-type, VPH1-GFP, and vac14 strains, uncovering detailed strain-specific cell and organelle size and shape variations. We show the utility of SXT data for precise 3D curvature analysis of entire organelles and cells and detection of fine morphological features using surface meshes. Our approach facilitates comparative analyses with high spatial precision and statistical throughput, uncovering subtle morphological features at the single cell and population level. This workflow significantly enhances our ability to characterize cell anatomy and supports scalable studies on the mesoscale, with applications in investigating cellular architecture, organelle biology, and genetic research across diverse biological contexts.

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Automated segmentation of soft X-ray tomography: native cellular structure with sub-micron resolution at high throughput for whole-cell quantitative imaging in yeast

Chen,

Mirvis,

Ekman

et al. 2024

Preprint

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Soft X-ray tomography analysis of mitochondria dynamics in Saccharomyces cerevisiae

Huang,

Chen,

Lin

et al. 2024

Biol Direct

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Background Mitochondria are highly dynamic organelles that constantly undergo processes of fission and fusion. The changes in mitochondrial dynamics shape the organellar morphology and influence cellular activity regulation. Soft X-ray tomography (SXT) allows for three-dimensional imaging of cellular structures while they remain in their natural, hydrated state, which omits the need for cell fixation and sectioning. Synchrotron facilities globally primarily use flat grids as sample carriers for SXT analysis, focusing on adherent cells. To investigate mitochondrial morphology and structure in hydrated yeast cells using SXT, it is necessary to establish a method that employs the flat grid system for examining cells in suspension. Results We developed a procedure to adhere suspended yeast cells to a flat grid for SXT analysis. Using this protocol, we obtained images of wild-type yeast cells, strains with mitochondrial dynamics defects, and mutant cells possessing distinctive mitochondria. The SXT images align well with the results from fluorescent microscopy. Optimized organellar visualization was achieved by constructing three-dimensional models of entire yeast cells. Conclusions In this study, we characterized the mitochondrial network in yeast cells using SXT. The optimized sample preparation procedure was effective for suspended cells like yeast, utilizing a flat grid system to analyze mitochondrial structure through SXT. The findings corresponded with the mitochondrial morphology observed under fluorescence microscopy, both in regular and disrupted dynamic equilibrium. With the acquired image of unique mitochondria in Δhap2 cells, our results revealed that intricate details of organelles, such as mitochondria and vacuoles in yeast cells, can be characterized using SXT. Therefore, this optimized system supports the expanded application of SXT for studying organellar structure and morphology in suspended cells.

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Subcellular Feature-Based Classification of α and β Cells Using Soft X-ray Tomography

Deshmukh,

Chang,

Cuala

et al. 2024

Cells

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The dysfunction of α and β cells in pancreatic islets can lead to diabetes. Many questions remain on the subcellular organization of islet cells during the progression of disease. Existing three-dimensional cellular mapping approaches face challenges such as time-intensive sample sectioning and subjective cellular identification. To address these challenges, we have developed a subcellular feature-based classification approach, which allows us to identify α and β cells and quantify their subcellular structural characteristics using soft X-ray tomography (SXT). We observed significant differences in whole-cell morphological and organelle statistics between the two cell types. Additionally, we characterize subtle biophysical differences between individual insulin and glucagon vesicles by analyzing vesicle size and molecular density distributions, which were not previously possible using other methods. These sub-vesicular parameters enable us to predict cell types systematically using supervised machine learning. We also visualize distinct vesicle and cell subtypes using Uniform Manifold Approximation and Projection (UMAP) embeddings, which provides us with an innovative approach to explore structural heterogeneity in islet cells. This methodology presents an innovative approach for tracking biologically meaningful heterogeneity in cells that can be applied to any cellular system.

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Automated 3D cytoplasm segmentation in soft X-ray tomography

Cited by 3 publications

References 39 publications

Automated segmentation of soft X-ray tomography: native cellular structure with sub-micron resolution at high throughput for whole-cell quantitative imaging in yeast

Automated segmentation of soft X-ray tomography: native cellular structure with sub-micron resolution at high throughput for whole-cell quantitative imaging in yeast

Soft X-ray tomography analysis of mitochondria dynamics in Saccharomyces cerevisiae

Subcellular Feature-Based Classification of α and β Cells Using Soft X-ray Tomography

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