Biology subjects

Le Gros, M.

Publications and source records attributed to Le Gros, M..

4 recordsLinked to original sources

bioRxiv2025

Interior soft x-ray tomography with sparse global sampling

Objective. To investigate the feasibility of interior imaging reconstruction in soft X-ray tomography to achieve higher spatial resolution cellular imaging, including whole-cell imaging. Approach. We develop an alignment and reconstruction algorithm that enables a combination of a low number of images from sparse whole-cell imaging with a high-resolution local interior scan. Based on numerical simulations, we demonstrate that combined reconstructions mitigate the depth of field limitation in high-resolution scans, enable radiation dose optimization, and yield quantitative X-ray absorption values with sparse sampling. Furthermore, we validate our numerical approach using experimental data from two different cell types and demonstrate that combined reconstruction is a reliable method for obtaining high and local spatial resolution within the volume of a whole cell. Significance. The developed sparse reconstruction algorithm provides a robust and faithful visualization of cellular organelles with soft X-ray tomography. A mesoscale imaging approach, such as an interior tomography scan, enables one to "scout" and zoom into the volumes of interest that contain organelles of interest. Utilizing sparse reconstructions, this increase in spatial resolution is achieved without sacrificing larger volume imaging, providing information on the relative position of all organelles within a cell.

cell biology↗

bioRxiv2024

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 (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. Significance StatementO_LISoft X-ray tomography offers many powerful features for whole-cell multi-organelle imaging, but, like other high resolution volumetric imaging modalities, is typically limited by low throughput due to laborious segmentation. C_LIO_LIAuto-segmentation for soft X-ray tomography overcomes this limitation, enabling statistical 3D morphometric analysis of multiple organelles in whole cells across cell populations. C_LIO_LIThe combination of high 3D resolution of SXT data with statistically useful throughput represents an avenue for more thorough characterizations of cells in toto and opens new mesoscale biological questions and statistical whole-cell modeling of organelle and cell morphology, interactions, and responses to perturbations. C_LI

cell biology↗

bioRxiv2024

Soft X-ray tomography reveals variations in B.subtilis biofilm structure upon tasA deletion

Bacterial biofilms are complex communities of cells within a self-produced extracellular matrix. They play crucial roles in healthcare, nutrition, agriculture and environmental research, yet an analysis of their elaborate 3D architecture remains challenging. Understanding mechanisms of biofilm formation, particularly the effects of chemical, physical, and genetic influences or modifications, is crucial but requires structural information at subcellular resolution to enable a community-level analysis of biofilms. In this work, we developed a "biofilm-in-capillary" growth method compatible with full-rotation soft X-ray tomography, providing high-resolution 3D imaging of bacterial cells and their surrounding extracellular matrix during biofilm formation, without drying or fixation steps. This approach offers 50 nm isotropic spatial resolution, rapid imaging time, and quantitative native analysis of biofilm structure. We demonstrate the potential of our method using Bacillus subtilis biofilms, detecting coherent alignment and chaining of wild-type cells while they are travelling towards the oxygen-rich capillary tip region. In stark contrast, the genetic knock-out {Delta}tasA shows a loss of cellular orientation, including changes in the extracellular matrix in volume and chemical density. Notably, we show that the addition of TasA protein to a culture of a {Delta}tasA strain restores the extracellular matrix density and leads to a compaction of cell assemblies, yet no chaining is observed as for the wildtype. Our approach to imaging biofilms is scalable and transferable, opening new avenues for examining biofilm structure and function across various species, including mixed biofilms, and observing 3D reorganization in response to genetic and environmental factors.

microbiology↗

bioRxiv2018

PSF correction in soft x-ray tomography

In this manuscript, we introduce a linear approximation of the forward model of soft x-ray tomography (SXT), such that the reconstruction is solvable by standard iterative schemes. This linear model takes into account the three-dimensional point spread function (PSF) of the optical system, which consequently enhances the reconstruction data. The feasibility of the model is demonstrated on both simulated and experimental data, based on theoretically estimated and experimentally measured PSFs.

cell biology↗