Mekbib, H. T. | bioRxiv Science

bioRxiv2025

Pan-ASLM: a high-resolution and large-field-of-view light sheet microscope for Expansion Microscopy

Expansion microscopy, a super-resolution fluorescence microscopy technique in which samples are expanded up to [~]8,000 times (after 20-fold expansion) their original volume, places high demands on the microscopes used to image the expanded samples. To reveal nanoscale cellular ultrastructure in meaningful sample volumes, the instruments need to feature a large field of view and working distance. Simultaneously, they need to offer a high three-dimensional resolution to avoid counteracting the resolution improvement achieved by the expansion process. Here, we present pan-ASLM, a high resolution, large field-of-view light-sheet microscope developed for expanded samples, based on the Axially Swept Light Sheet Microscopy (ASLM) technique. pan-ASLM allows imaging over a 640 {micro}m x 640 {micro}m field of view with lateral and axial resolutions of 566 nm and 457 nm, respectively, and features an image acquisition speed of up to 20 fps (183 Mvoxels/sec). It offers [~]1200x higher imaging speed, a [~]7x larger field of view, and [~]2x better axial resolution than the standard confocal microscopes typically used for expanded samples. We validate the new microscope design through imaging of pan-expanded HeLa cells as well as mouse kidney and brain tissue.

biophysics↗

bioRxiv2025

Scalable automated segmentation quantifies mitochondrial proteins and morphology at the nanoscale

Ultrastructural changes of mitochondria are closely associated with metabolic dysfunction, leading to a variety of human disorders. These changes can be visualized by pan-Expansion Microscopy (pan-ExM), a 3D microscopy technique requiring only standard fluorescence microscopes, at a throughput exceeding that of the current gold standard, 3D electron microscopy, by orders of magnitude. However, a lack of tools that enable the characterization and quantification of the observed ultrastructural features in the acquired 3D datasets at a comparable throughput has hindered the widespread adoption of pan-ExM for quantitative imaging. Here, we present an automated deep-learning based segmentation approach that utilizes pan-ExMs power to acquire multi-channel images and uses specific labeling as the annotation for the training of the segmentation network. This molecular annotation reduces the required manual annotation effort for mitochondria substructures to just a few hours when setting up the experiment and thereby provides access to 3D suborganellar morphology of mitochondria at an unprecedented throughput. Our approach, which we term MAPS (Mitochondrial Automated Pan-ExM Segmentation), enables for the first time to quantify mitochondrial ultrastructural morphology at scale. We demonstrate this power by characterizing the 3D mitochondrial morphology at the organelle and sub-organelle level in tens of HeLa cells under different treatments and localizing mitochondrial proteins in the sub-organellar context. To demonstrate our technology in tissue, we compare the ultrastructural morphology of mitochondria in proximal tubules of kidneys of mice exhibiting acute kidney injury (AKI) with those of untreated mice, revealing striking differences in their cristae structure. MAPS can easily be adapted to different cell and tissue types, allowing the analysis of tens of samples per day, and therefore provides a versatile tool for a comprehensive understanding of mitochondrial ultrastructural changes in many disease contexts. Requiring only standard fluorescence microscopes and computer infrastructure, MAPS is readily adoptable by any lab. HighlightsO_LIMAPS utilizes specific labeling to train a segmentation model for 3D super-resolution pan-ExM images C_LIO_LISuborganellar mitochondrial features and their changes in disease models are quantified at high throughput C_LIO_LI3D protein distributions are correlated to ultrastructural features in mitochondria C_LIO_LIRequiring only standard lab infrastructure, MAPS is readily adoptable C_LI

cell biology↗