Fiche publication
Date publication
mai 2020
Journal
Journal of structural biology
Auteurs
Membres identifiés du Cancéropôle Est :
Dr SCHULTZ Patrick
Tous les auteurs :
Spehner D, Steyer AM, Bertinetti L, Orlov I, Benoit L, Pernet-Gallay K, Schertel A, Schultz P
Lien Pubmed
Résumé
Focused Ion Beam-Scanning Electron Microscopy (FIB-SEM) is an invaluable tool to visualize the 3D architecture of cell constituents and map cell networks. Recently, amorphous ice embedding techniques have been associated with FIB-SEM to ensure that the biological material remains as close as possible to its native state. Here we have vitrified human HeLa cells and directly imaged them by cryo-FIB-SEM with the secondary electron InLens detector at cryogenic temperature and without any staining. Image stacks were aligned and processed by denoising, removal of ion beam milling artefacts and local charge imbalance. Images were assembled into a 3D volume and the major cell constituents were modelled. The data illustrate the power of the workflow to provide a detailed view of the internal architecture of the fully hydrated, close-to-native, entire HeLa cell. In addition, we have studied the feasibility of combining cryo-FIB-SEM imaging with live-cell protein detection. We demonstrate that internalized gold particles can be visualized by detecting back scattered primary electrons at low kV while simultaneously acquiring signals from the secondary electron detector to image major cell features. Furthermore, gold-conjugated antibodies directed against RNA polymerase II could be observed in the endo-lysosomal pathway while labelling of the enzyme in the nucleus was not detected, a shortcoming likely due to the inadequacy between the size of the gold particles and the voxel size. With further refinements, this method promises to have a variety of applications where the goal is to localize cellular antigens while visualizing the entire native cell in three dimensions.
Mots clés
3-D Cryo-FIB-SEM, 3D reconstruction and modelling, Image processing and machine learning, Immunolabeling
Référence
J. Struct. Biol.. 2020 May 5;:107528
