A workflow for streamlined acquisition and correlation of serial regions of interest in array tomography

BMC Biology

Steven Boeynaems¹  Sebastian Munck²  Christopher Cawthorne³  Frank Vernaillen⁴  Emiel Michiels⁵  Sergio Gabarre⁶  Pieter Baatsen⁶  Dorien Vandael⁶  Katlijn Vints⁶  Natalia V. Gounko⁶ 

[1] Department of Genetics, Stanford University School of Medicine;KU Leuven Department of Neurosciences, Leuven Brain Institute;MoSAIC-Molecular Small Animal Imaging Centre, KU Leuven;VIB BioInformatics Core;VIB Center for Brain and Disease Research;VIB-KU Leuven Center for Brain & Disease Research, Electron Microscopy Platform & VIB BioImaging Core;

关键词: Array tomography;    Integrated light and electron microscope;    3D EM;    CLEM;    Correlation;    Open source;   

DOI  :  10.1186/s12915-021-01072-7

来源: DOAJ

【 摘 要 】

Abstract Background Array tomography (AT) is a high-resolution imaging method to resolve fine details at the organelle level and has the advantage that it can provide 3D volumes to show the tissue context. AT can be carried out in a correlative way, combing light and electron microscopy (LM, EM) techniques. However, the correlation between modalities can be a challenge and delineating specific regions of interest in consecutive sections can be time-consuming. Integrated light and electron microscopes (iLEMs) offer the possibility to provide well-correlated images and may pose an ideal solution for correlative AT. Here, we report a workflow to automate navigation between regions of interest. Results We use a targeted approach that allows imaging specific tissue features, like organelles, cell processes, and nuclei at different scales to enable fast, directly correlated in situ AT using an integrated light and electron microscope (iLEM-AT). Our workflow is based on the detection of section boundaries on an initial transmitted light acquisition that serves as a reference space to compensate for changes in shape between sections, and we apply a stepwise refinement of localizations as the magnification increases from LM to EM. With minimal user interaction, this enables autonomous and speedy acquisition of regions containing cells and cellular organelles of interest correlated across different magnifications for LM and EM modalities, providing a more efficient way to obtain 3D images. We provide a proof of concept of our approach and the developed software tools using both Golgi neuronal impregnation staining and fluorescently labeled protein condensates in cells. Conclusions Our method facilitates tracing and reconstructing cellular structures over multiple sections, is targeted at high resolution ILEMs, and can be integrated into existing devices, both commercial and custom-built systems.

【 授权许可】

Unknown