At the CCR volume Electron Microscopy, our primary technique is focused ion beam scanning electron microscopy (FIB-SEM, otherwise known as ion abrasion scanning electron microscopy, or IA-SEM), often in conjunction with correlative light microscopy, to image large samples in 3D and at nanoscale resolutions. Recently, we have deployed array tomography (AT) to investigate large-volume samples.
At the CvEM, we have acquired a Zeiss Xbeam 550 FIB-SEM and a Zeiss Gemini 450 SEM for CvEM projects. For correlative microscopy we have a Zeiss LSM upright microscope equipped with an AiryScan detector. We also have a full suite of EM preparation equipment including a Leica EM ICE high-pressure freezer and freeze-substitution unit, ASP™-1000 mPrep automatic specimen processor, and a RMC Boeckeler ATUMtome. Additionally, we have a Talos L120C transmission electron microscope to screen samples and collect tomograms for projects requiring higher resolution.
We have developed protocols to render challenging samples amenable to CvEM imaging. Most recently, we have adapted high-pressure freezing (HPF) and quick freeze substitution (QFS) protocols to trap architectural intermediates in the rapidly developing C. elegans embryo (Rahman MM et al 2020, Chang YI et al 2021). We have recently acquired instrumentation to allow quick and consistent processing of larger tissue samples.
We have focused on rapidly and efficiently imaging targeted features of interest at high resolution, while also occasionally sampling the larger cellular context. The strategy of high-resolution “ROI imaging” and intermediate resolution “keyframe imaging”, developed in collaboration with Zeiss Inc and Fibics Inc, is now a central feature of FIB-SEM tomography acquisition software ATLAS3D (Narayan K et al 2014). We continue to explore methods of efficient vEM imaging in a variety of systems.
Combining multiple imaging modalities to either relocate or register features of interest is a continuing area of interest in the group. We have standardized protocols that allow for easy correlative imaging (LM + FIB-SEM) of adherent cells (Narayan et al 2014) and continue to work on methods that will allow more efficient FIB-SEM imaging of adherent cells. More recently we have developed cryoCLEM approaches to trap and image transient events in thick samples by cryo LM and room temperature FIB-SEM (Chang YI et al 2021).
Segmentation & Visualization
Still considered a bottleneck in CvEM pipelines, segmentation efforts are now bolstered by the application of machine learning and deep learning approaches. We have recently applied DL models for 3D visualization of vEM datasets (Cheng HC et al 2018), and most recently have curated and shared large datasets of relevant vEM images that we show greatly increase the performance of pre-trained DL models for downstream tasks such as mitochondrial segmentation (Conrad R and Narayan K 2021).
Data Handling and Standardization
A community effort to standardize vEM data and metadata formats is underway. We have contributed to the CLEM and CvEM portions of REMBI metadata recommendations, and we hope to continue to work in this space as the fields grows and matures (Sarkans U et al 2021)