High-precision targeting workflow for volume electron microscopy

Abstract

Cells are 3D objects. Therefore, volume EM (vEM) is often crucial for correct interpretation of ultrastructural data. Today, scanning EM (SEM) methods such as focused ion beam (FIB)–SEM are frequently used for vEM analyses. While they allow automated data acquisition, precise targeting of volumes of interest within a large sample remains challenging. Here, we provide a workflow to target FIB-SEM acquisition of fluorescently labeled cells or subcellular structures with micrometer precision. The strategy relies on fluorescence preservation during sample preparation and targeted trimming guided by confocal maps of the fluorescence signal in the resin block. Laser branding is used to create landmarks on the block surface to position the FIB-SEM acquisition. Using this method, we acquired volumes of specific single cells within large tissues such as 3D cultures of mouse mammary gland organoids, tracheal terminal cells in Drosophila melanogaster larvae, and ovarian follicular cells in adult Drosophila, discovering ultrastructural details that could not be appreciated before.

Sample preparation and warping accuracy for correlative multimodal imaging in the mouse olfactory bulb using 2-photon, synchrotron X-ray and volume electron microscopy

“…Importantly, sample preparation does not end with staining: trimming the specimen will be necessary to accommodate its dimensions to some acquisition techniques ( Supp. F3c ) (Karreman et al, 2016; Bosch et al, 2021; Ronchi et al, 2021), hard X-ray tomography imaging may inflict damage from radiation dose and temperature changes (Henderson, 1995; Howells et al, 2009; Du and Jacobsen, 2018), volume electron microscopy techniques imply slicing or destruction of the sample and therefore add their own risk of a specific sample becoming irreversibly damaged, and optimal targeting of the features of interest to be imaged with the distinct modalities precludes extracting the information sought by the initial experimental question (Bosch et al, 2020; Walter et al, 2020). In light of these successive challenges, it is imperative for a successful CMI experiment to prepare a sufficient number of samples at the first stage.…”

Niwaki Instead of Random Forests: Targeted Serial Sectioning Scanning Electron Microscopy With Reimaging Capabilities for Exploring Central Nervous System Cell Biology and Pathology

“…This is especially relevant if the ultrastructural morphology of the target region or its exact location are not known a priori or not obvious, for example, in randomly distributed sites of local pathologies, such as the plaques in Alzheimer’s disease. Despite the impossibility for reimaging and archiving in FIB-SEM or SB-SEM, for a given region of interest similar targeting strategies can be used with these techniques ( Heymann et al, 2006 ; Bosch et al, 2015 ; Blazquez-Llorca et al, 2017 ; Rodriguez-Moreno et al, 2017 ; Kikuchi et al, 2020 ; Ronchi et al, 2021 ). So while we are not explicitly referencing “single-shot” approaches below and rather focus on “multi-shot” techniques, many of the general points remain valid and beneficial for “one-shot” approaches, as described elsewhere ( Karreman et al, 2016b ; Lees et al, 2017b ; Luckner et al, 2018 ; Kremer et al, 2021 ).…”

“…Still, we feel it is valuable to point out some advantages of the "multi-shot" approaches for studying neuronal and glial cell biology and "rare" cellular pathology, as these approaches originated in "connectomics"style neuroscience (Helmstaedter et al, 2013;Kasthuri et al, 2015), as opposed to the complementary FIB-SEM approaches that have a stronger rooting in cell biology with recent developments toward high throughput (Xu et al, 2017;Hayworth et al, 2020). The latter have also received excellent coverage in recent reviews and original articles (Kizilyaprak et al, 2014;Narayan and Subramaniam, 2015;Karreman et al, 2016a;Luckner et al, 2018;Ronchi et al, 2021), to which we direct the interested reader. by collecting several hundreds or thousands sections on grids in a row (White et al, 1986).…”

Integrated Array Tomography for 3D Correlative Light and Electron Microscopy

“…For array tomography applications, ROIs can be targeted with increasing magnification through a sequence of feedback loops ( Gabarre et al, 2021 ). Similarly, strategies for rapidly screening sections have been developed for sequential CLEM to limit volume acquisitions to select ROI ( Burel et al, 2018 ; Ronchi et al, 2021 ).…”