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Micheva KD, Burden JJ, Schifferer M. Array tomography: trails to discovery. METHODS IN MICROSCOPY 2024; 1:9-17. [PMID: 39119254 PMCID: PMC11308915 DOI: 10.1515/mim-2024-0001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Accepted: 06/06/2024] [Indexed: 08/10/2024]
Abstract
Tissue slicing is at the core of many approaches to studying biological structures. Among the modern volume electron microscopy (vEM) methods, array tomography (AT) is based on serial ultramicrotomy, section collection onto solid support, imaging via light and/or scanning electron microscopy, and re-assembly of the serial images into a volume for analysis. While AT largely uses standard EM equipment, it provides several advantages, including long-term preservation of the sample and compatibility with multi-scale and multi-modal imaging. Furthermore, the collection of serial ultrathin sections improves axial resolution and provides access for molecular labeling, which is beneficial for light microscopy and immunolabeling, and facilitates correlation with EM. Despite these benefits, AT techniques are underrepresented in imaging facilities and labs, due to their perceived difficulty and lack of training opportunities. Here we point towards novel developments in serial sectioning and image analysis that facilitate the AT pipeline, and solutions to overcome constraints. Because no single vEM technique can serve all needs regarding field of view and resolution, we sketch a decision tree to aid researchers in navigating the plethora of options available. Lastly, we elaborate on the unexplored potential of AT approaches to add valuable insight in diverse biological fields.
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Affiliation(s)
| | | | - Martina Schifferer
- Center for Neurodegenerative Diseases (DZNE), Munich, Germany
- Munich Cluster of Systems Neurology (SyNergy), Munich, Germany
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Chang S, Li L, Hong B, Liu J, Xu Y, Pang K, Zhang L, Han H, Chen X. An intelligent workflow for sub-nanoscale 3D reconstruction of intact synapses from serial section electron tomography. BMC Biol 2023; 21:198. [PMID: 37743470 PMCID: PMC10519085 DOI: 10.1186/s12915-023-01696-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2022] [Accepted: 09/06/2023] [Indexed: 09/26/2023] Open
Abstract
BACKGROUND As an extension of electron tomography (ET), serial section electron tomography (serial section ET) aims to align the tomographic images of multiple thick tissue sections together, to break through the volume limitation of the single section and preserve the sub-nanoscale voxel size. It could be applied to reconstruct the intact synapse, which expands about one micrometer and contains nanoscale vesicles. However, there are several drawbacks of the existing serial section ET methods. First, locating and imaging regions of interest (ROIs) in serial sections during the shooting process is time-consuming. Second, the alignment of ET volumes is difficult due to the missing information caused by section cutting and imaging. Here we report a workflow to simplify the acquisition of ROIs in serial sections, automatically align the volume of serial section ET, and semi-automatically reconstruct the target synaptic structure. RESULTS We propose an intelligent workflow to reconstruct the intact synapse with sub-nanometer voxel size. Our workflow includes rapid localization of ROIs in serial sections, automatic alignment, restoration, assembly of serial ET volumes, and semi-automatic target structure segmentation. For the localization and acquisition of ROIs in serial sections, we use affine transformations to calculate their approximate position based on their relative location in orderly placed sections. For the alignment of consecutive ET volumes with significantly distinct appearances, we use multi-scale image feature matching and the elastic with belief propagation (BP-Elastic) algorithm to align them from coarse to fine. For the restoration of the missing information in ET, we first estimate the number of lost images based on the pixel changes of adjacent volumes after alignment. Then, we present a missing information generation network that is appropriate for small-sample of ET volume using pre-training interpolation network and distillation learning. And we use it to generate the missing information to achieve the whole volume reconstruction. For the reconstruction of synaptic ultrastructures, we use a 3D neural network to obtain them quickly. In summary, our workflow can quickly locate and acquire ROIs in serial sections, automatically align, restore, assemble serial sections, and obtain the complete segmentation result of the target structure with minimal manual manipulation. Multiple intact synapses in wild-type rat were reconstructed at a voxel size of 0.664 nm/voxel to demonstrate the effectiveness of our workflow. CONCLUSIONS Our workflow contributes to obtaining intact synaptic structures at the sub-nanometer scale through serial section ET, which contains rapid ROI locating, automatic alignment, volume reconstruction, and semi-automatic synapse reconstruction. We have open-sourced the relevant code in our workflow, so it is easy to apply it to other labs and obtain complete 3D ultrastructures which size is similar to intact synapses with sub-nanometer voxel size.
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Affiliation(s)
- Sheng Chang
- Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, 100190, Beijing, China
- State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
| | - Linlin Li
- Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China
| | - Bei Hong
- Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, 100190, Beijing, China
| | - Jing Liu
- Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China
| | - Yuxuan Xu
- School of Software and Microelectronics, Peking University, 100871, Beijing, China
| | - Keliang Pang
- School of Pharmaceutical Sciences, Tsinghua University, 100084, Beijing, China
| | - Lina Zhang
- Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China
| | - Hua Han
- Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China.
- State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.
- School of Future Technology, University of Chinese Academy of Sciences, Beijing, 101408, China.
| | - Xi Chen
- Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China.
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White IJ, Burden JJ. A practical guide to starting SEM array tomography-An accessible volume EM technique. Methods Cell Biol 2023; 177:171-196. [PMID: 37451766 DOI: 10.1016/bs.mcb.2022.12.023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/18/2023]
Abstract
The techniques collectively known as volume electron microscopy (vEM) each come with their own advantages and challenges, making them more or less suitable for any specific project. SEM array tomography (SEM-AT) is certainly no different in this respect. Requiring microtomy skills, and involving more data alignment post imaging, SEM-AT presents challenges to its users, nevertheless, as perhaps the most flexible, cost effective and potentially accessible vEM approach to regular EM facilities, it benefits those same users with multiple advantages due to its inherently non-destructive nature. The general principles and advantages/disadvantages of SEM-AT are described here, together with a step-by-step guide to the workflow, from block trimming, sectioning and collection on coverslips, to alignment of the high-resolution 3D dataset. With a suitable SEM/backscatter electron detector setup, and equipment readily found in an electron microscopy lab, it should be possible to begin to acquire 3D ultrastructural data. With the addition of appropriate SEM-AT imaging software, this process can be significantly enhanced to automatically image hundreds, potentially thousands, of sections. Hardware and software advances and future improvements will only make this easier, to the extent that SEM-AT could become a routine vEM technique throughout the world, rather than the privilege of a small number of experts in limited specialist facilities.
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Affiliation(s)
- Ian J White
- LMCB, University College London, London, United Kingdom
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Peddie CJ, Genoud C, Kreshuk A, Meechan K, Micheva KD, Narayan K, Pape C, Parton RG, Schieber NL, Schwab Y, Titze B, Verkade P, Aubrey A, Collinson LM. Volume electron microscopy. NATURE REVIEWS. METHODS PRIMERS 2022; 2:51. [PMID: 37409324 PMCID: PMC7614724 DOI: 10.1038/s43586-022-00131-9] [Citation(s) in RCA: 70] [Impact Index Per Article: 23.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Accepted: 05/10/2022] [Indexed: 07/07/2023]
Abstract
Life exists in three dimensions, but until the turn of the century most electron microscopy methods provided only 2D image data. Recently, electron microscopy techniques capable of delving deep into the structure of cells and tissues have emerged, collectively called volume electron microscopy (vEM). Developments in vEM have been dubbed a quiet revolution as the field evolved from established transmission and scanning electron microscopy techniques, so early publications largely focused on the bioscience applications rather than the underlying technological breakthroughs. However, with an explosion in the uptake of vEM across the biosciences and fast-paced advances in volume, resolution, throughput and ease of use, it is timely to introduce the field to new audiences. In this Primer, we introduce the different vEM imaging modalities, the specialized sample processing and image analysis pipelines that accompany each modality and the types of information revealed in the data. We showcase key applications in the biosciences where vEM has helped make breakthrough discoveries and consider limitations and future directions. We aim to show new users how vEM can support discovery science in their own research fields and inspire broader uptake of the technology, finally allowing its full adoption into mainstream biological imaging.
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Affiliation(s)
- Christopher J. Peddie
- Electron Microscopy Science Technology Platform, The Francis Crick Institute, London, UK
| | - Christel Genoud
- Electron Microscopy Facility, Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Anna Kreshuk
- Cell Biology and Biophysics Unit, European Molecular Biology Laboratory, Heidelberg, Germany
| | - Kimberly Meechan
- Cell Biology and Biophysics Unit, European Molecular Biology Laboratory, Heidelberg, Germany
- Present address: Faculty of Biosciences, Heidelberg University, Heidelberg, Germany
| | - Kristina D. Micheva
- Department of Molecular and Cellular Physiology, Stanford University, Palo Alto, CA, USA
| | - Kedar Narayan
- Center for Molecular Microscopy, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
- Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - Constantin Pape
- Cell Biology and Biophysics Unit, European Molecular Biology Laboratory, Heidelberg, Germany
| | - Robert G. Parton
- The Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
- Centre for Microscopy and Microanalysis, The University of Queensland, Brisbane, Queensland, Australia
| | - Nicole L. Schieber
- Centre for Microscopy and Microanalysis, The University of Queensland, Brisbane, Queensland, Australia
| | - Yannick Schwab
- Cell Biology and Biophysics Unit/ Electron Microscopy Core Facility, European Molecular Biology Laboratory, Heidelberg, Germany
| | | | - Paul Verkade
- School of Biochemistry, University of Bristol, Bristol, UK
| | - Aubrey Aubrey
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Lucy M. Collinson
- Electron Microscopy Science Technology Platform, The Francis Crick Institute, London, UK
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Lane R, Wolters AHG, Giepmans BNG, Hoogenboom JP. Integrated Array Tomography for 3D Correlative Light and Electron Microscopy. Front Mol Biosci 2022; 8:822232. [PMID: 35127826 PMCID: PMC8809480 DOI: 10.3389/fmolb.2021.822232] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2021] [Accepted: 12/15/2021] [Indexed: 12/22/2022] Open
Abstract
Volume electron microscopy (EM) of biological systems has grown exponentially in recent years due to innovative large-scale imaging approaches. As a standalone imaging method, however, large-scale EM typically has two major limitations: slow rates of acquisition and the difficulty to provide targeted biological information. We developed a 3D image acquisition and reconstruction pipeline that overcomes both of these limitations by using a widefield fluorescence microscope integrated inside of a scanning electron microscope. The workflow consists of acquiring large field of view fluorescence microscopy (FM) images, which guide to regions of interest for successive EM (integrated correlative light and electron microscopy). High precision EM-FM overlay is achieved using cathodoluminescent markers. We conduct a proof-of-concept of our integrated workflow on immunolabelled serial sections of tissues. Acquisitions are limited to regions containing biological targets, expediting total acquisition times and reducing the burden of excess data by tens or hundreds of GBs.
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Affiliation(s)
- Ryan Lane
- Imaging Physics, Delft University of Technology, Delft, Netherlands
| | - Anouk H. G. Wolters
- Department of Biomedical Sciences of Cells and Systems, University Groningen, University Medical Center Groningen, Groningen, Netherlands
| | - Ben N. G. Giepmans
- Department of Biomedical Sciences of Cells and Systems, University Groningen, University Medical Center Groningen, Groningen, Netherlands
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Baatsen P, Gabarre S, Vints K, Wouters R, Vandael D, Goodchild R, Munck S, Gounko NV. Preservation of Fluorescence Signal and Imaging Optimization for Integrated Light and Electron Microscopy. Front Cell Dev Biol 2022; 9:737621. [PMID: 34977003 PMCID: PMC8715528 DOI: 10.3389/fcell.2021.737621] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Accepted: 11/25/2021] [Indexed: 11/13/2022] Open
Abstract
Life science research often needs to define where molecules are located within the complex environment of a cell or tissue. Genetically encoded fluorescent proteins and or fluorescence affinity-labeling are the go-to methods. Although recent fluorescent microscopy methods can provide localization of fluorescent molecules with relatively high resolution, an ultrastructural context is missing. This is solved by imaging a region of interest with correlative light and electron microscopy (CLEM). We have adopted a protocol that preserves both genetically-encoded and antibody-derived fluorescent signals in resin-embedded cell and tissue samples and provides high-resolution electron microscopy imaging of the same thin section. This method is particularly suitable for dedicated CLEM instruments that combine fluorescence and electron microscopy optics. In addition, we optimized scanning EM imaging parameters for samples of varying thicknesses. These protocols will enable rapid acquisition of CLEM information from samples and can be adapted for three-dimensional EM.
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Affiliation(s)
- Pieter Baatsen
- VIB-KU Leuven Center for Brain and Disease Research, Electron Microscopy Platform and VIB-Bioimaging Core, Leuven, Belgium.,KU Leuven Department of Neurosciences, Leuven Brain Institute, Leuven, Belgium
| | - Sergio Gabarre
- VIB-KU Leuven Center for Brain and Disease Research, Electron Microscopy Platform and VIB-Bioimaging Core, Leuven, Belgium.,KU Leuven Department of Neurosciences, Leuven Brain Institute, Leuven, Belgium.,VIB-KU Leuven Center for Brain and Disease Research, Light Microscopy Expertise Unit and VIB Bioimaging Core, Leuven, Belgium
| | - Katlijn Vints
- VIB-KU Leuven Center for Brain and Disease Research, Electron Microscopy Platform and VIB-Bioimaging Core, Leuven, Belgium.,KU Leuven Department of Neurosciences, Leuven Brain Institute, Leuven, Belgium
| | - Rosanne Wouters
- KU Leuven Department of Neurosciences, Leuven Brain Institute, Leuven, Belgium.,VIB-KU Leuven Center for Brain and Disease Research, Laboratory for Membrane Trafficking, Leuven, Belgium
| | - Dorien Vandael
- VIB-KU Leuven Center for Brain and Disease Research, Electron Microscopy Platform and VIB-Bioimaging Core, Leuven, Belgium.,KU Leuven Department of Neurosciences, Leuven Brain Institute, Leuven, Belgium
| | - Rose Goodchild
- KU Leuven Department of Neurosciences, Leuven Brain Institute, Leuven, Belgium.,VIB-KU Leuven Center for Brain and Disease Research, Laboratory for Dystonia Research, Leuven, Belgium
| | - Sebastian Munck
- KU Leuven Department of Neurosciences, Leuven Brain Institute, Leuven, Belgium.,VIB-KU Leuven Center for Brain and Disease Research, Light Microscopy Expertise Unit and VIB Bioimaging Core, Leuven, Belgium
| | - Natalia V Gounko
- VIB-KU Leuven Center for Brain and Disease Research, Electron Microscopy Platform and VIB-Bioimaging Core, Leuven, Belgium.,KU Leuven Department of Neurosciences, Leuven Brain Institute, Leuven, Belgium
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Lewczuk B, Szyryńska N. Field-Emission Scanning Electron Microscope as a Tool for Large-Area and Large-Volume Ultrastructural Studies. Animals (Basel) 2021; 11:ani11123390. [PMID: 34944167 PMCID: PMC8698110 DOI: 10.3390/ani11123390] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2021] [Revised: 11/24/2021] [Accepted: 11/25/2021] [Indexed: 11/29/2022] Open
Abstract
Simple Summary Ultrastructural studies of cells and tissues are usually performed using transmission electron microscopy (TEM), which enables imaging at the highest possible resolution. The weak point of TEM is the limited ability to analyze the ultrastructure of large areas and volumes of biological samples. This limitation can be overcome by using modern field-emission scanning electron microscopy (FE-SEM) with high-sensitivity detection, which enables the creation of TEM-like images from the flat surfaces of resin-embedded biological specimens. Several FE-SEM-based techniques for two- and three-dimensional ultrastructural studies of cells, tissues, organs, and organisms have been developed in the 21st century. These techniques have created a new era in structural biology and have changed the role of the scanning electron microscope (SEM) in biological and medical laboratories. Since the premiere of the first commercially available SEM in 1965, these instruments were used almost exclusively to obtain topographical information over a large range of magnifications. Currently, FE-SEM offers many attractive possibilities in the studies of cell and tissue ultrastructure, and they are presented in this review. Abstract The development of field-emission scanning electron microscopes for high-resolution imaging at very low acceleration voltages and equipped with highly sensitive detectors of backscattered electrons (BSE) has enabled transmission electron microscopy (TEM)-like imaging of the cut surfaces of tissue blocks, which are impermeable to the electron beam, or tissue sections mounted on the solid substrates. This has resulted in the development of methods that simplify and accelerate ultrastructural studies of large areas and volumes of biological samples. This article provides an overview of these methods, including their advantages and disadvantages. The imaging of large sample areas can be performed using two methods based on the detection of transmitted electrons or BSE. Effective imaging using BSE requires special fixation and en bloc contrasting of samples. BSE imaging has resulted in the development of volume imaging techniques, including array tomography (AT) and serial block-face imaging (SBF-SEM). In AT, serial ultrathin sections are collected manually on a solid substrate such as a glass and silicon wafer or automatically on a tape using a special ultramicrotome. The imaging of serial sections is used to obtain three-dimensional (3D) information. SBF-SEM is based on removing the top layer of a resin-embedded sample using an ultramicrotome inside the SEM specimen chamber and then imaging the exposed surface with a BSE detector. The steps of cutting and imaging the resin block are repeated hundreds or thousands of times to obtain a z-stack for 3D analyses.
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