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  • Primer
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Volume electron microscopy

Nature Reviews Methods Primers volume 2, Article number: 51 (2022) Cite this article

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An Author Correction to this article was published on 07 September 2022

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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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Fig. 1: A typical vEM workflow.
Fig. 2: vEM encompasses a collection of closely related imaging modalities.
Fig. 3: vEM as a multiscale multimodal imaging technique.
Fig. 4: Correlative and comparative vEM for function–structure studies.
Fig. 5: A typical vEM image analysis pipeline.
Fig. 6: Examples of multiscale vEM on organelles and cells.
Fig. 7: Examples of multiscale vEM on model organisms and connectomes.
Fig. 8: Examples of multiscale vEM in tissues and the clinical setting.

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Code availability

The two example data analysis workflows for neuron and mitochondria segmentation can be found at: https://github.com/kreshuklab/vem-primer-examples.

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References

  1. Abbott, L. F. et al. The mind of a mouse. Cell 182, 1372–1376 (2020).

    Article  Google Scholar 

  2. Guerin, C. J. & Lippens, S. Correlative light and volume electron microscopy (vCLEM): how community participation can advance developing technologies. J. Microsc. 284, 97–102 (2021).

    Article  Google Scholar 

  3. Peddie, C. J. & Schieber, N. L. The importance of sample processing for correlative imaging (or, rubbish In, rubbish out). in Correlative Imaging: Focusing on the Future (eds Collinson, L. & Verkade, P.) 37–66 (Wiley, 2020).

  4. Hayat, M. A. Principles and Techniques of Electron Microscopy: Biological Applications 45–61 (Cambridge Univ. Press, 2000).

  5. Titze, B. & Genoud, C. Volume scanning electron microscopy for imaging biological ultrastructure. Biol. Cell 108, 307–323 (2016). This comprehensive review of the main SEM-based vEM techniques is a must read for those who are starting in the field, as the authors clearly address the potential and limitations of FIB-SEM, SBF-SEM and array tomography, from sample preparation to image analysis.

    Article  Google Scholar 

  6. Ströh, S., Hammerschmith, E. W., Tank, D. W., Seung, H. S. & Wanner, A. A. In situ X-ray assisted electron microscopy staining for large biological samples. Preprint at bioRxiv https://doi.org/10.1101/2021.06.19.448808 (2021).

    Article  Google Scholar 

  7. Russell, M. R. G. et al. 3D correlative light and electron microscopy of cultured cells using serial blockface scanning electron microscopy. J. Cell Sci. 130, 278–291 (2017).

    Google Scholar 

  8. Cortese, M. et al. Integrative imaging reveals SARS-CoV-2-induced reshaping of subcellular morphologies. Cell Host Microbe 28, 853–866.e5 (2020).

    Article  Google Scholar 

  9. Müller-Reichert, T., Hohenberg, H., O’Toole, E. T. & McDonald, K. Cryoimmobilization and three-dimensional visualization of C. elegans ultrastructure. J. Microsc. 212, 71–80 (2003).

    Article  MathSciNet  Google Scholar 

  10. Mulcahy, B. et al. A pipeline for volume electron microscopy of the Caenorhabditis elegans nervous system. Front. Neural Circuits 12, 94 (2018).

    Article  Google Scholar 

  11. Porrati, F., Grewe, D., Seybert, A., Frangakis, A. S. & Eltsov, M. FIB-SEM imaging properties of Drosophila melanogaster tissues embedded in Lowicryl HM20. J. Microsc. 273, 91–104 (2019).

    Article  Google Scholar 

  12. Yoshida, N. et al. The zebrafish as a novel model for the in vivo study of Toxoplasma gondii replication and interaction with macrophages. Dis. Model Mech. https://doi.org/10.1242/dmm.043091 (2020).

    Article  Google Scholar 

  13. Hildebrand, D. G. C. et al. Whole-brain serial-section electron microscopy in larval zebrafish. Nature 545, 345–349 (2017).

    Article  ADS  Google Scholar 

  14. Korogod, N., Petersen, C. C., Knott, G. W. & Häusser, M. Ultrastructural analysis of adult mouse neocortex comparing aldehyde perfusion with cryo fixation. eLife 4, e05793 (2015).

    Article  Google Scholar 

  15. Mikula, S. & Denk, W. High-resolution whole-brain staining for electron microscopic circuit reconstruction. Nat. Methods 12, 541–546 (2015).

    Article  Google Scholar 

  16. Polishchuk, R. S., Polishchuk, E. V. & Luini, A. Visualizing live dynamics and ultrastructure of intracellular organelles with preembedding correlative light-electron microscopy. Meth. Cell Biol. 111, 21–35 (2012).

    Article  Google Scholar 

  17. McDonald, K. Cryopreparation methods for electron microscopy of selected model systems. Meth. Cell Biol. 79, 23–56 (2007).

    Article  Google Scholar 

  18. McDonald, K. L., Morphew, M., Verkade, P. & Muller-Reichert, T. Recent advances in high-pressure freezing: equipment- and specimen-loading methods. Meth. Mol. Biol. 369, 143–173 (2007).

    Article  Google Scholar 

  19. McDonald, K. L. A review of high-pressure freezing preparation techniques for correlative light and electron microscopy of the same cells and tissues. J. Microsc. 235, 273–281 (2009).

    Article  MathSciNet  Google Scholar 

  20. Odriozola, A. et al. High contrast staining for serial block face scanning electron microscopy without uranyl acetate. Preprint at bioRxiv https://doi.org/10.1101/207472 (2017).

    Article  Google Scholar 

  21. Starborg, T. et al. Using transmission electron microscopy and 3View to determine collagen fibril size and three-dimensional organization. Nat. Protoc. 8, 1433–1448 (2013).

    Article  Google Scholar 

  22. Mikula, S., Binding, J. & Denk, W. Staining and embedding the whole mouse brain for electron microscopy. Nat. Methods 9, 1198–1201 (2012).

    Article  Google Scholar 

  23. Ronchi, P. et al. High-precision targeting workflow for volume electron microscopy. J. Cell Biol. https://doi.org/10.1083/jcb.202104069 (2021).

    Article  Google Scholar 

  24. Deerinck, T. J., Bushong, E., Thor, A. & Ellisman, M. H. NCMIR methods for 3D EM: a new protocol for preparation of biological specimens for serial block-face SEM. Microscopy 1, 6–8 (2010).

  25. Tapia, J. C. et al. High-contrast en bloc staining of neuronal tissue for field emission scanning electron microscopy. Nat. Protoc. 7, 193–206 (2012).

    Article  Google Scholar 

  26. Lu, Z. et al. En bloc preparation of Drosophila brains enables high-throughput FIB-SEM connectomics. Preprint at bioRxiv https://doi.org/10.1101/855130 (2019).

    Article  Google Scholar 

  27. Hua, Y., Laserstein, P. & Helmstaedter, M. Large-volume en-bloc staining for electron microscopy-based connectomics. Nat. Commun. 6, 7923 (2015). This important update in sample preparations for vEM imaging addresses the uneven staining gradients of samples up to 1 mm in diameter.

    Article  ADS  Google Scholar 

  28. Seligman, A. M., Wasserkrug, H. L. & Hanker, J. S. A new staining method (OTO) for enhancing contrast of lipid-containing membranes and droplets in osmium tetroxide-fixed tissue with osmiophilic thiocarbohydrazide (TCH). J. Cell Biol. 30, 424–432 (1966).

    Article  Google Scholar 

  29. Malick, L. E. & Wilson, R. B. Modified thiocarbohydrazide procedure for scanning electron microscopy: routine use for normal, pathological, or experimental tissues. Stain. Technol. 50, 265–269 (1975).

    Article  Google Scholar 

  30. Willingham, M. C. & Rutherford, A. V. The use of osmium–thiocarbohydrazide–osmium (OTO) and ferrocyanide-reduced osmium methods to enhance membrane contrast and preservation in cultured cells. J. Histochem. Cytochem. 32, 455–460 (1984).

    Article  Google Scholar 

  31. Genoud, C., Titze, B., Graff-Meyer, A. & Friedrich, R. W. Fast homogeneous en bloc staining of large tissue samples for volume electron microscopy. Front. Neuroanat. 12, 76 (2018).

    Article  Google Scholar 

  32. Nakakoshi, M., Nishioka, H. & Katayama, E. New versatile staining reagents for biological transmission electron microscopy that substitute for uranyl acetate. J. Electron. Microsc. 60, 401–407 (2011).

    Article  Google Scholar 

  33. Kuipers, J. & Giepmans, B. N. G. Neodymium as an alternative contrast for uranium in electron microscopy. Histochem. Cell Biol. 153, 271–277 (2020).

    Article  Google Scholar 

  34. McDonald, K. L. Out with the old and in with the new: rapid specimen preparation procedures for electron microscopy of sectioned biological material. Protoplasma 251, 429–448 (2013).

    Article  Google Scholar 

  35. Hoffman, D. P. et al. Correlative three-dimensional super-resolution and block-face electron microscopy of whole vitreously frozen cells. Science 367, eaaz5357 (2020).

    Article  Google Scholar 

  36. Luft, J. H. Improvements in epoxy resin embedding methods. J. Biophys. Biochem. Cytol. 9, 409–414 (1961).

    Article  Google Scholar 

  37. Glauert, A. M., Rogers, G. E. & Glauert, R. H. A new embedding medium for electron microscopy. Nature 178, 803–803 (1956).

    Article  Google Scholar 

  38. Kellenberger, E. Low Denaturation Embedding for Electron Microscopy of Thin Sections (Chemische Werke Lowi, 1980).

  39. Armbruster, B. et al. Specimen preparation for electron microscopy using low temperature embedding resins. J. Microsc. 126, 77–85 (1982).

    Article  Google Scholar 

  40. Stäubli, W. A new embedding technique for electron microscopy, combining a water-soluble epoxy resin (Durcupan) with water insoluble araldite. J. Cell Biol. 16, 197–201 (1963).

    Article  Google Scholar 

  41. Spurr, A. R. A low-viscosity epoxy resin embedding medium for electron microscopy. J. Ultrastruct. Res. 26, 31–43 (1969).

    Article  Google Scholar 

  42. Maco, B., Holtmaat, A., Jorstad, A., Fua, P. & Knott, G. W. Correlative in vivo 2-photon imaging and focused ion beam scanning electron microscopy: 3D analysis of neuronal ultrastructure. Meth. Cell Biol. 124, 339–361 (2014).

    Article  Google Scholar 

  43. Maco, B. et al. Semiautomated correlative 3D electron microscopy of in vivo-imaged axons and dendrites. Nat. Protoc. 9, 1354–1366 (2014). This work presents clever targeting of a ROI using an ultramicrotome to trim the sample and FIB-SEM to directly acquire the volume — an elegant example of vCLEM where in vivo imaging is registered to FIB-SEM.

    Article  Google Scholar 

  44. Karreman, M. A. et al. Fast and precise targeting of single tumor cells in vivo by multimodal correlative microscopy. J. Cell Sci. 129, 444–456 (2016). This work demonstrates the power of multimodal correlative microscopy for identifying, targeting and acquiring a specific ROI within a large sample volume.

    Google Scholar 

  45. Karreman, M. A. et al. Find your way with X-ray: using microCT to correlate in vivo imaging with 3D electron microscopy. Meth. Cell Biol. 140, 277–301 (2017).

    Article  Google Scholar 

  46. Musser, J. M. et al. Profiling cellular diversity in sponges informs animal cell type and nervous system evolution. Science 374, 717–723 (2021).

    Article  ADS  Google Scholar 

  47. Schieber, N. L. et al. Minimal resin embedding of multicellular specimens for targeted FIB-SEM imaging. Meth. Cell Biol. 140, 69–83 (2017).

    Article  Google Scholar 

  48. Belu, A. et al. Ultra‐thin resin embedding method for scanning electron microscopy of individual cells on high and low aspect ratio 3D nanostructures. J. Microsc. 263, 78–86 (2016).

    Article  Google Scholar 

  49. Van Donselaar, E. G. et al. Extremely thin layer plastification for focused-ion beam scanning electron microscopy: an improved method to study cell surfaces and organelles of cultured cells. J. Microsc. 270, 359–373 (2018).

    Article  Google Scholar 

  50. Vergara, H. M. et al. Whole-body integration of gene expression and single-cell morphology. Cell 184, 4819–4837.e22 (2021).

    Article  Google Scholar 

  51. Hayworth, K. J. et al. Ultrastructurally-smooth thick partitioning and volume stitching for larger-scale connectomics. Nat. Methods 12, 319–322 (2015).

    Article  Google Scholar 

  52. Xu, C. S. et al. Enhanced FIB-SEM systems for large-volume 3D imaging. eLife 6, e25916 (2017). This work is an example of how to optimize the use pattern of an instrument for a specific application by developing it into a fault-tolerant system, as opposed to re-engineering it to be fault free, in order to overcome some of the fundamental limitations of the hardware.

    Article  Google Scholar 

  53. Sano, T., Glazer, A. N. & Cantor, C. R. A streptavidin–metallothionein chimera that allows specific labeling of biological materials with many different heavy metal ions. Proc. Natl Acad. Sci. USA 89, 1534–1538 (1992).

    Article  ADS  Google Scholar 

  54. Mercogliano, C. P. & DeRosier, D. J. Concatenated metallothionein as a clonable gold label for electron microscopy. J. Struct. Biol. 160, 70–82 (2007).

    Article  Google Scholar 

  55. Diestra, E., Fontana, J., Guichard, P., Marco, S. & Risco, C. Visualization of proteins in intact cells with a clonable tag for electron microscopy. J. Struct. Biol. 165, 157–168 (2009).

    Article  Google Scholar 

  56. Morphew, M. K. et al. Metallothionein as a clonable tag for protein localization by electron microscopy of cells. J. Microsc. 260, 20–29 (2015).

    Article  Google Scholar 

  57. Gaietta, G. et al. Multicolor and electron microscopic imaging of connexin trafficking. Science 296, 503–507 (2002).

    Article  ADS  Google Scholar 

  58. Shu, X. et al. A genetically encoded tag for correlated light and electron microscopy of intact cells, tissues, and organisms. PLoS Biol. 9, e1001041 (2011).

    Article  Google Scholar 

  59. Martell, J. D., Deerinck, T. J., Lam, S. S., Ellisman, M. H. & Ting, A. Y. Electron microscopy using the genetically encoded APEX2 tag in cultured mammalian cells. Nat. Protoc. 12, 1792–1816 (2017). This work is an example of the use, strengths and limitations of genetically encoded stains for vEM.

    Article  Google Scholar 

  60. Lam, S. S. et al. Directed evolution of APEX2 for electron microscopy and proteomics. Nat. Methods 12, 51–54 (2015).

    Article  ADS  Google Scholar 

  61. Ariotti, N. et al. Modular detection of GFP-labeled proteins for rapid screening by electron microscopy in cells and organisms. Dev. Cell 35, 513–525 (2015).

    Article  Google Scholar 

  62. Ariotti, N. et al. Ultrastructural localisation of protein interactions using conditionally stable nanobodies. PLoS Biol. 16, e2005473 (2018).

    Article  Google Scholar 

  63. Han, Y. et al. Directed evolution of split APEX2 peroxidase. ACS Chem. Biol. 14, 619–635 (2019).

    Article  Google Scholar 

  64. Sengupta, R., Poderycki, M. J. & Mattoo, S. CryoAPEX — an electron tomography tool for subcellular localization of membrane proteins. J. Cell Sci. 132, jcs222315 (2019).

    Article  Google Scholar 

  65. Zhang, Q., Lee, W.-C. A., Paul, D. L. & Ginty, D. D. Multiplexed peroxidase-based electron microscopy labeling enables simultaneous visualization of multiple cell types. Nat. Neurosci. 22, 828–839 (2019).

    Article  Google Scholar 

  66. Mavlyutov, T. A. et al. APEX2-enhanced electron microscopy distinguishes σ-1 receptor localization in the nucleoplasmic reticulum. Oncotarget 8, 51317–51330 (2017).

    Article  Google Scholar 

  67. Rae, J. et al. A robust method for particulate detection of a genetic tag for 3D electron microscopy. eLife 10, e64630 (2021).

    Article  Google Scholar 

  68. Jiang, Z. et al. Genetically encoded tags for direct synthesis of EM-visible gold nanoparticles in cells. Nat. Methods 17, 937–946 (2020).

    Article  Google Scholar 

  69. Fulton, K. A. & Briggman, K. L. Permeabilization-free en bloc immunohistochemistry for correlative microscopy. eLife https://doi.org/10.7554/eLife.63392 (2021).

    Article  Google Scholar 

  70. Micheva, K. D. & Smith, S. J. Array tomography: a new tool for imaging the molecular architecture and ultrastructure of neural circuits. Neuron 55, 25–36 (2007).

    Article  Google Scholar 

  71. Smith, S. J. Q&A: array tomography. BMC Biol. 16, 98 (2018). This work is an accessible introduction to array tomography — light and electron microscopic applications.

    Article  Google Scholar 

  72. Oberti, D., Kirschmann, M. A. & Hahnloser, R. H. Correlative microscopy of densely labeled projection neurons using neural tracers. Front. Neuroanat. 4, 24 (2010).

    Google Scholar 

  73. Anderson, J. R. et al. Exploring the retinal connectome. Mol. Vis. 17, 355–379 (2011).

    Google Scholar 

  74. Shahidi, R. et al. A serial multiplex immunogold labeling method for identifying peptidergic neurons in connectomes. eLife https://doi.org/10.7554/eLife.11147 (2015).

    Article  Google Scholar 

  75. White, J. G., Southgate, E., Thomson, J. N. & Brenner, S. The structure of the nervous system of the nematode Caenorhabditis elegans. Philos. Trans. R. Soc. Lond. B Biol. Sci. 314, 1–340 (1986).

    Article  ADS  Google Scholar 

  76. Marsh, B. J., Volkmann, N., McIntosh, J. R. & Howell, K. E. Direct continuities between cisternae at different levels of the Golgi complex in glucose-stimulated mouse islet beta cells. Proc. Natl Acad. Sci. USA 101, 5565–5570 (2004).

    Article  ADS  Google Scholar 

  77. Marsh, B. J. Lessons from tomographic studies of the mammalian Golgi. Biochim. Biophys. Acta 1744, 273–292 (2005).

    Article  Google Scholar 

  78. Anderson, J. R. et al. A computational framework for ultrastructural mapping of neural circuitry. PLoS Biol. 7, e1000074 (2009).

    Article  Google Scholar 

  79. Bock, D. D. et al. Network anatomy and in vivo physiology of visual cortical neurons. Nature 471, 177–182 (2011).

    Article  ADS  Google Scholar 

  80. Potter, C. S., Pulokas, J., Smith, P., Suloway, C. & Carragher, B. Robotic grid loading system for a transmission electron microscope. J. Struct. Biol. 146, 431–440 (2004).

    Article  Google Scholar 

  81. Lefman, J., Morrison, R. & Subramaniam, S. Automated 100-position specimen loader and image acquisition system for transmission electron microscopy. J. Struct. Biol. 158, 318–326 (2007).

    Article  Google Scholar 

  82. Zheng, Z. et al. A complete electron microscopy volume of the brain of adult Drosophila melanogaster. Cell 174, 730–743.e22 (2018).

    Article  Google Scholar 

  83. Graham, B. J. et al. High-throughput transmission electron microscopy with automated serial sectioning. Preprint at bioRxiv https://doi.org/10.1101/657346 (2019).

    Article  Google Scholar 

  84. Phelps, J. S. et al. Reconstruction of motor control circuits in adult Drosophila using automated transmission electron microscopy. Cell 184, 759–774.e18 (2021).

    Article  Google Scholar 

  85. Yin, W. et al. A petascale automated imaging pipeline for mapping neuronal circuits with high-throughput transmission electron microscopy. Nat. Commun. 11, 4949 (2020).

    Article  ADS  Google Scholar 

  86. Briggman, K. L. & Denk, W. Towards neural circuit reconstruction with volume electron microscopy techniques. Curr. Opin. Neurobiol. 16, 562–570 (2006).

    Article  Google Scholar 

  87. Peddie, C. J. & Collinson, L. M. Exploring the third dimension: volume electron microscopy comes of age. Micron 61, 9–19 (2014). This work is the first articulation of the term vEM in the context that is now accepted and extended by the community.

    Article  Google Scholar 

  88. Kubota, Y., Sohn, J. & Kawaguchi, Y. Large volume electron microscopy and neural microcircuit analysis. Front. Neural Circuits 12, 98 (2018).

    Article  Google Scholar 

  89. Smith, D. & Starborg, T. Serial block face scanning electron microscopy in cell biology: applications and technology. Tissue Cell 57, 111–122 (2019).

    Article  Google Scholar 

  90. Sergey, G. et al. Oxygen plasma focused ion beam scanning electron microscopy for biological samples. Preprint at bioRxiv https://doi.org/10.1101/457820 (2018).

    Article  Google Scholar 

  91. Motta, A. et al. Dense connectomic reconstruction in layer 4 of the somatosensory cortex. Science 366, eaay3134 (2019).

    Article  Google Scholar 

  92. Denk, W. & Horstmann, H. Serial block-face scanning electron microscopy to reconstruct three-dimensional tissue nanostructure. PLoS Biol. 2, e329 (2004). This work is one of the first examples of vEM imaging of resin-embedded cellular samples, effected by automated in situ serial sectioning and SEM imaging.

    Article  Google Scholar 

  93. Lippens, S., Kremer, A., Borghgraef, P. & Guérin, C. J. Serial block face-scanning electron microscopy for volume electron microscopy. Meth. Cell Biol. 152, 69–85 (2019).

    Article  Google Scholar 

  94. Titze, B. & Denk, W. Automated in‐chamber specimen coating for serial block‐face electron microscopy. J. Microsc. 250, 101–110 (2013).

    Article  Google Scholar 

  95. Deerinck, T. J. et al. High-performance serial block-face SEM of nonconductive biological samples enabled by focal gas injection-based charge compensation. J. Microsc. https://doi.org/10.1111/jmi.12667 (2017).

    Article  Google Scholar 

  96. Knott, G., Marchman, H., Wall, D. & Lich, B. Serial section scanning electron microscopy of adult brain tissue using focused ion beam milling. J. Neurosci. 28, 2959–2964 (2008). This work is one of the first examples of vEM imaging of resin-embedded cellular samples, effected by automated in situ FIB milling and SEM imaging.

    Article  Google Scholar 

  97. Knott, G., Rosset, S. & Cantoni, M. Focussed ion beam milling and scanning electron microscopy of brain tissue. J. Vis. Exp. https://doi.org/10.3791/2588 (2011).

    Article  Google Scholar 

  98. Narayan, K. & Subramaniam, S. Focused ion beams in biology. Nat. Methods 12, 1021–1031 (2015).

    Article  Google Scholar 

  99. Xu, C. S. et al. An open-access volume electron microscopy atlas of whole cells and tissues. Nature 599, 147–151 (2021).

    Article  ADS  Google Scholar 

  100. Baena, V. et al. FIB-SEM as a volume electron microscopy approach to study cellular architectures in SARS-CoV-2 and other viral infections: a practical primer for a virologist. Viruses https://doi.org/10.3390/v13040611 (2021).

    Article  Google Scholar 

  101. Burnett, T. L. et al. Large volume serial section tomography by Xe plasma FIB dual beam microscopy. Ultramicroscopy 161, 119–129 (2016).

    Article  Google Scholar 

  102. Winiarski, B. et al. Broad ion beam serial section tomography. Ultramicroscopy 172, 52–64 (2017).

    Article  Google Scholar 

  103. Gholinia, A. et al. Coupled broad ion beam-scanning electron microscopy (BIB-SEM) for polishing and three dimensional (3D) serial section tomography (SST). Ultramicroscopy 214, 112989 (2020).

    Article  Google Scholar 

  104. Wang, J. et al. Reactive oxygen FIB spin milling enables correlative workflow for 3D super-resolution light microscopy and serial FIB/SEM of cultured cells. Sci. Rep. 11, 13162 (2021).

    Article  ADS  Google Scholar 

  105. Hayworth, K. J. et al. Gas cluster ion beam SEM for imaging of large tissue samples with 10 nm isotropic resolution. Nat. Methods 17, 68–71 (2020).

    Article  Google Scholar 

  106. Burel, A. et al. A targeted 3D EM and correlative microscopy method using SEM array tomography. Development 145, dev160879 (2018).

    Article  Google Scholar 

  107. Wacker, I. et al. Hierarchical imaging: a new concept for targeted imaging of large volumes from cells to tissues. BMC Cell Biol. 17, 38 (2016).

    Article  Google Scholar 

  108. Collman, F. et al. Mapping synapses by conjugate light-electron array tomography. J. Neurosci. 35, 5792–5807 (2015).

    Article  Google Scholar 

  109. Gabarre, S. et al. A workflow for streamlined acquisition and correlation of serial regions of interest in array tomography. BMC Biol. 19, 152 (2021).

    Article  Google Scholar 

  110. Franke, T. & Kolotuev, I. Array tomography workflow for the targeted acquisition of volume information using scanning electron microscopy. J Vis Exp https://doi.org/10.3791/61847 (2021).

    Article  Google Scholar 

  111. Koike, T. et al. A device for ribbon collection for array tomography with scanning electron microscopy. Acta Histochem. Cytochem. 50, 135–140 (2017).

    Article  Google Scholar 

  112. Templier, T. MagC, magnetic collection of ultrathin sections for volumetric correlative light and electron microscopy. eLife https://doi.org/10.7554/eLife.45696 (2019).

    Article  Google Scholar 

  113. Li, X. et al. Large scale three-dimensional reconstruction of an entire Caenorhabditis elegans larva using AutoCUTS-SEM. J. Struct. Biol. 200, 87–96 (2017).

    Article  Google Scholar 

  114. Schlek, R. et al. ATUM-based SEM for high-speed large-volume biological reconstructions. Microsc. Microanal. 18, 572–573 (2012).

    Article  Google Scholar 

  115. Eberle, A. L. et al. High-resolution, high-throughput imaging with a multibeam scanning electron microscope. J. Microsc. 259, 114–120 (2015).

    Article  Google Scholar 

  116. Shapson-Coe, A. et al. A connectomic study of a petascale fragment of human cerebral cortex. Preprint at bioRxiv https://doi.org/10.1101/2021.05.29.446289 (2021). This work is a tour de force on vEM and automated segmentation of a cubic millimetre of human cerebral cortex with excellent ultrastructural preservation that allows analysis of human cortical circuits at the synaptic level, including identification of main neuron types, glia and blood vessels.

    Article  Google Scholar 

  117. Kruit, P. & Zuidema, W. A dedicated multi-beam SEM for transmission imaging of thin samples. Microsc. Microanal. 25, 1034–1035 (2019).

    Article  ADS  Google Scholar 

  118. Peddie, C. J. et al. Correlative super-resolution fluorescence and electron microscopy using conventional fluorescent proteins in vacuo. J. Struct. Biol. 199, 120–131 (2017).

    Article  Google Scholar 

  119. Lane, R., Wolters, A. H. G., Giepmans, B. N. G. & Hoogenboom, J. P. Integrated array tomography for 3D correlative light and electron microscopy. Front. Mol. Biosci. 8, 822232 (2021).

    Article  Google Scholar 

  120. Guérin, C. J., Kremer, A., Borghgraef, P., Shih, A. Y. & Lippens, S. Combining serial block face and focused ion beam scanning electron microscopy for 3D studies of rare events. Meth. Cell Biol. 152, 87–101 (2019).

    Article  Google Scholar 

  121. Greenwood, D. J. et al. Subcellular antibiotic visualization reveals a dynamic drug reservoir in infected macrophages. Science 364, 1279–1282 (2019).

    Article  ADS  Google Scholar 

  122. Kislinger, G. et al. ATUM-FIB microscopy for targeting and multiscale imaging of rare events in mouse cortex. STAR. Protoc. 1, 100232 (2020).

    Article  Google Scholar 

  123. Hegermann, J. et al. Volume-CLEM: a method for correlative light and electron microscopy in three dimensions. Am. J. Physiol. Lung Cell Mol. Physiol. 317, L778–L784 (2019).

    Article  Google Scholar 

  124. Narayan, K. et al. Multi-resolution correlative focused ion beam scanning electron microscopy: applications to cell biology. J. Struct. Biol. 185, 278–284 (2014).

    Article  Google Scholar 

  125. Wanner, A. A., Genoud, C. & Friedrich, R. W. 3-Dimensional electron microscopic imaging of the zebrafish olfactory bulb and dense reconstruction of neurons. Sci. Data 3, 160100 (2016).

    Article  Google Scholar 

  126. Maclachlan, C., Sahlender, D. A., Hayashi, S., Molnár, Z. & Knott, G. Block face scanning electron microscopy of fluorescently labeled axons without using near infra-red branding. Front. Neuroanat. 12, 88 (2018).

    Article  Google Scholar 

  127. Luckner, M. & Wanner, G. From light microscopy to analytical scanning electron microscopy (SEM) and focused ion beam (FIB)/SEM in biology: fixed coordinates, flat embedding, absolute references. Microsc. Microanal. 24, 526–544 (2018).

    Article  ADS  Google Scholar 

  128. Lees, R. M., Peddie, C. J., Collinson, L. M., Ashby, M. C. & Verkade, P. Correlative two-photon and serial block face scanning electron microscopy in neuronal tissue using 3D near-infrared branding maps. Meth. Cell Biol. 140, 245–276 (2017).

    Article  Google Scholar 

  129. Kremer, A. et al. A workflow for 3D-CLEM investigating liver tissue. J. Microsc. 281, 231–242 (2021).

    Article  Google Scholar 

  130. Thomas, C. I. et al. Targeting functionally characterized synaptic architecture using inherent fiducials and 3D correlative microscopy. Microsc. Microanal. 27, 156–169 (2021).

    Article  ADS  Google Scholar 

  131. Dalecká, M. et al. Invadopodia structure in 3D environment resolved by near-infrared branding protocol combining correlative confocal and FIB-SEM microscopy. Int. J. Mol. Sci. https://doi.org/10.3390/ijms22157805 (2021).

    Article  Google Scholar 

  132. Agronskaia, A. V. et al. Integrated fluorescence and transmission electron microscopy. J. Struct. Biol. 164, 183–189 (2008).

    Article  Google Scholar 

  133. Kukulski, W. et al. Correlated fluorescence and 3D electron microscopy with high sensitivity and spatial precision. J. Cell Biol. 192, 111–119 (2011).

    Article  Google Scholar 

  134. Liv, N. et al. Simultaneous correlative scanning electron and high-NA fluorescence microscopy. PLoS ONE 8, e55707 (2013).

    Article  ADS  Google Scholar 

  135. Zonnevylle, A. C. et al. Integration of a high‐NA light microscope in a scanning electron microscope. J. Microsc. 252, 58–70 (2013).

    Article  Google Scholar 

  136. Peddie, C. J. et al. Correlative and integrated light and electron microscopy of in-resin GFP fluorescence, used to localise diacylglycerol in mammalian cells. Ultramicroscopy 143, 3–14 (2014).

    Article  Google Scholar 

  137. Luby-Phelps, K., Ning, G., Fogerty, J. & Besharse, J. C. Visualization of identified GFP-expressing cells by light and electron microscopy. J. Histochem. Cytochem. 51, 271–274 (2003).

    Article  Google Scholar 

  138. Nixon, S. J. et al. A single method for cryofixation and correlative light, electron microscopy and tomography of zebrafish embryos. Traffic 10, 131–136 (2009). This work is one of the first demonstrations of preservation of fluorescent protein emission after embedding in resin for electron microscopy, raising the possibility of high-accuracy post-embedding vCLEM.

    Article  Google Scholar 

  139. Bloss, E. B. et al. Single excitatory axons form clustered synapses onto CA1 pyramidal cell dendrites. Nat. Neurosci. 21, 353–363 (2018).

    Article  Google Scholar 

  140. Mastronarde, D. N. Automated electron microscope tomography using robust prediction of specimen movements. J. Struct. Biol. 152, 36–51 (2005).

    Article  Google Scholar 

  141. Hayworth, K. J. et al. Imaging ATUM ultrathin section libraries with WaferMapper: a multi-scale approach to EM reconstruction of neural circuits. Front. Neural Circuits 8, 68 (2014).

    Article  Google Scholar 

  142. Titze, B., Genoud, C. & Friedrich, R. W. SBEMimage: versatile acquisition control software for serial block-face electron microscopy. Front. Neural Circuits https://doi.org/10.3389/fncir.2018.00054 (2018).

    Article  Google Scholar 

  143. Serra Lleti, J. M. et al. CLEMSite, a software for automated phenotypic screens using light microscopy and FIB-SEM. Preprint at bioRxiv https://doi.org/10.1101/2021.03.19.436113 (2021).

    Article  Google Scholar 

  144. Mastronarde, D. N. & Held, S. R. Automated tilt series alignment and tomographic reconstruction in IMOD. J. Struct. Biol. 197, 102–113 (2017).

    Article  Google Scholar 

  145. Lowe, D. G. Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60, 91–110 (2004).

    Article  Google Scholar 

  146. Saalfeld, S., Cardona, A., Hartenstein, V. & Tomančák, P. As-rigid-as-possible mosaicking and serial section registration of large ssTEM datasets. Bioinformatics 26, i57–i63 (2010).

    Article  Google Scholar 

  147. Cardona, A. et al. TrakEM2 software for neural circuit reconstruction. PLoS ONE 7, e38011 (2012).

    Article  ADS  MathSciNet  Google Scholar 

  148. Rueden, C. T. et al. ImageJ2: ImageJ for the next generation of scientific image data. BMC Bioinformatics 18, 529 (2017).

    Article  Google Scholar 

  149. Schindelin, J., Rueden, C. T., Hiner, M. C. & Eliceiri, K. W. The ImageJ ecosystem: an open platform for biomedical image analysis. Mol. Reprod. Dev. 82, 518–529 (2015).

    Article  Google Scholar 

  150. Hennies, J. et al. AMST: Alignment to Median Smoothed Template for focused ion beam scanning electron microscopy image stacks. Sci. Rep. 10, 2004 (2020).

    Article  ADS  Google Scholar 

  151. Mitchell, E., Keselj, S., Popovych, S., Buniatyan, D. & Seung, H. S. Siamese encoding and alignment by multiscale learning with self-supervision. Preprint at https://ui.adsabs.harvard.edu/abs/2019arXiv190402643M (2019).

  152. Macrina, T. et al. Petascale neural circuit reconstruction: automated methods. Preprint at bioRxiv https://doi.org/10.1101/2021.08.04.455162 (2021).

    Article  Google Scholar 

  153. Haskins, G., Kruger, U. & Yan, P. Deep learning in medical image registration: a survey. Mach. Vis. Appl. 31, 8 (2020).

    Article  Google Scholar 

  154. Pizer, S. M. et al. Adaptive histogram equalization and its variations. Comput. Vis. Graphics Image Process. 39, 355–368 (1987).

    Article  ADS  Google Scholar 

  155. Buchholz, T.-O. et al. Content-aware image restoration for electron microscopy. Meth. Cell Biol. 152, 277–289 (2019).

    Article  Google Scholar 

  156. Bogovic, J. A. et al. An unbiased template of the Drosophila brain and ventral nerve cord. PLoS ONE 15, e0236495 (2021).

    Article  Google Scholar 

  157. Mais, L. et al. PatchPerPixMatch for automated 3D search of neuronal morphologies in light microscopy. Preprint at bioRxiv https://doi.org/10.1101/2021.07.23.453511 (2021).

    Article  Google Scholar 

  158. Heinrich, L. et al. Whole-cell organelle segmentation in volume electron microscopy. Nature https://doi.org/10.1038/s41586-021-03977-3 (2021).

    Article  Google Scholar 

  159. Paul-Gilloteaux, P. et al. eC-CLEM: flexible multidimensional registration software for correlative microscopies. Nat. Methods 14, 102–103 (2017).

    Article  Google Scholar 

  160. Bogovic, J. A., Hanslovsky, P., Wong, A. & Saalfeld, S. Robust registration of calcium images by learned contrast synthesis. in IEEE 13th Int. Symp. Biomedical Imaging (ISBI) 1123–1126 (IEEE, 2016).

  161. Klein, S., Staring, M., Murphy, K., Viergever, M. A. & Pluim, J. P. W. elastix: a toolbox for intensity-based medical image registration. IEEE Trans. Med. Imaging 29, 196–205 (2010).

    Article  Google Scholar 

  162. Shamonin, D. et al. Fast parallel image registration on CPU and GPU for diagnostic classification of Alzheimer’s disease. Front. Neuroinform. https://doi.org/10.3389/fninf.2013.00050 (2014).

    Article  Google Scholar 

  163. Müller, A. et al. 3D FIB-SEM reconstruction of microtubule–organelle interaction in whole primary mouse β cells. J. Cell Biol. 220, e202010039 (2020). This compelling work illustrates the power of FIB-SEM imaging at a 4 nm isovoxel resolution — the fine detail of the most intimate parts of a mammalian endocrine cell is depicted and state-of-the-art morphometric analysis enables the description of inter-organelle interactions.

    Article  Google Scholar 

  164. Berg, S. et al. ilastik: interactive machine learning for (bio)image analysis. Nat. Methods 16, 1226–1232 (2019).

    Article  Google Scholar 

  165. Belevich, I., Joensuu, M., Kumar, D., Vihinen, H. & Jokitalo, E. Microscopy Image Browser: a platform for segmentation and analysis of multidimensional datasets. PLoS Biol. 14, e1002340 (2016).

    Article  Google Scholar 

  166. Kremer, J. R., Mastronarde, D. N. & McIntosh, J. R. Computer visualization of three-dimensional image data using IMOD. J. Struct. Biol. 116, 71–76 (1996).

    Article  Google Scholar 

  167. Fiala, J. C. Reconstruct: a free editor for serial section microscopy. J. Microsc. 218, 52–61 (2005).

    Article  MathSciNet  Google Scholar 

  168. Boergens, K. M. et al. webKnossos: efficient online 3D data annotation for connectomics. Nat. Methods 14, 691–694 (2017).

    Article  Google Scholar 

  169. Berger, D. R., Seung, H. S. & Lichtman, J. W. VAST (volume annotation and segmentation tool): efficient manual and semi-automatic labeling of large 3D image stacks. Front. Neural Circuits 12, 88 (2018).

    Article  Google Scholar 

  170. Tischer, C. et al. BigDataProcessor2: a free and open-source Fiji plugin for inspection and processing of TB sized image data. Bioinformatics 37, 3079–3081 (2021).

    Article  Google Scholar 

  171. Linkert, M. et al. Metadata matters: access to image data in the real world. J. Cell Biol. 189, 777–782 (2010).

    Article  Google Scholar 

  172. Arganda-Carreras, I. et al. Crowdsourcing the creation of image segmentation algorithms for connectomics. Front. Neuroanat. https://doi.org/10.3389/fnana.2015.00142 (2015).

    Article  Google Scholar 

  173. Scheffer, L. K. et al. A connectome and analysis of the adult Drosophila central brain. eLife 9, e57443 (2020).

    Article  Google Scholar 

  174. Legland, D., Arganda-Carreras, I. & Andrey, P. MorphoLibJ: integrated library and plugins for mathematical morphology with ImageJ. Bioinformatics 32, 3532–3534 (2016).

    Google Scholar 

  175. Schmidt, U., Weigert, M., Broaddus, C. & Myers, G. Cell detection with star-convex polygon. in Medical Image Computing and Computer Assisted Intervention–MICCAI 2018. Lecture Notes in Computer Science vol. 11071 (eds Frangi, A., Schnabel, J., Davatzikos, C., Alberola-López, C. & Fichtinger, G.) 265–273 (Springer, 2018).

  176. Stringer, C., Wang, T., Michaelos, M. & Pachitariu, M. Cellpose: a generalist algorithm for cellular segmentation. Nat. Methods 18, 100–106 (2021).

    Article  Google Scholar 

  177. Ronneberger, O., Fischer, P. & Brox, T. U-Net: convolutional networks for biomedical image segmentation. Preprint at https://doi.org/10.48550/arXiv.1505.04597 (2015).

  178. Arganda-Carreras, I. et al. Trainable Weka segmentation: a machine learning tool for microscopy pixel classification. Bioinformatics 33, 2424–2426 (2017).

    Article  Google Scholar 

  179. Gómez-de-Mariscal, E. et al. DeepImageJ: a user-friendly environment to run deep learning models in ImageJ. Nat. Methods 18, 1192–1195 (2021).

    Article  Google Scholar 

  180. Belevich, I. & Jokitalo, E. DeepMIB: user-friendly and open-source software for training of deep learning network for biological image segmentation. PLoS Comput. Biol. 17, e1008374 (2021).

    Article  ADS  Google Scholar 

  181. Haberl, M. G. et al. CDeep3M — plug-and-play cloud-based deep learning for image segmentation. Nat. Methods 15, 677–680 (2018).

    Article  Google Scholar 

  182. Conrad, R. & Narayan, K. Instance segmentation of mitochondria in electron microscopy images with a generalist deep learning model. Preprint at bioRxiv https://doi.org/10.1101/2022.03.17.484806 (2022).

    Article  Google Scholar 

  183. Conrad, R. & Narayan, K. CEM500K, a large-scale heterogeneous unlabeled cellular electron microscopy image dataset for deep learning. eLife 10, e65894 (2021).

    Article  Google Scholar 

  184. von Chamier, L. et al. Democratising deep learning for microscopy with ZeroCostDL4Mic. Nat. Commun. 12, 2276 (2021).

    Article  ADS  Google Scholar 

  185. Funke, J. et al. Large scale image segmentation with structured loss based deep learning for connectome reconstruction. IEEE Trans. Pattern Anal. Mach. Intell. 41, 1669–1680 (2019).

    Article  Google Scholar 

  186. Wolf, S. et al. The Mutex watershed and its objective: efficient, parameter-free graph partitioning. IEEE Trans. Pattern Anal. Mach. Intell. 43, 3724–3738 (2021).

    Article  Google Scholar 

  187. Berning, M., Boergens, Kevin, M. & Helmstaedter, M. SegEM: efficient Image analysis for high-resolution connectomics. Neuron 87, 1193–1206 (2015).

    Article  Google Scholar 

  188. Januszewski, M. et al. High-precision automated reconstruction of neurons with flood-filling networks. Nat. Methods 15, 605–610 (2018).

    Article  Google Scholar 

  189. Heinrich, L., Funke, J., Pape, C., Nunez-Iglesias, J. & Saalfeld, S. Synaptic cleft segmentation in non-isotropic volume electron microscopy of the complete Drosophila brain. Preprint at https://doi.org/10.48550/arXiv.1805.02718 (2018).

  190. Buhmann, J. et al. Automatic detection of synaptic partners in a whole-brain Drosophila electron microscopy data set. Nat. Methods 18, 771–774 (2021).

    Article  Google Scholar 

  191. Sofroniew, N. et al. napari. Zenodo https://doi.org/10.5281/zenodo.5587893 (2021).

    Article  Google Scholar 

  192. Dorkenwald, S. et al. FlyWire: online community for whole-brain connectomics. Nat. Methods 19, 119–128 (2022).

    Article  Google Scholar 

  193. Leite, V. et al. Paintera. Zenodo https://doi.org/10.5281/zenodo.5028021 (2021).

    Article  Google Scholar 

  194. Kaynig, V. et al. Large-scale automatic reconstruction of neuronal processes from electron microscopy images. Med. Image Anal. 22, 77–88 (2015).

    Article  Google Scholar 

  195. Pape, C. et al. MoBIE: a Fiji plugin for sharing and exploration of multi-modal cloud-hosted big image data. Preprint at bioRxiv https://doi.org/10.1101/2022.05.27.493763 (2022).

    Article  Google Scholar 

  196. Lu, R., Zlateski, A. & Seung, H. S. Large-scale image segmentation based on distributed clustering algorithms. Preprint at https://ui.adsabs.harvard.edu/abs/2021arXiv210610795L (2021).

  197. Madany, M., Marcus, K., Peltier, S., Ellisman, M. H. & Altintas, I. NeuroKube: an automated and autoscaling neuroimaging reconstruction framework using cloud native computing and A.I. in 2020 IEEE Int. Conf. Big Data (Big Data) 320–330 (IEEE, 2020).

  198. Kim, J. S. et al. Space-time wiring specificity supports direction selectivity in the retina. Nature 509, 331–336 (2014).

    Article  Google Scholar 

  199. Spiers, H. et al. Deep learning for automatic segmentation of the nuclear envelope in electron microscopy data, trained with volunteer segmentations. Traffic 22, 240–253 (2021).

    Article  Google Scholar 

  200. Pietzsch, T., Saalfeld, S., Preibisch, S. & Tomancak, P. BigDataviewer: visualization and processing for large image data sets. Nat. Methods 12, 481–483 (2015).

    Article  Google Scholar 

  201. Tischer, C. et al. MoBIE: a free and open-source platform for integration and cloud-based sharing of multi-modal correlative big image data. Microsc. Microanal. 27, 2588–2589 (2021).

    Article  Google Scholar 

  202. Saalfeld, S., Cardona, A., Hartenstein, V. & Tomančák, P. CATMAID: collaborative annotation toolkit for massive amounts of image data. Bioinformatics 25, 1984–1986 (2009).

    Article  Google Scholar 

  203. Maitin-Shepard, J. et al. neuroglancer. Zenodo https://doi.org/10.5281/zenodo.5573294 (2021).

    Article  Google Scholar 

  204. Helmstaedter, M., Briggman, K. L. & Denk, W. High-accuracy neurite reconstruction for high-throughput neuroanatomy. Nat. Neurosci. 14, 1081–1088 (2011).

    Article  Google Scholar 

  205. Porter, K. R. & Palade, G. E. Studies on the endoplasmic reticulum: III. Its form and distribution in striated muscle cells. J. Biophys. Biochem. Cytol. 3, 269–300 (1957).

    Article  Google Scholar 

  206. Redemann, S. et al. C. elegans chromosomes connect to centrosomes by anchoring into the spindle network. Nat. Commun. 8, 15288 (2017).

    Article  ADS  Google Scholar 

  207. Terasaki, M. et al. Stacked endoplasmic reticulum sheets are connected by helicoidal membrane motifs. Cell 154, 285–296 (2013).

    Article  Google Scholar 

  208. Puhka, M., Vihinen, H., Joensuu, M. & Jokitalo, E. Endoplasmic reticulum remains continuous and undergoes sheet-to-tubule transformation during cell division in mammalian cells. J. Cell Biol. 179, 895–909 (2007).

    Article  Google Scholar 

  209. Puhka, M., Joensuu, M., Vihinen, H., Belevich, I. & Jokitalo, E. Progressive sheet-to-tubule transformation is a general mechanism for endoplasmic reticulum partitioning in dividing mammalian cells. Mol. Biol. Cell 23, 2424–2432 (2012).

    Article  Google Scholar 

  210. Pain, C., Kriechbaumer, V., Kittelmann, M., Hawes, C. & Fricker, M. Quantitative analysis of plant ER architecture and dynamics. Nat. Commun. 10, 984 (2019).

    Article  ADS  Google Scholar 

  211. Wu, Y. et al. Contacts between the endoplasmic reticulum and other membranes in neurons. Proc. Natl Acad. Sci. USA 114, E4859–E4867 (2017).

    Article  Google Scholar 

  212. Yang, K. et al. ER exit sites in Drosophila display abundant ER-Golgi vesicles and pearled tubes but no megacarriers. Cell Rep. 36, 109707 (2021).

    Article  Google Scholar 

  213. Weigel, A. V. et al. ER-to-Golgi protein delivery through an interwoven, tubular network extending from ER. Cell 184, 2412–2429.e16 (2021).

    Article  Google Scholar 

  214. Fermie, J. et al. Single organelle dynamics linked to 3D structure by correlative live-cell imaging and 3D electron microscopy. Traffic 19, 354–369 (2018).

    Article  Google Scholar 

  215. Loginov, S. V. et al. Correlative organelle microscopy: fluorescence guided volume electron microscopy of intracellular processes. Front. Cell Dev. Biol. https://doi.org/10.3389/fcell.2022.829545 (2022).

    Article  Google Scholar 

  216. Prinz, W. A., Toulmay, A. & Balla, T. The functional universe of membrane contact sites. Nat. Rev. Mol. Cell Biol. 21, 7–24 (2020).

    Article  Google Scholar 

  217. Parlakgül, G. et al. High resolution 3D imaging of liver reveals a central role for subcellular architectural organization in metabolism. Preprint at bioRxiv https://doi.org/10.1101/2020.11.18.387803 (2020).

    Article  Google Scholar 

  218. Cortese, M. et al. Ultrastructural characterization of Zika virus replication factories. Cell Rep. 18, 2113–2123 (2017).

    Article  Google Scholar 

  219. Hackstadt, T. et al. Disruption of the Golgi apparatus and contribution of the endoplasmic reticulum to the SARS-CoV-2 replication complex. Viruses https://doi.org/10.3390/v13091798 (2021).

    Article  Google Scholar 

  220. Lee, J. H., Pasquarella, J. R. & Kalejta, R. F. Cell line models for human cytomegalovirus latency faithfully mimic viral entry by macropinocytosis and endocytosis. J Virol. https://doi.org/10.1128/jvi.01021-19 (2019).

    Article  Google Scholar 

  221. Felts, R. L. et al. 3D visualization of HIV transfer at the virological synapse between dendritic cells and T cells. Proc. Natl Acad. Sci. USA 107, 13336–13341 (2010).

    Article  ADS  Google Scholar 

  222. Nkwe, D. O., Pelchen-Matthews, A., Burden, J. J., Collinson, L. M. & Marsh, M. The intracellular plasma membrane-connected compartment in the assembly of HIV-1 in human macrophages. BMC Biol. 14, 50 (2016).

    Article  Google Scholar 

  223. Aggarwal, A., Stella, A. O., Henry, C. C., Narayan, K. & Turville, S. G. Embedding of HIV egress within cortical F-actin. Pathogens 11, 56 (2022).

    Article  Google Scholar 

  224. Mayorova, T. D. et al. Placozoan fiber cells: mediators of innate immunity and participants in wound healing. Sci. Rep. 11, 23343 (2021).

    Article  ADS  Google Scholar 

  225. Decelle, J. et al. Algal remodeling in a ubiquitous planktonic photosymbiosis. Curr. Biol. 29, 968–978.e4 (2019).

    Article  Google Scholar 

  226. Uwizeye, C. et al. Cytoklepty in the plankton: a host strategy to optimize the bioenergetic machinery of endosymbiotic algae. Proc. Natl Acad. Sci. USA https://doi.org/10.1073/pnas.2025252118 (2021).

    Article  Google Scholar 

  227. Weiner, A. & Enninga, J. The pathogen–host interface in three dimensions: correlative FIB/SEM applications. Trends Microbiol. 27, 426–439 (2019).

    Article  Google Scholar 

  228. Beckwith, M. S. et al. Seeing a mycobacterium-infected cell in nanoscale 3D: correlative imaging by light microscopy and FIB/SEM tomography. PLoS ONE 10, e0134644 (2015).

    Article  Google Scholar 

  229. Bernard, E. M. et al. M. tuberculosis infection of human iPSC-derived macrophages reveals complex membrane dynamics during xenophagy evasion. J. Cell Sci. https://doi.org/10.1242/jcs.252973 (2020).

    Article  Google Scholar 

  230. Hunt, A. et al. Differential requirements for cyclase-associated protein (CAP) in actin-dependent processes of Toxoplasma gondii. eLife https://doi.org/10.7554/eLife.50598 (2019).

    Article  Google Scholar 

  231. Kolba, M. D. et al. Tunneling nanotube-mediated intercellular vesicle and protein transfer in the stroma-provided imatinib resistance in chronic myeloid leukemia cells. Cell Death Dis. 10, 817 (2019).

    Article  Google Scholar 

  232. Zenker, J. et al. A microtubule-organizing center directing intracellular transport in the early mouse embryo. Science 357, 925–928 (2017).

    Article  ADS  Google Scholar 

  233. Frías-Anaya, E. et al. Age-related ultrastructural neurovascular changes in the female mouse cortex and hippocampus. Neurobiol. Aging 101, 273–284 (2021).

    Article  Google Scholar 

  234. Sheu, S.-H. et al. A serotonergic axon–cilium synapse drives nuclear signaling to maintain chromatin accessibility. Preprint at bioRxiv https://doi.org/10.1101/2021.09.27.461878 (2021).

    Article  Google Scholar 

  235. Pipkin, J. E., Bushong, E. A., Ellisman, M. H. & Kristan, W. B. Jr Verifying, challenging, and discovering new synapses among fully EM-reconstructed neurons in the leech ganglion. Front. Neuroanat. 12, 95 (2018).

    Article  Google Scholar 

  236. Tran, H. T., Lucas, M. S., Ishikawa, T., Shahmoradian, S. H. & Padeste, C. A compartmentalized neuronal cell-culture platform compatible with cryo-fixation by high-pressure freezing for ultrastructural imaging. Front. Neurosci. 15, 726763 (2021).

    Article  Google Scholar 

  237. Morgan, J. L. & Lichtman, J. W. An individual interneuron participates in many kinds of inhibition and innervates much of the mouse visual thalamus. Neuron 106, 468–481.e2 (2020).

    Article  Google Scholar 

  238. Baena, V. & Terasaki, M. Three-dimensional organization of transzonal projections and other cytoplasmic extensions in the mouse ovarian follicle. Sci. Rep. 9, 1262 (2019).

    Article  ADS  Google Scholar 

  239. Boulogne, C. et al. Functional organisation of the endomembrane network in the digestive gland of the Venus flytrap: revisiting an old story with a new microscopy toolbox. J. Microsc. 280, 86–103 (2020).

    Article  Google Scholar 

  240. Czymmek, K. et al. Plant endosomes, methods and protocols. Meth. Mol. Biol. 2177, 69–81 (2020).

    Article  Google Scholar 

  241. Polilov, A. A., Makarova, A. A., Pang, S., Xu, C. S. & Hess, H. Protocol for preparation of heterogeneous biological samples for 3D electron microscopy: a case study for insects. Sci. Rep. 11, 4717 (2021).

    Article  ADS  Google Scholar 

  242. Wang, X.-Q. et al. Three-dimensional reconstruction of a whole insect reveals its phloem sap-sucking mechanism at nano-resolution. eLife 10, e62875 (2021).

    Article  Google Scholar 

  243. Kaji, T., Kakui, K., Miyazaki, N., Murata, K. & Palmer, A. R. Mesoscale morphology at nanoscale resolution: serial block-face scanning electron microscopy reveals fine 3D detail of a novel silk spinneret system in a tube-building tanaid crustacean. Front. Zool. 13, 14 (2016).

    Article  Google Scholar 

  244. Denk, W., Briggman, K. L. & Helmstaedter, M. Structural neurobiology: missing link to a mechanistic understanding of neural computation. Nat. Rev. Neurosci. 13, 351–358 (2012).

    Article  Google Scholar 

  245. Briggman, K. L., Helmstaedter, M. & Denk, W. Wiring specificity in the direction-selectivity circuit of the retina. Nature 471, 183–188 (2011).

    Article  ADS  Google Scholar 

  246. Ryan, K., Lu, Z. & Meinertzhagen, I. A. The CNS connectome of a tadpole larva of Ciona intestinalis (L.) highlights sidedness in the brain of a chordate sibling. eLife 5, e16962 (2016).

    Article  Google Scholar 

  247. Ohyama, T. et al. A multilevel multimodal circuit enhances action selection in Drosophila. Nature 520, 633–639 (2015).

    Article  ADS  Google Scholar 

  248. Consortium, M. et al. Functional connectomics spanning multiple areas of mouse visual cortex. Preprint at bioRxiv https://doi.org/10.1101/2021.07.28.454025 (2021).

    Article  Google Scholar 

  249. Pfeiffer, R. L. et al. A pathoconnectome of early neurodegeneration: network changes in retinal degeneration. Exp. Eye Res. 199, 108196 (2020).

    Article  Google Scholar 

  250. Witvliet, D. et al. Connectomes across development reveal principles of brain maturation. Nature 596, 257–261 (2021).

    Article  ADS  Google Scholar 

  251. Wilson, C. E. et al. Taste bud connectome: implications for taste information processing. J. Neurosci. https://doi.org/10.1523/jneurosci.0838-21.2021 (2021).

    Article  Google Scholar 

  252. Randel, N. et al. Inter-individual stereotypy of the Platynereis larval visual connectome. eLife 4, 27 (2015).

    Article  Google Scholar 

  253. Verasztó, C. et al. Whole-animal connectome and cell-type complement of the three-segmented Platynereis dumerilii larva. Preprint at bioRxiv https://doi.org/10.1101/2020.08.21.260984 (2020). This work is a fascinating example of the power of vEM to obtain the complete whole-body synaptic connectome of the larva of a marine annelid, including the specific pathways from sensory neurons to different effector systems. This approach addresses how synaptic circuits control the entire body of the animal.

    Article  Google Scholar 

  254. Kasthuri, N. et al. Saturated reconstruction of a volume of neocortex. Cell 162, 648–661 (2015).

    Article  Google Scholar 

  255. Terasaki, M. Axonal endoplasmic reticulum is very narrow. J. Cell Sci. 131, jcs210450 (2018).

    Article  Google Scholar 

  256. Yao, P. J. et al. Mitochondrial protrusions in neuronal cells. iScience 23, 101514 (2020).

    Article  ADS  Google Scholar 

  257. Tomassy, G. S. et al. Distinct profiles of myelin distribution along single axons of pyramidal neurons in the neocortex. Science 344, 319–324 (2014).

    Article  ADS  Google Scholar 

  258. Erlandson, R. A. Role of electron microscopy in modern diagnostic surgical pathology. Mod. Surg. Pathol. https://doi.org/10.1016/b978-1-4160-3966-2.00005-9 (2009).

    Article  Google Scholar 

  259. Shoemark, A. et al. International consensus guideline for reporting transmission electron microscopy results in the diagnosis of primary ciliary dyskinesia (BEAT PCD TEM criteria). Eur. Respir. J. https://doi.org/10.1183/13993003.00725-2019 (2020).

    Article  Google Scholar 

  260. Shoemark, A. et al. Primary ciliary dyskinesia with normal ultrastructure: three-dimensional tomography detects absence of DNAH11. Eur. Respir. J. 51, 1701809 (2018).

    Article  Google Scholar 

  261. Takaki, T., Ohno, N., Saitoh, S., Nagai, M. & Joh, K. Podocyte penetration of the glomerular basement membrane to contact on the mesangial cell at the lesion of mesangial interposition in lupus nephritis: a three-dimensional analysis by serial block-face scanning electron microscopy. Clin. Exp. Nephrol. 23, 773–781 (2019).

    Article  Google Scholar 

  262. Nagai, M. et al. Glomerular cellular interactions following disruption of the glomerular basement membrane in IgA nephropathy: ultrastructural analyses by 3-dimensional serial block-face scanning electron microscopy. Kidney Med. 2, 222–225 (2020).

    Article  Google Scholar 

  263. Domínguez-Álvaro, M., Montero-Crespo, M., Blazquez-Llorca, L., DeFelipe, J. & Alonso-Nanclares, L. 3D electron microscopy study of synaptic organization of the normal human transentorhinal cortex and its possible alterations in Alzheimer’s disease. eNeuro https://doi.org/10.1523/eneuro.0140-19.2019 (2019).

    Article  Google Scholar 

  264. Shahmoradian, S. H. et al. Lewy pathology in Parkinson’s disease consists of crowded organelles and lipid membranes. Nat. Neurosci. 22, 1099–1109 (2019).

    Article  Google Scholar 

  265. Riesterer, J. L. et al. A workflow for visualizing human cancer biopsies using large-format electron microscopy. Meth. Cell Biol. 158, 163–181 (2020).

    Article  Google Scholar 

  266. Johnson, B. E. et al. An omic and multidimensional spatial atlas from serial biopsies of an evolving metastatic breast cancer. Cell Rep. Med. 3, 100525 (2022).

    Article  Google Scholar 

  267. de Senneville, B. D. et al. Deciphering tumour tissue organization by 3D electron microscopy and machine learning. Commun. Biol. 4, 1390 (2021).

    Article  Google Scholar 

  268. Schneider, J. P., Hegermann, J. & Wrede, C. Volume electron microscopy: analyzing the lung. Histochem. Cell Biol. 155, 241–260 (2021).

    Article  Google Scholar 

  269. Goggin, P. et al. Development of protocols for the first serial block-face scanning electron microscopy (SBF SEM) studies of bone tissue. Bone 131, 115107 (2020).

    Article  Google Scholar 

  270. Nelson, G. et al. QUAREP-LiMi: a community-driven initiative to establish guidelines for quality assessment and reproducibility for instruments and images in light microscopy. J. Microsc. 284, 56–73 (2021).

    Article  Google Scholar 

  271. Sarkans, U. et al. REMBI: Recommended Metadata for Biological Images — enabling reuse of microscopy data in biology. Nat. Methods 18, 1418–1422 (2021).

    Article  Google Scholar 

  272. Scheres, S. H. RELION: implementation of a Bayesian approach to cryo-EM structure determination. J. Struct. Biol. 180, 519–530 (2012).

    Article  Google Scholar 

  273. Borrett, S. & Hughes, L. Reporting methods for processing and analysis of data from serial block face scanning electron microscopy. J. Microsc. 263, 3–9 (2016).

    Article  Google Scholar 

  274. Warfield, S. K., Zou, K. H. & Wells, W. M. Simultaneous truth and performance level estimation (STAPLE): an algorithm for the validation of image segmentation. IEEE Trans. Med. Imaging 23, 903–921 (2004).

    Article  Google Scholar 

  275. Destrempes, F. et al. Assessment of inter-expert variability and of an automated segmentation method of 40 and 60 MHz IVUS images of coronary arteries. PLoS ONE 12, e0168332 (2017).

    Article  Google Scholar 

  276. Moore, J. et al. OME-NGFF: a next-generation file format for expanding bioimaging data-access strategies. Nat. Methods 18, 1496–1498 (2021).

    Article  Google Scholar 

  277. Iudin, A., Korir, P. K., Salavert-Torres, J., Kleywegt, G. J. & Patwardhan, A. EMPIAR: a public archive for raw electron microscopy image data. Nat. Methods 13, 387–388 (2016).

    Article  Google Scholar 

  278. Hartley, M. et al. The BioImage archive — building a home for life-sciences microscopy data. J. Mol. Biol. https://doi.org/10.1016/j.jmb.2022.167505 (2022).

    Article  Google Scholar 

  279. Vidavsky, N. et al. Cryo-FIB-SEM serial milling and block face imaging: large volume structural analysis of biological tissues preserved close to their native state. J. Struct. Biol. 196, 487–495 (2016).

    Article  Google Scholar 

  280. Zhu, Y. et al. Serial cryoFIB/SEM reveals cytoarchitectural disruptions in leigh syndrome patient cells. Structure 29, 82–87.e83 (2021).

    Article  Google Scholar 

  281. Groen, J., Conesa, J. J., Valcárcel, R. & Pereiro, E. The cellular landscape by cryo soft X-ray tomography. Biophys. Rev. 11, 611–619 (2019).

    Article  Google Scholar 

  282. Dittmayer, C. et al. Why misinterpretation of electron micrographs in SARS-CoV-2-infected tissue goes viral. Lancet 396, e64–e65 (2020).

    Article  Google Scholar 

  283. Withers, P. J. et al. X-ray computed tomography. Nat. Rev. Methods Primers 1, 18 (2021).

    Article  Google Scholar 

  284. Rawson, S. D., Maksimcuka, J., Withers, P. J. & Cartmell, S. H. X-ray computed tomography in life sciences. BMC Biol. 18, 21 (2020).

    Article  Google Scholar 

  285. Bosch, C. et al. Functional and multiscale 3D structural investigation of brain tissue through correlative in vivo physiology, synchrotron microtomography and volume electron microscopy. Nat. Commun. 13, 2923 (2022).

    Article  Google Scholar 

  286. Bushong, E. A. et al. X-ray microscopy as an approach to increasing accuracy and efficiency of serial block-face imaging for correlated light and electron microscopy of biological specimens. Microsc. Microanal. 21, 231–238 (2015).

    Article  ADS  Google Scholar 

  287. Kuan, A. T. et al. Dense neuronal reconstruction through X-ray holographic nano-tomography. Nat. Neurosci. 23, 1637–1643 (2020).

    Article  Google Scholar 

  288. Immel, A. et al. Effect of X-ray irradiation on ancient DNA in sub-fossil bones — guidelines for safe X-ray imaging. Sci. Rep. 6, 32969 (2016).

    Article  ADS  Google Scholar 

  289. Wallander, H. & Wallentin, J. Simulated sample heating from a nanofocused X-ray beam. J. Synchrotron Radiat. 24, 925–933 (2017).

    Article  Google Scholar 

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Acknowledgements

The authors thank E. Duke (European Molecular Biology Laboratory (EMBL) Hamburg) for comments on the manuscript. The work of C.J.P. and L.M.C. was supported by the Francis Crick Institute, which receives its core funding from Cancer Research UK (FC001999), the UK Medical Research Council (FC001999) and the Wellcome Trust (FC001999). R.G.P is supported by the National Health and Medical Research Council of Australia (grant APP1150083 and fellowship APP1156489) and by the Australian Research Council (DP200102559). K.D.M. is supported by the National Institutes of Health (NIH) (grant NS094499). A.W. was supported by the Howard Hughes Medical Institute, Janelia Research Campus. K.N. is funded in whole or in part with Federal funds from the National Cancer Institute, NIH, under Contract No. 75N91019D00024. The content of this publication does not necessarily reflect the views or policies of the Department of Health and Human Services, nor does mention of trade names, commercial products or organizations imply endorsement by the US government. K.M. collaborates for a joint PhD degree between EMBL and Heidelberg University, Faculty of Biosciences, Heidelberg, Germany.

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Author notes

  1. Kimberly Meechan

    Present address: Faculty of Biosciences, Heidelberg University, Heidelberg, Germany

Authors and Affiliations

  1. Electron Microscopy Science Technology Platform, The Francis Crick Institute, London, UK

    Christopher J. Peddie & Lucy M. Collinson

  2. Electron Microscopy Facility, Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland

    Christel Genoud

  3. Cell Biology and Biophysics Unit, European Molecular Biology Laboratory, Heidelberg, Germany

    Anna Kreshuk, Kimberly Meechan & Constantin Pape

  4. Department of Molecular and Cellular Physiology, Stanford University, Palo Alto, CA, USA

    Kristina D. Micheva

  5. Center for Molecular Microscopy, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA

    Kedar Narayan

  6. Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, MD, USA

    Kedar Narayan

  7. The Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia

    Robert G. Parton

  8. Centre for Microscopy and Microanalysis, The University of Queensland, Brisbane, Queensland, Australia

    Robert G. Parton & Nicole L. Schieber

  9. Cell Biology and Biophysics Unit/ Electron Microscopy Core Facility, European Molecular Biology Laboratory, Heidelberg, Germany

    Yannick Schwab

  10. Nanosurf AG, Liestal, Switzerland

    Benjamin Titze

  11. School of Biochemistry, University of Bristol, Bristol, UK

    Paul Verkade

  12. Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA

    Aubrey Weigel

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Contributions

Introduction (L.M.C. and P.V.); Experimentation (L.M.C., C.J.P., C.G., K.M., K.D.M., R.G.P., N.L.S., Y.S., B.T. and P.V.); Results (L.M.C., C.G., K.M., K.D.M., C.P., B.T. and A.W.); Applications (L.M.C., C.J.P., C.G., K.D.M., R.G.P., N.L.S., Y.S., P.V. and A.W.); Reproducibility and data deposition (L.M.C., A.K., K.N. and Y.S.); Limitations and optimizations (L.M.C., K.N. and Y.S.); Outlook (L.M.C.); overview of the Primer (L.M.C.).

Corresponding author

Correspondence to Lucy M. Collinson.

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Competing interests

K.D.M. has founder’s equity interests in Aratome, LLC (Menlo Park, CA, USA), an enterprise that produces array tomography materials and services. All other authors declare no competing interests.

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Nature Reviews Methods Primers thanks Jacob Hoogenboom, Saskia Lippens and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Related links

3D Slicer: www.slicer.org

APEER: https://www.apeer.com/home/

ariadne: https://ariadne.ai/

BigDataProcessor: https://github.com/bigdataprocessor/bigdataprocessor2

BigWarp: https://imagej.net/plugins/bigwarp

Bio-Formats: https://www.openmicroscopy.org/bio-formats/

Bioimage Model Zoo: https://bioimage.io/#/

CDeep3M: https://github.com/CRBS/cdeep3m

CLEMSite: https://git.embl.de/lleti/clemsite

Dask: https://dask.org/

DeepImageJ: https://deepimagej.github.io/deepimagej/

DeepMIB: https://github.com/Ajaxels/MIB2

ec-CLEM: https://icy.bioimageanalysis.org/plugin/ec-clem/

elastix: https://elastix.lumc.nl/

Example data analysis workflows: https://github.com/kreshuklab/vem-primer-examples

Fiji Weka plugin: https://imagej.net/plugins/tws/

ilastik: https://www.ilastik.org/

Microscopy Image Browser: http://mib.helsinki.fi/

MitoEM data challenge: https://mitoem.grand-challenge.org/

MorphoLibJ: https://imagej.net/plugins/morpholibj

piTEAM: https://github.com/AllenInstitute/piTEAM

Reconstruct: https://synapseweb.clm.utexas.edu/software-0

SBEMimage: https://github.com/SBEMimage

scalable minds: https://scalableminds.com/

SegEM: http://segem.brain.mpg.de/

SerialEM: https://bio3d.colorado.edu/SerialEM/

The Lichtman lab at Harvard University: https://lichtmanlab.fas.harvard.edu/

TrakEM2: https://imagej.net/plugins/trakem2/

VAST: https://lichtman.rc.fas.harvard.edu/vast/

vEM community website: https://www.volumeem.org/

WaferMapper: https://lichtman.rc.fas.harvard.edu/LGN/WaferMapper.html

webKnossos: https://webknossos.org/

ZeroCostDL4Mic: https://github.com/HenriquesLab/ZeroCostDL4Mic

Supplementary information

Glossary

Connectomics

An interdisciplinary field combining systems neuroscience, applied physics and computer science to map the physical and functional connections of the brain in a range of species.

Vibratome

An instrument for sectioning tissue that utilizes a vibrating blade.

Membrane tubulation

The process of membrane reorganization that produces tubular membrane invaginations or outward-facing tubular membrane projections.

Ultra-thin sections

Sections thin enough to allow transmission of an electron beam (usually 100 nm or less).

Diamond knife

A specialized tool used in electron microscopy where diamond is used to produce an extremely sharp and durable knife edge capable of cutting ultra-thin sections of tissue (down to about 30 nm).

Ultramicrotome

An instrument for preparing ultra-thin sections for electron microscopy (EM).

Neural tracers

Fluorescent probes for imaging neuronal morphology.

Charging

A process that occurs in poorly conducting specimens in the scanning electron microscope as electrons are trapped and their excess negative charge causes surface potentials that distort the signal amplitude, focus and image geometry.

Isotropic voxels

3D pixels with the same size in x, y and z.

Segmentation

The process of delineating objects within an image, usually with the aim of visualizing and/or analysing different object classes. Segmentation may be performed manually or using algorithms.

Noise

Unwanted alterations of the pixel intensities in an image caused by different stages of the imaging process (electron gun, signal detection, amplification).

Fiducial

An object added to the field of view of a microscope during imaging that can be used as a point of reference during sample preparation, imaging and image analysis.

Classifier

An algorithm that categorizes an observation based on quantifiable properties.

Watershed transform

An image processing technique that can be used for image segmentation by modelling the intensity of greyscale images as topographical peaks.

Connectome

A map of the synaptic connections of the brain.

Choanocytes

Specialized cells found in sponges, contributing to feeding function via water circulation and filtration.

Tunnelling nanotubes

Extensions of the plasma membrane that form channels between cells whose proposed functions are to open communication channels between cells, for the exchange of organelles or other intracellular components.

Vitrification

Transformation of a substance into a non-crystalline, amorphous, glass-like solid. In cryo electron microscopy (cryo-EM), this involves cryopreservation of materials without hexagonal ice crystal formation, to maintain cellular components in a near-native state.

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Peddie, C.J., Genoud, C., Kreshuk, A. et al. Volume electron microscopy. Nat Rev Methods Primers 2, 51 (2022). https://doi.org/10.1038/s43586-022-00131-9

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