4.3 Article

Volume electron microscopy

期刊

NATURE REVIEWS METHODS PRIMERS
卷 2, 期 1, 页码 -

出版社

SPRINGERNATURE
DOI: 10.1038/s43586-022-00131-9

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资金

  1. Francis Crick Institute
  2. Cancer Research UK [FC001999]
  3. UK Medical Research Council [FC001999]
  4. Wellcome Trust
  5. National Health and Medical Research Council of Australia [APP1150083, APP1156489]
  6. Australian Research Council [DP200102559]
  7. National Institutes of Health (NIH) [NS094499]
  8. Howard Hughes Medical Institute, Janelia Research Campus
  9. National Cancer Institute, NIH [75N91019D00024]
  10. Australian Research Council [DP200102559] Funding Source: Australian Research Council

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Volume electron microscopy (vEM) is an emerging technique that allows deep imaging of cellular and tissue structures. This article provides an introduction to the different vEM imaging modalities, specialized sample processing, and image analysis pipelines, showcasing key applications in the biosciences.
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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