Volume scanning electron microscopy for imaging biological ultrastructure. (12th August 2016)

• Record Type: Journal Article
• Authors: Titze, Benjamin; Genoud, Christel
• Is Part Of: Biology of the cell. Volume 108:Number 11(2016)
• Page Start: 307
• Page End: 323
• Publication Date: 2016-08-12
• Subjects: Brain/nervous system -- Cellular imaging -- Electron microscopy -- Systems biology; Cytology -- Periodicals; Electron microscopy -- Periodicals; 571.6
• DOI: 10.1111/boc.201600024
• Languages: English
• ISSNs: 0248-4900

Abstract

Abstract : Novel volume imaging methods based on scanning electron microscopy (SEM) allow researchers to acquire biological specimens at <10–50 nm resolution in three dimensions. Recent advances have increased reliability and speed of acquisitions, improved image quality and reduced the required amount of manual labour. Volume SEM has the potential to revolutionise ultrastructural investigations of cells and tissues in volumes spanning distances of tens or even hundreds of microns. Abstract : Electron microscopy (EM) has been a key imaging method to investigate biological ultrastructure for over six decades. In recent years, novel volume EM techniques have significantly advanced nanometre‐scale imaging of cells and tissues in three dimensions. Previously, this had depended on the slow and error‐prone manual tasks of cutting and handling large numbers of sections, and imaging them one‐by‐one with transmission EM. Now, automated volume imaging methods mostly based on scanning EM (SEM) allow faster and more reliable acquisition of serial images through tissue volumes and achieve higher z ‐resolution. Various software tools have been developed to manipulate the acquired image stacks and facilitate quantitative analysis. Here, we introduce three volume SEM methods: serial block‐face electron microscopy (SBEM), focused ion beam SEM (FIB‐SEM) and automated tape‐collecting ultramicrotome SEM (ATUM‐SEM). We discuss and compare their capabilities, provide an overview of the full volume SEM workflow for obtaining 3D datasets and showcase different applications for biological research.