Serial Block Face-Scanning Electron Microscopy as a Burgeoning Technology

被引:19
作者
Marshall, Andrea G. [1 ]
Neikirk, Kit [1 ]
Stephens, Dominique C. [1 ]
Vang, Larry [1 ]
Vue, Zer [1 ]
Beasley, Heather K. [1 ]
Crabtree, Amber [1 ]
Scudese, Estevao [2 ,3 ]
Lopez, Edgar Garza [4 ]
Shao, Bryanna [1 ]
Krystofiak, Evan [5 ]
Rutledge, Sharifa [6 ]
Davis, Jaimaine [7 ]
Murray, Sandra A. [8 ]
Damo, Steven M. [9 ,10 ]
Katti, Prasanna [11 ]
Hinton, Antentor, Jr. [1 ]
机构
[1] Vanderbilt Univ, Dept Mol Physiol & Biophys, Nashville, TN 37232 USA
[2] Fed Univ State Rio De Janeiro UNIRIO, Lab Biosci Human Motr LABIMH, Rio De Janeiro, Brazil
[3] Catholic Univ Petropolis UCP, Sport Sci & Exercise Lab LaCEE, BR-25685100 Catholic, Brazil
[4] Univ Iowa, Dept Internal Med, Iowa City, IA 52242 USA
[5] Vanderbilt Univ, Dept Cell & Dev Biol, Nashville, TN 37232 USA
[6] Univ Alabama Huntsville, Dept Chem, Huntsville, AL 35899 USA
[7] Meharry Med Coll, Dept Biochem Canc Biol Neurosci Pharmacol, Nashville, TN 37232 USA
[8] Univ Pittsburgh, Dept Cell Biol, Pittsburgh, PA 15261 USA
[9] Fisk Univ, Dept Life & Phys Sci, Nashville, TN 37208 USA
[10] Vanderbilt Univ, Ctr Struct Biol, Nashville, TN 37232 USA
[11] Natl Heart Lung & Blood Inst, NIH, 9000 Rockville Pike, Bethesda, MD 20892 USA
来源
ADVANCED BIOLOGY | 2023年 / 7卷 / 08期
基金
美国国家卫生研究院;
关键词
3D EM; CLEM; machine learning; SBF-SEM; 3D CORRELATIVE LIGHT; MORPHOLOGY; DIAGNOSIS; REVEALS;
D O I
10.1002/adbi.202300139
中图分类号
TB3 [工程材料学]; R318.08 [生物材料学];
学科分类号
0805 ; 080501 ; 080502 ;
摘要
Serial block face scanning electron microscopy (SBF-SEM), also referred to as serial block-face electron microscopy, is an advanced ultrastructural imaging technique that enables three-dimensional visualization that provides largerx- and y-axis ranges than other volumetric EM techniques. While SEM is first introduced in the 1930s, SBF-SEM is developed as a novel method to resolve the 3D architecture of neuronal networks across large volumes with nanometer resolution by Denk and Horstmann in 2004. Here, the authors provide an accessible overview of the advantages and challenges associated with SBF-SEM. Beyond this, the applications of SBF-SEM in biochemical domains as well as potential future clinical applications are briefly reviewed. Finally, the alternative forms of artificial intelligence-based segmentation which may contribute to devising a feasible workflow involving SBF-SEM, are also considered.
引用
收藏
页数:7
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