title: Automated transmission-mode scanning electron microscopy (tSEM) for large volume analysis at nanoscale resolution.
abstract: Transmission-mode scanning electron microscopy (tSEM) on a field emission SEM platform was developed for efficient and cost-effective imaging of circuit-scale volumes from brain at nanoscale resolution. Image area was maximized while optimizing the resolution and dynamic range necessary for discriminating key subcellular structures, such as small axonal, dendritic and glial processes, synapses, smooth endoplasmic reticulum, vesicles, microtubules, polyribosomes, and endosomes which are critical for neuronal function. Individual image fields from the tSEM system were up to 4,295 µm(2) (65.54 µm per side) at 2 nm pixel size, contrasting with image fields from a modern transmission electron microscope (TEM) system, which were only 66.59 µm(2) (8.160 µm per side) at the same pixel size. The tSEM produced outstanding images and had reduced distortion and drift relative to TEM. Automated stage and scan control in tSEM easily provided unattended serial section imaging and montaging. Lens and scan properties on both TEM and SEM platforms revealed no significant nonlinear distortions within a central field of ∼100 µm(2) and produced near-perfect image registration across serial sections using the computational elastic alignment tool in Fiji/TrakEM2 software, and reliable geometric measurements from RECONSTRUCT™ or Fiji/TrakEM2 software. Axial resolution limits the analysis of small structures contained within a section (∼45 nm). Since this new tSEM is non-destructive, objects within a section can be explored at finer axial resolution in TEM tomography with current methods. Future development of tSEM tomography promises thinner axial resolution producing nearly isotropic voxels and should provide within-section analyses of structures without changing platforms. Brain was the test system given our interest in synaptic connectivity and plasticity; however, the new tSEM system is readily applicable to other biological systems.
authors.affiliation: Center for Learning and Memory, The University of Texas at Austin, Austin, Texas, United States of America.
authors.forename: Masaaki
authors.initials: M
authors.lastname: Kuwajima
authors.forename: John M
authors.initials: JM
authors.lastname: Mendenhall
authors.forename: Laurence F
authors.initials: LF
authors.lastname: Lindsey
authors.forename: Kristen M
authors.initials: KM
authors.lastname: Harris
mesh_terms: D000818:Animals; D016477:Artifacts; D001331:Automation; D001921:Brain / Q000648:ultrastructure; D003362:Cost-Benefit Analysis; D004548:Elasticity; D007091:Image Processing, Computer-Assisted; D007909:Lenses; D008855:Microscopy, Electron, Scanning / Q000191:economics / Q000295:instrumentation / Q000379:methods*; D036103:Nanotechnology / Q000191:economics / Q000295:instrumentation / Q000379:methods*; D051381:Rats
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