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 µm2 (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 µm2 (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 µm2 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.

Large-scale automatic reconstruction of neuronal processes from electron microscopy images

“…To reconstruct the neuronal circuit at the level of individual cells, the field of neuroanatomy faces the challenge to acquire and analyze data volumes that cover a brain tissue volume large enough to allow meaningful analysis of circuits and detailed enough to detect synapses and thus the connectivity structure of the circuit. Recently, significant progress has been made in the automation of sample preparation (Hayworth et al, 2006) and automatic image acquisition (Kuwajima et al, 2013a; Bock et al, 2011; Knott et al, 2008; Denk and Horstmann, 2004) for electron microscopy. These techniques allow neuroscientists to acquire large datasets in the GB-TB range.…”

Correlated light and electron microscopy: ultrastructure lights up!

“…Only recently, specific protocols have been developed to allow 2D automated TEM acquisition and stitching of areas up to 1 mm 2 at macromolecular resolution 52,73,74 , an approach also referred to as nanotomy (for nanoanatomy; http://www.nanotomy.org/). With scanning-based detection (scanning EM), even larger FOVs (for example, 32,000 × 32,000 pixels) can be acquired with quality similar to that of transmission EM 75,76 . Analysis of these large data sets remains a bottleneck; this is still typically done by manual annotation, sometimes by many people 77 .…”

Automated Serial Section Large-field Transmission-Mode Scanning Electron Microscopy (tSEM) for Volume Analysis of Hippocampus Ultrastructure

“…Image fields from the tSEM system were up to 65 µm per side at 2 nm pixel size, contrasting with image fields from a modern TEM system at about 8 µm per side at the same pixel size. The tSEM produced quality images with negligible scan or beam-caused distortion, no apparent charging effects, and with reduced drift relative to TEM [6]. Automated stage and scan control in this tSEM system and additional software provided unattended and stable serial section imaging and montaging across multiple grids on overnight runs.…”