Software tools for automated Transmission Electron Microscopy
By Martin Schorb, I. Haberbosch, W. Hagen, Y. Schwab, David N. Mastronarde. Nature Methods, 2018
420 citations recorded by the source corpus.
The demand for high-throughput data collection in electron microscopy is increasing for applications in structural and cellular biology. Here we present a combination of software tools that enable automated acquisition guided by image analysis for a variety of transmission electron microscopy acquisition schemes. SerialEM controls microscopes and detectors and can trigger automated tasks at multiple positions with high flexibility. Py-EM interfaces with SerialEM to enact specimen-specific image-analysis pipelines that enable feedback microscopy. As example applications, we demonstrate dose reduction in cryo-electron microscopy experiments, fully automated acquisition of every cell in a plastic section and automated targeting on serial sections for 3D volume imaging across multiple grids. Py-EM and SerialEM enable automated microscope control for high-throughput data acquisition in diverse transmission electron microscopy imaging experiments.
Subfields: Biophysics, Structural Biology
View Paper: https://www.semanticscholar.org/paper/27c4a8866d8d7f509bbdad47b2f23fb9ba5e778f
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
Citations in the corpus
- Moyamoya disease factor RNF213 is a giant E3 ligase with a dynein-like core and a distinct ubiquitin-transfer mechanism (2020, methodology): “Additionally, promising negative stain grids were imaged on the FEI Tecnai G2 20 using SerialEM (Schorb et al., 2019) with a defocus range of – 1.5 mm to –2.5 mm and a pixel size of 1.85 Å/px. Negative staining micrographs were analyzed using Relion 2.1 (Scheres, 2012).”