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Related Concept Videos

Electron Microscope Tomography and Single-particle Reconstruction01:07

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Transmission electron microscopy (TEM) can be used to determine the 3D structure of biological samples with the help of techniques such as electron microscope tomography and single-particle reconstruction. While single-particle reconstruction can examine macromolecules and macromolecular complexes in vitro conditions only, tomography permits the study of cell components or small cells in vivo.
Electron Tomography
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Related Experiment Video

Updated: Jun 17, 2025

Using Tomoauto: A Protocol for High-throughput Automated Cryo-electron Tomography
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Using Tomoauto: A Protocol for High-throughput Automated Cryo-electron Tomography

Published on: January 30, 2016

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Smart parallel automated cryo-electron tomography.

Fabian Eisenstein1,2, Yoshiyuki Fukuda3, Radostin Danev4

  • 1Graduate School of Medicine, University of Tokyo, Tokyo, Japan.

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Summary
This summary is machine-generated.

We developed SPACEtomo, a machine learning workflow that fully automates cryo-electron tomography. This automation is key for high-resolution macromolecule structure determination in cells.

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User-friendly, High-throughput, and Fully Automated Data Acquisition Software for Single-particle Cryo-electron Microscopy
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Area of Science:

  • Structural Biology
  • Biophysics
  • Cell Biology

Background:

  • In situ cryo-electron tomography (cryo-ET) allows visualization of macromolecules within their native cellular environment.
  • Recent advancements in hardware and software have increased sample accessibility for cryo-ET.
  • Current cryo-ET data collection is labor-intensive, requiring expert operators and significant microscope time for target selection and tilt series acquisition.

Purpose of the Study:

  • To develop a fully automated workflow for cryo-electron tomography.
  • To eliminate the need for human intervention in cryo-ET data collection.
  • To enable high-throughput acquisition of statistically relevant datasets for structural analysis.

Main Methods:

  • Developed SPACEtomo, a novel workflow integrating machine learning.
  • Implemented automated lamella detection and biological feature segmentation.
  • Enabled automated target selection and parallel tilt series acquisition.

Main Results:

  • SPACEtomo fully automates the entire cryo-electron tomography process.
  • The workflow requires no human intervention.
  • Facilitates high-throughput data collection for structural studies.

Conclusions:

  • SPACEtomo significantly advances the automation of cryo-electron tomography.
  • This automation is crucial for obtaining statistically relevant datasets.
  • Enables high-resolution structure determination of macromolecules in their native cellular context.