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Related Concept Videos

Cryo-electron Microscopy01:28

Cryo-electron Microscopy

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Conventional electron microscopy (EM) involves dehydration, fixation, and staining of biological samples, which distorts the native state of biological molecules and results in several artifacts. Also, the high-energy electron beam damages the sample and makes it difficult to obtain high-resolution images. These issues can be addressed using cryo-EM, which uses frozen samples and gentler electron beams. The technique was developed by Jacques Dubochet, Joachim Frank, and Richard Henderson, for...
4.0K

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Strategies for Optimization of Cryogenic Electron Tomography Data Acquisition
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TranSPHIRE: automated and feedback-optimized on-the-fly processing for cryo-EM.

Markus Stabrin1, Fabian Schoenfeld1, Thorsten Wagner1

  • 1Department of Structural Biochemistry, Max Planck Institute of Molecular Physiology, Otto-Hahn-Straße 11, 44227, Dortmund, Germany.

Nature Communications
|November 12, 2020
PubMed
Summary

TranSPHIRE offers a fully-automated solution for single particle cryo-electron microscopy (cryo-EM) data processing during acquisition. This software enhances throughput and enables high-resolution structure determination with its adaptive machine learning approach.

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Single Particle Cryo-Electron Microscopy: From Sample to Structure
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Area of Science:

  • Structural Biology
  • Biophysics
  • Computational Biology

Background:

  • High-throughput structure determination using single particle cryo-electron microscopy (cryo-EM) necessitates full automation.
  • Existing software automates parts of the cryo-EM pipeline but lacks a complete, reliable on-the-fly processing solution for high-resolution results.

Purpose of the Study:

  • To introduce TranSPHIRE, a software package designed for fully-automated processing of cryo-EM datasets during data acquisition.
  • To enable efficient and high-resolution structure determination through continuous, adaptive data processing.

Main Methods:

  • TranSPHIRE automates data transfer, pre-processing, particle picking, 2D clustering, and 3D refinement in parallel with image recording.
  • Incorporates a machine learning-based feedback loop for live re-training of the particle picking model, adapting to specific datasets.
  • Supports automated processing of filaments and collects key metrics and microscope settings for real-time data evaluation.

Main Results:

  • TranSPHIRE effectively processes cryo-EM data, producing high-quality particle stacks suitable for high-resolution structure determination.
  • The adaptive machine learning approach enhances processing efficiency and reliability across diverse datasets.
  • Real-time monitoring of metrics and settings aids users in assessing data quality during acquisition.

Conclusions:

  • TranSPHIRE provides a comprehensive, automated solution for the cryo-EM pipeline, significantly improving throughput and data processing efficiency.
  • The software's adaptive capabilities and real-time feedback mechanism facilitate reliable, high-resolution structure determination.
  • TranSPHIRE is a valuable tool for accelerating structural biology research through automated cryo-EM data analysis.