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Cryo-electron Microscopy

Cryo-electron Microscopy

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...

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Related Experiment Video

Updated: Jan 12, 2026

Author Spotlight: Optimizing Cryo-EM Analysis with CryoSieve for Enhanced Particle Selection Efficiency

Author Spotlight: Optimizing Cryo-EM Analysis with CryoSieve for Enhanced Particle Selection Efficiency

Published on: May 10, 2024

CryoSift: an accessible and automated CNN-driven tool for cryo-EM 2D class selection.

Jan Hannes Schäfer¹, Austin Calza¹, Keegan Hom¹

¹Department of Integrative Structural and Computational Biology, Scripps Research, La Jolla, California, USA.

Acta Crystallographica. Section F, Structural Biology Communications

|November 7, 2025

Summary

This summary is machine-generated.

CryoSift, a new tool using convolutional neural networks (CNNs), automates particle selection in cryo-electron microscopy (cryo-EM) data processing. This enhances high-resolution structure determination by improving the efficiency of data analysis for all users.

Keywords:

automation cryo-EM data processing deep learning single-particle cryo-EM

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A Robust Single-Particle Cryo-Electron Microscopy cryo-EM Processing Workflow with cryoSPARC, RELION, and Scipion

A Robust Single-Particle Cryo-Electron Microscopy cryo-EM Processing Workflow with cryoSPARC, RELION, and Scipion

Published on: January 31, 2022

Area of Science:

Structural Biology
Biophysics
Computational Biology

Background:

Single-particle cryo-electron microscopy (cryo-EM) is crucial for determining molecular structures.
Automating data processing in cryo-EM remains a significant challenge, hindering high-throughput analysis.
Current methods often require extensive manual intervention, limiting accessibility and speed.

Purpose of the Study:

To develop an automated tool for assessing the quality of 2D averages in cryo-EM data.
To enable automatic selection of suitable particles for high-resolution cryo-EM reconstructions.
To integrate this tool into a fully automated processing pipeline for enhanced efficiency.

Main Methods:

Development of a platform-independent convolutional neural network (CNN) named CryoSift.
Integration of CryoSift into an automated processing pipeline utilizing cryosparc-tools.
Implementation of a customizable 2D assessment workflow for particle selection.

Main Results:

CryoSift effectively assesses the quality of 2D averages, facilitating automatic particle selection.
The integrated pipeline streamlines cryo-EM data processing, reducing manual effort.
The workflow supports high-throughput processing, benefiting both novice and experienced users.

Conclusions:

CryoSift offers a robust solution for automating particle selection in cryo-EM.
The developed workflow significantly improves the efficiency and accessibility of cryo-EM data analysis.
This advancement supports the broader application of cryo-EM for high-resolution structure determination.