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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: Jul 10, 2025

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

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

Published on: July 29, 2021

Automated pipelines for rapid evaluation during cryoEM data acquisition.

Joshua H Mendez¹, Eugene Y D Chua¹, Mohammadreza Paraan¹

¹Simons Electron Microscopy Center, New York Structural Biology Center, New York, NY, USA.

Current Opinion in Structural Biology

|November 21, 2023

Summary

On-the-fly data processing in cryo-electron microscopy (cryoEM) speeds up biomolecular structure determination. This review assesses real-time software to enhance data quality and collection efficiency for researchers.

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Last Updated: Jul 10, 2025

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

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Published on: July 29, 2021

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Single Particle Cryo-Electron Microscopy: From Sample to Structure

Single Particle Cryo-Electron Microscopy: From Sample to Structure

Published on: May 29, 2021

Area of Science:

Structural biology
Biophysics
Biochemistry

Background:

Cryo-electron microscopy (cryoEM) is crucial for high-resolution biomolecular structure determination.
Data processing in cryoEM is often time-consuming, posing a challenge for new researchers.
On-the-fly processing software aims to automate tasks like motion correction and CTF estimation.

Purpose of the Study:

To review on-the-fly data processing software for cryoEM.
To identify metrics for assessing data quality during data collection.
To evaluate the features and performance of ideal on-the-fly processing systems.

Main Methods:

Literature review of existing on-the-fly cryoEM data processing software.
Definition of minimal metrics for real-time data quality assessment.
Comparative analysis of three selected on-the-fly processing software packages.

Main Results:

On-the-fly processing significantly improves data collection efficiency and instrument productivity.
Software packages vary in performance, computational needs, and automation levels.
Key metrics for real-time quality control were identified.

Conclusions:

Automated on-the-fly processing is vital for efficient cryoEM data acquisition.
Selecting appropriate software requires consideration of performance and computational resources.
Standardized quality metrics aid in optimizing cryoEM data collection.