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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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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.
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Related Experiment Video

Updated: May 12, 2026

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

Cryo-Electron Microscopy Structural Ensemble Optimization Using Individual Particles.

David Silva-Sánchez¹, Alison M Berezuk², Xing Zhu²

¹Department of Applied and Computational Mathematics, Yale University, New Haven, Connecticut 06520, United States.

Journal of Chemical Theory and Computation

|May 11, 2026

Summary

This summary is machine-generated.

This study introduces a new cryo-electron microscopy (cryo-EM) method for optimizing biomolecule structures and their populations. The technique accurately determines conformational ensembles, crucial for understanding biomolecular function.

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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
Computational biology

Background:

Biomolecules exist in dynamic conformational states essential for function.
Cryo-electron microscopy (cryo-EM) determines atomic resolution structures but characterizing dynamic ensembles remains challenging.
Existing methods infer population weights (ensemble reweighting) but cannot simultaneously determine structures and weights.

Purpose of the Study:

To develop a novel method for simultaneous inference of conformational structures and their population weights from cryo-EM data.
To enable comprehensive characterization of flexible biomolecules' conformational landscapes.
To advance the capabilities of cryo-EM for studying biomolecular dynamics.

Main Methods:

Developed a cryo-EM ensemble optimization method using Bayesian optimization.
Iteratively optimized structures and weights directly from cryo-EM particle images.
Employed a projected gradient descent-inspired approach for physical prior projection.

Main Results:

Successfully recovered accurate structures and population weights across various systems, from toy models to large proteins.
Demonstrated robustness under diverse experimental conditions.
Showed effective performance even when the number of inferred structures did not match the true number of states.

Conclusions:

The cryo-EM ensemble optimization method provides a powerful new tool for structural biology.
Enables detailed analysis of complex, multimodal conformational landscapes in flexible biomolecules.
Paves the way for advanced cryo-EM studies of biomolecular dynamics and function.