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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...

Electron Microscope Tomography and Single-particle Reconstruction

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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
Electron tomography can be performed either in TEM or STEM (scanning transmission...

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Related Experiment Video

Updated: Jul 27, 2025

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

Ensemble Reweighting Using Cryo-EM Particle Images.

Wai Shing Tang^1,2, David Silva-Sánchez^1,3, Julian Giraldo-Barreto²

¹Center for Computational Mathematics, Flatiron Institute, New York, New York 10010, United States.

The Journal of Physical Chemistry. B

|June 9, 2023

Summary

This summary is machine-generated.

This study introduces a new Bayesian framework to analyze cryo-electron microscopy (cryo-EM) data from biomolecules with conformational heterogeneity. The method refines conformational ensembles, enabling accurate recovery of molecular states and free energies.

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Single Particle Cryo-Electron Microscopy: From Sample to Structure

Single Particle Cryo-Electron Microscopy: From Sample to Structure

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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:

Cryo-electron microscopy (cryo-EM) is a powerful technique for determining high-resolution structures of biological macromolecules.
Existing cryo-EM reconstruction methods struggle with samples exhibiting significant conformational heterogeneity, limiting their ability to capture the full range of molecular states.
Single-molecule data from heterogeneous samples contain valuable information about conformational distributions that is often lost.

Purpose of the Study:

To develop a computational framework for analyzing cryo-electron microscopy (cryo-EM) data from conformationally heterogeneous biomolecular samples.
To enable the accurate estimation of ensemble densities and conformational distributions from single-molecule cryo-EM data.
To overcome the limitations of current reconstruction tools in handling molecular flexibility.

Main Methods:

Developed an ensemble refinement framework based on a Bayesian approach.
Utilized reweighting of prior conformational ensembles (e.g., from molecular dynamics or structure prediction) to estimate ensemble density.
Applied the framework to a toy model and synthetic cryo-EM data of a protein with multiple conformations.

Main Results:

The framework successfully estimates the equilibrium probability density of biomolecules in conformational space from single-molecule data.
Demonstrated the extraction of state populations and free energies for a toy model.
Validated the approach using synthetic cryo-EM data of a simulated protein exhibiting folded and unfolded conformations.

Conclusions:

The developed ensemble refinement framework provides a general and effective method for analyzing heterogeneous cryo-EM data.
This approach allows for the recovery of detailed conformational landscapes, including state populations and free energies.
The method significantly advances the application of cryo-EM to study dynamic and flexible biological macromolecules.