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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

Electron Microscope Tomography and Single-particle Reconstruction

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: Jan 16, 2026

Cryo-EM and Single-Particle Analysis with Scipion

Cryo-EM and Single-Particle Analysis with Scipion

Published on: May 29, 2021

CryoEMNet driven symmetry-aware molecular reconstruction through deep learning enhanced electron microscopy.

Saksham Arora¹, Shin-Hung Pan², Sudhakar Kumar¹

¹CSE, Chandigarh College of Engineering and Technology, Sector 26, Chandigarh, India.

Scientific Reports

|October 3, 2025

Summary

CryoEMNet uses symmetry-aware deep learning for cryo-electron microscopy (cryo-EM) to create accurate 3D molecular reconstructions. This method improves resolution and structural consistency, outperforming current techniques.

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Last Updated: Jan 16, 2026

Cryo-EM and Single-Particle Analysis with Scipion

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Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

Area of Science:

Structural Biology
Biophysics
Computational Biology

Background:

Cryo-electron microscopy (cryo-EM) is crucial for determining molecular structures.
Current cryo-EM reconstruction methods face challenges with noise, heterogeneity, and particle alignment.
High-resolution, structurally consistent reconstructions are vital for detailed molecular analysis.

Purpose of the Study:

To develop a novel deep learning framework for enhanced molecular reconstruction in cryo-EM.
To incorporate molecular symmetry constraints into the deep learning reconstruction process.
To improve the accuracy, resolution, and interpretability of cryo-EM density maps.

Main Methods:

Developed CryoEMNet, a symmetry-aware deep learning framework.
Utilized unsupervised and transfer learning techniques for refining molecular details and particle orientations.
Incorporated molecular symmetry constraints directly into the deep learning model.

Main Results:

Achieved an average resolution of 3.78 Å to 3.81 Å in reconstructions.
Demonstrated superior performance compared to existing methods like EMPIAR.
Significantly improved the interpretability and structural consistency of density maps.

Conclusions:

CryoEMNet provides a reliable and scalable methodology for cryo-EM reconstruction.
The symmetry-aware deep learning approach overcomes key limitations in current methods.
This advancement facilitates more precise structural analyses and accelerates progress in structural biology.