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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: Jun 28, 2025

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

Overcoming the preferred orientation problem in cryoEM with self-supervised deep-learning.

Yun-Tao Liu^1,2, Hongcheng Fan^1,2, Jason J Hu^1,2,3

¹Department of Microbiology, Immunology, and Molecular Genetics, University of California, Los Angeles, CA, USA.

Biorxiv : the Preprint Server for Biology

|April 22, 2024

Summary

This summary is machine-generated.

spIsoNet is a new deep-learning software that computationally solves the preferred orientation problem in cryo-electron microscopy. It improves 3D reconstruction accuracy and isotropy without complex sample preparation.

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Optimizing Sample Preparation for Cryogenic Electron Microscopy

Optimizing Sample Preparation for Cryogenic Electron Microscopy

Published on: April 11, 2025

Area of Science:

Structural biology
Biophysics
Computational biology

Background:

Single-particle cryo-electron microscopy (cryo-EM) achieves atomic resolution for macromolecular complexes.
Particle orientation bias, or the preferred orientation problem, complicates 3D reconstruction in cryo-EM.
Current solutions involve complex biochemical and physical specimen manipulation.

Approach:

Developed spIsoNet, an end-to-end self-supervised deep-learning software.
Utilizes preferred-orientation views to enhance information in under-sampled views.
Improves angular distribution and particle alignment accuracy in 3D reconstruction.

Key Points:

spIsoNet generates near-isotropic reconstructions from limited views.
Demonstrated efficacy on ribosomes, β-galactosidases, and a challenging hemagglutinin trimer dataset.
Applicable to both single-particle analysis and subtomogram averaging.

Conclusions:

spIsoNet offers a general computational solution to the preferred orientation problem in cryo-EM.
Eliminates the need for additional, complex specimen preparation procedures.
Enhances map isotropy and particle alignment for preferentially oriented molecules.