Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

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...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Highly Stereoselective Synthesis of β-Glycosyl Esters via <i>N</i>-Heterocyclic Carbene Catalysis under Aerobic Conditions.

Organic letters·2026

Same author

Laparoscopic Anatomical Resection of Liver Segment IV Guided by Key Venous Landmarks.

Annals of surgical oncology·2026

Same author

Effects of ultrasound-guided Iliopsoas Plane Block on postoperative immune function and recovery quality in elderly patients undergoing total hip arthroplasty.

Annals of medicine and surgery (2012)·2026

Same author

Anoikis in cancer: molecular mechanisms, resistance, and therapeutic strategies.

Apoptosis : an international journal on programmed cell death·2026

Same author

Next-Generation CDK Inhibitors in Breast Cancer: Expanding the Therapeutic Landscape beyond CDK4/6 Inhibitors.

Breast care (Basel, Switzerland)·2026

Same author

Opportunities and challenges for the application of single-cell RNA sequencing and spatial transcriptomics in ischemic stroke.

Experimental neurology·2026

Search research articles

Related Experiment Video

Updated: Feb 25, 2026

Single-Particle Cryo-EM Data Collection with Stage Tilt using Leginon

Single-Particle Cryo-EM Data Collection with Stage Tilt using Leginon

Published on: July 1, 2022

Massively parallel unsupervised single-particle cryo-EM data clustering via statistical manifold learning.

Jiayi Wu^1,2, Yong-Bei Ma², Charles Congdon³

¹State Key Laboratory for Artificial Microstructure and Mesoscopic Physics, Institute of Condensed Matter Physics, School of Physics, Center for Quantitative Biology, Peking University, Beijing, China.

|August 9, 2017

Summary

This study introduces a new unsupervised clustering algorithm, generative topographic mapping (GTM), for single-particle cryo-electron microscopy (cryo-EM). GTM enhances classification accuracy for low signal-to-noise ratio data, improving structural heterogeneity assessment.

More Related Videos

Single Particle Cryo-Electron Microscopy: From Sample to Structure

Single Particle Cryo-Electron Microscopy: From Sample to Structure

Published on: May 29, 2021

Cryo-EM and Single-Particle Analysis with Scipion

Cryo-EM and Single-Particle Analysis with Scipion

Published on: May 29, 2021

Related Experiment Videos

Last Updated: Feb 25, 2026

Single-Particle Cryo-EM Data Collection with Stage Tilt using Leginon

Single-Particle Cryo-EM Data Collection with Stage Tilt using Leginon

Published on: July 1, 2022

Single Particle Cryo-Electron Microscopy: From Sample to Structure

Single Particle Cryo-Electron Microscopy: From Sample to Structure

Published on: May 29, 2021

Cryo-EM and Single-Particle Analysis with Scipion

Cryo-EM and Single-Particle Analysis with Scipion

Published on: May 29, 2021

Area of Science:

Structural biology
Biophysics
Computational biology

Background:

Single-particle cryo-electron microscopy (cryo-EM) faces challenges with structural heterogeneity.
Traditional unsupervised classification methods struggle with low signal-to-noise ratio (SNR) data and high computational costs.

Purpose of the Study:

To develop an advanced unsupervised clustering algorithm for cryo-EM data processing.
To improve the accuracy and efficiency of identifying structural heterogeneity in cryo-EM datasets.

Main Methods:

Introduced an unsupervised clustering algorithm based on generative topographic mapping (GTM), a statistical manifold learning framework.
Implemented and optimized the GTM algorithm within a high-performance computing (HPC) environment for massively parallel processing.

Main Results:

Achieved approximately 40% improvement in classification accuracy for low SNR data without input references.
Demonstrated the ability to detect subtle structural differences using a hierarchical clustering strategy.
Generated thousands of reference-free class averages within hours, enabling rapid ab initio 3D reconstruction.

Conclusions:

Generative topographic mapping (GTM) offers a robust solution for unsupervised classification in cryo-EM, particularly for heterogeneous datasets.
The developed HPC-optimized software significantly accelerates data processing and enhances the purification of homogeneous datasets for high-resolution structure determination.