Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Electron Microscope Tomography and Single-particle Reconstruction01:07

Electron Microscope Tomography and Single-particle Reconstruction

2.4K
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...
2.4K
Cryo-electron Microscopy01:28

Cryo-electron Microscopy

3.4K
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...
3.4K

You might also read

Related Articles

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

Sort by
Same author

Heterogeneous reconstruction algorithms for cryoEM achieve limited particle classification accuracy on real benchmark datasets.

bioRxiv : the preprint server for biology·2026
Same author

Engineered Cas9 complexes establish an experimentally grounded benchmark for heterogeneous cryoEM reconstruction methods.

bioRxiv : the preprint server for biology·2026
Same author

A conserved archaeal ribosome-associated factor linking bacterial hibernation and eukaryotic energy sensing.

bioRxiv : the preprint server for biology·2026
Same author

Capturing ribosomal structures in cellular extracts with cryoPRISM: A purification-free cryoEM approach reveals novel structural states.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same author

Ribosome-associated quality control of aberrant protein production during amino acid limitation.

bioRxiv : the preprint server for biology·2026
Same author

NCOA4 initiates ferritinophagy by avidly binding GATE16 using two short linear interaction motifs.

Life science alliance·2026

Related Experiment Video

Updated: Jul 24, 2025

Averaging of Viral Envelope Glycoprotein Spikes from Electron Cryotomography Reconstructions using Jsubtomo
08:29

Averaging of Viral Envelope Glycoprotein Spikes from Electron Cryotomography Reconstructions using Jsubtomo

Published on: October 21, 2014

12.3K

Learning structural heterogeneity from cryo-electron sub-tomograms with tomoDRGN.

Barrett M Powell1, Joseph H Davis1,2

  • 1Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02139.

Biorxiv : the Preprint Server for Biology
|July 3, 2023
PubMed
Summary
This summary is machine-generated.

TomoDRGN analyzes structural heterogeneity in cryo-electron tomography (cryo-ET) data. This deep learning tool reconstructs diverse macromolecular structures, revealing complex conformational changes in situ.

More Related Videos

Determination of Molecular Structures of HIV Envelope Glycoproteins using Cryo-Electron Tomography and Automated Sub-tomogram Averaging
07:29

Determination of Molecular Structures of HIV Envelope Glycoproteins using Cryo-Electron Tomography and Automated Sub-tomogram Averaging

Published on: December 1, 2011

41.6K
Leveraging Virtual Reality for Immersive Segmentation and Analysis of Cryo-Electron Tomography Data
07:17

Leveraging Virtual Reality for Immersive Segmentation and Analysis of Cryo-Electron Tomography Data

Published on: January 24, 2025

969

Related Experiment Videos

Last Updated: Jul 24, 2025

Averaging of Viral Envelope Glycoprotein Spikes from Electron Cryotomography Reconstructions using Jsubtomo
08:29

Averaging of Viral Envelope Glycoprotein Spikes from Electron Cryotomography Reconstructions using Jsubtomo

Published on: October 21, 2014

12.3K
Determination of Molecular Structures of HIV Envelope Glycoproteins using Cryo-Electron Tomography and Automated Sub-tomogram Averaging
07:29

Determination of Molecular Structures of HIV Envelope Glycoproteins using Cryo-Electron Tomography and Automated Sub-tomogram Averaging

Published on: December 1, 2011

41.6K
Leveraging Virtual Reality for Immersive Segmentation and Analysis of Cryo-Electron Tomography Data
07:17

Leveraging Virtual Reality for Immersive Segmentation and Analysis of Cryo-Electron Tomography Data

Published on: January 24, 2025

969

Area of Science:

  • Structural Biology
  • Biophysics
  • Computational Biology

Background:

  • Cryo-electron tomography (cryo-ET) enables visualization of macromolecular complexes in their native environments.
  • Current analysis tools assume structural homogeneity, limiting the study of dynamic or heterogeneous complexes.
  • Existing methods struggle to represent macromolecules undergoing continuous conformational changes.

Approach:

  • Extended the cryoDRGN deep learning architecture for sub-tomogram analysis in cryo-ET.
  • Developed tomoDRGN to learn continuous low-dimensional representations of structural heterogeneity.
  • Enabled reconstruction of heterogeneous macromolecular ensembles from cryo-ET data.

Key Points:

  • TomoDRGN addresses limitations in analyzing structural diversity within cryo-ET datasets.
  • The tool learns representations of heterogeneity and reconstructs multiple structures from the same data.
  • Benchmarked architectural choices specific to cryo-ET data requirements.

Conclusions:

  • TomoDRGN effectively reveals extensive structural heterogeneity in macromolecular complexes.
  • Demonstrated utility in analyzing ribosomes imaged in situ, capturing conformational variability.
  • Provides a powerful new method for studying dynamic biological systems at high resolution.