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

2.7K
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...
2.7K
Cryo-electron Microscopy01:28

Cryo-electron Microscopy

3.9K
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...
3.9K

You might also read

Related Articles

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

Sort by
Same author

A knowledge-guided deep learning framework for quantitative nucleic acid testing.

Briefings in bioinformatics·2026
Same author

DyMamba: dynamic Mamba for microscopy image semantic segmentation.

Bioinformatics (Oxford, England)·2026
Same author

Earthworm-Inspired Self-Powered Multistimuli Neuromorphic Vision Skin with Homogeneous Ion Heterogel Arrays.

ACS applied materials & interfaces·2026
Same author

A variational framework with composite sparse regularization for cryo-electron tomography reconstruction.

Bioinformatics (Oxford, England)·2026
Same author

Corrective osteotomy for distal radius malunion using 3D-printed patient-specific guides and spacers: a retrospective comparative study.

BMC musculoskeletal disorders·2026
Same author

MSFSNet: Multi-Source Few-Shot Adaptation Network for Cross-Subject Depression Recognition from EEG Signals.

IEEE journal of biomedical and health informatics·2026

Related Experiment Video

Updated: Nov 12, 2025

Do's and Don'ts of Cryo-electron Microscopy: A Primer on Sample Preparation and High Quality Data Collection for Macromolecular 3D Reconstruction
09:25

Do's and Don'ts of Cryo-electron Microscopy: A Primer on Sample Preparation and High Quality Data Collection for Macromolecular 3D Reconstruction

Published on: January 9, 2015

46.6K

Macromolecules Structural Classification With a 3D Dilated Dense Network in Cryo-Electron Tomography.

Shan Gao, Renmin Han, Xiangrui Zeng

    IEEE/ACM Transactions on Computational Biology and Bioinformatics
    |March 17, 2021
    PubMed
    Summary
    This summary is machine-generated.

    A new deep learning method, 3D-Dilated-DenseNet, accurately classifies macromolecules from cryo-electron tomography data. This approach improves 3D structural analysis by overcoming noise and artifacts, enhancing biological insights.

    More Related Videos

    Single Particle Cryo-Electron Microscopy: From Sample to Structure
    11:52

    Single Particle Cryo-Electron Microscopy: From Sample to Structure

    Published on: May 29, 2021

    9.2K
    Cryo-Electron Tomography Remote Data Collection and Subtomogram Averaging
    08:55

    Cryo-Electron Tomography Remote Data Collection and Subtomogram Averaging

    Published on: July 12, 2022

    5.4K

    Related Experiment Videos

    Last Updated: Nov 12, 2025

    Do's and Don'ts of Cryo-electron Microscopy: A Primer on Sample Preparation and High Quality Data Collection for Macromolecular 3D Reconstruction
    09:25

    Do's and Don'ts of Cryo-electron Microscopy: A Primer on Sample Preparation and High Quality Data Collection for Macromolecular 3D Reconstruction

    Published on: January 9, 2015

    46.6K
    Single Particle Cryo-Electron Microscopy: From Sample to Structure
    11:52

    Single Particle Cryo-Electron Microscopy: From Sample to Structure

    Published on: May 29, 2021

    9.2K
    Cryo-Electron Tomography Remote Data Collection and Subtomogram Averaging
    08:55

    Cryo-Electron Tomography Remote Data Collection and Subtomogram Averaging

    Published on: July 12, 2022

    5.4K

    Area of Science:

    • Structural biology
    • Biophysics
    • Computational biology

    Background:

    • Cryo-electron tomography (Cryo-ET) combined with subtomogram averaging (STA) visualizes 3D macromolecule structures in near-native states.
    • Accurate classification of diverse macromolecules within cellular tomograms is crucial for high-resolution 3D structural analysis.
    • Poor signal-to-noise ratio and ray artifacts in tomograms present significant challenges for precise macromolecule classification.

    Purpose of the Study:

    • To develop a novel convolutional neural network, 3D-Dilated-DenseNet, for enhanced macromolecule classification in Cryo-ET.
    • To improve the accuracy of macromolecule classification by leveraging dense connections and dilated convolutions.
    • To evaluate the performance of 3D-Dilated-DenseNet against existing methods using both synthetic and experimental data.

    Main Methods:

    • Implementation of a novel 3D convolutional neural network, 3D-Dilated-DenseNet.
    • Utilizing dense connections within the network to maximize feature map utilization.
    • Incorporating dilated convolutions to capture multi-level feature information.
    • Comparative analysis with baseline 3D-C-DenseNet and state-of-the-art SHREC-CNN on synthetic and experimental datasets.

    Main Results:

    • 3D-Dilated-DenseNet and 3D-C-DenseNet outperformed the SHREC-CNN method on synthetic data.
    • 3D-Dilated-DenseNet demonstrated significant improvements in F1 metric for tiny (0.393) and small (0.213) macromolecules on synthetic data.
    • On experimental data, 3D-Dilated-DenseNet achieved a 2.1% increase in classification performance compared to 3D-C-DenseNet.

    Conclusions:

    • 3D-Dilated-DenseNet effectively enhances macromolecule classification accuracy in Cryo-ET.
    • The proposed network architecture addresses challenges posed by low SNR and artifacts in tomographic data.
    • This advancement holds promise for more precise structural determination of macromolecules in cellular contexts.