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

EasyGrid: a versatile platform for automated cryo-EM sample preparation and quality control.

Nature methods·2026

Same author

Deciphering Membrane Protein Complexes in Plasmodium falciparum Gametocytes via Integrative Structural Systems Biology.

Molecular & cellular proteomics : MCP·2026

Same author

TRIM2 E3 ligase substrate discovery reveals zinc-mediated regulation of TMEM106B in the endolysosomal pathway.

EMBO reports·2026

Same author

Cryo-EM Structure of FcεRI Bound IgE Reveals Multiple Defined Conformations of the Fab-Fc Hinge.

Allergy·2025

Same author

Design of Orthogonal Far-Red, Orange and Green Fluorophore-binding Proteins for Multiplex Imaging.

bioRxiv : the preprint server for biology·2025

Same author

Effects of base temperature, immersion medium, and EM grid material on devitrification thresholds in cryogenic optical super-resolution microscopy.

Journal of structural biology·2025

Same journal

Ultrastructural evidence of autophagy-related processes and mitochondrial remodeling in the myxozoan parasite Henneguya piaractus.

Journal of structural biology·2026

Same journal

Architecture and dynamics of a supramolecular oxygen transport system in human homogentisate 1,2-Dioxygenase.

Journal of structural biology·2026

Same journal

Connecting pathways between mineralized fibrocartilage and bone at the Achilles tendon insertion.

Journal of structural biology·2026

Same journal

Structural and functional characterization of thermostable EstS1 esterase for BHET degradation.

Journal of structural biology·2026

Same journal

Following the white rabbit: multiscale 2D3D correlative imaging of bone structure.

Journal of structural biology·2026

Same journal

The mantis shrimp eye imaged in 3D using 4th generation synchrotron multiscale phase contrast tomography.

Journal of structural biology·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 28, 2026

A 3D Cartographic Description of the Cell by Cryo Soft X-ray Tomography

A 3D Cartographic Description of the Cell by Cryo Soft X-ray Tomography

Published on: March 15, 2021

ColabSeg: An interactive tool for editing, processing, and visualizing membrane segmentations from cryo-ET data.

Marc Siggel¹, Rasmus K Jensen², Valentin J Maurer¹

¹European Molecular Biology Laboratory (EMBL) Hamburg, Notkestrasse 85, Hamburg 20607, Germany; Centre of Structural Systems Biology (CSSB), Notkestrasse 85, Hamburg 20607, Germany.

Journal of Structural Biology

|February 17, 2024

Summary

This summary is machine-generated.

ColabSeg simplifies lipid membrane segmentation from cellular cryo-electron tomography (cryo-ET) data. This tool aids structural biologists in analyzing complex cellular structures and generating training data for automated segmentation methods.

Keywords:

Cryo-electron tomography (Cryo-ET)Electron microscopy Image analysis Lipid membranes Segmentation

More Related Videos

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

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

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

Published on: January 24, 2025

Related Experiment Videos

Last Updated: Jun 28, 2026

A 3D Cartographic Description of the Cell by Cryo Soft X-ray Tomography

A 3D Cartographic Description of the Cell by Cryo Soft X-ray Tomography

Published on: March 15, 2021

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

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

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

Published on: January 24, 2025

Area of Science:

Structural Biology
Cell Biology
Biophysics

Background:

Cellular cryo-electron tomography (cryo-ET) provides high-resolution 3D views of cellular structures.
Accurate segmentation of lipid membranes from noisy cryo-ET data is crucial for quantitative analysis but often requires extensive manual effort.
Existing methods lack user-friendly tools for processing and refining membrane segmentations.

Purpose of the Study:

To introduce ColabSeg, a novel Python-based software tool for processing, visualizing, editing, and fitting membrane segmentations from cryo-ET data.
To provide structural biologists with accessible point-cloud processing algorithms via a graphical user interface (GUI) for cryo-ET applications.
To facilitate high-throughput membrane segmentation for downstream analysis and the development of automated segmentation techniques.

Main Methods:

Development of ColabSeg, a Python-based software with a GUI.
Integration of established point-cloud processing algorithms.
Application to a large dataset of 50 Mycoplasma pneumoniae tomograms.

Main Results:

ColabSeg enables efficient processing, visualization, editing, and fitting of membrane segmentations from cryo-ET data.
The tool demonstrated utility across various use cases and biological examples.
High-throughput membrane segmentation was achieved on a large dataset, generating valuable training data for convolutional neural network (CNN)-based segmentation.

Conclusions:

ColabSeg significantly reduces user intervention in cryo-ET membrane segmentation.
The tool democratizes access to advanced point-cloud processing for structural biologists.
ColabSeg supports both manual curation and the development of automated segmentation strategies for cellular imaging.