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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...

Electron Microscope Tomography and Single-particle Reconstruction

Electron Microscope Tomography and Single-particle Reconstruction

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...

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Related Experiment Video

Updated: Mar 29, 2026

Preparation and Cryo-FIB micromachining of Saccharomyces cerevisiae for Cryo-Electron Tomography

Preparation and Cryo-FIB micromachining of Saccharomyces cerevisiae for Cryo-Electron Tomography

Published on: November 20, 2021

ICECREAM: high-fidelity equivariant cryo-electron tomography.

Vinith Kishore¹, Valentin Debarnot², Ricardo D Righetto³

¹Department of Mathematics and Computer Science, University of Basel, 4051 Basel, Switzerland.

Acta Crystallographica. Section D, Structural Biology

|March 27, 2026

Summary

This summary is machine-generated.

ICECREAM enhances cryo-electron tomography (cryo-ET) by improving 3D cellular structure visualization. This deep learning method offers superior denoising and fills missing data, overcoming key challenges in cryo-ET analysis.

Keywords:

cryogenic electron tomography deep learning machine learning self-supervised learning tomography

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

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

Area of Science:

Structural biology
Microscopy
Computational imaging

Background:

Cryo-electron tomography (cryo-ET) is crucial for visualizing 3D cellular structures.
Current deep learning methods struggle with low signal-to-noise ratios and the 'missing wedge' artifact in cryo-ET.
Lack of ground truth data hinders algorithm development.

Purpose of the Study:

To introduce ICECREAM, a novel deep learning framework for cryo-ET.
To improve denoising and missing wedge correction in cryo-ET data.
To provide a robust method applicable to various tomography problems.

Main Methods:

ICECREAM integrates self-supervised learning with an equivariant imaging framework.
The method utilizes two statistically independent views of the volume, obtainable via dose splitting or angular partitioning.
It is designed to be broadly applicable to tomography datasets.

Main Results:

ICECREAM demonstrates superior denoising performance compared to existing methods.
It achieves more reliable correction of the 'missing wedge' artifact.
The framework shows effectiveness across diverse experimental cryo-ET datasets.

Conclusions:

ICECREAM significantly advances cryo-electron tomography data processing.
The method offers improved accuracy and reliability in reconstructing 3D cellular architecture.
ICECREAM provides a valuable tool for structural biology research.