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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...

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Updated: Dec 24, 2025

Cryo-EM and Single-Particle Analysis with Scipion

Cryo-EM and Single-Particle Analysis with Scipion

Published on: May 29, 2021

MicrographCleaner: A python package for cryo-EM micrograph cleaning using deep learning.

Ruben Sanchez-Garcia¹, Joan Segura², David Maluenda¹

¹National Center of Biotechnology (CSIC)/Instruct Image Processing Center, C/ Darwin n° 3, Campus of Cantoblanco, 28049 Madrid, Spain.

Journal of Structural Biology

|April 11, 2020

Summary

This summary is machine-generated.

MicrographCleaner is a new deep learning tool that automatically identifies usable areas in cryo-electron microscopy images. This preprocessing enhances particle picking for more accurate macromolecular structure determination.

Keywords:

Carbon Cleaning Contaminants Cryo-EM Deep learning Micrographs

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Area of Science:

Structural biology
Biophysics
Computational biology

Background:

Cryo-electron microscopy single particle analysis (Cryo-EM SPA) requires numerous high-quality particle projections for macromolecular structure determination.
Conventional particle picking methods often yield high false-positive rates due to contaminants like carbon, hindering 3D reconstruction.
Effective preprocessing is crucial to improve the efficiency and accuracy of Cryo-EM SPA workflows.

Purpose of the Study:

To develop an automated deep learning tool, MicrographCleaner, for preprocessing cryo-EM micrographs.
To enhance the discrimination between particle-containing regions and unwanted contaminants in micrographs.
To improve the overall efficiency of particle picking in Cryo-EM SPA.

Main Methods:

Development of MicrographCleaner, a deep learning package utilizing a U-net-like architecture.
Training the model on a manually curated dataset of over 500 micrographs.
Benchmarking the performance on approximately 100 independent micrographs.

Main Results:

MicrographCleaner effectively discriminates between suitable and unsuitable regions for particle picking in cryo-EM micrographs.
The deep learning approach significantly reduces false positives caused by carbon and other contaminants.
Benchmarking demonstrates MicrographCleaner as an efficient method for micrograph preprocessing.

Conclusions:

MicrographCleaner offers an automated and efficient solution for cryo-EM micrograph preprocessing.
The tool improves the quality of data input for particle picking, thereby facilitating more accurate macromolecular structure determination.
MicrographCleaner is accessible as a package and a Scipion/Xmipp protocol, promoting its adoption in the research community.