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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: Aug 1, 2025

Cryo-Structured Illumination Microscopic Data Collection from Cryogenically Preserved Cells

Cryo-Structured Illumination Microscopic Data Collection from Cryogenically Preserved Cells

Published on: May 28, 2021

Measuring cryo-TEM sample thickness using reflected light microscopy and machine learning.

Mart G F Last¹, Lenard M Voortman¹, Thomas H Sharp¹

¹Department of Cell and Chemical Biology, Leiden University Medical Center, 2300 RC Leiden, the Netherlands.

Journal of Structural Biology

|April 26, 2023

Summary

This summary is machine-generated.

We developed a machine learning method using reflected light microscopy to quickly assess cryo-transmission electron microscopy (cryo-TEM) sample thickness. This approach improves throughput for cellular structural biology and correlative imaging workflows.

Keywords:

Cellular EM Correlative light and electron microscopy Cryo-TEM Image-to-image translation Neural network

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Last Updated: Aug 1, 2025

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Published on: July 26, 2014

Area of Science:

Biophysics
Microscopy
Machine Learning

Background:

Sample thickness is critical for cryo-transmission electron microscopy (cryo-TEM) image quality.
Correlating cryo-TEM with other imaging methods necessitates precise sample thickness control.
Current methods for thickness assessment can be time-consuming, limiting throughput.

Purpose of the Study:

To present a novel, rapid method for assessing cryo-TEM sample thickness.
To enable pre-imaging thickness evaluation using reflected light microscopy and machine learning.
To enhance throughput for structural biology and correlative imaging workflows.

Main Methods:

Utilized thin-film interference patterns from reflected narrow-band LED light.
Trained a neural network to correlate reflection images with sample thickness.
Applied the method to mammalian cells grown on TEM grids.

Main Results:

Accurately predicted cryo-TEM sample thickness using light microscopy.
Demonstrated high similarity between predicted and measured sample thicknesses.
Developed and shared open-source software for thickness prediction.

Conclusions:

The developed method offers a fast and accurate alternative to cryo-TEM screening for sample thickness assessment.
This approach can be integrated into correlative imaging to identify optimal sites for high-resolution cryo-TEM.
The method has the potential to significantly improve the efficiency of in situ cellular structural biology studies.