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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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Cryo-Electron Tomography Remote Data Collection and Subtomogram Averaging

Cryo-Electron Tomography Remote Data Collection and Subtomogram Averaging

Published on: July 12, 2022

MRCZ - A file format for cryo-TEM data with fast compression.

Robert A McLeod¹, Ricardo Diogo Righetto¹, Andy Stewart²

¹Center for Cellular Imaging and NanoAnalytics (C-CINA), University of Basel, Basel, Switzerland.

Journal of Structural Biology

|November 28, 2017

Summary

This summary is machine-generated.

Fast CMOS detectors in transmission electron microscopy generate big data. This study demonstrates a 10x improvement in data storage and transfer times using integer decimation and lossless compression for direct-detection device data.

Keywords:

Data management File archiving Lossless compression

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Area of Science:

Scientific instrumentation
Data science
Materials science

Background:

Fast CMOS detectors are transforming transmission electron microscopy (TEM) into a big data field.
Automated data pipelines in TEM increase data transfer and archiving demands.

Purpose of the Study:

To demonstrate a method for improving data storage and read/write times for TEM data.
To address the challenges posed by big data in modern electron microscopy.

Main Methods:

Implementing a combination of integer decimation and lossless compression.
Testing the method on data from a Gatan K2 direct-detection device at a dose rate of 1 e-/pix/frame.

Main Results:

Achieved a 10x improvement in storage and read/write times.
Successfully reduced data handling requirements for high-throughput TEM.

Conclusions:

The combined technique of integer decimation and lossless compression offers a practical solution for managing big data in TEM.
This approach enhances the efficiency of data transfer and archiving in electron microscopy workflows.