Cryogenic electron microscopy

Summary

Cryogenic electron microscopy (cryo-EM) is a cryomicroscopy technique applied on samples cooled to cryogenic temperatures. For biological specimens, the structure is preserved by embedding in an environment of vitreous ice. An aqueous sample solution is applied to a grid-mesh and plunge-frozen in liquid ethane or a mixture of liquid ethane and propane. While development of the technique began in the 1970s, recent advances in detector technology and software algorithms have allowed for the determination of biomolecular structures at near-atomic resolution. This has attracted wide attention to the approach as an alternative to X-ray crystallography or NMR spectroscopy for macromolecular structure determination without the need for crystallization. In 2017, the Nobel Prize in Chemistry was awarded to Jacques Dubochet, Joachim Frank, and Richard Henderson "for developing cryo-electron microscopy for the high-resolution structure determination of biomolecules in solution."

Official source

https://en.wikipedia.org/wiki/Cryogenic_electron_microscopy

About this result

This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.

Related publications (100)

Related publications

Related people (13)

Related people

Related units (6)

Related units

Related concepts (21)

Related concepts

Related courses (15)

Related courses

Related lectures (18)

Related lectures

Related publications (100)

3D Poses Recovery in Single-Particle Cryo-EM from Learned Pairwise Projection Distances

Jelena Banjac

Single-particle cryo-electron microscopy (cryo-EM) is a technology that allows the observation and the high-resolution 3D structure determination of biomolecules. In this project, the goal is to estimate the angles at which we imaged the 2D projections from a given 3D protein. We developed deep learning models to estimate the angles from learned pairwise projection distances. We designed a two-step method: 1) distance estimation using a Siamese neural network to learn the distance between pairs of projections, and 2) angle recovery that includes a minimization scheme in order to estimate the angles at which each projection was taken. The current results obtained are discussed depending on the different combinations of approaches used and experimental conditions.

2020

Differential Visual Proteomics: Enabling the Proteome-Wide Comparison of Protein Structures of Single-Cells

Henning Paul-Julius Stahlberg, Anastasia Syntychaki

Proteins are involved in all tasks of life, and their characterization is essential to understand the underlying mechanisms of biological processes. We present a method called "differential visual proteomics" geared to study proteome-wide structural changes of proteins and protein-complexes between a disturbed and an undisturbed cell or between two cell populations. To implement this method, the cells are lysed and the lysate is prepared in a lossless manner for single-particle electron microscopy (EM). The samples are subsequently imaged in the EM. Individual particles are computationally extracted from the images and pooled together, while keeping track of which particle originated from which specimen. The extracted particles are then aligned and classified. A final quantitative analysis of the particle classes found identifies the particle structures that differ between positive and negative control samples. The algorithm and a graphical user interface developed to perform the analysis and to visualize the results were tested with simulated and experimental data. The results are presented, and the potential and limitations of the current implementation are discussed. We envisage the method as a tool for the untargeted profiling of the structural changes in the proteome of single-cells as a response to a disturbing force.

American Chemical Society (ACS)2019

We present a sample preparation method for cryo-electron microscopy (cryo-EM) that requires only 320 nL of sample to prepare a cryo-EM grid, depending on the protocol used. The sample is applied and spread on the grid by a microcapillary. The procedure does not involve any blotting steps, and realtime monitoring allows the water film thickness to be assessed and decreased to an optimum value prior to vitrification. We demonstrate that the method is suitable for high-resolution cryo-EM and will enable alternative electron microscopy approaches, such as single-cell visual proteomics. (C) 2016 The Author(s). Published by Elsevier Inc.

Elsevier BV2017