Cryo-EM requires that protein samples be frozen before they are imaged with an electron microscope. This revolutionary method and its application to structural biology were the focus of the 2017 Nobel Prize in Chemistry and contributed to a wave of new structural information for proteins that were difficult or impossible to prepare for X-ray crystallography. As cryo-EM has become more popular, new techniques and tools have emerged to improve the technique and make it more effective. Many people are now working on how to prepare samples to make it easier to get high-resolution structural information from each experiment. Once the samples are prepared, there are a number of automated tools that have been developed to collect the frozen particles to be processed. It is during the study of these tools that the Doctor of Philosophy. student Mateusz Olek and his supervisor Pejun Zhang with the help of Yuri Chaban and Donovan Web discovered an unusual problem. Their findings were published in a journal Structure.
Blind zones of automation
Automated particle collectors are designed to analyze images and automatically select the best particles for experiments. After studying the performance of these tools, the team noticed that there were areas without elections in the pictures. These voids were difficult to explain, especially when the team visually inspected the images and clearly saw that particles were present. For some reason, automated particle collectors were blind to some areas of the image. This problem may have implications for cryo-EM experiments. If the automation software leaves viable particles, scientists will not be able to gather all the data needed for the experiment.
the team quickly noticed that the background images did not match. Some parts were darker and others lighter. This may be the cause of some malfunctions in the automation software, which relies on measuring the contrast of the protein particle in the background. For cryo-EM protein particles are suspended in a thin film of ice, so the team concluded that image background inconsistencies were related to different ice thicknesses. This has caused a number of problems for researchers using cryo-EM. First, one obvious solution would be to try to make the ice film more uniform. Unfortunately, despite the fact that great efforts are being made to improve the preparation of cryo-EM samples, it is still very difficult to create a uniform ice film. Often an ice thickness gradient can be seen in the cryo-EM sample.
Starting from scratch
Mateusz and his team started from scratch to develop a new method of dealing with the problem of ice. They started with segmenting different images and analyzing the background. This allowed their collector to identify the particles regardless of the background that came from the ice. This innovation meant that in this experiment, researchers could reliably gather more structural information by analyzing more protein particles. However, this was not the end of the road. Although collecting more particles is extremely valuable for researchers using cryo-EM, the amount of ice affects the quality of the particles, and hence the quality of the cryo-EM cards that can be recovered. Mateusz and the research team knew they could extend the software by instantly giving researchers information about the quality of the protein particles being filmed.
In a recent publication, the research team identified two main problems that can arise when ice does not have an optimal thickness. First, very thick ice creates more background noise in the image. This makes it difficult to obtain high-resolution data from protein particles. Conversely, if the ice film is too thin, it may not support the proteins properly. This problem is highly dependent on the specific proteins that the researcher is investigating. For example, some proteins are small and tightly bound in a spherical configuration. These proteins can be contained in relatively thin layers of ice. However, many proteins are large with long protruding branches that can fall out of the ice film if it is too thin. This means that thin ice makes it difficult to image these types of proteins, even when the background noise is very low. Optimal ice thickness is something that minimizes background, but is thick enough to fully support protein. Often the optimal ice thickness will depend on nature protein is being studied and will vary depending on the experiment.
Their new software tool, IceBreaker, could fully automate particle selection. However, the team has chosen an approach that gives more flexibility to researchers who use it. Instead of making decisions, the software comments on each particle, giving the researcher an indication of quality. This allows them to direct their experiments exactly as they need to achieve optimal results. Sometimes fewer high-quality particles and sometimes more poor-quality particles from thick ice are enough to show unique particle orientations that are not supported by thin ice. Using the new IceBreaker software, researchers have complete control over the data they collect. IcreBreaker is now being implemented as part of Diamond’s data collection pipeline Electronic Bioimaging Center (eBIC) and is freely available on GitHub.
Mateusz Olek and others, IceBreaker: Software for single-particle high-resolution cryo-EM with uneven ice, Structure (2022). DOI: 10.1016 / j.p.2022.01.005
Diamond light source
Citation: The solution to the problem of ice in cryoelectron microscopy (2022, February 14) was obtained on February 14, 2022 from https://phys.org/news/2022-02-ice-problem-cryo-electron-microscopy.html
This document is subject to copyright. Except for any honest transaction for the purpose of private study or research, no part may be reproduced without written permission. The content is provided for informational purposes only.
https://phys.org/news/2022-02-ice-problem-cryo-electron-microscopy.html Solving the ice problem in cryoelectron microscopy