Routine single particle CryoEM sample and grid characterization by tomography
- New York Structural Biology Center, United States
- City College of New York, United States
- Columbia University, United States
- National Institutes of Health, United States
Abstract
Single particle cryo-electron microscopy (cryoEM) is often performed under the assumption that particles are not adsorbed to the air-water interfaces and in thin, vitreous ice. In this study, we performed fiducial-less tomography on over 50 different cryoEM grid/sample preparations to determine the particle distribution within the ice and the overall geometry of the ice in grid holes. Surprisingly, by studying particles in holes in 3D from over 1,000 tomograms, we have determined that the vast majority of particles (approximately 90%) are adsorbed to an air-water interface. The implications of this observation are wide-ranging, with potential ramifications regarding protein denaturation, conformational change, and preferred orientation. We also show that fiducial-less cryo-electron tomography on single particle grids may be used to determine ice thickness, optimal single particle collection areas and strategies, particle heterogeneity, and de novo models for template picking and single particle alignment.
Data availability
Several representative tilt-series from the datasets have been deposited to the Electron Microscopy Data Bank (EMDB) in the form of binned by 4 or 8 tomograms and to the Electron Microscopy Pilot Image Archive (EMPIAR) in the form of unaligned tilt-series images (one including super-resolution frames), Appion-Protomo tilt-series alignment runs, and aligned tilt-series stacks.Protomo estimations for the orientation of the local ice normal based on the tilt-series alignment of the particles in the ice, which includes potential systematic stage and beam axis error, are available in all deposited EMPIAR datasets as a plot located: protomo_alignments/tiltseries####/media/angle_refinement/series####_orientation.gifA Docker-based version of Appion-Protomo fiducial-less tilt-series alignment is available at http://github.com/nysbc/appion-protomo.
Article and author information
Author details
Funding
Simons Foundation (SF349247)
- Clinton S Potter
- Bridget Carragher
National Institutes of Health (R01 GM084162)
- David Jeruzalmi
New York State Foundation for Science, Technology and Innovation
- Clinton S Potter
- Bridget Carragher
National Institute of General Medical Sciences (GM103310)
- Clinton S Potter
- Bridget Carragher
Agouron Institute (F00316)
- Clinton S Potter
- Bridget Carragher
National Institutes of Health (S10 OD019994-01)
- Clinton S Potter
- Bridget Carragher
National Institute on Minority Health and Health Disparities (5G12MD007603-30)
- David Jeruzalmi
National Institute of Allergy and Infectious Diseases (Intramural Funding from the Vaccine Research Center)
- Peter D Kwong
Agency for Science, Technology and Research
- Yong Zi Tan
National Institutes of Health (R01-MH1148175)
- Lawrence Shapiro
The authors declare that the funders played no role in this work, including the experimental design, data collection, or data analysis.
Copyright
This is an open-access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.
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Routine single particle CryoEM sample and grid characterization by tomography
eLife 7:e34257.
https://doi.org/10.7554/eLife.34257
Further reading
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- Structural Biology and Molecular Biophysics
Segmentation is a critical data processing step in many applications of cryo-electron tomography. Downstream analyses, such as subtomogram averaging, are often based on segmentation results, and are thus critically dependent on the availability of open-source software for accurate as well as high-throughput tomogram segmentation. There is a need for more user-friendly, flexible, and comprehensive segmentation software that offers an insightful overview of all steps involved in preparing automated segmentations. Here, we present Ais: a dedicated tomogram segmentation package that is geared towards both high performance and accessibility, available on GitHub. In this report, we demonstrate two common processing steps that can be greatly accelerated with Ais: particle picking for subtomogram averaging, and generating many-feature segmentations of cellular architecture based on in situ tomography data. Featuring comprehensive annotation, segmentation, and rendering functionality, as well as an open repository for trained models at aiscryoet.org, we hope that Ais will help accelerate research and dissemination of data involving cryoET.
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