Optimized cryo‐EM data‐acquisition workflow by sample‐thickness determination. Issue 5 (5th May 2021)

• Record Type: Journal Article
• Authors: Rheinberger, Jan; Oostergetel, Gert; Resch, Guenter P.; Paulino, Cristina
• Is Part Of: Acta crystallographica. Volume 77:Issue 5(2021)
• Page Start: 565
• Page End: 571
• Publication Date: 2021-05-05
• Subjects: single‐particle cryo‐electron microscopy -- sample thickness -- automation -- Digital Micrograph -- SerialEM; X-ray crystallography -- Periodicals; Crystallography -- Periodicals; Molecular biology -- Periodicals; Molecular structure -- Periodicals; Biomolecules -- Structure -- Periodicals; Cytology -- Periodicals; Biomolecules -- Structure; Crystallography; Cytology; Molecular biology; Molecular structure; X-ray crystallography; Periodicals; 548
• DOI: 10.1107/S205979832100334X
• Languages: English
• ISSNs: 2059-7983

Abstract

Abstract : Sample thickness is a key parameter in single‐particle cryo‐electron microscopy. Determining the sample thickness before data acquisition allows the targeting of optimal areas and the maximization of the data‐output quality of single‐particle cryo‐electron microscopy sessions. Scripts and optimized workflows for EPU and SerialEM are presented and are available as open source. Abstract : Sample thickness is a known key parameter in cryo‐electron microscopy (cryo‐EM) and can affect the amount of high‐resolution information retained in the image. Yet, common data‐acquisition approaches in single‐particle cryo‐EM do not take it into account. Here, it is demonstrated how the sample thickness can be determined before data acquisition, allowing the identification of optimal regions and the restriction of automated data collection to images with preserved high‐resolution details. This quality‐over‐quantity approach almost entirely eliminates the time‐ and storage‐consuming collection of suboptimal images, which are discarded after a recorded session or during early image processing due to a lack of high‐resolution information. It maximizes the data‐collection efficiency and lowers the electron‐microscopy time required per data set. This strategy is especially useful if the speed of data collection is restricted by the microscope hardware and software, or if microscope access time, data transfer, data storage and computational power are a bottleneck.