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.
Highlights
In single-particle cryo-electron microscopy, the thin layer of vitreous ice embedding the protein or macromolecule complex of interest is a key parameter in sample preparation and optimization
With the functions available through Digital Micrograph, we developed a script that allows the user to calculate and monitor the sample thickness of a grid square with a holey-carbon support film at low magnification
Sample thickness is a key player in cryo-electron microscopy (cryo-EM) data quality, whereby ice that is too thick lowers the amount of high-resolution information retained and ice that is too thin
Summary
In single-particle cryo-electron microscopy (cryo-EM), the thin layer of vitreous ice embedding the protein or macromolecule complex of interest is a key parameter in sample preparation and optimization. The thickness of the vitreous ice layer has an impact on electron transparency and image formation and is a key criterion of image quality. An ice layer that is too thin might not be sufficient to fully embed the target protein or molecule of interest. This might lead to its denaturation at the air–water interface, or push it towards the edge of the support film hole, limiting the number of copies in the field of view (Noble et al, 2018; D’Imprima et al, 2019). The importance and urgency of the optimization of cryo-EM sample preparation is reflected by the development of new techniques and devices in recent years (Dandey et al, 2018; Rubinstein et al, 2019; Ravelli et al, 2020; Tan & Rubinstein, 2020; Arnold et al, 2017; Kontziampasis et al, 2019; Maeots et al, 2020)
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