Suppr超能文献

通过确定样品厚度来优化冷冻电镜数据采集工作流程。

Optimized cryo-EM data-acquisition workflow by sample-thickness determination.

机构信息

Department of Structural Biology and Membrane Enzymology at the Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, Groningen, The Netherlands.

Nexperion e.U. - Solutions for Electron Microscopy, Vienna, Austria.

出版信息

Acta Crystallogr D Struct Biol. 2021 May 1;77(Pt 5):565-571. doi: 10.1107/S205979832100334X. Epub 2021 Apr 27.

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.

摘要

样本厚度是冷冻电子显微镜(cryo-EM)中的一个已知关键参数,会影响图像中保留的高分辨率信息量。然而,单颗粒冷冻电子显微镜中的常见数据采集方法并未考虑到这一点。本研究展示了如何在数据采集之前确定样本厚度,从而可以识别最佳区域,并将自动化数据采集限制在保留高分辨率细节的图像上。这种注重质量而非数量的方法几乎可以完全消除耗时且浪费的次优图像的采集,这些图像会在记录会话后或早期图像处理过程中由于缺乏高分辨率信息而被丢弃。它最大限度地提高了数据采集效率,并降低了每个数据集所需的电子显微镜时间。如果数据采集速度受到显微镜硬件和软件的限制,或者显微镜访问时间、数据传输、数据存储和计算能力是瓶颈,那么这种策略尤其有用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a566/8098475/129c4f6fc463/d-77-00565-fig1.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验