Routine Collection of High-Resolution cryo-EM Datasets Using 200 KV Transmission Electron Microscope

自动化 计算机科学 吞吐量 低温电子显微 数据收集 显微镜 数据质量 滤波器(信号处理) 图像质量 分辨率(逻辑) 显微镜 数据挖掘 人工智能 计算机视觉 光学 物理 图像(数学) 电信 工程类 数学 公制(单位) 无线 统计 机械工程 核磁共振 运营管理
作者
Adrian Koh,Sagar Khavnekar,Wen Ye,Dimple Karia,Dennis Cats,Rob van der Ploeg,Fanis Grollios,Oliver Raschdorf,Abhay Kotecha,Daniel Němeček
出处
期刊:Journal of Visualized Experiments [MyJoVE Corporation]
卷期号: (181) 被引量:4
标识
DOI:10.3791/63519
摘要

Cryo-electron microscopy (cryo-EM) has been established as a routine method for protein structure determination during the past decade, taking an ever-increasing share of published structural data. Recent advances in TEM technology and automation have boosted both the speed of data collection and quality of acquired images while simultaneously decreasing the required level of expertise for obtaining cryo-EM maps at sub-3 Å resolutions. While most of such high-resolution structures have been obtained using state-of-the-art 300 kV cryo-TEM systems, high-resolution structures can be also obtained with 200 kV cryo-TEM systems, especially when equipped with an energy filter. Additionally, automation of microscope alignments and data collection with real-time image quality assessment reduces system complexity and assures optimal microscope settings, resulting in increased yield of high-quality images and overall throughput of data collection. This protocol demonstrates the implementation of recent technological advances and automation features on a 200 kV cryo-transmission electron microscope and shows how to collect data for the reconstruction of 3D maps that are sufficient for de novo atomic model building. We focus on best practices, critical variables, and common issues that must be considered to enable the routine collection of such high-resolution cryo-EM datasets. Particularly the following essential topics are reviewed in detail: i) automation of microscope alignments, ii) selection of suitable areas for data acquisition, iii) optimal optical parameters for high-quality, high-throughput data collection, iv) energy filter tuning for zero-loss imaging, and v) data management and quality assessment. Application of the best practices and improvement of achievable resolution using an energy filter will be demonstrated on the example of apo-ferritin that was reconstructed to 1.6 Å, and Thermoplasma acidophilum 20S proteasome reconstructed to 2.1-Å resolution using a 200 kV TEM equipped with an energy filter and a direct electron detector.

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