configuring and utilizing cryosieve for enhanced particle sieving in cryo-electron microscopy

Enhancing Cryo-EM Map Quality with CryoSieve Particle Selection

Cryogenic electron microscopy (cryo-EM) single-particle analysis (SPA) has become a dominant method to determine high-resolution three-dimensional density maps of biological macromolecules. Due to a series of technological innovations1,2,3,4,5,6, named resolution revolution7, cryo-EM has the capability to determine the structures of biological macromolecules with up to atomic resolution at an unprecedented rate. This breakthrough marks the beginning of a new era in molecular insights, overtaking X-ray crystallography as the predominant technique and answering longstanding biological questions.
Cryo-EM SPA diverges from X-ray crystallography by not requiring the crystallization of biological macromolecules. Instead, a solution containing the target biological macromolecules is rapidly frozen in vitreous ice. It is then imaged with an electron beam to produce a series of micrographs, bypassing the need for crystallization8. Subsequently, particle-picking algorithms are utilized to extract individual raw particles from these micrographs4,9,10,11,12. As cryo-EM does not depend on crystallization, it is natural that extracted particles are predominantly damaged or in undesired conformational states, necessitating multiple rounds of particle selection to achieve a high-resolution density map. In cryo-EM SPA image processing, particle selection is therefore crucial for obtaining high-resolution density maps13.
In cryo-EM SPA, standard particle selection methods include two-dimensional (2D) and three-dimensional (3D) classification14. 2D classification categorizes particles into a predefined number of groups, yielding an average image and an estimated 2D resolution for each class. Researchers can then visually inspect these classes, removing particles from lower resolution groups to use the remaining ones in reconstructions aimed at achieving higher resolution. Once particle poses are established using refinement algorithms, researchers will proceed with 3D classification, clustering particles into multiple classes. This enables visual inspection of the reconstructed density map for each class, allowing for the exclusion of undesirable particles, such as those from undesired conformations. Following multiple rounds of classification, a final stack comprising relatively high-quality particles is obtained. These final stacks are instrumental in producing atomic or near-atomic resolution density maps.
Zhu and her colleagues have demonstrated that further particle selection can be conducted on these final stacks15. CryoSieve15, an innovative iterative method for particle selection, can be applied to enhance the quality of the final density map by significantly reducing the number of particles. While other particle sorting criteria and software, such as the normalized cross-correlation (NCC) method16, the angular graph consistency (AGC) approach17, and non-alignment classification5, are currently in use within the field, this method has been shown to outperform these algorithms in terms of effectiveness.
In this study, we present a detailed guide to the entire process. As a case study, we applied this new method to the dataset of the influenza hemagglutinin trimer (EMPIAR entry: 10097)18, which includes 130,000 particles in its final stack. Our procedure successfully discarded about 73.8% of the particles from the final stack of this dataset, improving the resolution of the reconstructed density map from 4.11 &#197; to 3.62 &#197;. In addition to the influenza hemagglutinin trimer, results from multiple datasets are presented in earlier publication15, showcasing a variety of resolutions and molecular weights of biomolecules.

Author Spotlight: Optimizing Cryo-EM Analysis with CryoSieve for Enhanced Particle Selection Efficiency

Enhancing Density Maps by Removing the Majority of Particles in Single Particle Cryogenic Electron Microscopy Final Stacks

Cryogenic electron microscopy is essential for determining near-atomic structures of biological macromolecules. Despite its reliance on averaging numerous low-signal-to-noise images, the optimum number of particles needed for specific resolution remains unknown. This limitation hinders progress in sample analysis and preparation methods.To tackle this, we introduce an iterative sorting method, CryoSieve. Standard protocol selection includes two-dimensional and three-dimensional classification, other protocol sorting criteria such as the normalized cross-correlation method, the angular graph consistency approach, and the non-alignment classification are currently in use. Extensive experiments demonstrate that CryoSieve outperforms other cryo-EM particle sorting algorithms, revealing that most particles are unnecessary in final stacks.The minority of particles remaining in the final stacks yields very high-resolution amplitude in reconstructive density maps. For some data sets, the size of the finest subset approaches the theoretical limit. In cryo-EM, sample preparation hinders workflow.Due to the lack of standard metrics for protocol comparison, the ratio of selected to collected particles could serve as a quality metric. Examining their spatial and temporal distribution may also highlight key physical factors in preparation effectiveness.

Configuring and Utilizing CryoSieve for Enhanced Particle Sieving in Cryo-Electron Microscopy

configuring-utilizing-cryosieve-for-enhanced-particle-sieving-cryo

Research

JoVE Journal

Biology

203 次观看.  Shenzhen Medical Academy of Research and Translation. 要配置 GPU 加速环境，请打开终端并输入命令 nvidia-smi。验证该命令是否成功显示有关 GPU 卡的所有信息，以及 CUDA 版本是否高于 10.2。然后执行命令 conda V 以确认 conda 的安装。输入以下命令。设置虚拟环境并等待几分钟，直到成功配置环境。执行命令 conda activate CRYOSIEVE_ENV。激活新创建的虚拟环境。要安装 CryoSieve，请运行 pip install cryosieve 命令。安装后，输入命令 ...

视频: 配置和利用 CryoSieve 在冷冻电子显微镜中增强颗粒筛分

Over the past decade, advancements in technology and methodology within the field of cryogenic electron microscopy (cryo-EM) single-particle analysis (SPA) have substantially improved our capacity for high-resolution structural examination of biological macromolecules. This advancement has ushered in a new era of molecular insights, replacing X-ray crystallography as the dominant method and providing answers to longstanding questions in biology. Since cryo-EM does not depend on crystallization, which is a significant limitation of X-ray crystallography, it captures particles of varying quality. Consequently, the selection of particles is crucial, as the quality of the selected particles directly influences the resolution of the reconstructed density map. An innovative iterative approach for particle selection, termed CryoSieve, significantly improves the quality of reconstructed density maps by effectively reducing the number of particles in the final stack. Experimental evidence shows that this method can eliminate the majority of particles in final stacks, resulting in a notable enhancement in the quality of density maps. This article outlines the detailed workflow of this approach and showcases its application on a real-world dataset.

An advanced particle selection method for cryo-EM, namely CryoSieve, improves density map resolution by removing a majority of particles in final stacks, as demonstrated through its application on a real-world dataset.

Over the past decade, advancements in technology and methodology within the field of cryogenic electron microscopy (cryo-EM) single-particle analysis ...