Name: enhancing density maps by removing the majority of particles in single particle cryogenic electron microscopy final stacks
Uploaded: 2024-05-10T14:35:00.000+00:00
Duration: 6 min 41 s
Description: Read this Scientific Article Protocol about Author Spotlight: Optimizing Cryo-EM Analysis with CryoSieve for Enhanced Particle Selection Efficiency at JoVE.com

This work was supported by Shenzhen Academy of Research and Translation (to M.H.), the Advanced Innovation Center for Structural Biology (to M.H.), the Beijing Frontier Research Center for Biological Structure (to M.H.), the National Key R&#38;D Program of China (No.2021YFA1001300) (to C.B.), the National Natural Science Foundation of China (No.12271291) (to C.B.), and the National Natural Science Foundation of China (No.12071244) (to Z.S.).

1. Installation
<ol>
	<li>Check and configure GPU-acceleration environment
	<ol>
		<li>Open the terminal and enter the command: nvidia-smi. Make sure the command successfully displays all information about the GPU card(s) and the CUDA version is higher than 10.2. Execute the command: conda -V to check if Conda is installed (Supplementary Figure 1).</li>
	</ol>
	</li>
	<li>Configure virtual environment
	<ol>
		<li>Enter the following command to set up the virtual environment, replacing CRYOSIEVE_ENV with your desired environment name: conda create -n CRYOSIEVE_ENV python=3.8 cudatoolkit=10.2 cupy=10.0 pytorch=1.10 -c pytorch -c conda-forge. Wait for a few minutes until the environment is successfully configured (Supplementary Figure 2). 
		NOTE: Users have the flexibility to modify the environment name as needed. The provided command is specific to CUDA 10.2. If a different CUDA version is desired, adjust the version number for cudatoolkit.</li>
	</ol>
	</li>
	<li>Install CryoSieve
	<ol>
		<li>Activate the environment by executing the command: conda activate CRYOSIEVE_ENV. Install the software by running: pip install cryosieve or conda install -c mxhulab cryosieve (Supplementary Figure 3). Enter cryosieve -h and ensure that help information is correctly displayed (Supplementary Figure 4).</li>
	</ol>
	</li>
</ol>
2. Particle sieving
<ol>
	<li>Retrieve the data
	<ol>
		<li>Download the EMPIAR-10097 final stack dataset from EMPIAR (see Table of Materials). Download the star file, the mask file (mask.mrc) and the initial model (for the re-estimate step; initial.mrc) from Github (see Table of Materials). Place all these files in a folder together (Supplementary Figure 5). 
		NOTE: The repository at https://github.com/mxhulab/cryosieve-demos utilizes Git Large File Storage (Git LFS). Installing Git LFS is essential for cloning the entire repository. Alternatively, access the file via the GitHub link and click the&#160;Download raw file button to download an individual file.</li>
	</ol>
	</li>
	<li>Process particle sieving
	<ol>
		<li>Open the terminal and use the command: cd FILEPATH to navigate to the folder where the dataset is located. Activate the Conda environment by: conda activate CRYOSIEVE_ENV.</li>
		<li>Enter the following command to start our particle sieving experiment: cryosieve --reconstruct_software relion_reconstruct --postprocess_software relion_postprocess --i T40_HA_130K-Equalized_run-data_CryoSPARC_refined.star --o output/ --mask mask.mrc --angpix 1.3099979 --num_iters 10 --frequency_start 40 --frequency_end 3 --retention_ratio 0.8 --sym C3 --num_gpus 1 --balance (Supplementary Figure 5). During the execution, the terminal will display the output logs for each iteration. 
		NOTE: Detailed instructions for each option can be found in Supplementary File 1. Processing time and the minimum requirements for execution are detailed in Supplementary File 2. T40_HA_130K-Equalized_run-data_CryoSPARC_refined.star was refined by CryoSPARC from T40_HA_130K-Equalized_run-data.star (downloaded from EMPIAR) to mitigate the effects brought about by advancements in orientation estimation techniques.</li>
	</ol>
	</li>
</ol>
3. Finding the optimal iteration
<ol>
	<li>Check resolutions
	<ol>
		<li>Use the command: grep &#34;+ FINAL RESOLUTION:&#34; output/_postprocess*.txt to print resolution results for the 10 iterations of sieving (Figure 1). Since the particle stack filtered in the 7th iteration has the highest resolution with the fewest particles, it is likely to provide the optimal result. 
		NOTE: To avoid unintentional information transfer from discarded to retained particles15&#160;and to ensure that the particle stack posts the 7th iteration is indeed optimal, users are required to execute a re-estimation step for nearby iterations. In this protocol, iterations 4, 5, 6, 7, and 8&#160;are subjected to verification.</li>
	</ol>
	</li>
	<li>Import sieved particles
	<ol>
		<li>Open the CryoSPARC web interface and follow these steps: Enter a workspace and click on the Builder button at the top right of the panel. In the panel, select and click on the Import Particle Stack option. In the Parameters section of the Particle Stack Import panel, specify the Particle meta path as the _iter{n}.star file located in the output folder of the completed results and the particle data path to the folder where the mrcs file is stored. Click the Queue Job button, then click the Queue button to initiate the process. Use the same way to import the remaining iterations that need re-estimation (Supplementary Figure 6A).</li>
	</ol>
	</li>
	<li>Import initial model
	<ol>
		<li>Click on the Builder button at the top right of the panel. In the panel, select and click on the Import 3D Volumes option.</li>
		<li>Specify the Volume data path as the initial.mrc file. Click the Queue Job button, then click the Queue button to initiate the process (Supplementary Figure 6B). 
		NOTE: The initial model can also be generated through ab initio reconstruction (Supplementary File 3).</li>
	</ol>
	</li>
	<li>Homogeneous refinement (Build job)
	<ol>
		<li>Click on the Builder button at the top right of the panel. In the panel, select and click on the Homogeneous Refinement option. 
		NOTE: Non-uniform refinement is also applicable.</li>
	</ol>
	</li>
	<li>Homogeneous refinement (Import particles)
	<ol>
		<li>In the main panel on the left, open the job for importing the particle stack of the 5th iteration (or the desired iteration). Drag the imported particles module from the right side of the main panel and drop it into the Particle stacks section of the Builder on the right. Close the Import Particle Stack job by clicking on the red X in the top right corner of the main panel.</li>
		<li>Open the job for importing 3D volumes. Drag the imported volumes module from the right side of the main panel and drop it into the Initial volume section of the Builder on the right.</li>
	</ol>
	</li>
	<li>Homogeneous refinement (Modify the parameters)
	<ol>
		<li>Under the Parameters fold, locate the Symmetry option and set it to C3. Find the Force re-do GS split option and disable it. Click the Queue Job button, then click the Queue button to initiate the Homogeneous Refinement. Perform Homogeneous Refinement for the remaining iterations using the same method (Supplementary Figure 6C-D). 
		NOTE: The Force re-do GS split option is critical. Disabling this option ensures that CryoSPARC retains the gold-standard split given by the star file, avoiding overfitting. A detailed rationale for disabling Force Re-do GS Split can be found in Supplementary File 4.</li>
	</ol>
	</li>
	<li>Wait until all the jobs finish running to obtain the results. Based on the results, it is confirmed that the particle stack filtered in the 6th iteration is&#160;the actual optimal result. 
	NOTE: It is normal for the results obtained to have minor random deviations from the results provided in this protocol. These deviations do not affect the overall conclusion.</li>
</ol>

Enhancing Cryo-EM Map Quality with CryoSieve Particle Selection

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All other authors declare no competing interests.

Cryo-EM stands as a pivotal technique for elucidating the structures of biological molecules. In this process, after data collection via microscopy, particle extraction from micrographs is essential, followed by their classification in multiple stages to compile the final stack. A common challenge is the predominance of damaged or undesirably conformed particles, underscoring the need for repeated particle selection to attain high-resolution density maps. This makes particle selection a critical step in cryo-EM SPA for achieving high-quality density maps. Existing particle selection techniques include the statistical non-tilt validation algorithm22, the z-score-based approach23, and the angular accuracy estimation method24.
CryoSieve emerges as a valuable tool in this context, adept at eliminating a significant number of extraneous particles from the final stack. This reduction not only enhances the reconstruction&#39;s computational efficiency but also streamlines the process. It offers a comprehensive suite for particle selection, where the extent of particle discard and the consequent improvement in resolution largely hinge on the initial data quality and the methodologies employed in data processing.
In this manuscript, we have presented a complete workflow of particle sieving using the real case dataset of influenza hemagglutinin trimer (EMPIAR entry: 10097). The steps covered and discussed here can be summarized as particle sieving and pose re-estimation. The final 3D reconstructed volume achieved a resolution of 3.62 &#197;, and side chains in alpha-helices were clearer in the post-processed volume compared to the published density map.
CryoSieve is an open-source method which is available on GitHub (https://github.com/mxhulab/cryosieve). A detailed tutorial can also be found on its homepage. Users can install and use it by following the tutorial. Additionally, two modules, cryosieve-csrefine and cryosieve-csrhbfactor, are provided. The cryosieve-csrefine module is specifically crafted to automate the sequential execution of various operations within CryoSPARC (Supplementary File 5). These operations include importing particle stacks and conducting ab initio, homogeneous refinement, or non-uniform refinement jobs. On the other hand, the cryosieve-csrhbfactor module is designed to automate the determination of the Rosenthal-Henderson B-factor by leveraging the capabilities of cryosieve-csrefine (Supplementary File 6).
Presently, this method&#39;s application is confined to single conformation scenarios. Consequently, in instances where particles represent multiple conformations, their capabilities are limited. Users are advised to initially engage in 3D classification to segregate particles of disparate conformations before employing it for refined particle selection. Moreover, although the method demonstrates proficiency in filtering out over 50% of particles from the final stack, the origins of these discarded particles and the underlying reasons for their negligible contribution to reconstruction quality remain unclear. This gap in understanding necessitates additional research to comprehensively address and potentially rectify this limitation.
There are three possible existing methods of particle sorting or particle sieving. First of all, cisTEM4&#160;can report a score for each single particle image after 3D refinement. Users could sort particles using the cisTEM score to discard particles. The angular graph consistency (AGC) approach17&#160;is also a method to discard misaligned particles. Furthermore, the non-alignment classification5&#160;is a traditional way to discard particles using 3D classification. We compared the quality of particles retained by these methods with CryoSieve and found that the retained particles of CryoSieve are of higher quality15. The method presented here significantly outperforms alternative methods and achieves the smallest number of particles at the same resolution.
As demonstrated in the result, the majority of particles in a cryo-EM final stack do not contribute to density map reconstruction. In other words, among all particles gathered during image acquisition, only a select few, namely the finest subset, actually contribute to the final reconstruction. Consequently, the ratio of this final subset to the total number of collected particles could serve as a quantitative metric for assessing sample quality. The higher this ratio, the better the sample quality. Despite technical advancements that have made cryo-EM more accessible to structural biologists, sample preparation remains a major bottleneck in the workflow. Scientists and engineers are thus focusing their efforts on this challenge25. In single-particle analysis (SPA), sample preparation consists of two crucial steps: sample optimization and grid preparation. The former involves purifying the specimen while maintaining its optimal biochemical state. The latter entails preparing the sample for analysis in the microscope, including chemical or plasma treatment of the grid, sample deposition, and vitrification. Numerous techniques have been proposed to address macromolecular instability, but the efficacy of one approach over another depends on the sample&#39;s characteristics25,26. Currently, grid preparation results are heavily influenced by the user&#39;s expertise and experience, which can make the process time-consuming and challenging27,28. The numerous variables encountered in sample and grid preparation pose challenges in establishing cause-and-effect relationships, as researchers can only assess the sample at the molecular level using the microscope. As a result, quantitative statistics from comparisons of different sample and grid preparation protocols are still lacking, and a systematic approach is necessary to investigate trends and comprehend the fundamental mechanisms of sample behavior29.

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.

<table><tbody><tr><td>CryoSPARC</td><td>Structura Biotechnology Inc. Toronto, Canada</td><td></td><td>CryoSPARC (Cryo-EM Single Particle Ab-Initio Reconstruction and Classification) is a state of the art HPC software solution for complete processing of single-particle cryo-electron microscopy (cryo-EM) data. CryoSPARC is useful for solving cryo-EM structures of membrane proteins, viruses, complexes, flexible molecules, small particles, phase plate data and negative stain data.</td></tr><tr><td>EMPIAR-10097 Dataset</td><td></td><td>https://ftp.ebi.ac.uk/empiar/world_availability/10097/data/Particle-Stack/T40_HA_130K-Equalized-Particle-Stack.mrcs</td><td>This dataset comprises single-particle cryo-EM data of the Influenza Hemagglutinin trimer, characterized by its highly preferred orientation, collected using a 40-degree tilted collection strategy.</td></tr><tr><td>initial.mrc</td><td></td><td>https://github.com/mxhulab/cryosieve-demos/tree/master/EMPIAR-10097</td><td></td></tr><tr><td>mask.mrc</td><td></td><td>https://github.com/mxhulab/cryosieve-demos/tree/master/EMPIAR-10097</td><td></td></tr><tr><td>RELION</td><td></td><td>4.0-beta-2</td><td>RELION (REgularised LIkelihood OptimisatioN) is an open-source software for cryo-electron microscopy (cryo-EM) data processing, particularly for refining macromolecular structures. Utilizing a Bayesian approach, it excels in separating signal from noise, enabling high-resolution structure determination. RELION supports single-particle analysis, tomography, and sub-tomogram averaging, and has become widely used in structural biology due to its effectiveness and user-friendly interface.</td></tr><tr><td>T40_HA_130K-Equalized_run-data_CryoSPARC_refined.star</td><td></td><td>https://github.com/mxhulab/cryosieve-demos/tree/master/EMPIAR-10097</td><td>Metadata file for the final stack of particles from EMPIAR-10097</td></tr></tbody></table>

enhancing density maps by removing the majority of particles in single particle cryogenic electron microscopy final stacks

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.

In this protocol, we utilized the influenza hemagglutinin trimer dataset (EMPIAR entry: 10097) as a demonstration of the efficacy of this process. Due to the preferred orientation of the sample, data acquisition required tilting at 40&#176;. The protein exhibits C3 symmetry and has a molecular weight of 150 kDa.
We have implemented the protocol described earlier to process the final particle stack. It progressively removed 20% of the particles in each iteration, resulting in a retention ratio of 80.0%, 64.0%, 51.2%, and so on. As depicted in Figure 1&#160;and Figure 2, the resolution of the retained particles initially improved but eventually decreased. Among the iterations, the 6th iteration was identified as the most optimal subset, containing the fewest particles yet achieving the highest resolution. Our algorithm successfully identified a subset of particles comprising only 26.2% of the original stack, resulting in an improved resolution from 4.19 &#197; to 3.62 &#197; (re-estimated by CryoSPARC), shown in Figure 2. Furthermore, density maps before and after using CryoSieve were compared in Figure 3. Model-to-map Fourier Shell Correlation (FSC) curve and half-maps FSC curve of the reconstructed density maps before and after the method are also shown (Figure 3A-B). Raw density maps and sharp density maps obtained were also compared, with the equivalent contour level applied (Figure 3C). The side chains of sharp density maps were compared, showing the enhancement of reconstructed density maps. The estimated Rosenthal-Henderson B-factor was also adopted for the criteria of particle quality19. After removing the majority of particles in the final stack, the Rosenthal-Henderson B-factor raised from 226.9 &#197;2 to 146.2 &#197;2&#160;(Figure 3D). Local resolution, local B-factor20, and ResLog21&#160;were also utilized for comparison, indicating that CryoSieve indeed enhances both the quality of the density maps and the particles (Figure 4).
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/66617/66617fig01.jpg" /> 
Figure 1: Resolutions of each iteration. Resolutions that have been reported are highlighted in red boxes. <a href="https://www.jove.com/files/ftp_upload/66617/66617fig01large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/66617/66617fig02.jpg" /> 
Figure 2: Resolutions of each iteration. Resolutions identified by homogeneous refinement jobs are highlighted in red boxes. <a href="https://www.jove.com/files/ftp_upload/66617/66617fig02large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/66617/66617fig03.jpg" /> 
Figure 3: Density maps. (A) Comparison of model-to-map FSC curve of reconstructed density maps before and after using CryoSieve. The y-axis represents FSC, while the x-axis represents resolution. The red dashed line marks the threshold of 0.5 for the FSC. The vertical dashed line illustrates the resolution of the density maps obtained under a threshold of 0.5. (B) Half-maps FSC curve were obtained from reconstructed density maps before and after using CryoSieve via CryoSPARC. The y-axis represents FSC, while the x-axis represents resolution. (C) Raw density maps and sharp density maps were shown for both the CryoSieve-retained particles and the complete set of particles in the final stacks. The equivalent contour level of 0.65 was applied for raw density maps. The equivalent contour level of 0.84 was applied for sharp density maps. Sharp density maps were directly obtained by CryoSPARC. The sharp density maps were auto-postprocessed, first FSC-weighted (based on FSCs given by CryoSPARC). Then, the B-factor was sharpened using the auto-determined B-factors (232.0 &#197;2 for all particles in the final stack and 160.8 &#197;2 for CryoSieve). The side chains in the sharp density maps were compared, incorporating atomic models for reference. Red arrows highlight the improved regions. (D) The estimated Rosenthal-Henderson B-factor was shown for both the CryoSieve-retained particles and the complete set of particles in the final stacks. The y-axis represents the number of particles used, and the x-axis represents the reciprocal of the square of the resolution. Moving from top to bottom, each point represents half the particles of the previous one. The resolutions were determined by refinement. B-factors were determined using a least-squares approximation of the measured points, as shown by the fitting curves. The estimated Rosenthal and Henderson&#39;s B-factors are indicated in the legends: orange represents particles retained by CryoSieve, while blue denotes all particles in the final stack. <a href="https://www.jove.com/files/ftp_upload/66617/66617fig03large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/66617/66617fig04.jpg" /> 
Figure 4: Comparison of various metrics of density maps. (A) Comparison of local resolution maps before and after using CryoSieve obtained by CryoSPARC. The local resolution ranges between 7 &#197; (red) and 3.5 &#197; (blue). (B) Comparison of density maps before and after using CryoSieve, colored with the local B-factor map obtained by LocBFactor using a resolution range of [20-3.5] &#197;. (C), Comparison of ResLog plots before and after using CryoSieve obtained by CryoSPARC. <a href="https://www.jove.com/files/ftp_upload/66617/66617fig04large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Supplementary Figure 1: Using commands nvidia-smi and conda -V to verify the prerequisites. If the prerequisites are met, typing the command nvidia-smi will display the GPU driver version, the CUDA version, and the status of the GPU cards. Similarly, entering the command conda -V should correctly display the installed version of Conda. <a href="https://www.jove.com/files/ftp_upload/66617/Supplementary_Figure_1.zip" target="_blank">Please click here to download this File.</a>
Supplementary Figure 2: The process of creating new GPU-acceleration environments. The screen displays the output generated by the command used to create the Conda environment. <a href="https://www.jove.com/files/ftp_upload/66617/Supplementary_Figure_2.zip" target="_blank">Please click here to download this File.</a>
Supplementary Figure 3: Installation of CryoSieve in the GPU-acceleration environment. After activating the newly created Conda environment, the screen displays the output resulting from executing the command to install CryoSieve using Pip. <a href="https://www.jove.com/files/ftp_upload/66617/Supplementary_Figure_3.zip" target="_blank">Please click here to download this File.</a>
Supplementary Figure 4: Help information. <a href="https://www.jove.com/files/ftp_upload/66617/Supplementary_Figure_4.zip" target="_blank">Please click here to download this File.</a>
Supplementary Figure 5: Running process. Upon executing CryoSieve through the command line, the screen then displays information regarding the running process. <a href="https://www.jove.com/files/ftp_upload/66617/Supplementary_Figure_5.zip" target="_blank">Please click here to download this File.</a>
Supplementary Figure 6: The configuration of CryoSPARC&#39;s jobs. (A) Import particle stack. (B) Import 3D volumes. (C-D) Homogeneous refinement. <a href="https://www.jove.com/files/ftp_upload/66617/Supplementary_Figure_6.zip" target="_blank">Please click here to download this File.</a>
Supplementary File 1: Options of CryoSieve. <a href="https://www.jove.com/files/ftp_upload/66617/supplementary_1.pdf" target="_blank">Please click here to download this File.</a>
Supplementary File 2: Processing time and minimal requirement for running Cryosieve. <a href="https://www.jove.com/files/ftp_upload/66617/supplementary_2.pdf" target="_blank">Please click here to download this File.</a>
Supplementary File 3: Generation of initial model by CryoSPARC. <a href="https://www.jove.com/files/ftp_upload/66617/supplementary_3.pdf" target="_blank">Please click here to download this File.</a>
Supplementary File 4: Rationale for disabling force re-do GS split. <a href="https://www.jove.com/files/ftp_upload/66617/supplementary_4.pdf" target="_blank">Please click here to download this File.</a>
Supplementary File 5: Options of cryosieve-csrefine. <a href="https://www.jove.com/files/ftp_upload/66617/supplementary_5.pdf" target="_blank">Please click here to download this File.</a>
Supplementary File 6: Options of cryosieve-csrhbfactor. <a href="https://www.jove.com/files/ftp_upload/66617/supplementary_6.pdf" target="_blank">Please click here to download this File.</a>

Read this Scientific Article Protocol about Author Spotlight: Optimizing Cryo-EM Analysis with CryoSieve for Enhanced Particle Selection Efficiency at JoVE.com

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

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.

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

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

enhancing-density-maps-removing-majority-particles-single-particle

Research

JoVE Journal

Biology

1.5K Views.  Shenzhen Medical Academy of Research and Translation. Cryogene elektronenmicroscopie is essentieel voor het bepalen van bijna-atomaire structuren van biologische macromoleculen. Ondanks de afhankelijkheid van het gemiddelde van talloze beelden met een laag signaal-ruis, blijft het optimale aantal deeltjes dat nodig is voor een specifieke resolutie onbekend. Deze beperking belemmert de vooruitgang in de analyse van monsters en de bereidingsmethoden.Om dit aan te pakken, introduceren we een iteratieve sorteermethode, CryoSieve. Standaard pr...

Video: Auteur in de schijnwerpers: Cryo-EM-analyse optimaliseren met CryoSieve voor verbeterde deeltjesselectie-efficiëntie

High-resolution Single Particle Analysis from Electron Cryo-microscopy Images Using SPHIRE

SPHIRE (SPARX for High-Resolution Electron Microscopy) is a novel open-source, user-friendly software suite for the semi-automated processing of single particle electron cryo-microscopy (cryo-EM) data. The protocol presented here describes in detail how to obtain a near-atomic resolution structure starting from cryo-EM micrograph movies by guiding users through all steps of the single particle structure determination pipeline. These steps are controlled from the new SPHIRE graphical user interface and require minimum user intervention. Using this protocol, a 3.5 &#197; structure of TcdA1, a Tc toxin complex from Photorhabdus luminescens, was derived from only 9500 single particles. This streamlined approach will help novice users without extensive processing experience and a priori structural information, to obtain noise-free and unbiased atomic models of their purified macromolecular complexes in their native state.

This paper presents a protocol for processing cryo-EM images using the software suite SPHIRE. The present protocol can be applied for nearly all single particle EM projects that target near-atomic resolution.

SPHIRE (SPARX for High-Resolution Electron Microscopy) is a novel open-source, user-friendly software suite for the semi-automated processing of single ...

Biochemistry

Cryo-EM and Single-Particle Analysis with Scipion

Cryo-electron microscopy has become one of the most important tools in biological research to reveal the structural information of macromolecules at near-atomic resolution. In single-particle analysis, the vitrified sample is imaged by an electron beam and the detectors at the end of the microscope column produce movies of that sample. These movies contain thousands of images of identical particles in random orientations. The data need to go through an image processing workflow with multiple steps to obtain the final 3D reconstructed volume. The goal of the image processing workflow is to identify the acquisition parameters to be able to reconstruct the specimen under study. Scipion provides all the tools to create this workflow using several image processing packages in an integrative framework, also allowing the traceability of the results. In this article the whole image processing workflow in Scipion is presented and discussed with data coming from a real test case, giving all the details necessary to go from the movies obtained by the microscope to a high resolution final 3D reconstruction. Also, the power of using consensus tools that allow combining methods, and confirming results along every step of the workflow, improving the accuracy of the obtained results, is discussed.

Single-particle analysis in cryo-electron microscopy is one of the main techniques used to determine the structure of biological ensembles at high resolution. Scipion provides the tools to create the whole pipeline to process the information acquired by the microscope and achieve a 3D reconstruction of the biological specimen.

Cryo-electron microscopy has become one of the most important tools in biological research to reveal the structural information of macromolecules at ...

Single Particle Cryo-Electron Microscopy: From Sample to Structure

Cryo-electron microscopy (cryoEM) is a powerful technique for structure determination of macromolecular complexes, via single particle analysis (SPA). The overall process involves i) vitrifying the specimen in a thin film supported on a cryoEM grid; ii) screening the specimen to assess particle distribution and ice quality; iii) if the grid is suitable, collecting a single particle dataset for analysis; and iv) image processing to yield an EM density map. In this protocol, an overview for each of these steps is provided, with a focus on the variables which a user can modify during the workflow and the troubleshooting of common issues. With remote microscope operation becoming standard in many facilities, variations on imaging protocols to assist users in efficient operation and imaging when physical access to the microscope is limited will be described.

Structure determination of macromolecular complexes using cryoEM has become routine for certain classes of proteins and complexes. Here, this pipeline is summarized (sample preparation, screening, data acquisition and processing) and readers are directed towards further detailed resources and variables that may be altered in the case of more challenging specimens.

Cryo-electron microscopy (cryoEM) is a powerful technique for structure determination of macromolecular complexes, via single particle analysis (SPA). The ...

A Robust Single-Particle Cryo-Electron Microscopy (cryo-EM) Processing Workflow with cryoSPARC, RELION, and Scipion

Recent advances in both instrumentation and image processing software have made single-particle cryo-electron microscopy (cryo-EM) the preferred method for structural biologists to determine high-resolution structures of a wide variety of macromolecules. Multiple software suites are available to new and expert users for image processing and structure calculation, which streamline the same basic workflow: movies acquired by the microscope detectors undergo correction for beam-induced motion and contrast transfer function (CTF) estimation. Next, particle images are selected and extracted from averaged movie frames for iterative 2D and 3D classification, followed by 3D reconstruction, refinement, and validation. Because various software packages employ different algorithms and require varying levels of expertise to operate, the 3D maps they generate often differ in quality and resolution. Thus, users regularly transfer data between a variety of programs for optimal results. This paper provides a guide for users to navigate a workflow across the popular software packages: cryoSPARC v3, RELION-3, and Scipion 3 to obtain a near-atomic resolution structure of the adeno-associated virus (AAV). We first detail an image processing pipeline with cryoSPARC v3, as its efficient algorithms and easy-to-use GUI allow users to quickly arrive at a 3D map. In the next step, we use PyEM and in-house scripts to convert and transfer particle coordinates from the best quality 3D reconstruction obtained in cryoSPARC v3 to RELION-3 and Scipion 3 and recalculate 3D maps. Finally, we outline steps for further refinement and validation of the resultant structures by integrating algorithms from RELION-3 and Scipion 3. In this article, we describe how to effectively utilize three processing platforms to create a single and robust workflow applicable to a variety of data sets for high-resolution structure determination.

A Robust Single-Particle Cryo-Electron Microscopy cryo-EM Processing Workflow with cryoSPARC, RELION, and Scipion

This article describes how to effectively utilize three cryo-EM processing platforms, i.e., cryoSPARC v3, RELION-3, and Scipion 3, to create a single and robust workflow applicable to a variety of single-particle data sets for high-resolution structure determination.

Recent advances in both instrumentation and image processing software have made single-particle cryo-electron microscopy (cryo-EM) the preferred method ...

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

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 &#197; 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 &#197;, and Thermoplasma acidophilum 20S proteasome reconstructed to 2.1-&#197; resolution using a 200 kV TEM equipped with an energy filter and a direct electron detector.

High-resolution cryo-EM maps of macromolecules can be also achieved by using 200 kV TEM microscopes. This protocol shows the best practices for setting accurate optics alignments, data acquisition schemes, and selection of imaging areas that are all essential for the successful collection of high-resolution datasets using a 200 kV TEM.

Cryo-electron microscopy (cryo-EM) has been established as a routine method for protein structure determination during the past decade, taking an ...

Single-Particle Cryo-EM Data Collection with Stage Tilt using Leginon

Single-particle analysis (SPA) by cryo-electron microscopy (cryo-EM) is now a mainstream technique for high-resolution structural biology. Structure determination by SPA relies upon obtaining multiple distinct views of a macromolecular object vitrified within a thin layer of ice. Ideally, a collection of uniformly distributed random projection orientations would amount to all possible views of the object, giving rise to reconstructions characterized by isotropic directional resolution. However, in reality, many samples suffer from preferentially oriented particles adhering to the air-water interface. This leads to non-uniform angular orientation distributions in the dataset and inhomogeneous Fourier-space sampling in the reconstruction, translating into maps characterized by anisotropic resolution. Tilting the specimen stage provides a generalizable solution to overcoming resolution anisotropy by virtue of improving the uniformity of orientation distributions, and thus the isotropy of Fourier space sampling. The present protocol describes a tilted-stage automated data collection strategy using Leginon, a software for automated image acquisition. The procedure is simple to implement, does not require any additional equipment or software, and is compatible with most standard transmission electron microscopes (TEMs) used for imaging biological macromolecules.

The present protocol describes a generalized and easy-to-implement scheme for tilted single-particle data collection in cryo-EM experiments. Such a procedure is especially useful for obtaining a high-quality EM map for samples suffering from preferential orientation bias due to adherence to the air-water interface.

Single-particle analysis (SPA) by cryo-electron microscopy (cryo-EM) is now a mainstream technique for high-resolution structural biology. Structure ...

Coating CryoEM Grids with Monolayer Graphene to Improve Cryo-Sample Preparation

Application of Monolayer Graphene to Cryo-Electron Microscopy Grids for High-resolution Structure Determination

In cryogenic electron microscopy (cryoEM), purified macromolecules are applied to a grid bearing a holey carbon foil; the molecules are then blotted to remove excess liquid and rapidly frozen in a roughly 20-100 nm thick layer of vitreous ice, suspended across roughly 1 &#181;m wide foil holes. The resulting sample is imaged using cryogenic transmission electron microscopy, and after image processing using suitable software, near-atomic resolution structures can be determined. Despite cryoEM&#39;s widespread adoption, sample preparation remains a severe bottleneck in cryoEM workflows, with users often encountering challenges related to samples behaving poorly in the suspended vitreous ice. Recently, methods have been developed to modify cryoEM grids with a single continuous layer of graphene, which acts as a support surface that often increases particle density in the imaged area and can reduce interactions between particles and the air-water interface. Here, we provide detailed protocols for the application of graphene to cryoEM grids and for rapidly assessing the relative hydrophilicity of the resulting grids. Additionally, we describe an EM-based method to confirm the presence of graphene by visualizing its characteristic diffraction pattern. Finally, we demonstrate the utility of these graphene supports by rapidly reconstructing a 2.7 &#197; resolution density map of a Cas9 complex using a pure sample at a relatively low concentration.

Author Spotlight: Enhancing CryoEM Sample Preparation Using Graphene Monolayer on Microscopy Grids 

The application of support layers to cryogenic electron microscopy (cryoEM) grids can increase particle density, limit interactions with the air-water interface, reduce beam-induced motion, and improve the distribution of particle orientations. This paper describes a robust protocol for coating cryoEM grids with a monolayer of graphene for improved cryo-sample preparation.

In cryogenic electron microscopy (cryoEM), purified macromolecules are applied to a grid bearing a holey carbon foil; the molecules are then blotted to ...

Optimizing Sample Preparation for Cryogenic Electron Microscopy

Cryogenic electron microscopy (cryo-EM) has revolutionized structural biology by enabling the study of macromolecular structures in near-native conditions, suspended in vitreous ice. This technique allows for the high-resolution visualization of proteins and other biomolecules without the need for crystallization, offering significant insights into their function and mechanism. Recent advancements in single-particle analysis, coupled with improved computational data processing, have made cryo-EM an indispensable tool in modern structural biology. Despite its growing adoption, cryo-EM faces persistent challenges that can limit its effectiveness, particularly uneven particle distribution. This issue often leads to poor resolution and reduced accuracy in reconstructed protein structures. This article outlines a simple, practical approach to address this challenge, using the small heat-shock protein from Methanocaldococcus jannaschii (MjsHSP16.5) as an example. The method optimizes sample preparation to minimize preferential adsorption, ensuring more homogeneous particle distribution and higher-quality protein cryo-EM structures. This technique offers valuable guidance for researchers aiming to overcome similar challenges in structural studies.

This protocol presents a practical method using Methanocaldococcus jannaschii (MjsHSP16.5) as an example to address uneven particle distribution through sample preparation optimization, providing a reference for researchers to efficiently elucidate macromolecular structures using cryogenic electron microscopy (cryo-EM).

Cryogenic electron microscopy (cryo-EM) has revolutionized structural biology by enabling the study of macromolecular structures in near-native ...

User-friendly, High-throughput, and Fully Automated Data Acquisition Software for Single-particle Cryo-electron Microscopy

In the past several years, technological and methodological advancements in single-particle cryo-electron microscopy (cryo-EM) have paved a new avenue for the high-resolution structure determination of biological macromolecules. Despite the remarkable advances in cryo-EM, there is still scope for improvement in various aspects of the single-particle analysis workflow. Single-particle analysis demands a suitable software package for high-throughput automatic data acquisition. Several automatic data acquisition software packages were developed for automatic imaging for single-particle cryo-EM in the last eight years. This paper presents an application of a fully automated image acquisition pipeline for vitrified biomolecules under low-dose conditions. 
It demonstrates a software package, which can collect cryo-EM data fully, automatically, and precisely. Additionally, various microscopic parameters are easily controlled by this software package. This protocol demonstrates the potential of this software package in automated imaging of the severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) spike protein with a 200 keV cryo-electron microscope equipped with a direct electron detector (DED). Around 3,000 cryo-EM movie images were acquired in a single session (48 h) of data collection, yielding an atomic-resolution structure of the spike protein of SARS-CoV-2. Furthermore, this structural study indicates that the spike protein adopts two major conformations, 1-RBD (receptor-binding domain) up open and all RBD down closed conformations.

Single-particle cryo-electron microscopy demands a suitable software package and user-friendly pipeline for high-throughput automatic data acquisition. Here, we present the application of a fully automated image acquisition software package, Latitude-S, and a practical pipeline for data collection of vitrified biomolecules under low-dose conditions.

In the past several years, technological and methodological advancements in single-particle cryo-electron microscopy (cryo-EM) have paved a new avenue for ...

Immunology and Infection

Do's and Don'ts of Cryo-electron Microscopy: A Primer on Sample Preparation and High Quality Data Collection for Macromolecular 3D Reconstruction

Cryo-electron microscopy (cryoEM) entails flash-freezing a thin layer of sample on a support, and then visualizing the sample in its frozen hydrated state by transmission electron microscopy (TEM). This can be achieved with very low quantity of protein and in the buffer of choice, without the use of any stain, which is very useful to determine structure-function correlations of macromolecules. When combined with single-particle image processing, the technique has found widespread usefulness for 3D structural determination of purified macromolecules. 
The protocol presented here explains how to perform cryoEM and examines the causes of most commonly encountered problems for rational troubleshooting; following all these steps should lead to acquisition of high quality cryoEM images. The technique requires access to the electron microscope instrument and to a vitrification device. Knowledge of the 3D reconstruction concepts and software is also needed for computerized image processing. Importantly, high quality results depend on finding the right purification conditions leading to a uniform population of structurally intact macromolecules. 
The ability of cryoEM to visualize macromolecules combined with the versatility of single particle image processing has proven very successful for structural determination of large proteins and macromolecular machines in their near-native state, identification of their multiple components by 3D difference mapping, and creation of pseudo-atomic structures by docking of x-ray structures. The relentless development of cryoEM instrumentation and image processing techniques for the last 30 years has resulted in the possibility to generate de novo 3D reconstructions at atomic resolution level.

This paper describes the cryo-electron microscopy methodology, which is used to obtain high quality microscopic images of macromolecules in their near-native state. The method yields images suitable for further computerized processing using the single particle approach, devised to generate the 3D reconstruction of a macromolecule.

Cryo-electron microscopy (cryoEM) entails flash-freezing a thin layer of sample on a support, and then visualizing the sample in its frozen hydrated state ...

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

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