We thank Carlos Oscar Sorzano for help with Scipion3 installation and Kilian Schnelle and Arne Moeller for help with data transfer between different processing platforms. A portion of this research was supported by NIH grant U24GM129547 and performed at the PNCC at OHSU and accessed through EMSL (grid.436923.9), a DOE Office of Science User Facility sponsored by the Office of Biological and Environmental Research. This study was supported by a start-up grant from Rutgers University to Arek Kulczyk.

1. Creating a new cryoSPARC v3 project and importing data
NOTE: Data was acquired at Oregon Health and Science University (OHSU) in Portland using a 300 kV Titan Krios electron microscope equipped with a Falcon 3 direct electron detector. Images were collected in a counting mode with a total dose of 28.38 e&#8722;/&#197;2 fractioned across 129 frames, and a defocus range from -0.5 &#181;m to -2.5 &#181;m, at a pixel size of 1.045 &#197; using EPU. The sampl...

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The authors have nothing to disclose.

In this article, we present a robust SPA workflow for cryo-EM data processing across various software platforms to achieve high-resolution 3D reconstructions (Figure 1). This workflow is applicable to a wide variety of biological macromolecules. The subsequent steps of the protocol are outlined in Figure 4, including movie pre-processing, particle picking and classification, and multiple methods for structure refinements (Table 1</strong...

Cryo-electron microscopy (cryo-EM) and single-particle analysis (SPA) enable structure determination of a wide variety of biomolecular assemblies in their hydrated state, helping to illuminate the roles of these macromolecules in atomic detail. Improvements in microscope optics, computer hardware, and image processing software have made it possible to determine structures of biomolecules at resolution reaching beyond 2 &#197;1,2,3. More than 2,300 cryo-EM structures were deposited in the Protein Data Bank (PDB) in 2020, compared to 192 structures in 20144, indicating that cryo-EM has become the method of choice for many structural biologists. Here, we describe a workflow combining three different SPA programs for high-resolution structure determination (Figure 1).
The goal of SPA is to reconstruct 3D volumes of a target specimen from noisy 2D images recorded by a microscope detector. Detectors collect images as movies with individual frames of the same field of view. In order to preserve the sample, frames are collected with a low electron dose and thus have a poor signal-to-noise ratio (SNR). Additionally, electron exposure can induce motion within the vitrified cryo-EM grids, resulting in image-blurring. To overcome these issues, frames are aligned to correct for beam-induced motion and averaged to yield a micrograph with an increased SNR. These micrographs then undergo Contrast Transfer Function (CTF) estimation to account for the effects of defocus and aberrations imposed by the microscope. From the CTF-corrected micrographs, individual particles are selected, extracted, and sorted into 2D class averages representing different orientations adopted by the specimen in vitreous ice. The resultant homogeneous set of particles is used as input for ab initio&#160;3D reconstruction to generate a coarse model or models, which are then iteratively refined to produce one or more high-resolution structures. After reconstruction, structural refinements are performed to further improve the quality and resolution of the cryo-EM map. Finally, either an atomic model is directly derived from the map, or the map is fitted with atomic coordinates obtained elsewhere.
Different software packages are available to accomplish the tasks outlined above, including Appion5, cisTEM6, cryoSPARC7, EMAN8, IMAGIC9, RELION10, Scipion11, SPIDER12, Xmipp13, and others. While these programs follow similar processing steps, they employ different algorithms, for example, to pick particles, generate initial models, and refine reconstructions. Additionally, these programs require a varying level of user knowledge and intervention to operate, as some depend on the fine-tuning of parameters that can act as a hurdle for new users. These discrepancies often result in maps with inconsistent quality and resolution across platforms14, prompting many researchers to use multiple software packages to refine and validate results. In this article, we highlight the use of cryoSPARC v3, RELION-3, and Scipion 3 to obtain a high-resolution 3D reconstruction of AAV, a widely used vector for gene therapy15. The aforementioned software packages are free to academic users; cryoSPARC v3 and Scipion 3 require licenses.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>CryoSPARC</td>
			<td>Structura Biotechnology Inc.</td>
			<td>https://cryosparc.com/</td>
			<td></td>
		</tr>
		<tr>
			<td>CTFFIND 4</td>
			<td>Howard Hughes Medical Institute, UMass Chan Medical School</td>
			<td>https://grigoriefflab.umassmed.edu/ctffind4</td>
			<td></td>
		</tr>
		<tr>
			<td>MotionCorr2</td>
			<td>UCSF Macromolecular Structure Group</td>
			<td>https://msg.ucsf.edu/software</td>
			<td></td>
		</tr>
		<tr>
			<td>Phenix</td>
			<td>Computational Tools for Macromolecular Neutron Crystallography (MNC)</td>
			<td>http://www.phenix-online.org/</td>
			<td></td>
		</tr>
		<tr>
			<td>PyEM</td>
			<td>Univerisity of California, San Francisco</td>
			<td>https://github.com/asarnow/pyem</td>
			<td></td>
		</tr>
		<tr>
			<td>RELION</td>
			<td>MRC Laboratory of Structural Biology</td>
			<td>https://www3.mrc-lmb.cam.ac.uk/relion/index.php/Main_Page</td>
			<td></td>
		</tr>
		<tr>
			<td>Scipion</td>
			<td>Instruct Image Processing Center (I2PC), SciLifeLab</td>
			<td>http://scipion.i2pc.es/</td>
			<td></td>
		</tr>
		<tr>
			<td>UCSF Chimera</td>
			<td>UCSF Resource for Biocomputing, Visualization, and Informatics</td>
			<td>https://www.cgl.ucsf.edu/chimera/</td>
			<td></td>
		</tr>
	</tbody>
</table>

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.

We have presented a comprehensive SPA pipeline to obtain a high-resolution structure using three different processing platforms: cryoSPARC v3, RELION-3, and Scipion 3. Figure 1 and Figure 4 summarize the general processing workflow, and Table 1 details refinement protocols. These protocols were used during refinements of a 2.3 &#197; structure of AAV, achieving near Nyquist resolution.
Movies were first imported to cr...

Watch this Scientific Journal Video about A Robust Single-Particle Cryo-Electron Microscopy cryo-EM Processing Workflow with cryoSPARC, RELION, and Scipion at JoVE.com

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.

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 ...

Single-particle cryo-electron microscopy is the method for structured determinations of macromolecules at near atomic resolution. Multiple software packages are available for image processing and structure calculations, yet the result on 3D maps often vary in quality and resolution due to the differences in algorithms applied during calculations. Thus, using a combination of different programs is often beneficial to achieve the optimal results.This protocol guides users to navigate workflow across three different cryo-EM processing platforms:CryoSPARC, RELION, and Scipion. We will demonstrate how to use this pipeline to obtain a high-resolution structure of the adeno-associated virus which is a widely used vector for gene therapy. After opening CryoSPARC v3 in a web browser and creating the workspace for the project, navigate to the new workspace and open the job builder on the right panel.Click on import movies and provide the movies path and gain reference file path. Then set the acquisition parameters. Next, click on queue, select a lane to run the job and a workspace and click on create.Open patch motion correction and the import movies job card. Then drag the imported_movies output to the movies placeholder on the new job and queue the job. To perform the contrast transfer function or CTF estimation, open patch CTF estimation.Input the generated micrographs and queue the job. To inspect the averaged and CTF corrected micrographs and select a subset for further processing, open curate exposures, input the exposures obtained from the previous step and queue the job. After the job enters waiting mode, click on the interactive tab on the job card.Adjust the parameter thresholds and accept or reject individual micrographs for further processing. While processing the current data, set the upper threshold of astigmatism to 400 angstroms, CTF fit resolution to five angstroms and relative ice thickness to two. Then click on done to select the micrographs for downstream processing.For manual-based picking, open the manual picker. Input the accepted exposures and queue the job. Then click on the interactive tab, set the box size to 300 pixels and click on a few hundred particles across multiple micrographs, avoiding overlapping particles.Once finished with the selection, click on done picking extract particles. Next, to generate templates for automated particle picking, click on 2D classification and input the generated particle picks. Then change the number of 2D classes to 10 and queue the job.Next, open select 2D classes and input the particles and class averages obtained in the previous step. Then click on the interactive tab, select representative 2D classes with a good signal-to-noise ratio and click on done. For template-based particle picking, open template picker and input the selected 2D classes and micrographs.Then set the particle diameter to 220 angstroms and queue the job. Finally, open extract from micrographs and input the micrographs and particles obtained from inspecting particle picks. Then set the extracted box size to 300 pixels and queue the job.For 2D classification, click on 2D classification and input the extracted particles. Then set the number of 2D classes to 50 and queue the job. Next, open select 2D classes and input the obtained particles and class averages.Click on the interactive tab. Choose 2D classes based upon the resolution and the number of particles in the class and click on done. To generate an initial 3D volume, open ab-initio reconstruction and input the particles from the final 2D classification.Then adjust symmetry to icosahedral and queue the job. Next, open homogenous refinement, input the volume from the previous step and particles from the final 2D classification. Then change the symmetry and queue the job.When the job is finished, inspect the Fourier shell correlation or FSC curve and download the volume to examine in UCSF Chimera. In CryoSPARC v3, open the job card of the select 2D class job from the final 2D classification. Then on the details tab, click on export job.Using PyEM, convert the particles_exported. cs file to star format by executing the indicated command. After clicking on particle extraction, in the I/O tab, input the CTF corrected micrographs and coordinates.Then click on the extract tab, change the particle box size to 300 pixels and run the job. For 3D refinement, use the map generated in CryoSPARC v3 as an initial model in RELION-3. Select the import method and set the indicated parameters in the I/O tab.Then on the others tab, select the CryoSPARC v3 map as the input file, change node type to 3D reference and run the job. Next, select the 3D auto-refine and on the I/O tab, set input images as the particles. star file from the last selection job.Use the CryoSPARC v3 reconstruction as the reference map, click on the reference tab and change initial low-pass filter to 50 angstroms and symmetry to icosahedral. Then on the optimization tab, change the mask diameter to 280 angstroms and run the job. For post processing, click on post-processing.And on the I/O tab, input the created half maps and mask and set the calibrated pixel size to 1.045 angstroms. Then on the sharpen tab, for estimate B-factor automatically, input yes. For lowest resolution for auto-B fit, input 10.And for use your own B-factor, input no. Finally, on the filter tab, set skip FSC-weighting to no and run the job. For polishing training, open Bayesian polishing.And on the I/O tab, input the motion corrected micrographs, particles, and PostProcess. star file obtained previously. Click the training tab and set train optimal parameters to yes, fraction of Fourier pixels for testing to 0.5 and use this many particles to 5, 000.Then run the job. Once the training job is finished, click on Bayesian polishing. Then in the training tab, set train optimal parameters to no.Select the polish tab and in optimized parameter file, specify the path to the opt_params_all_groups. txt file from the previous step and click on run. To estimate higher order aberrations, open CTF refinement.And on the I/O tab under particles, select the path to the star file containing polished particles from the recent refined 3D job. Then under postprocess STAR file, set the path to the output from the latest post-processing job. Next, select the fit tab and set estimate anisotropic magnification to no.Perform CTF parameter fitting to no. Estimate beamtilt to yes. Also estimate trefoil to yes.And estimate fourth order aberrations to yes. Then run the job. To further refine and validate the RELION-3 map, launch Scipion 3 and create a new project.On the left protocols panel, select the imports dropdown and click on import particles. Change the parameters import from to RELION-3 and star file to postprocess.star. Then specify the acquisition parameters as demonstrated earlier and click on execute.Next, click on the imports dropdown and select import volumes. Under import from, give the path to the RELION-3 map, change pixel size sampling rate to 1.045 angstroms per pixel and execute. To perform a global alignment, select the refine dropdown menu from the protocols panel and click on xmipp3-highres.Input the imported particles and volumes from the previous steps as full size images and initial volumes respectively and set the symmetry group to icosahedral. Then on the image alignment tab under angular assignment, choose global, set the max target resolution to three angstroms and run the job. To perform a local alignment, select xmipp3-highres global, change continue from previous run to yes and select the previous job.Then on the angular assignment tab, change image alignment to local and set the max target resolution to 2.1 angstroms. After finishing, in the xmipp3-highres results window, click on display resolution plots to see how the FSC has changed after the refinement and click on plot histogram with angular changes to see if the Euler angle assignments have changed. Inspect the volume in UCSF Chimera.Zoom in and look for high-resolution features. Micrographs with good CTF fit and low astigmatism were selected for further processing, while those with high astigmatism and forfeit were discarded. 2D class averages containing well-defined classes were selected, and those with low resolution, noise, and partial particles were rejected.Regions of the cryo-electron microscopy map fitted with atomic coordinates of an adeno-associated virus are shown here. Well-defined EM densities allow for fitting side chains of individual amino acids, water molecules and magnesium ions. FSC curves calculated using CryoSPARC v3, RELION-3, and Scipion indicate increasing resolution across the workflow, resolution estimates at four different slices through the structures and resolution histograms demonstrate the incremental improvement in local resolution between the maps throughout the workflow.Combining cryo-EM processing algorithms of CryoSPARC, RELION-3, Scipion, and Phoenix resulted in a resolution increase of the adeno-associated virus structures from 2.9 to 2.3 angstroms across the processing pipeline. It is important to remember that the parameters specified in each step are sample and microscope-dependent. Additionally, the ability to reach high resolution greatly depends upon the quality of the sample and of the raw data.In this video, we present the robust SP workflow for processing of cryo-EM data across various software platforms. By using this approach, one can implement algorithms for multiple programs to refine and validate cryo structures. This workflow can be applied to structure calculations of a wide variety of macromolecular assemblies.

a-robust-single-particle-cryo-electron-microscopy-cryo-em-processing

Research

JoVE Journal

Biochemistry

11.8K Görüntüleme.  Rutgers University. Tek parçacıklı kriyo-elektron mikroskobu, makromoleküllerin atomik çözünürlüğe yakın yapılandırılmış tayinleri için kullanılan yöntemdir. Görüntü işleme ve yapı hesaplamaları için birden fazla yazılım paketi mevcuttur, ancak 3D haritalardaki sonuç, hesaplamalar sırasında uygulanan algoritmalardaki farklılıklar nedeniyle genellikle kalite ve çözünürlükte farklılık gösterir. Bu nedenle, farklı programların bir kombinasyonunu kullanmak, en iyi sonuçlar...