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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 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.
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 Å1,2,3. 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 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.
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−/Å2 fractioned across 129 frames, and a defocus range from -0.5 µm to -2.5 µm, at a pixel size of 1.045 Å using EPU. The sample of AAV-DJ was provided by the staff of OHSU.
2. CryoSPARC v3 - movie alignment and CTF estimation
3. CryoSPARC v3 - manual and template-based particle picking
4. CryoSPARC v3 - 2D classification
5. CryoSPARC v3 - ab-initio reconstruction and homogeneous refinement
6. Exporting particle coordinates from cryoSPARC v3 and importing them to RELION-3 using PyEM
NOTE: Particle coordinates carry information about the location of individual particles in each micrograph. Transfer of coordinates instead of particle stacks to RELION-3 allows for running refinement steps which otherwise would not be available. For example, particle polishing requires access to initial movie frames. Hence, prior to exporting particle coordinates from cryoSPARC v3 to RELION-3, import movies and perform motion correction and CTF estimation in RELION-3. See the RELION-3 tutorial19 for details.
7. RELION-3 - Particle extraction and 2D classification
8. RELION-3 - 3D refinement, mask creation, and post-processing
9. RELION-3 - Polishing training and particle polishing
10. RELION-3 - CTF and per-particle refinements
11. Transferring RELION-3 particle coordinates and 3D map to Scipion 3
12. Scipion 3 - High - resolution refinement
13. Scipion 3 - Map validation
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 Å structure of AAV, achieving near Nyquist resolution.
Movies were first imported to cr...
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
The authors have nothing to disclose.
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.
Name | Company | Catalog Number | Comments |
CryoSPARC | Structura Biotechnology Inc. | https://cryosparc.com/ | |
CTFFIND 4 | Howard Hughes Medical Institute, UMass Chan Medical School | https://grigoriefflab.umassmed.edu/ctffind4 | |
MotionCorr2 | UCSF Macromolecular Structure Group | https://msg.ucsf.edu/software | |
Phenix | Computational Tools for Macromolecular Neutron Crystallography (MNC) | http://www.phenix-online.org/ | |
PyEM | Univerisity of California, San Francisco | https://github.com/asarnow/pyem | |
RELION | MRC Laboratory of Structural Biology | https://www3.mrc-lmb.cam.ac.uk/relion/index.php/Main_Page | |
Scipion | Instruct Image Processing Center (I2PC), SciLifeLab | http://scipion.i2pc.es/ | |
UCSF Chimera | UCSF Resource for Biocomputing, Visualization, and Informatics | https://www.cgl.ucsf.edu/chimera/ |
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