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

Summary

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.

Abstract

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.

Introduction

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 Å^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 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.

Protocol

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⁻/Ų 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.

1. Open cryoSPARC v3 in a web browser and click the Projects header. Select + Add to create a new project. Title the project accordingly and provide a path to an existing directory where jobs and data will be saved.
2. Create a workspace for the project by opening the project, clicking + Add, and selecting New Workspace. Title the workspace and click on Create.
3. Navigate to the new workspace and open the Job Builder on the right panel. This tab displays all functions available in cryoSPARC v3. Click on Import Movies and provide the movies path, gain reference file path, and set acquisition parameters as follows: Raw Pixel Size 1.045 Å, Accelerating Voltage 300 kV, Spherical Aberration 2.7 mm, Total Exposure Dose 28.38 e^-/Å^2.
4. Click on Queue, select a lane to run the job and a workspace, and click on Create.
   NOTE: The acquisition parameters are sample and microscope dependent.

2. CryoSPARC v3 - movie alignment and CTF estimation

1. Open Patch Motion Correction (Multi). This job requires the movies imported in step 1.3 as input. Open the import movies job card in the workspace and drag the Imported_movies output to the movies placeholder on the new job. Queue the job.
   NOTE: For more information about the cryoSPARC methods outlined in this article, see the cryoSPARC tutorial¹⁶.
2. To perform CTF estimation, open Patch CTF Estimation (Multi). Input the micrographs generated in step 2.1 and Queue the job.
3. To inspect the averaged and CTF-corrected micrographs and select a subset for further processing, open Curate Exposures and input the exposures obtained in step 2.2. Queue the job.
4. After the job enters Waiting mode, click on the Interaction tab on the job card, adjust parameter thresholds, and accept or reject individual micrographs for further processing. Accept micrographs with well-matched estimated and experimental CTFs (Figure 2) and discard those with high astigmatism, poor CTF fit, and thick ice.
5. While processing the current data, set the upper threshold of Astigmatism to 400 Å, CTF fit resolution to 5 Å, and relative ice thickness to 2. Click on Done to select the micrographs for downstream processing.

3. CryoSPARC v3 - manual and template-based particle picking

1. Open Manual Picker, input the accepted exposures from steps 2.4-2.5, and Queue the job. Click on the Interactive tab, set the Box Size (px) to 300, and click on a few hundred particles across multiple micrographs and avoid selecting overlapping particles. Here, 340 particles across 29 micrographs were selected. When finished, click on Done Picking! Extract Particles.
   NOTE: This protocol uses manual particle picking to generate templates for automatic selection. However, other methods are also available¹⁷.
2. To generate templates for automated particle picking, click on 2D Classification and input the particle picks generated in step 3.1. Change the number of 2D Classes to 10 and Queue the job.
3. Open Select 2D classes. Input the particles and class averages obtained in step 3.2 and click on the Interactive tab. Select representative 2D classes with good SNR and click on Done.
   NOTE: The class averages reflect different particle views. Select class averages that reflect each view. The goal is to produce well-defined templates representing different views of the specimen for automated picking.
4. Open Template Picker and input the 2D classes selected in step 3.3 and micrographs from steps 2.4-2.5. Set the Particle Diameter (Å) to 220 Å and Queue the job.
5. To inspect the automated picks, open Select Particle Picks, input the particles and micrographs generated in step 3.4, and Queue the job.
6. On the Select Particle Picks job card, click on the Interactive tab and set the Box size (px) to 300. Click on an individual micrograph, adjust the lowpass filter until particles are clearly visible, and set the Normalized Cross Correlation (NCC) Threshold to 0.41 and Power Threshold between 54000 and 227300 .
7. Inspect several micrographs and, if needed, adjust thresholds such that most particles are selected without including false positives. When finished, click Done Picking! Extract Particles.
   NOTE: True particles typically have a high NCC and power score, indicating they are similar to the template and have a high SNR, respectively.
8. Open Extract from Micrographs and input the micrographs and particles from step 3.7. Set the Extracted Box Size (px) to 300 and Queue the job.

4. CryoSPARC v3 - 2D classification

1. Click on 2D Classification and input the extracted particles from step 3.8. Set the Number of 2D classes to 50 and Queue the job.
2. To select the best 2D classes for further processing, open Select 2D classes. Input the particles and class averages obtained in step 4.1. Click on the Interactive tab and choose 2D classes based upon the resolution and the number of particles in the class (Figure 3). Do not select classes containing artifacts. After selecting, click on Done.
   NOTE: Usually, multiple rounds of 2D classification are required to remove particles, which do not converge into distinct, well-defined classes. Run as many rounds of 2D classification as needed to remove such particles from the data set (Figure 3).

5. CryoSPARC v3 - ab-initio reconstruction and homogeneous refinement

1. To generate an initial 3D volume, open Ab-initio Reconstruction and input the particles obtained in step 4.2 or from the final 2D classification. Adjust Symmetry to icosahedral. Queue the job.
   NOTE: Symmetry is sample-dependent and should be changed accordingly. If unknown, use C1 symmetry.
2. Open Homogeneous Refinement. Input the volume from step 5.1 and particles from 4.2 or the final 2D classification. Change the Symmetry and Queue the job. When the job is finished, inspect the Fourier Shell Correlation (FSC) curve and download the volume to examine in UCSF Chimera¹⁸.

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.

1. Navigate to the RELION-3 project directory and launch RELION-3.
2. Open Import from the job-type browser and specify the path to the movies and acquisition parameters as in step 1.3.
3. To perform motion correction, use UCSF MotionCor2²⁰ through the RELION-3 GUI, open Motion Correction and set the default parameters as in the UCSF MotionCor2 manual²¹. Input the path to the movies imported in step 6.2. On the Motion tab, specify the path to motioncor2 executable.
   NOTE: MotionCor2 can be run in parallel using multiple GPUs.
4. Perform CTF estimation using CTFFIND-4.1²² through the RELION-3 GUI. Open CTF Estimation and input the micrographs.star generated in step 6.3. On the CTFFIND-4.1 tab, specify the path to CTFFIND-4.1 executable and set parameters as in the RELION-3.1 tutorial¹⁹.
5. In order to import particle stacks from cryoSPARC v3 to RELION-3, they first must be exported from cryoSPARC v3. In cryoSPARC v3, open the job card of the Select 2D class job from step 4.2 or the final 2D classification. On the Details tab, click on Export Job. Export job outputs the particles_exported.cs file.
6. Prior to importing particle coordinates from cryoSPARC v3 to RELION-3, the particles_exported.cs file from step 6.5 must be converted to .star format. Using PyEM²³, convert the particles_exported.cs file to .star format by executing the following command: csparc2star.py particles_exported.cs particles_exported.star
7. In RELION-3, click on the Manual Picking tab and on the I/O tab, input the micrographs from CTF refinement described in step 6.4. On the Display tab, input the following parameters: Particle diameter (A): 220, Lowpass Filter (A): -1 , Scale for CTF image: 0.5. Run the job. A directory called ManualPick is generated in the RELION-3 home folder.
   NOTE: This step is performed to create a manual picking folder structure in RELION-3. While running manual picking, a single .star file containing coordinates of picked particles is created for each averaged micrograph used for picking in the RELION-3 GUI.
8. Navigate to the folder containing the particles_exported.star file from step 6.6 and run a home-written script producing a single manualpick.star file for each averaged micrograph used for picking of cryo-SPARC v3 particles, which contributed to the final 2D classification exported in step 6.5. The resultant coordinate files are saved in the ManualPick/Movies folder.
9. Return to RELION-3 and re-open the Manual Picking job. Click on Continue. This will display particles previously picked in cryoSPARC v3 in the RELION-3 GUI. Inspect a few micrographs to verify if the transfer of particle coordinates has been accomplished and if particles are properly selected.

7. RELION-3 - Particle extraction and 2D classification

1. Click on Particle Extraction. On the I/O tab, input the CTF corrected micrographs from step 6.4 and coordinates from step 6.9. Click on the Extract tab and change the Particle Box Size (pix) to 300. Run the job.
2. Perform 2D classification to further clean the particle set generated in cryoSPARC v3 to achieve a higher-resolution reconstruction. Click on 2D Classification and on the I/O tab, input the particles.star file generated in step 7.1. On the Optimisation tab, set the Number of Classes to 50 and Mask Diameter (A) to 280. Run the job.
   NOTE: The mask should encompass the entire particle.
3. To choose the best 2D classes, click on the Subset Selection method, input the _model.star file from step 7.2, and Run the job. Select classes as described in step 4.2.
4. Repeat steps 7.2 and 7.3 to remove non-converging particles.

8. RELION-3 - 3D refinement, mask creation, and post-processing

1. Use the map generated in cryoSPARC v3 (step 5.2) as an initial model for 3D refinement in RELION-3. Select the Import method and set the following parameters on the I/O tab: Import Raw Movies/Micrographs: No, Raw Input Files: Movies/*.mrc.
2. Supply the MTF file and input the movie acquisition parameters as described in step 1.3. On the Others tab, select the cryoSPARC v3 map as the input file, change Node Type to 3D reference (.mrc), and Run the job.
3. Select 3D Auto-Refine and on the I/O tab, set Input Images as the particles.star file from step 7.3 or the last selection job. Give the cryoSPARC v3 reconstruction as the Reference Map. Click on the Reference tab and change Initial Low-Pass filter (Å) to 50 and Symmetry to icosahedral. On the Optimisation tab, change the Mask Diameter (Å) to 280 and Run the job.
4. After the run is finished, open run_class001.mrc in UCSF Chimera.
5. In UCSF Chimera, click on Tools and under Volume Data, select Volume Viewer. This will open a new window to adjust volume settings. Change the Step to 1 and adjust the slider until reaching the level value where the map has no noise. Record this value, as it will be used for mask creation in the next step.
6. The map produced from auto-refinement does not reflect the true FSC, as noise from the surrounding solvent lowers the resolution. Before post-processing, create a mask to distinguish the specimen from the solvent region.
   1. Click on Mask Creation and input run_class001.mrc from step 8.3.
   2. Click on the Mask tab and adjust parameters as follows: Lowpass Filter Map (Å): 10, Pixel Size (Å): 1.045, Initial Binarization Threshold: the level value obtained in step 8.5, Extend Binary Map this many Pixels: 3, and Add a Soft-Edge of this many Pixels: 3. Run the job.
7. Examine the mask in UCSF Chimera. If the mask is too tight, increase Extend Binary Map this many Pixels and/or Add a Soft-Edge of this many Pixels. It is important to create a mask with soft edges, as a sharp mask may lead to overfitting.
8. Click on Post-Processing and on the I/O tab, input the half-maps created in step 8.3 and mask from 8.6. Set Calibrated Pixel Size to 1.045 Å. On the Sharpen tab, input the following: Estimate B-Factor Automatically?: Yes, Lowest Resolution for Auto-B Fit (A): 10, Use Your Own B-Factor?: No. On the Filter tab, set Skip Fsc-Weighting? to No. Run the job.

9. RELION-3 - Polishing training and particle polishing

1. Before correcting for per-particle beam-induced motion, first use the training mode to identify optimal motion tracks for the data set. Open Bayesian Polishing and on the I/O tab, input the motion-corrected micrographs from step 6.3, particles from step 8.3, and postprocess .star file from step 8.8. Click the Training tab and set the following parameters: Train Optimal Parameters: Yes, Fraction of Fourier Pixels for Testing: 0.5, Use this many Particles: 5000. Run the job.
   NOTE: This script will produce opt_params_all_groups.txt file in the RELION-3 Polish folder containing optimized polishing parameters required for executing the following step.
2. Once the training job has finished, click on Bayesian Polishing. Click on the Training tab and set Train Optimal Parameters? to No. Select the Polish tab and in Optimised Parameter File specify the path to the opt_params_all_groups.txt file from step 9.1. Click on Run.
3. Repeat 3D refinement (step 8.3) and post-processing (step 8.8) with a set of polished particles.

10. RELION-3 - CTF and per-particle refinements

1. 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 Refine 3D job (run_data.star).
   1. Under Postprocess Star File, set the path to the output from the latest post-processing job (step 9.3).
   2. Select the Fit tab and set the following parameters: Estimate (Anisotropic Magnification): No, Perform CTF Parameter Fitting? No, Estimate Beamtilt: Yes, Also Estimate Trefoil? Yes, Estimate 4^th Order Abberations? Yes. Run the job.
2. Repeat step 10.1 using as input Particles (from Refine3D) generated in the previous job (particles_ctf_refine.star). On the Fit tab, change Estimate (Anisotropic Magnification) to Yes and Run the job.
3. Repeat step 10.2 using as input Particles (from Refine3D) produced in the previous job (particles_ctf_refine.star). On the Fit tab, set the following parameters: Estimate (Anisotropic Magnification): No, Perform CTF Parameter Fitting?: Yes, Fit Defocus?: Per-particle, Fit Astigmatism? Per-micrograph, Fit B-factor?: No, Fit Phase-Shift: No, Estimate Beamtilt?: No, Estimate 4^th Order Aberrations?: No. Run it.
   NOTE: Given the particle has sufficient contrast, the Fit Astigmatism? tab can be set to Per-particle. For this dataset, Per-Particle astigmatism refinement did not improve the quality and resolution of the map.
4. Repeat 3D refinement with the particles from step 10.3 and on the I/O tab, set Use Solvent-Flattened FSCs? to yes. When finished running, execute a post-processing job (step 8.8) and examine the map in UCSF Chimera (step 5.2).

11. Transferring RELION-3 particle coordinates and 3D map to Scipion 3

1. To further refine and validate the RELION-3 map, first import the volume and particles from the last post-processing job (step 10.4) to Scipion 3. Launch Scipion 3 and create a new project.
2. On the left Protocols panel, select the Imports drop-down and click on Import Particles. Change the following parameters: Import From: RELION-3, Star File: postprocess.star, and specify acquisition parameters as in step 1.3. Click on Execute.
3. Click on the Imports drop-down and select Import Volumes. Under Import From give the path to the RELION-3 map. Change Pixel Size (Sampling Rate) Å/px to 1.045 and Execute.

12. Scipion 3 - High - resolution refinement

1. First, perform a global alignment. Select the Refine drop-down on the Protocols panel and click on Xmipp3 - highres²⁴. Input the imported particles and volumes from steps 11.2 and 11.3 as Full-Size Images and Initial Volumes, respectively and set the Symmetry Group to icosahedral. On the Image Alignment tab under Angular Assignment, choose Global and set the Max Target Resolution to 3 Å, and Run the job.
2. When the job is finished, click on Analyze Results. In the new window, click on the UCSF Chimera icon to examine the refined volume. Additionally, click on Display Resolution Plots (FSC) to see how the FSC has changed after the refinement, as well as Plot Histogram with Angular Changes to see if the Euler angle assignments have changed.
   NOTE: Depending on the resolution of the input RELION-3 structure, this step may be repeated several times with different values set for the Max Target Resolution under the Angular Assignment tab. For more information see the Scipion tutorial²⁵.
3. Repeat step 12.1 with a local alignment. Copy the previous job and change Select Previous Run to Xmipp3 - highres Global. On the Angular Assignment tab, change Image Alignment to Local. Set the Max Target Resolution to 2.1 Å.
4. Examine the refined map in UCSF Chimera and analyze the change in FSC and angular assignments (step 12.2). Repeat local refinement until resolution does not improve and the angular assignments have converged, adjusting Max Target Resolution as needed.
5. The output map from Scipion 3 can be additionally density-modified and sharpened in Phenix²⁶.

13. Scipion 3 - Map validation

1. Examine the local resolution of the final map generated in Xmipp3 - highres. Open Xmipp - local MonoRes²⁷ and input the final volume from the previous job and mask generated in step 8.6. Set Resolution Range from 1 to 6 Å with a 0.1 Å interval and Execute the job.
2. When finished running, click on Analyze Results and examine the resolution histogram and volume slices colored by resolution.
3. To see if particles are well-aligned, open Multireference Alignability²⁸ and input the particles and volume from step 12.3. Click on Analyze Results to display the validation plot. Ideally, all points should be clustered around (1.0,1.0).
4. Open Xmipp3 - Validate Overfitting. Input the particles and volumes from step 12.3. When finished running, analyze results and inspect the overfitting plot. Crossing of the aligned Gaussian noise and aligned particles curves indicates overfitting.

Results

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 cryoSPA...

Discussion

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

Materials

List of materials used in this article

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/