Name: cryo-em and single-particle analysis with scipion
Uploaded: 2021-05-29T13:00:00
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Description: Watch this Scientific Journal Video about Cryo-EM and Single-Particle Analysis with Scipion at JoVE.com

The authors would like to acknowledge economical support from: The Spanish Ministry of Science and Innovation through Grants: PID2019-104757RB-I00/AEI/10.13039/501100011033, the &#34;Comunidad Aut&#243;noma de Madrid&#34; through Grant: S2017/BMD-3817, Instituto de Salud Carlos III, PT17/0009/0010 (ISCIII-SGEFI/ERDF), European Union (EU) and Horizon 2020 through grant: INSTRUCT - ULTRA (INFRADEV-03-2016-2017, Proposal: 731005), EOSC Life (INFRAEOSC-04-2018, Proposal: 824087), iNEXT - Discovery (Proposal: 871037), and HighResCells (ERC - 2018 - SyG, Proposal: 810057). The project that gave rise to these results received the support of a fellowship from &#34;la Caixa&#34; Foundation (ID 100010434). The fellowship code is LCF/BQ/DI18/11660021. This project has received funding from the European Union&#39;s Horizon 2020 research and innovation programme under the Marie Sk&#322;odowska-Curie grant agreement No. 713673. The authors acknowledge the support and the use of resources of Instruct, a Landmark ESFRI project.

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The authors have nothing to disclose.

Currently, cryo-EM is a key tool to reveal the 3D structure of biological samples. When good data is collected with the microscope, the available processing tools will allow us to obtain a 3D reconstruction of the macromolecule under study. Cryo-EM data processing is able to achieve near-atomic resolution, which is key to understanding the functional behavior of a macromolecule and is also crucial in drug discovery.
Scipion is a software that allows creating the whole workflow combining the most relevant image processing packages in an integrative way, which helps the traceability and reproducibility of the entire image-processing workflow. Scipion provides a very complete set of tools to carry out the processing; however, obtaining high resolutions reconstructions depends completely on the quality of the acquired data and how these data is processed. 
To get a high resolution 3D reconstruction, the first requirement is to obtain good movies from the microscope, which preserve structural information to high resolution. If this is not the case, the workflow will not be able to extract high definition information from the data. Then, a successful processing workflow should be able to extract particles that really correspond to the structure and to find the orientations of these particles in the 3D space. If any of the steps in the workflow fails, the quality of the reconstructed volume will be degraded. Scipion allows for using different packages in any of the processing steps, which helps to find the most adequate approach to process the data. Moreover, thanks to having many packages available, consensus tools, that boost the accuracy by finding an agreement in the estimated outputs of different methods, can be used. Also, it has been discussed in detail in the Representative Results section several validation tools and how to identify accurate and inaccurate results in every step of the workflow, to detect potential problems, and how to try to solve them. There are several checkpoints along the protocol that could help to realize if the protocol is running properly or not. Some of the most relevant are: picking, 2D classification, initial volume estimation, and 3D alignment. Checking the inputs, repeating the step with a different method, or using consensus, are options available in Scipion that the user can use to find solutions when issues appear. 
Regarding the previous approaches to package integration in the Cryo-EM field, Appion31 is the only one that allows real integration of different software packages. However, Appion is tightly connected with Leginon32, a system for automated collection of images from electron microscopes. The main difference with Scipion is that data model and storage are less coupled. In such a way, to create a new protocol in Scipion, only a Python script needs to be developed. However, in Appion, the developer must write the script and change the underlying database. In summary, Scipion was developed to simplify maintenance and extensibility.
We have presented in this manuscript a complete workflow for Cryo-EM processing, using the real case dataset of the Plasmodium falciparum 80S Ribosome (EMPIAR entry: 10028, EMDB entry: 2660). The steps covered and discussed here can be summarized as movie alignment, CTF estimation, particle picking, 2D classification, initial map estimation, 3D classification, 3D refinement, evaluation, and post-processing. Different packages have been used and consensus tools were applied in several of these steps. The final 3D reconstructed volume achieved a resolution of 3 &#197; and, in the post-processed volume, some secondary structures can be distinguished, like alpha-helices, which helps to describe how atoms are arranged in space.
The workflow presented in this manuscript shows how Scipion can be used to combine different Cryo-EM packages in a straightforward and integrative way to simplify the processing, and obtain more reliable result at the same time.
In the future, the development of new methods and packages will keep growing and software like Scipion to easily integrate all of them will be even more important for the researchers. Consensus approaches will be more relevant even then, when plenty of methods with different basis will be available, helping to obtain more accurate estimations of all the parameters involve in the reconstruction process in Cryo-EM. Tracking and reproducibility are key in the research process and easier to achieve with Scipion thanks to having a common framework for the execution of complete workflows.

In cryo-electron microscopy (cryo-EM), single particle analysis (SPA) of vitrified frozen-hydrated specimens is one of the most widely used and successful variants of imaging for biological macromolecules, as it allows to understand molecular interactions and the function of biological ensembles1. This is thanks to the recent advances in this imaging technique that gave rise to the "resolution revolution"2 and have allowed the successful determination of biological 3D structures with near-atomic resolution. Currently, the highest resolution achieved in SPA cryo-EM was 1.15 &#197; for apoferritin3 (EMDB entry: 11668). These technological advances comprise improvements in the sample preparation4, the image acquisition5, and the image processing methods6. This article is focused on this last point. 
Briefly, the goal of the image processing methods is to identify all the acquisition parameters to invert the imaging process of the microscope and recover the 3D structure of the biological specimen under study. These parameters are the gain of the camera, the beam-induced movement, the aberrations of the microscope (mainly the defocus), the 3D angular orientation and translation of each particle, and the conformational state in case of having a specimen with conformational changes. However, the number of parameters is very high and cryo-EM requires using low-dose images to avoid radiation damage, which significantly reduces the Signal-to-Noise Ratio (SNR) of the acquired images. Thus, the problem cannot be unequivocally solved and all the parameters to be calculated only can be estimations. Along the image processing workflow, the correct parameters should be identified, discarding the remaining ones to finally obtain a high-resolution 3D reconstruction.
The data generated by the microscope are gathered in frames. Simplifying, a frame contains the number of electrons that have arrived at a particular position (pixel) in the image, whenever electron-counting detectors are used. In a particular field of view, several frames are collected and this is called a movie. As low electron doses are used to avoid radiation damage that could destroy the sample, the SNR is very low and the frames corresponding to the same movie need to be averaged to obtain an image revealing structural information about the sample. However, not only a simple average is applied, the sample can suffer shifts and other kinds of movements during the imaging time due to the beam-induced movement that need to be compensated. The shift-compensated and averaged frames originate a micrograph.
Once the micrographs are obtained, we need to estimate the aberrations introduced by the microscope for each of them, called Contrast Transfer Function (CTF), which represents the changes in the contrast of the micrograph as a function of frequency. Then, the particles can be selected and extracted, which is called particle picking. Every particle should be a small image containing only one copy of the specimen under study. There are three families of algorithms for particle picking: 1) the ones that only use some basic parameterization of the appearance of the particle to find them in the whole set of micrographs (e.g., particle size), 2) the ones that learn how the particles look like from the user or a pretrained set, and 3) the ones that use image templates. Each family has different properties that will be shown later.
The extracted set of particles found in the micrographs will be used in a 2D classification process that has two goals: 1) cleaning the set of particles by discarding the subset containing pure noise images, overlapping particles, or other artifacts, and 2) the averaged particles representing each class could be used as initial information to calculate a 3D initial volume. 
The 3D initial volume calculation is the next crucial step. The problem of obtaining the 3D structure can be seen as an optimization problem in a multidimensional solution landscape, where the global minimum is the best 3D volume that represents the original structure, but several local minima representing suboptimal solutions can be found, and where it is very easy to get trapped. The initial volume represents the starting point for the searching process, so bad initial volume estimation could prevent us to find the global minimum. From the initial volume, a 3D classification step will help to discover different conformational states and to clean again the set of particles; the goal is to obtain a structurally homogeneous population of particles. After that, a 3D refinement step will be in charge of refining the angular and translation parameters for every particle to get the best 3D volume possible. 
Finally, in the last steps, the obtained 3D reconstruction can be sharpened and polished. Sharpening is a process of boosting the high frequencies of the reconstructed volume, and the polishing is a step to further refine some parameters, as CTF or beam-induced movement compensation, at the level of particles. Also, some validation procedures could be used to better understand the achieved resolution at the end of the workflow. 
After all these steps, the tracing and docking processes7 will help to give a biological meaning to the obtained 3D reconstruction, by building atomic models de novo or fitting existing models. If high resolution is achieved, these processes will tell us the positions of the biological structures, even of the different atoms, in our structure.
Scipion8 allows creating the whole workflow combining the most relevant image processing packages in an integrative way. Xmipp9, Relion10, CryoSPARC11, Eman12, Spider13, Cryolo14, Ctffind15, CCP416, Phenix17, and many more packages can be included in Scipion. Also, it incorporates all the necessary tools to benefit the integration, interoperability, traceability, and reproducibility to make a full tracking of the entire image-processing workflow8.
One of the most powerful tools that Scipion allows us to use is the consensus, which means to compare the results obtained with several methods in one step of the processing, making a combination of the information conveyed by different methods to generate a more accurate output. This could help to boost the performance and improve the achieved quality in the estimated parameters. Note that a simpler workflow can be build without the use of consensus methods; however, we have seen the power of this tool22,25 and the workflow presented in this manuscript will use it in several steps. 
All the steps that have been summarized in the previous paragraphs will be explained in detail in the following section and combined in a complete workflow using Scipion. Also, how to use the consensus tools to achieve a higher agreement in the generated outputs will be shown. To that end, the example dataset of the Plasmodium falciparum 80S Ribosome has been chosen (EMPIAR entry: 10028, EMDB entry: 2660). The dataset is formed by 600 movies of 16 frames of size 4096x4096 pixels at a pixel size of 1.34&#197; taken at an FEI POLARA 300 with an FEI FALCON II camera, with a reported resolution at EMDB is 3.2&#197;18 .

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
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			<td>no material is used in this article</td>
			<td>-</td>
			<td>-</td>
			<td>-</td>
		</tr>
	</tbody>
</table>

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.

Watch this Scientific Journal Video about Cryo-EM and Single-Particle Analysis with Scipion at JoVE.com

Cryo-EM and Single-Particle Analysis with Scipion

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 ...

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