Cryo-EM and Single-Particle Analysis with Scipion

Abstract

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.

Scipion-EM-ProDy: A Graphical Interface for the ProDy Python Package within the Scipion Workflow Engine Enabling Integration of Databases, Simulations and Cryo-Electron Microscopy Image Processing

“…In this context, we recently created the Scipion Flexibility Hub [ 32 ], a framework for combining continuous flexibility methods into pipelines with shared interactive analysis tools connected to the rest of the Scipion workflow engine for Cryo-EM image analysis and structural biology (version 3.0) [ 33 , 34 ]. This framework provides common tools for several continuous heterogeneity methods, including the CryoDRGN deep reconstruction generative network (supporting version 1.0.0, 1.1.0, 2.1.0 and 2.3.0) [ 11 ], HEMNMA [ 24 , 35 ], MDSPACE [ 27 ], and related methods presented in ContinuousFlex v3.4.0 [ 13 ], and some approaches based on the Zernike3D deformation framework [ 12 , 36 ].…”