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. 2019 Oct 1;75(Pt 10):882-894.
doi: 10.1107/S2059798319011860. Epub 2019 Oct 1.

Flexible workflows for on-the-fly electron-microscopy single-particle image processing using Scipion

D Maluenda  1 , T Majtner  1 , P Horvath  1 , J L Vilas  1 , A Jiménez-Moreno  1 , J Mota  1 , E Ramírez-Aportela  1 , R Sánchez-García  1 , P Conesa  1 , L Del Caño  1 , Y Rancel  1 , Y Fonseca  1 , M Martínez  1 , G Sharov  2 , C A García  3 , D Strelak  1 , R Melero  1 , R Marabini  4 , J M Carazo  1 , C O S Sorzano  1
Affiliations

Flexible workflows for on-the-fly electron-microscopy single-particle image processing using Scipion

D Maluenda et al. Acta Crystallogr D Struct Biol. .

Abstract

Electron microscopy of macromolecular structures is an approach that is in increasing demand in the field of structural biology. The automation of image acquisition has greatly increased the potential throughput of electron microscopy. Here, the focus is on the possibilities in Scipion to implement flexible and robust image-processing workflows that allow the electron-microscope operator and the user to monitor the quality of image acquisition, assessing very simple acquisition measures or obtaining a first estimate of the initial volume, or the data resolution and heterogeneity, without any need for programming skills. These workflows can implement intelligent automatic decisions and they can warn the user of possible acquisition failures. These concepts are illustrated by analysis of the well known 2.2 Å resolution β-galactosidase data set.

Keywords: Scipion; electron microscopy; electron-microscopy facilities; image processing; single-particle analysis; stream processing.

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Figures

Figure 1
Figure 1
Example of automatic CTF selection for the 2.2 Å resolution β-galactosidase data set. Left: CTF disabled as Xmipp CTF estimates defocus U as 1.35 µm and defocus V as 1.21 µm, resulting in 0.14 µm of astigmatism in this case (less than 1% of the micrographs are disabled by this criterion). Centre: CTF disabled owing to poor visibility of the Thon rings (about 5% of the micrographs are disabled owing to this criterion). Right: CTF disabled owing to a large discrepancy between Xmipp CTF and CTFFind4 (about 5% of the micrograph are disabled owing to this criterion). After visual inspection, we realized that CTFFind4 has failed in this case.
Figure 2
Figure 2
HTML summary of monitoring for the acquisition simulation of EMPIAR data set 10061. (a) Project summary, (b) the maximum resolution and defocusing histograms according to the CTF estimations, (c) CTF values (resolution, defocusing and phase) over time, (d) gain evolution, (e) system monitoring showing the CPU and RAM load and the swap over time and (f) the micrograph list showing the thumbnails of the aligned micrographs, the shift drift during the alignment, the estimated CTF and other estimated parameters, such as defocusing, astigmatism, maximum resolution, cross-correlation with the theoretical CTF and the astigmatism ratio.
Figure 3
Figure 3
Particle-picking stage. Left: semi-automatic picking workflow, where manual picking trains the Xmipp auto-picking and fixes the particle size for EMAN2 SPARX. Right: fully automatic picking workflow using EMAN2 SPARX and Sphire-crYOLO, where the box size is estimated by Xmipp. The AND and OR consensus strategy is followed for both workflows and the resulting selections for certain micrographs from EMPIAR data set 10061, are shown.
Figure 4
Figure 4
Automatic particle rejection for two micrographs from EMPIAR data set 10061. The red circles correspond to those particles that were labeled as incorrect, whereas the green circles correspond to those that were considered suitable to continue in the pipeline. The number beside each rejected particle corresponds to the reason why it was rejected (see Section 3).
Figure 5
Figure 5
Automatic 2D class selection. Top: classes automatically selected for further processing. Bottom: classes automatically disabled as having either a heterogeneous background or representing very few particles.
Figure 6
Figure 6
Workflow example of 2D classification (purple boxes) and initial volume reconstruction (brown boxes). Since the 2D analysis is carried out on downsampled images, the resulting initial volume is resized to the original size (green box). The volume shown at the side of the workflow corresponds to the classification of 5000 particles from the 2.2 Å resolution β-galactosidase data set, which results in 17 automatically selected classes from a total of 32 (16 for CL2D and 16 for RELION 2D). In addition, Xmipp Swarm consensus has merged 21 initial volumes (ten from EMAN, ten from Xmipp Ransac and one from Xmipp Significant).
Figure 7
Figure 7
Example of a workflow downloaded from the public repository http://workflows.scipion.i2pc.es.
Figure 8
Figure 8
Example of a high-level tool that can create a configurable image-processing workflow in streaming. The tool gives a choice between different algorithmic alternatives for each of the steps and of the configuration of the hardware (in particular GPU and CPU) usage.
Figure 9
Figure 9
Example of a high-level web tool that can import an existing processing streaming workflow.
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