Image processing for electron microscopy single-particle analysis using XMIPP

Abstract

We describe a collection of standardized image processing protocols for electron microscopy single-particle analysis using the XMIPP software package. These protocols allow performing the entire processing workflow starting from digitized micrographs up to the final refinement and evaluation of 3D models. A particular emphasis has been placed on the treatment of structurally heterogeneous data through maximum-likelihood refinements and self-organizing maps as well as the generation of initial 3D models for such data sets through random conical tilt reconstruction methods. All protocols presented have been implemented as stand-alone, executable python scripts, for which a dedicated graphical user interface has been developed. Thereby, they may provide novice users with a convenient tool to quickly obtain useful results with minimum efforts in learning about the details of this comprehensive package. Examples of applications are presented for a negative stain random conical tilt data set on the hexameric helicase G40P and for a structurally heterogeneous data set on 70S Escherichia coli ribosomes embedded in vitrified ice.

Figure 1

A generalized XMIPP processing workflow. The protocols developed may be divided in data preprocessing, 2D processing and 3D processing (light-blue boxes). Computationally demanding protocols that allow multiprocessor computing (via message-passing interface, MPI) are shown in orange; all other protocols are shown in yellow.

Figure 2

Micrograph selection. This is based on (a–c) CTFs and on (d–f) particle appearance. (a) A suitable CTF has several rotationally symmetric rings. CTFs should be discarded if they present drift, that is, (b) fading in a particular direction, or astigmatism, that is, (c) rotationally asymmetric. Furthermore, suitable micrographs should present a (d) homogenous population of well-separated particles. (e) Micrographs should be discarded if the particle density is too high, that is, the particles are so close to each other that they almost overlap, or (f) if the particles are very heterogeneous in size or appearance, indicating aggregation or other forms of particle instability. Panels d–f are on the same scale, and the scale bar in panel d represents 50 nm.

Figure 3

Example of class selection from a self-organizing map of rotational spectra. The user interactively identifies distinct classes, each of which may comprehend several (neighboring) code vectors. In this example, two classes were identified, one with sixfold symmetry and one with threefold symmetry. Code vectors were identified as belonging to the sixfold symmetric class when they contained a single peak at the sixfold harmonic. Code vectors were identified to belong to the threefold symmetric class when they contained the largest peak at the threefold harmonic and a secondary peak at the sixfold harmonic. The graphical interface of the xmipp_show program (Steps 14 and 20) allows calculating averages for all experimental measurements corresponding to the selected classes (insets).

Figure 4

Anticipated results for the G40P case. Tilted pairs of digitized micrographs were processed to yield a data set of 14,000 particle pairs. The untilted particles were classified in threefold and sixfold symmetric particles based on their rotational spectra. For both classes, the untilted particles were aligned using maximum-likelihood refinement, and the tilted particles were used to calculate the corresponding random conical tilt reconstructions.

Figure 5

Anticipated results for the ribosome case. A structurally heterogeneous data set of 70S E. coli ribosome particles in complex with or without EFG were classified using ML3D classification, and the corresponding class with particles lacking EFG was further refined using either projection matching or multiresolution refinement.