Using Scipion for stream image processing at Cryo-EM facilities.

Three dimensional electron microscopy is becoming a very data-intensive field in which vast amounts of experimental images are acquired at high speed. To manage such large-scale projects, we had previously developed a modular workflow system called Scipion (de la Rosa-Trevín et al., 2016). We present here a major extension of Scipion that allows processing of EM images while the data is being acquired. This approach helps to detect problems at early stages, saves computing time and provides users with a detailed evaluation of the data quality before the acquisition is finished. At present, Scipion has been deployed and is in production mode in seven Cryo-EM facilities throughout the world.

A new algorithm for high-resolution reconstruction of single particles by electron microscopy.

The Map Challenge organized by the Electron Microscopy Data Bank has prompted the development of an Xmipp high resolution reconstruction protocol (which we will refer to as highres) that is integrated in the software platform Scipion. In this work we describe the details of the image angular alignment and map reconstruction steps in our new method. This algorithm is similar to the standard projection matching approach with some important modifications, especially in the area of detecting significant features in the reconstructed volume. We show that the new method is able to produce higher resolution maps than the current de facto standard as measured by the Fourier Shell Correlation, the Monogenic Local Resolution and EMRinger.

Blind estimation of DED camera gain in Electron Microscopy.

The introduction of Direct Electron Detector (DED) videos in the Electron Microscope field has boosted Single Particle Analysis to a point in which it is currently considered to be a key technique in Structural Biology. In this article we introduce an approach to estimate the DED camera gain at each pixel from the movies themselves. This gain is needed to have the set of recorded frames into a coherent gray level range, homogeneous over the whole image. The algorithm does not need any other input than the DED movie itself, being capable of providing an estimate of the camera gain image, helping to identify dead pixels and cases of incorrectly calibrated cameras. We propose the algorithm to be used either to validate the experimentally acquired gain image (for instance, to follow its possible change over time) or to verify that there is no residual gain image after experimentally correcting for the camera gain. We show results for a number of DED camera models currently in use (DE, Falcon II, Falcon 3, and K2).

A Survey of the Use of Iterative Reconstruction Algorithms in Electron Microscopy.

One of the key steps in Electron Microscopy is the tomographic reconstruction of a three-dimensional (3D) map of the specimen being studied from a set of two-dimensional (2D) projections acquired at the microscope. This tomographic reconstruction may be performed with different reconstruction algorithms that can be grouped into several large families: direct Fourier inversion methods, back-projection methods, Radon methods, or iterative algorithms. In this review, we focus on the latter family of algorithms, explaining the mathematical rationale behind the different algorithms in this family as they have been introduced in the field of Electron Microscopy. We cover their use in Single Particle Analysis (SPA) as well as in Electron Tomography (ET).

A review of resolution measures and related aspects in 3D Electron Microscopy.

Fourier Shell Correlation, Spectral Signal-to-Noise Ratio, Fourier Neighbour Correlation, and Differential Phase Residual are different measures that have been proposed over time to determine the spatial resolution achieved by a certain 3D reconstruction. Estimates of B-factors to describe the reduction in signal-to-noise ratio with increasing resolution is also a useful parameter. All these concepts are interrelated and different thresholds have been given for each one of them. However, the problem of resolution assessment in 3DEM is still far from settled and preferences are normally adopted in order to choose the "correct" threshold. In this paper we review the different concepts, their theoretical foundations and the derivation of their statistical distributions (the basis for establishing sensible thresholds). We provide theoretical justifications for some common practices in the field for which a formal justification was missing. We also analyze the relationship between SSNR and B-factors, the electron dose needed for achieving a given contrast and resolution, the number of images required, etc. Finally, we review the consequences for the number of particles needed to achieve a certain resolution and how to analyze the Signal-to-Noise Ratio for a sequence of imaging operations.

Fast and automatic identification of particle tilt pairs based on Delaunay triangulation.

Random conical tilt (RCT) and orthogonal tilt reconstruction (OTR) are two remarkable methods for reconstructing the three-dimensional structure of macromolecules at low resolution. These techniques use two images at two different sample tilts. One of the most demanding steps in these methods at the image processing level is to identify corresponding particles on both micrographs, and manual or semiautomatic matching methods are usually used. Here we present an approach to solve this bottleneck with a fully automatic method for assigning particle tilt pairs. This new algorithm behaves correctly with a variety of samples, covering the range from small to large macromolecules and from sparse to densely populated fields of view. It is also more rapid than previous approaches. The roots of the method lie in a Delaunay triangulation of the set of independently picked coordinates on both the untilted and tilted micrographs. These triangulations are then used to search an affine transformation between the untilted and tilted triangles. The affine transformation that maximizes the number of correspondences between the two micrographs defines the coordinate matching.

Scipion: A software framework toward integration, reproducibility and validation in 3D electron microscopy.

In the past few years, 3D electron microscopy (3DEM) has undergone a revolution in instrumentation and methodology. One of the central players in this wide-reaching change is the continuous development of image processing software. Here we present Scipion, a software framework for integrating several 3DEM software packages through a workflow-based approach. Scipion allows the execution of reusable, standardized, traceable and reproducible image-processing protocols. These protocols incorporate tools from different programs while providing full interoperability among them. Scipion is an open-source project that can be downloaded from http://scipion.cnb.csic.es.

Local analysis of strains and rotations for macromolecular electron microscopy maps.

Macromolecular complexes perform their physiological functions by local rearrangements of their constituents and biochemically interacting with their reaction partners. These rearrangements may involve local rotations and the induction of local strains causing different mechanical efforts and stretches at the different areas of the protein. The analysis of these local deformations may reveal important insight into the way proteins perform their tasks. In this paper we introduce a method to perform this kind of local analysis using Electron Microscopy volumes in a fully objective and automatic manner. For doing so, we exploit the continuous nature of the result of an elastic image registration using B-splines as its basis functions. We show that the results obtained by the new automatic method are consistent with previous observations on these macromolecules.

Cryo-EM and the elucidation of new macromolecular structures: Random Conical Tilt revisited.

Cryo-Electron Microscopy (cryo-EM) of macromolecular complexes is a fundamental structural biology technique which is expanding at a very fast pace. Key to its success in elucidating the three-dimensional structure of a macromolecular complex, especially of small and non-symmetric ones, is the ability to start from a low resolution map, which is subsequently refined with the actual images collected at the microscope. There are several methods to produce this first structure. Among them, Random Conical Tilt (RCT) plays a prominent role due to its unbiased nature (it can create an initial model based on experimental measurements). In this article, we revise the fundamental mathematical expressions supporting RCT, providing new expressions handling all key geometrical parameters without the need of intermediate operations, leading to improved automation and overall reliability, essential for the success of cryo-EM when analyzing new complexes. We show that the here proposed RCT workflow based on the new formulation performs very well in practical cases, requiring very few image pairs (as low as 13 image pairs in one of our examples) to obtain relevant 3D maps.

A statistical approach to the initial volume problem in Single Particle Analysis by Electron Microscopy.

Cryo Electron Microscopy is a powerful Structural Biology technique, allowing the elucidation of the three-dimensional structure of biological macromolecules. In particular, the structural study of purified macromolecules -often referred as Single Particle Analysis(SPA)- is normally performed through an iterative process that needs a first estimation of the three-dimensional structure that is progressively refined using experimental data. It is well-known the local optimisation nature of this refinement, so that the initial choice of this first structure may substantially change the final result. Computational algorithms aiming to providing this first structure already exist. However, the question is far from settled and more robust algorithms are still needed so that the refinement process can be performed with sufficient guarantees. In this article we present a new algorithm that addresses the initial volume problem in SPA by setting it in a Weighted Least Squares framework and calculating the weights through a statistical approach based on the cumulative density function of different image similarity measures. We show that the new algorithm is significantly more robust than other state-of-the-art algorithms currently in use in the field. The algorithm is available as part of the software suite Xmipp (http://xmipp.cnb.csic.es) and Scipion (http://scipion.cnb.csic.es) under the name "Significant".

Xmipp 3.0: an improved software suite for image processing in electron microscopy.

Xmipp is a specialized software package for image processing in electron microscopy, and that is mainly focused on 3D reconstruction of macromolecules through single-particles analysis. In this article we present Xmipp 3.0, a major release which introduces several improvements and new developments over the previous version. A central improvement is the concept of a project that stores the entire processing workflow from data import to final results. It is now possible to monitor, reproduce and restart all computing tasks as well as graphically explore the complete set of interrelated tasks associated to a given project. Other graphical tools have also been improved such as data visualization, particle picking and parameter "wizards" that allow the visual selection of some key parameters. Many standard image formats are transparently supported for input/output from all programs. Additionally, results have been standardized, facilitating the interoperation between different Xmipp programs. Finally, as a result of a large code refactoring, the underlying C++ libraries are better suited for future developments and all code has been optimized. Xmipp is an open-source package that is freely available for download from: http://xmipp.cnb.csic.es.

A pattern matching approach to the automatic selection of particles from low-contrast electron micrographs.

MOTIVATION: Structural information of macromolecular complexes provides key insights into the way they carry out their biological functions. Achieving high-resolution structural details with electron microscopy requires the identification of a large number (up to hundreds of thousands) of single particles from electron micrographs, which is a laborious task if it has to be manually done and constitutes a hurdle towards high-throughput. Automatic particle selection in micrographs is far from being settled and new and more robust algorithms are required to reduce the number of false positives and false negatives. RESULTS: In this article, we introduce an automatic particle picker that learns from the user the kind of particles he is interested in. Particle candidates are quickly and robustly classified as particles or non-particles. A number of new discriminative shape-related features as well as some statistical description of the image grey intensities are used to train two support vector machine classifiers. Experimental results demonstrate that the proposed method: (i) has a considerably low computational complexity and (ii) provides results better or comparable with previously reported methods at a fraction of their computing time. AVAILABILITY: The algorithm is fully implemented in the open-source Xmipp package and downloadable from http://xmipp.cnb.csic.es.

Particle quality assessment and sorting for automatic and semiautomatic particle-picking techniques.

Three-dimensional reconstruction of biological specimens using electron microscopy by single particle methodologies requires the identification and extraction of the imaged particles from the acquired micrographs. Automatic and semiautomatic particle selection approaches can localize these particles, minimizing the user interaction, but at the cost of selecting a non-negligible number of incorrect particles, which can corrupt the final three-dimensional reconstruction. In this work, we present a novel particle quality assessment and sorting method that can separate most erroneously picked particles from correct ones. The proposed method is based on multivariate statistical analysis of a particle set that has been picked previously using any automatic or manual approach. The new method uses different sets of particle descriptors, which are morphology-based, histogram-based and signal to noise analysis based. We have tested our proposed algorithm with experimental data obtaining very satisfactory results. The algorithm is freely available as a part of the Xmipp 3.0 package [http://xmipp.cnb.csic.es].

FASTDEF: fast defocus and astigmatism estimation for high-throughput transmission electron microscopy.

In this work we present a fast and automated algorithm for estimating the contrast transfer function (CTF) of a transmission electron microscope. The approach is very suitable for High Throughput work because: (a) it does not require any initial defocus estimation, (b) it is almost an order of magnitude faster than existing approaches, (c) it opens the way to well-defined extensions to the estimation of higher order aberrations, at the same time that provides defocus and astigmatism estimations comparable in accuracy to well established methods, such as Xmipp and CTFFIND3 approaches. The new algorithm is based on obtaining the wrapped modulating phase of the power spectra density pattern by the use of a quadrature filter. This phase is further unwrapped in order to obtain the continuous and smooth absolute phase map; then a Zernike polynomial fitting is performed and the defocus and astigmatism parameters are determined. While the method does not require an initial estimation of the defocus parameters or any non-linear optimization procedure, these approaches can be used if further refinement is desired. Results of the CTF estimation method are presented for standard negative stained images, cryo-electron microscopy images in the absence of carbon support, as well as micrographs with only ice. Additionally, we have also tested the proposed method with micrographs acquired from tilted and untilted samples, obtaining good results. The algorithm is freely available as a part of the Xmipp package [http://xmipp.cnb.csic.es].

Semiautomatic, high-throughput, high-resolution protocol for three-dimensional reconstruction of single particles in electron microscopy.

In this chapter we describe the steps needed for reconstructing the three-dimensional structure of a macromolecular complex starting from its projections collected in electron micrographs. The concepts are shown through the use of Xmipp 3.0, a software suite specifically designed for the image processing of biological structures imaged with electron or X-ray microscopy. We illustrate the image processing workflow by applying it to the images of Bovine Papilloma virus published in Wolf et al. (Proc Natl Acad Sci USA 107:6298-6303, 2010). We show that in the case of high-quality, homogeneous datasets with a priori knowledge about the initial volume, we can have a high-resolution 3D reconstruction in less than 1 day using a computer cluster with only 32 processors.