Consideration of sample motion in cryo-tomography based on alignment residual interpolation.

Recently, it has been shown that the resolution in cryo-tomography could be improved by considering the sample motion in tilt-series alignment and reconstruction, where a set of quadratic polynomials were used to model this motion. One requirement of this polynomial method is the optimization of a large number of parameters, which may limit its practical applicability. In this work, we propose an alternative method for modeling the sample motion. Starting from the standard fiducial-based tilt-series alignment, the method uses the alignment residual as local estimates of the sample motion at the 3D fiducial positions. Then, a scattered data interpolation technique characterized by its smoothness and a closed-form solution is applied to model the sample motion. The motion model is then integrated in the tomographic reconstruction. The new method improves the tomogram quality similar to the polynomial one, with the important advantage that the determination of the motion model is greatly simplified, thereby overcoming one of the major limitations of the polynomial model. Therefore, the new method is expected to make the beam-induced motion correction methodology more accessible to the cryoET community.

Databases and Archiving for CryoEM.

CryoEM in structural biology is currently served by three public archives-EMDB for 3DEM reconstructions, PDB for models built from 3DEM reconstructions, and EMPIAR for the raw 2D image data used to obtain the 3DEM reconstructions. These archives play a vital role for both the structural community and the wider biological community in making the data accessible so that results may be reused, reassessed, and integrated with other structural and bioinformatics resources. The important role of the archives is underpinned by the fact that many journals mandate the deposition of data to PDB and EMDB on publication. The field is currently undergoing transformative changes where on the one hand high-resolution structures are becoming a routine occurrence while on the other hand electron tomography is enabling the study of macromolecules in the cellular context. Concomitantly the archives are evolving to best serve their stakeholder communities. In this chapter, we describe the current state of the archives, resources available for depositing, accessing, searching, visualizing and validating data, on-going community-wide initiatives and opportunities, and challenges for the future.

Accurate membrane tracing in three-dimensional reconstructions from electron cryotomography data.

The connection between the extracellular matrix and the cell is of major importance for mechanotransduction and mechanobiology. Electron cryo-tomography, in principle, enables better than nanometer-resolution analysis of these connections, but restrictions of data collection geometry hamper the accurate extraction of the ventral membrane location from these tomograms, an essential prerequisite for the analysis. Here, we introduce a novel membrane tracing strategy that enables ventral membrane extraction at high fidelity and extraordinary accuracy. The approach is based on detecting the boundary between the inside and the outside of the cell rather than trying to explicitly trace the membrane. Simulation studies show that over 99% of the membrane can be correctly modeled using this principle and the excellent match of visually identifiable membrane stretches with the extracted boundary of experimental data indicates that the accuracy is comparable for actual data.

M-free: scoring the reference bias in sub-tomogram averaging and template matching.

Cryo-electron tomography provides a snapshot of the cellular proteome. With template matching, the spatial positions of various macromolecular complexes within their native cellular context can be detected. However, the growing awareness of the reference bias introduced by the cross-correlation based approaches, and more importantly the lack of a reliable confidence measurement in the selection of these macromolecular complexes, has restricted the use of these applications. Here we propose a heuristic, in which the reference bias is measured in real space in an analogous way to the R-free value in X-ray crystallography. We measure the reference bias within the mask used to outline the area of the template, and do not modify the template itself. The heuristic works by splitting the mask into a working and a testing area in a volume ratio of 9:1. While the working area is used during the calculation of the cross-correlation function, the information from both areas is explored to calculate the M-free score. We show using artificial data, that the M-free score gives a reliable measure for the reference bias. The heuristic can be applied in template matching and in sub-tomogram averaging. We further test the applicability of the heuristic in tomograms of purified macromolecules, and tomograms of whole Mycoplasma cells.

Tuner: principled parameter finding for image segmentation algorithms using visual response surface exploration.

In this paper we address the difficult problem of parameter-finding in image segmentation. We replace a tedious manual process that is often based on guess-work and luck by a principled approach that systematically explores the parameter space. Our core idea is the following two-stage technique: We start with a sparse sampling of the parameter space and apply a statistical model to estimate the response of the segmentation algorithm. The statistical model incorporates a model of uncertainty of the estimation which we use in conjunction with the actual estimate in (visually) guiding the user towards areas that need refinement by placing additional sample points. In the second stage the user navigates through the parameter space in order to determine areas where the response value (goodness of segmentation) is high. In our exploration we rely on existing ground-truth images in order to evaluate the "goodness" of an image segmentation technique. We evaluate its usefulness by demonstrating this technique on two image segmentation algorithms: a three parameter model to detect microtubules in electron tomograms and an eight parameter model to identify functional regions in dynamic Positron Emission Tomography scans.

A distributed multi-GPU system for high speed electron microscopic tomographic reconstruction.

Full resolution electron microscopic tomographic (EMT) reconstruction of large-scale tilt series requires significant computing power. The desire to perform multiple cycles of iterative reconstruction and realignment dramatically increases the pressing need to improve reconstruction performance. This has motivated us to develop a distributed multi-GPU (graphics processing unit) system to provide the required computing power for rapid constrained, iterative reconstructions of very large three-dimensional (3D) volumes. The participating GPUs reconstruct segments of the volume in parallel, and subsequently, the segments are assembled to form the complete 3D volume. Owing to its power and versatility, the CUDA (NVIDIA, USA) platform was selected for GPU implementation of the EMT reconstruction. For a system containing 10 GPUs provided by 5 GTX295 cards, 10 cycles of SIRT reconstruction for a tomogram of 4096(2) × 512 voxels from an input tilt series containing 122 projection images of 4096(2) pixels (single precision float) takes a total of 1845 s of which 1032 s are for computation with the remainder being the system overhead. The same system takes only 39 s total to reconstruct 1024(2) × 256 voxels from 122 1024(2) pixel projections. While the system overhead is non-trivial, performance analysis indicates that adding extra GPUs to the system would lead to steadily enhanced overall performance. Therefore, this system can be easily expanded to generate superior computing power for very large tomographic reconstructions and especially to empower iterative cycles of reconstruction and realignment.

Fast three-dimensional noise reduction for real-time electron tomography.

Electron tomography (ET) is the leading imaging technique for visualizing the molecular architecture of complex biological specimens. Real-time ET systems allow scientists to acquire experimental datasets and obtain a preliminary version of three-dimensional structure of the specimen. This rough structure allows assessment of the quality of the sample and can also be used as a guide to collect more datasets. However, the low signal-to-noise ratio of the ET datasets precludes detailed interpretation and makes their assessment difficult. Therefore, noise reduction methods should be integrated in these real-time ET systems for their full exploitation. However, feature-preserving noise reduction methods are typically computationally intensive, which hinders real-time response. This work proposes and evaluates fast implementations of a sophisticated noise reduction method with capabilities of preservation of biologically relevant features. These implementations are designed to exploit the high performance computing (HPC) capabilities of modern multicore platforms and of graphics processing units. It is shown that the use of HPC on modern platforms makes this noise reduction method able to provide datasets appropriate for assessment in a matter of seconds, thereby making it suitable for integration in current real-time ET systems.

Maximum likelihood based classification of electron tomographic data.

Classification and averaging of sub-tomograms can improve the fidelity and resolution of structures obtained by electron tomography. Here we present a three-dimensional (3D) maximum likelihood algorithm--MLTOMO--which is characterized by integrating 3D alignment and classification into a single, unified processing step. The novelty of our approach lies in the way we calculate the probability of observing an individual sub-tomogram for a given reference structure. We assume that the reference structure is affected by a 'compound wedge', resulting from the summation of many individual missing wedges in distinct orientations. The distance metric underlying our probability calculations effectively down-weights Fourier components that are observed less frequently. Simulations demonstrate that MLTOMO clearly outperforms the 'constrained correlation' approach and has advantages over existing approaches in cases where the sub-tomograms adopt preferred orientations. Application of our approach to cryo-electron tomographic data of ice-embedded thermosomes revealed distinct conformations that are in good agreement with results obtained by previous single particle studies.

High performance computing approaches for 3D reconstruction of complex biological specimens.

Knowledge of the structure of specimens is crucial to determine the role that they play in cellular and molecular biology. To yield the three-dimensional (3D) reconstruction by means of tomographic reconstruction algorithms, we need the use of large projection images and high processing time. Therefore, we propose the use of the high performance computing (HPC) to cope with the huge computational demands of this problem. We have implemented a HPC strategy where the distribution of tasks follows the master-slave paradigm. The master processor distributes a slab of slices, a piece of the final 3D structure to reconstruct, among the slave processors and receives reconstructed slices of the volume. We have evaluated the performance of our HPC approach using different sizes of the slab. We have observed that it is possible to find out an optimal size of the slab for the number of processor used that minimize communications time while maintaining a reasonable grain of parallelism to be exploited by the set of processors.

Vectorization with SIMD extensions speeds up reconstruction in electron tomography.

Electron tomography allows structural studies of cellular structures at molecular detail. Large 3D reconstructions are needed to meet the resolution requirements. The processing time to compute these large volumes may be considerable and so, high performance computing techniques have been used traditionally. This work presents a vector approach to tomographic reconstruction that relies on the exploitation of the SIMD extensions available in modern processors in combination to other single processor optimization techniques. This approach succeeds in producing full resolution tomograms with an important reduction in processing time, as evaluated with the most common reconstruction algorithms, namely WBP and SIRT. The main advantage stems from the fact that this approach is to be run on standard computers without the need of specialized hardware, which facilitates the development, use and management of programs. Future trends in processor design open excellent opportunities for vector processing with processor's SIMD extensions in the field of 3D electron microscopy.