A fast reconstruction algorithm for electron microscope tomography

We have implemented a Fast Fourier Summation algorithm for tomographic reconstruction of three-dimensional biological data sets obtained via transmission electron microscopy. We designed the fast algorithm to reproduce results obtained by the direct summation algorithm (also known as filtered or R-weighted backprojection). For two-dimensional images, the new algorithm scales as O(N(theta)M log M)+O(MN log N) operations, where N(theta) is the number of projection angles and M x N is the size of the reconstructed image. Three-dimensional reconstructions are constructed from sequences of two-dimensional reconstructions. We demonstrate the algorithm on real data sets. For typical sizes of data sets, the new algorithm is 1.5-2.5 times faster than using direct summation in the space domain. The speed advantage is even greater as the size of the data sets grows. The new algorithm allows us to use higher order spline interpolation of the data without additional computational cost. The algorithm has been incorporated into a commonly used package for tomographic reconstruction.     

A "flip-flop" rotation stage for routine dual-axis electron cryotomography

Electron cryotomography can be used to solve the three-dimensional structures of individual large macromolecules, assemblies, and even small intact cells to medium (approximately 4-8 nm) resolution in a near-native state, but restrictions in the range of accessible views are a major limitation. Here we report on the design, characterization, and demonstration of a new "flip-flop" rotation stage that allows facile and routine collection of two orthogonal tilt-series of cryosamples. Single- and dual-axis tomograms of a variety of samples are compared to illustrate qualitatively the improvement produced by inclusion of the second tilt-series. Exact quantitative expressions are derived for the volume of the remaining "missing pyramid" in reciprocal space. When orthogonal tilt-series are recorded to +/-65 degrees in each direction, as this new cryostage permits, only 11% of reciprocal space is left unmeasured. The tomograms suggest that further improvement could be realized, however, through better software to align and merge dual-axis tilt-series of cryosamples.     

A novel approach for intracellular 3D immuno-labeling for electron tomography

Electron tomography (ET) is an indispensable high-resolution tool for three dimensional (3D) imaging in cell biology. When applied to immuno-labeled cells, ET can provide essential insights in both the cellular architecture and the dynamics. Current protocols for 3D immuno-labeling of intracellular antigens include permeabilization steps that cause random, extensive cell membrane disruption. This permeabilization results in a poor cell ultrastructure, limiting the usefulness of the specimens for high-resolution studies. Here we describe a novel method, based on a well-controlled permeabilization by targeted laser cell perforation, that allows for the 3D immuno-localization of cytoplasmic antigens in cultured cells. The approach is unique since it is applicable to both chemically and cryo-fixed cells and leads to a superior ultrastructural preservation for electron microscopy and tomography.     

Performance improvements for iterative electron tomography reconstruction using graphics processing units (GPUs)

Iterative reconstruction algorithms are becoming increasingly important in electron tomography of biological samples. These algorithms, however, impose major computational demands. Parallelization must be employed to maintain acceptable running times. Graphics Processing Units (GPUs) have been demonstrated to be highly cost-effective for carrying out these computations with a high degree of parallelism. In a recent paper by Xu et al. (2010), a GPU implementation strategy was presented that obtains a speedup of an order of magnitude over a previously proposed GPU-based electron tomography implementation. In this technical note, we demonstrate that by making alternative design decisions in the GPU implementation, an additional speedup can be obtained, again of an order of magnitude. By carefully considering memory access locality when dividing the workload among blocks of threads, the GPU’s cache is used more efficiently, making more effective use of the available memory bandwidth.     

A resolution criterion for electron tomography based on cross-validation

Despite much progress in electron tomography, quantitative assessment of resolution has remained a problematic issue. The criteria that are used in single particle analysis, based on gauging the consistency between density maps calculated from half data sets, are not directly applicable because of the uniqueness of a tomographic volume. Here, we propose two criteria based on a cross-validation approach. One, called FSC(e/o), is based on a Fourier shell correlation comparison between tomograms calculated from the even and odd members of a tilt series. The other, called noise-compensated leave-one-out (NLOO), is based on Fourier ring correlation comparisons between an original projection and the corresponding reprojection of the tomogram calculated from all the other projections, taking into account the differing noise statistics. Plotted as a function of tilt angle, they allow assessment of the angular dependence of resolution and quality control over the series of projections. Integrated over all projections, the results give a global figure for resolution. Tests on simulated tomograms established consistency between these criteria and the FSC(ref), a correlation coefficient calculated between a known reference structure and the corresponding portion of a tomogram containing that structure. The two criteria-FSC(e/o) and NLOO-are mutually consistent when residual noise is the major resolution-limiting factor. When the size of the tilt increment becomes a significant factor, NLOO provides a more reliable criterion, as expected, although it is computationally intensive. Applicable to entire tomograms or selected structures, NLOO has also been tested on experimental tomographic data.     

A novel automatic 3D stitching algorithm for optical coherence tomography angiography and its application in dermatology

Optical coherence tomography (OCT) and OCT angiography (OCTA) techniques offer numerous advantages in clinical skin applications but the field of view (FOV) of current commercial systems are relatively limited to cover the entire skin lesion. The typical method to expand the FOV is to apply wide field objective lens. However, lateral resolution is often sacrificed when scanning with these lenses. To overcome this drawback, we developed an automated 3D stitching method for creating high-resolution skin structure and vascular volumes with large field of view, which was realized by montaging multiple adjacent OCT and OCTA volumes. The proposed stitching method is demonstrated by montaging 3 × 3 OCT and OCTA volumes (nine OCT/OCTA volumes as one data set with each volume covers 2.5 cm × 2.5 cm area) of healthy thin and thick skin from six volunteers. The proposed stitching protocol achieves high flexibility and repeatable for all the participants. Moreover, according to evaluation of structural similarity index and feature similarity index, our proposed stitched result has a superior similarity to single scanning protocol in large-scaled. We had also verified its improved performance through assessing metrics of vessel contrast-noise-ratio (CNR) from 2.07 ± 0.44 (single large-scaled scanning protocol) to 3.05 ± 0.51 (proposed 3 × 3 sub-volume stitching method).     

A novel algorithm for finding interspersed repeat regions

The analysis of repeats in the DNA sequences is an important subject in bioinformatics. In this paper, we propose a novel projection-assemble algorithm to find unknown interspersed repeats in DNA sequences. The algorithm employs random projection algorithm to obtain a candidate fragment set, and exhaustive search algorithm to search each pair of fragments from the candidate fragment set to find potential linkage, and then assemble them together. The complexity of our projection-assemble algorithm is nearly linear to the length of the genome sequence, and its memory usage is limited by the hardware. We tested our algorithm with both simulated data and real biology data, and the results show that our projection-assemble algorithm is efficient. By means of this algorithm, we found an un-labeled repeat region that occurs five times in Escherichia coil genome, with its length more than 5,000 bp, and a mismatch probability less than 4%.     

A novel algorithm for ranking RNA structure candidates

RNA research is advancing at an ever increasing pace. The newest and most state-of-the-art instruments and techniques have made possible the discoveries of new RNAs, and they have carried the field to new frontiers of disease research, vaccine development, therapeutics, and architectonics. Like proteins, RNAs show a marked relationship between structure and function. A deeper grasp of RNAs requires a finer understanding of their elaborate structures. In pursuit of this, cutting-edge experimental and computational structure-probing techniques output several candidate geometries for a given RNA, each of which is perfectly aligned with experimentally determined parameters. Identifying which structure is the most accurate, however, remains a major obstacle. In recent years, several algorithms have been developed for ranking candidate RNA structures in order from most to least probable, though their levels of accuracy and transparency leave room for improvement. Most recently, advances in both areas are demonstrated by rsRNASP, a novel algorithm proposed by Tan et al. rsRNASP is a residue-separation-based statistical potential for three-dimensional structure evaluation, and it outperforms the leading algorithms in the field.     

Single versus dual-axis cryo-electron tomography of microtubules assembled in vitro: Limits and perspectives

Single-axis cryo-electron tomography of vitrified specimens has become a method of choice to reconstruct in three dimensions macromolecular assemblies in their cellular context or prepared from purified components. Here, we asked how a dual-axis acquisition scheme would improve three-dimensional reconstructions of microtubules assembled in vitro. We show that in single-axis tomograms, microtubules oriented close to the perpendicular of the tilt axis display diminished contrast, and ultimately transform into sets of parallel lines oriented in the direction of the electron beam when observed in cross-section. Analysis of their three-dimensional Fourier transform indicates that this imaging artifact is due to a decrease in the angular sampling of their equatorial components. Although the second orthogonal series does not fully complement the first one at the specimen level due to increased radiation damage, it still allows elongated features oriented in any directions to be correctly reconstructed, which might be essential for highly heterogeneous specimens such as cells.     

An iterative deconvolution algorithm for image recovery in clinical CT: A phantom study

PurposeTo study the feasibility of using an iterative reconstruction algorithm to improve previously reconstructed CT images which are judged to be non-diagnostic on clinical review. A novel rapidly converging, iterative algorithm (RSEMD) to reduce noise as compared with standard filtered back-projection algorithm has been developed.Materials and methodsThe RSEMD method was tested on in-silico, Catphan^®500, and anthropomorphic 4D XCAT phantoms. The method was applied to noisy CT images previously reconstructed with FBP to determine improvements in SNR and CNR. To test the potential improvement in clinically relevant CT images, 4D XCAT phantom images were used to simulate a small, low contrast lesion placed in the liver.ResultsIn all of the phantom studies the images proved to have higher resolution and lower noise as compared with images reconstructed by conventional FBP. In general, the values of SNR and CNR reached a plateau at around 20 iterations with an improvement factor of about 1.5 for in noisy CT images. Improvements in lesion conspicuity after the application of RSEMD have also been demonstrated. The results obtained with the RSEMD method are in agreement with other iterative algorithms employed either in image space or with hybrid reconstruction algorithms.ConclusionsIn this proof of concept work, a rapidly converging, iterative deconvolution algorithm with a novel resolution subsets-based approach that operates on DICOM CT images has been demonstrated. The RSEMD method can be applied to sub-optimal routine-dose clinical CT images to improve image quality to potentially diagnostically acceptable levels.     

A novel neural response algorithm for protein function prediction

Background

Large amounts of data are being generated by high-throughput genome sequencing methods. But the rate of the experimental functional characterization falls far behind. To fill the gap between the number of sequences and their annotations, fast and accurate automated annotation methods are required. Many methods, such as GOblet, GOFigure, and Gotcha, are designed based on the BLAST search. Unfortunately, the sequence coverage of these methods is low as they cannot detect the remote homologues. Adding to this, the lack of annotation specificity advocates the need to improve automated protein function prediction.

Results

We designed a novel automated protein functional assignment method based on the neural response algorithm, which simulates the neuronal behavior of the visual cortex in the human brain. Firstly, we predict the most similar target protein for a given query protein and thereby assign its GO term to the query sequence. When assessed on test set, our method ranked the actual leaf GO term among the top 5 probable GO terms with accuracy of 86.93%.

Conclusions

The proposed algorithm is the first instance of neural response algorithm being used in the biological domain. The use of HMM profiles along with the secondary structure information to define the neural response gives our method an edge over other available methods on annotation accuracy. Results of the 5-fold cross validation and the comparison with PFP and FFPred servers indicate the prominent performance by our method. The program, the dataset, and help files are available at http://www.jjwanglab.org/NRProF/.

     

A novel algorithm for network-based prediction of cancer recurrence

To develop accurate prognostic models is one of the biggest challenges in “omics”-based cancer research. Here, we propose a novel computational method for identifying dysregulated gene subnetworks as biomarkers to predict cancer recurrence. Applying our method to the DNA methylome of endometrial cancer patients, we identified a subnetwork consisting of differentially methylated (DM) genes, and non-differentially methylated genes, termed Epigenetic Connectors (EC), that are topologically important for connecting the DM genes in a protein-protein interaction network. The ECs are statistically significantly enriched in well-known tumorgenesis and metastasis pathways, and include known epigenetic regulators. Importantly, combining the DMs and ECs as features using a novel random walk procedure, we constructed a support vector machine classifier that significantly improved the prediction accuracy of cancer recurrence and outperformed several alternative methods, demonstrating the effectiveness of our network-based approach.     

TOMOBFLOW: feature-preserving noise filtering for electron tomography

Background  

Noise filtering techniques are needed in electron tomography to allow proper interpretation of datasets. The standard linear filtering techniques are characterized by a tradeoff between the amount of reduced noise and the blurring of the features of interest. On the other hand, sophisticated anisotropic nonlinear filtering techniques allow noise reduction with good preservation of structures. However, these techniques are computationally intensive and are difficult to be tuned to the problem at hand.     

Evaluation of denoising algorithms for biological electron tomography

Tomograms of biological specimens derived using transmission electron microscopy can be intrinsically noisy due to the use of low electron doses, the presence of a "missing wedge" in most data collection schemes, and inaccuracies arising during 3D volume reconstruction. Before tomograms can be interpreted reliably, for example, by 3D segmentation, it is essential that the data be suitably denoised using procedures that can be individually optimized for specific data sets. Here, we implement a systematic procedure to compare various nonlinear denoising techniques on tomograms recorded at room temperature and at cryogenic temperatures, and establish quantitative criteria to select a denoising approach that is most relevant for a given tomogram. We demonstrate that using an appropriate denoising algorithm facilitates robust segmentation of tomograms of HIV-infected macrophages and Bdellovibrio bacteria obtained from specimens at room and cryogenic temperatures, respectively. We validate this strategy of automated segmentation of optimally denoised tomograms by comparing its performance with manual extraction of key features from the same tomograms.     

An improved strategy for automated electron microscopic tomography

A prediction-based scheme is proposed and implemented for automated electron microscopic tomography. By assuming that the sample follows a simple geometric rotation and that the optical system can be characterized in terms of an offset between the optical and mechanical axes, it is found that the image movement in the x, y, and z directions due to stage tilt can be dynamically predicted with desired accuracy (15 nm in x-y position and 100 nm in focus). Thus, the microscope optical system (beam/image shift and focus) can be automatically adjusted to compensate for the predicted image movement prior to taking the projected image at each tilt angle. As a consequence, it is not necessary to either record additional images for tracking and focusing during the course of data collections or to spend valuable setup time in a lengthy pre-calibration of stage motions. Furthermore, this scheme is also found to tolerate a significant degree of non-eucentricity and to be quite robust in the collection of regular and cryo low-dose images on thin or thick samples even at magnifications greater than 62000x and angular step as large as 10 degrees. For interested users the software can be freely downloaded for non-profit use at http://www.msg.ucsf.edu/tomography.     

Object shape dependency of in-plane resolution for iterative reconstruction of computed tomography

The present study aimed to investigate whether the in-plane resolution property of iterative reconstruction (IR) of computed tomography (CT) data is object shape-dependent by testing columnar shapes with diameters of 3, 7, and 10 cm (circular edge method) and a cubic shape with 5-cm side lengths (linear edge method). For each shape, objects were constructed of acrylic (contrast in Hounsfield units [ΔHU] = 120) as well as a soft tissue equivalent material (ΔHU = 50). For each shape, we measured the modulation transfer functions (MTFs) of IR and filtered back projection (FBP) using two multi-slice CT scanners at scan doses of 5 and 10 mGy. In addition, we evaluated a thin metal wire using the conventional method at 10 mGy. For FBP images, the MTF results of the tested objects and the wire method showed substantial agreement, thus demonstrating the validity of our analysis technique. For IR images, the MTF results of different shapes were nearly identical for each object contrast and dose combination, and we did not observe shape-dependent effects of the resolution properties of either tested IR. We conclude that both the circular edge method and linear edge method are equally useful for evaluating the resolution properties of IRs.     

A novel prosthetic group for site-selective labeling of peptides for positron emission tomography

Efficient methodologies for the radiolabeling of peptides with [(18)F]fluoride are a prerequisite to enabling commercialization of peptide-containing radiotracers for positron emission tomography (PET) imaging. It was the purpose of this study to investigate a novel chemoselective ligation reaction comprising conjugation of an [(18)F]-N-methylaminooxy-containing prosthetic group to a functionalized peptide. Twelve derivatives of general formula R1-CO-NH-Lys-Gly-Phe-Gly-Lys-OH were synthesized where R1 was selected from a short list of moieties anticipated to be reactive toward the N-methylaminooxy group. Conjugation reactions were initially carried out with nonradioactive precursors to assess, in a qualitative manner, their general suitability for PET chemistry with only the most promising pairings progressing to full radiochemical assessment. Best results were obtained for the ligation of O-[2-(2-[(18)F]fluoroethoxy)ethyl]-N-methyl-N-hydroxylamine 18 to the maleimidopropionyl-Lys-Gly-Phe-Gly-Lys-OH precursor 10 in acetate buffer (pH 5) after 1 h at 70 degrees C. The non-decay-corrected isolated yield was calculated to be 8.5%. The most encouraging result was observed with the combination 18 and 4-(2-nitrovinyl)benzoyl-Lys-Gly-Phe-Gly-Lys-OH, 9, where the conjugation reaction proceeded rapidly to completion at 30 degrees C after only 5 min. The corresponding non-decay-corrected radiochemical yield for the isolated (18)F-labeled product 27 was 12%. The preliminary results from this study demonstrate the considerable potential of this novel strategy for the radiolabeling of peptides.     

A novel functional module detection algorithm for protein-protein interaction networks

Background  

The sparse connectivity of protein-protein interaction data sets makes identification of functional modules challenging. The purpose of this study is to critically evaluate a novel clustering technique for clustering and detecting functional modules in protein-protein interaction networks, termed STM.