@article {1454, title = {3D segmentation of cell boundaries from whole cell cryogenic electron tomography volumes.}, journal = {Journal of Structural Biology}, volume = {170}, year = {2010}, month = {2010 Apr}, pages = {134-45}, abstract = {

Cryogenic electron tomography (cryo-ET) has gained increasing interest in recent years due to its ability to image whole cells and subcellular structures in 3D at nanometer resolution in their native environment. However, due to dose restrictions and the inability to acquire high tilt angle images, the reconstructed volumes are noisy and have missing information. Thus, features are unreliable, and precision extraction of the cell boundary is difficult, manual and time intensive. This paper presents an efficient recursive algorithm called BLASTED (Boundary Localization using Adaptive Shape and Texture Discovery) to automatically extract the cell boundary using a conditional random field (CRF) framework in which boundary points and shape are jointly inferred. The algorithm learns the texture of the boundary region progressively, and uses a global shape model and shape-dependent features to propose candidate boundary points on a slice of the membrane. It then updates the shape of that slice by accepting the appropriate candidate points using local spatial clustering, the global shape model, and trained boosted texture classifiers. The BLASTED algorithm segmented the cell membrane over an average of 93\% of the length of the cell in 19 difficult cryo-ET datasets.

}, keywords = {Algorithms, Cell Membrane, Cryoelectron Microscopy, Electron Microscope Tomography}, issn = {1095-8657}, doi = {10.1016/j.jsb.2009.12.015}, author = {Moussavi, Farshid and Heitz, Geremy and Amat, Fernando and Comolli, Luis R and Koller, Daphne and Horowitz, Mark} } @article {1450, title = {Alignment of cryo-electron tomography datasets.}, journal = {Methods in Enzymology}, volume = {482}, year = {2010}, month = {2010}, pages = {343-67}, abstract = {

Data acquisition of cryo-electron tomography (CET) samples described in previous chapters involves relatively imprecise mechanical motions: the tilt series has shifts, rotations, and several other distortions between projections. Alignment is the procedure of correcting for these effects in each image and requires the estimation of a projection model that describes how points from the sample in three-dimensions are projected to generate two-dimensional images. This estimation is enabled by finding corresponding common features between images. This chapter reviews several software packages that perform alignment and reconstruction tasks completely automatically (or with minimal user intervention) in two main scenarios: using gold fiducial markers as high contrast features or using relevant biological structures present in the image (marker-free). In particular, we emphasize the key decision points in the process that users should focus on in order to obtain high-resolution reconstructions.

}, keywords = {Computer-Assisted, Cryoelectron Microscopy, Electron Microscope Tomography, Image Processing}, issn = {1557-7988}, doi = {10.1016/S0076-6879(10)82014-2}, author = {Amat, Fernando and Casta{\~n}o-Diez, Daniel and Lawrence, Albert and Moussavi, Farshid and Winkler, Hanspeter and Horowitz, Mark} } @article {1452, title = {Analysis of the intact surface layer of Caulobacter crescentus by cryo-electron tomography.}, journal = {Journal of Bacteriology}, volume = {192}, year = {2010}, month = {2010 Nov}, pages = {5855-65}, abstract = {

The surface layers (S layers) of those bacteria and archaea that elaborate these crystalline structures have been studied for 40 years. However, most structural analysis has been based on electron microscopy of negatively stained S-layer fragments separated from cells, which can introduce staining artifacts and allow rearrangement of structures prone to self-assemble. We present a quantitative analysis of the structure and organization of the S layer on intact growing cells of the Gram-negative bacterium Caulobacter crescentus using cryo-electron tomography (CET) and statistical image processing. Instead of the expected long-range order, we observed different regions with hexagonally organized subunits exhibiting short-range order and a broad distribution of periodicities. Also, areas of stacked double layers were found, and these increased in extent when the S-layer protein (RsaA) expression level was elevated by addition of multiple rsaA copies. Finally, we combined high-resolution amino acid residue-specific Nanogold labeling and subtomogram averaging of CET volumes to improve our understanding of the correlation between the linear protein sequence and the structure at the 2-nm level of resolution that is presently available. The results support the view that the U-shaped RsaA monomer predicted from negative-stain tomography proceeds from the N terminus at one vertex, corresponding to the axis of 3-fold symmetry, to the C terminus at the opposite vertex, which forms the prominent 6-fold symmetry axis. Such information will help future efforts to analyze subunit interactions and guide selection of internal sites for display of heterologous protein segments.

}, keywords = {Amino Acids, Bacterial Proteins, Caulobacter crescentus, Computer-Assisted, Cryoelectron Microscopy, Electron Microscope Tomography, Image Processing, Membrane Glycoproteins, Metal Nanoparticles, Staining and Labeling}, issn = {1098-5530}, doi = {10.1128/JB.00747-10}, author = {Amat, Fernando and Comolli, Luis R and Nomellini, John F and Moussavi, Farshid and Downing, Kenneth H and Smit, John and Horowitz, Mark} } @article {1453, title = {Subtomogram alignment by adaptive Fourier coefficient thresholding.}, journal = {Journal of Structural Biology}, volume = {171}, year = {2010}, month = {2010 Sep}, pages = {332-44}, abstract = {

In the past few years, three-dimensional (3D) subtomogram alignment has become an important tool in cryo-electron tomography (CET). This technique allows one to produce higher resolution images of structures which can not be reconstructed using single-particle methods. Building on previous work, we present a new dissimilarity measure between subtomograms that works well for the noisy images that often occur in CET images. A technique that is more robust to noise provides the ability to analyze macromolecules in thicker samples such as whole cells or lower the defocus in thinner samples to push the first zero of the Contrast Transfer Function (CTF). Our method, Threshold Constrained Cross-Correlation (TCCC), uses statistics of the noise to automatically select only a small percentage of the Fourier coefficients to compute the cross-correlation, which has two main advantages: first, it reduces the influence of the noise by looking at only those peaks dominated by signal; and second, it avoids the missing wedge normalization problem since we consider the same number of coefficients for all possible pairs of subtomograms. We present results with synthetic and real data to compare our approach with other existing methods under different SNR and missing wedge conditions, and show that TCCC improves alignment results for datasets with SNR\<0.1. We have made our source code freely available for the community.

}, keywords = {Algorithms, Electron Microscope Tomography}, issn = {1095-8657}, doi = {10.1016/j.jsb.2010.05.013}, author = {Amat, Fernando and Comolli, Luis R and Moussavi, Farshid and Smit, John and Downing, Kenneth H and Horowitz, Mark} } @article {1455, title = {Markov random field based automatic image alignment for electron tomography.}, journal = {Journal of Structural Biology}, volume = {161}, year = {2008}, month = {2008 Mar 28}, pages = {260-75}, abstract = {

We present a method for automatic full-precision alignment of the images in a tomographic tilt series. Full-precision automatic alignment of cryo electron microscopy images has remained a difficult challenge to date, due to the limited electron dose and low image contrast. These facts lead to poor signal to noise ratio (SNR) in the images, which causes automatic feature trackers to generate errors, even with high contrast gold particles as fiducial features. To enable fully automatic alignment for full-precision reconstructions, we frame the problem probabilistically as finding the most likely particle tracks given a set of noisy images, using contextual information to make the solution more robust to the noise in each image. To solve this maximum likelihood problem, we use Markov Random Fields (MRF) to establish the correspondence of features in alignment and robust optimization for projection model estimation. The resulting algorithm, called Robust Alignment and Projection Estimation for Tomographic Reconstruction, or RAPTOR, has not needed any manual intervention for the difficult datasets we have tried, and has provided sub-pixel alignment that is as good as the manual approach by an expert user. We are able to automatically map complete and partial marker trajectories and thus obtain highly accurate image alignment. Our method has been applied to challenging cryo electron tomographic datasets with low SNR from intact bacterial cells, as well as several plastic section and X-ray datasets.

}, keywords = {Algorithms, Computer-Assisted, Cryoelectron Microscopy, Image Processing, Markov Chains, Tomography}, issn = {1095-8657}, doi = {10.1016/j.jsb.2007.07.007}, author = {Amat, Fernando and Moussavi, Farshid and Comolli, Luis R and Elidan, Gal and Downing, Kenneth H and Horowitz, Mark} }