Benchmarking tomographic acquisition schemes for high-resolution structural biology.

Cryo electron tomography with subsequent subtomogram averaging is a powerful technique to structurally analyze macromolecular complexes in their native context. Although close to atomic resolution in principle can be obtained, it is not clear how individual experimental parameters contribute to the attainable resolution. Here, we have used immature HIV-1 lattice as a benchmarking sample to optimize the attainable resolution for subtomogram averaging. We systematically tested various experimental parameters such as the order of projections, different angular increments and the use of the Volta phase plate. We find that although any of the prominently used acquisition schemes is sufficient to obtain subnanometer resolution, dose-symmetric acquisition provides considerably better outcome. We discuss our findings in order to provide guidance for data acquisition. Our data is publicly available and might be used to further develop processing routines.

Quantifying Variability of Manual Annotation in Cryo-Electron Tomograms.

Although acknowledged to be variable and subjective, manual annotation of cryo-electron tomography data is commonly used to answer structural questions and to create a "ground truth" for evaluation of automated segmentation algorithms. Validation of such annotation is lacking, but is critical for understanding the reproducibility of manual annotations. Here, we used voxel-based similarity scores for a variety of specimens, ranging in complexity and segmented by several annotators, to quantify the variation among their annotations. In addition, we have identified procedures for merging annotations to reduce variability, thereby increasing the reliability of manual annotation. Based on our analyses, we find that it is necessary to combine multiple manual annotations to increase the confidence level for answering structural questions. We also make recommendations to guide algorithm development for automated annotation of features of interest.

Alignment algorithms and per-particle CTF correction for single particle cryo-electron tomography.

Single particle cryo-electron tomography (cryoSPT) extracts features from cryo-electron tomograms, followed by 3D classification, alignment and averaging to generate improved 3D density maps of such features. Robust methods to correct for the contrast transfer function (CTF) of the electron microscope are necessary for cryoSPT to reach its resolution potential. Many factors can make CTF correction for cryoSPT challenging, such as lack of eucentricity of the specimen stage, inherent low dose per image, specimen charging, beam-induced specimen motions, and defocus gradients resulting both from specimen tilting and from unpredictable ice thickness variations. Current CTF correction methods for cryoET make at least one of the following assumptions: that the defocus at the center of the image is the same across the images of a tiltseries, that the particles all lie at the same Z-height in the embedding ice, and/or that the specimen, the cryo-electron microscopy (cryoEM) grid and/or the carbon support are flat. These experimental conditions are not always met. We have developed a CTF correction algorithm for cryoSPT without making any of the aforementioned assumptions. We also introduce speed and accuracy improvements and a higher degree of automation to the subtomogram averaging algorithms available in EMAN2. Using motion-corrected images of isolated virus particles as a benchmark specimen, recorded with a DE20 direct detection camera, we show that our CTF correction and subtomogram alignment routines can yield subtomogram averages close to 4/5 Nyquist frequency of the detector under our experimental conditions.

Local regularization of tilt projections reduces artifacts in electron tomography.

Electron tomography produces very high resolution 3D image volumes useful for investigating the structure and function of cellular components. Unfortunately, unavoidable discontinuities and physical constraints in the acquisition geometry lead to a range of artifacts that can affect the reconstructed image. In particular, highly electron dense regions, such as gold nanoparticles, can hide proximal biological structures and degrade the overall quality of the reconstructed tomograms. In this work we introduce a pre-reconstruction non-conservative non-linear isotropic diffusion (NID) filter that automatically identifies and reduces local irregularities in the tilt projections. We illustrate the improvement in quality obtained using this approach for reconstructed tomograms generated from samples of malaria parasite-infected red blood cells. A quantitative and qualitative evaluation for our approach on both simulated and real data is provided.

Improving the quality of electron tomography image volumes using pre-reconstruction filtering.

Electron tomography produces highly magnified 3D image volumes useful for investigating the structure and function of cellular components. Image quality is degraded by multiple scattering events and quantum noise, which depend on the angle at which individual tilt projections are collected. We have adapted a biomedical imaging approach to improve image quality by enhancing individual tilt projections prior to volumetric reconstruction. Specifically, we have developed a family of non-linear anisotropic diffusion (NAD) filters parameterized by the tilt angle. We give a quantitative and qualitative evaluation of our pre-processing approach and the NAD filter. We show an improvement in the reconstructed volumes for tomograms generated from both plastic-embedded and cryo-stabilized samples of malaria parasite-infected erythrocytes.

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.