Developing a denoising filter for electron microscopy and tomography data in the cloud

Spaghetti Tracer: A Framework for Tracing Semiregular Filamentous Densities in 3D Tomograms

Within cells, cytoskeletal filaments are often arranged into loosely aligned bundles. These fibrous bundles are dense enough to exhibit a certain regularity and mean direction, however, their packing is not sufficient to impose a symmetry between-or specific shape on-individual filaments. This intermediate regularity is computationally difficult to handle because individual filaments have a certain directional freedom, however, the filament densities are not well segmented from each other (especially in the presence of noise, such as in cryo-electron tomography). In this paper, we develop a dynamic programming-based framework, Spaghetti Tracer, to characterizing the structural arrangement of filaments in the challenging 3D maps of subcellular components. Assuming that the tomogram can be rotated such that the filaments are oriented in a mean direction, the proposed framework first identifies local seed points for candidate filament segments, which are then grown from the seeds using a dynamic programming algorithm. We validate various algorithmic variations of our framework on simulated tomograms that closely mimic the noise and appearance of experimental maps. As we know the ground truth in the simulated tomograms, the statistical analysis consisting of precision, recall, and F1 scores allows us to optimize the performance of this new approach. We find that a bipyramidal accumulation scheme for path density is superior to straight-line accumulation. In addition, the multiplication of forward and backward path densities provides for an efficient filter that lifts the filament density above the noise level. Resulting from our tests is a robust method that can be expected to perform well (F1 scores 0.86-0.95) under experimental noise conditions.

TomoSim: Simulation of Filamentous Cryo-Electron Tomograms

As automated filament tracing algorithms in cryo-electron tomography (cryo-ET) continue to improve, the validation of these approaches has become more incumbent. Having a known ground truth on which to base predictions is crucial to reliably test predicted cytoskeletal filaments because the detailed structure of the filaments in experimental tomograms is obscured by a low resolution, as well as by noise and missing Fourier space wedge artifacts. We present a software tool for the realistic simulation of tomographic maps (TomoSim) based on a known filament trace. The parameters of the simulated map are automatically matched to those of a corresponding experimental map. We describe the computational details of the first prototype of our approach, which includes wedge masking in Fourier space, noise color, and signal-to-noise matching. We also discuss current and potential future applications of the approach in the validation of concurrent filament tracing methods in cryo-ET.

Tracing Filaments in Simulated 3D Cryo-Electron Tomography Maps Using a Fast Dynamic Programming Algorithm

We propose a fast, dynamic programming-based framework for tracing actin filaments in 3D maps of subcellular components in cryo-electron tomography. The approach can identify high-density filament segments in various orientations, but it takes advantage of the arrangement of actin filaments within cells into more or less tightly aligned bundles. Assuming that the tomogram can be rotated such that the filaments can be oriented to be directed in a dominant direction (i.e., the X, Y, or Z axis), the proposed framework first identifies local seed points that form the origin of candidate filament segments (CFSs), which are then grown from the seeds using a fast dynamic programming algorithm. The CFS length l can be tuned to the nominal resolution of the tomogram or the separation of desired features, or it can be used to restrict the curvature of filaments that deviate from the overall bundle direction. In subsequent steps, the CFSs are filtered based on backward tracing and path density analysis. Finally, neighboring CFSs are fused based on a collinearity criterion to bridge any noise artifacts in the 3D map that would otherwise fractionalize the tracing. We validate our proposed framework on simulated tomograms that closely mimic the features and appearance of experimental maps.

[Progress in filters for denoising cryo-electron microscopy images]

Cryo-electron microscopy (cryo-EM) imaging has the unique potential to bridge the gap between cellular and molecular biology. Therefore, cryo-EM three-dimensional (3D) reconstruction has been rapidly developed in recent several years and applied widely in life science research to reveal the structures of large macromolecular assemblies and cellular complexes, which is critical to understanding their functions at all scales. Although the technical breakthrough in recent years, for example, the introduction of the direct detection device (DDD) camera and the development of cryo-EM software tools, made the three cryo-EM pioneers share the 2017 Nobel Prize, several bottleneck problems still exist that hamper the further increase of the resolution of single-particle reconstruction and hold back the application of in situ subnanometer structure determination by cryo-tomography. Radiation damage is still the key limiting factor in cryo-EM. In order to minimize the radiation damage and preserve as much resolution as possible, the imaging conditions of a low dose and weak contrast make cryo-EM images extremely noisy with very low signal-to-noise ratios (SNR), generally about 0.1. The high noise will obscure the fine details in cryo-EM images or reconstructed maps. Thus, a method to reduce the level of noise and improve the resolution has become an important issue. In this paper, we systematically reviewed and compared some robust filters in the cryo-EM field of two aspects, single-particle analysis (SPA) and cryo-electron tomography (cryo-ET), and especially studied their applications, such as, 3D reconstruction, visualization, structural analysis, and interpretation. Conventional approaches to noise reduction in cryo-EM imaging include the use of Gaussian, median, and bilateral filters, among other means. A Gaussian filter selects an appropriate filter kernel to conduct spatial convolution with a noisy image. Although noise with larger standard deviations in cryo-EM images can be suppressed and satisfactory performance is achieved in certain cases, this filter also blurs the images and over-smooths small-scale image features. This is especially detrimental when precise quantitative information needs to be extracted. Unlike a Gaussian filter, a median filter is based on the order statistics of the image and selects the median intensity in a window of the adjacent pixels to denoise the image. Although this filter is robust to outliers, it suffers from aliasing problems that possibly result in incorrect information for cryo-EM structure interpretation. A bilateral filter is a nonlinear filter that performs spatial weighted averaging and is more selective in the pixels allowing to contribute to the weighted sum, excluding the high frequency noise from the smoothing process. Thus, this filter can be used to smooth out noise while maintaining the edge details, which is similar to an anisotropic diffusion filter, and distinct from a Gaussian filter but its utility will be limited when the SNR of a cryo-EM image is very low. Generally, spatial filtering methods have the disadvantage of losing image resolution when reducing noise. A wavelet transform can exploit the wavelet's natural ability to separate a signal from noise at multiple image scales to allow for joint resolution in both the spatial and frequency domains, and thus has the potential to outperform existing methods. The modified wavelet shrinkage filter we developed can offer a remarkable improvement in image quality with a good compromise between detail preservation and noise smoothing. We expect that our review study on different filters can provide benefits to cryo-EM applications and the interpretation of biological structures.

Cryo-Electron Tomography and Proteomics studies of centrosomes from differentiated quiescent thymocytes

We have used cryo Electron Tomography, proteomics and immunolabeling to study centrosomes isolated from the young lamb thymus, an efficient source of quiescent differentiated cells. We compared the proteome of thymocyte centrosomes to data published for KE37 cells, focusing on proteins associated with centriole disengagement and centrosome separation. The data obtained enhances our understanding of the protein system joining the centrioles, a system comprised of a branched network of fibers linked to an apparently amorphous density that was partially characterized here. A number of proteins were localized to the amorphous density by immunolabeling (C-NAP1, cohesin SMC1, condensin SMC4 and NCAPD2), yet not DNA. In conjuction, these data not only extend our understanding of centrosomes but they will help refine the model that focus on the protein system associated with the centriolar junction.

An overview of state-of-the-art image restoration in electron microscopy

In Life Science research, electron microscopy (EM) is an essential tool for morphological analysis at the subcellular level as it allows for visualization at nanometer resolution. However, electron micrographs contain image degradations such as noise and blur caused by electromagnetic interference, electron counting errors, magnetic lens imperfections, electron diffraction, etc. These imperfections in raw image quality are inevitable and hamper subsequent image analysis and visualization. In an effort to mitigate these artefacts, many electron microscopy image restoration algorithms have been proposed in the last years. Most of these methods rely on generic assumptions on the image or degradations and are therefore outperformed by advanced methods that are based on more accurate models. Ideally, a method will accurately model the specific degradations that fit the physical acquisition settings. In this overview paper, we discuss different electron microscopy image degradation solutions and demonstrate that dedicated artefact regularisation results in higher quality restoration and is applicable through recently developed probabilistic methods.

Tracing Actin Filament Bundles in Three-Dimensional Electron Tomography Density Maps of Hair Cell Stereocilia

Cryo-electron tomography (cryo-ET) is a powerful method of visualizing the three-dimensional organization of supramolecular complexes, such as the cytoskeleton, in their native cell and tissue contexts. Due to its minimal electron dose and reconstruction artifacts arising from the missing wedge during data collection, cryo-ET typically results in noisy density maps that display anisotropic XY versus Z resolution. Molecular crowding further exacerbates the challenge of automatically detecting supramolecular complexes, such as the actin bundle in hair cell stereocilia. Stereocilia are pivotal to the mechanoelectrical transduction process in inner ear sensory epithelial hair cells. Given the complexity and dense arrangement of actin bundles, traditional approaches to filament detection and tracing have failed in these cases. In this study, we introduce BundleTrac, an effective method to trace hundreds of filaments in a bundle. A comparison between BundleTrac and manually tracing the actin filaments in a stereocilium showed that BundleTrac accurately built 326 of 330 filaments (98.8%), with an overall cross-distance of 1.3 voxels for the 330 filaments. BundleTrac is an effective semi-automatic modeling approach in which a seed point is provided for each filament and the rest of the filament is computationally identified. We also demonstrate the potential of a denoising method that uses a polynomial regression to address the resolution and high-noise anisotropic environment of the density map.

Exploring an optimal wavelet-based filter for cryo-ET imaging

Cryo-electron tomography (cryo-ET) is one of the most advanced technologies for the in situ visualization of molecular machines by producing three-dimensional (3D) biological structures. However, cryo-ET imaging has two serious disadvantages—low dose and low image contrast—which result in high-resolution information being obscured by noise and image quality being degraded, and this causes errors in biological interpretation. The purpose of this research is to explore an optimal wavelet denoising technique to reduce noise in cryo-ET images. We perform tests using simulation data and design a filter using the optimum selected wavelet parameters (three-level decomposition, level-1 zeroed out, subband-dependent threshold, a soft-thresholding and spline-based discrete dyadic wavelet transform (DDWT)), which we call a modified wavelet shrinkage filter; this filter is suitable for noisy cryo-ET data. When testing using real cryo-ET experiment data, higher quality images and more accurate measures of a biological structure can be obtained with the modified wavelet shrinkage filter processing compared with conventional processing. Because the proposed method provides an inherent advantage when dealing with cryo-ET images, it can therefore extend the current state-of-the-art technology in assisting all aspects of cryo-ET studies: visualization, reconstruction, structural analysis, and interpretation.

Computational opportunities for remote collaboration and capacity building afforded by Web 2.0 and cloud computing

In this paper, we state our aims and aspirations for building a global network of likeminded people interested in developing and encouraging students in the field of computational biophysics (CB). Global capacity building efforts have uncovered local computational talent in virtually every community regardless of where the students reside. Our vision is to discover and encourage these aspiring investigators by suggesting ways that they and other "garage scientists" can participate in new science even if they have no access to sophisticated research infrastructure. We argue that participatory computing in the "cloud" is particularly suitable for CB and available to any budding computational biophysicist if he or she is provided with open-minded mentors who have the necessary skills and generosity. We recognize that there are barriers to the development of such remote collaborations, and we discuss possible pathways to overcome these barriers. We point out that this Special Issue of Biophysical Reviews provides a much-needed forum for the development of several specific applications of CB.