A differential structure approach to membrane segmentation in electron tomography

Disruption of the mitochondrial network in a mouse model of Huntington's disease visualized by in-tissue multiscale 3D electron microscopy

Huntington's disease (HD) is an inherited neurodegenerative disorder caused by an expanded CAG repeat in the coding sequence of huntingtin protein. Initially, it predominantly affects medium-sized spiny neurons (MSSNs) of the corpus striatum. No effective treatment is still available, thus urging the identification of potential therapeutic targets. While evidence of mitochondrial structural alterations in HD exists, previous studies mainly employed 2D approaches and were performed outside the strictly native brain context. In this study, we adopted a novel multiscale approach to conduct a comprehensive 3D in situ structural analysis of mitochondrial disturbances in a mouse model of HD. We investigated MSSNs within brain tissue under optimal structural conditions utilizing state-of-the-art 3D imaging technologies, specifically FIB/SEM for the complete imaging of neuronal somas and Electron Tomography for detailed morphological examination, and image processing-based quantitative analysis. Our findings suggest a disruption of the mitochondrial network towards fragmentation in HD. The network of interlaced, slim and long mitochondria observed in healthy conditions transforms into isolated, swollen and short entities, with internal cristae disorganization, cavities and abnormally large matrix granules.

Computational Methods Toward Unbiased Pattern Mining and Structure Determination in Cryo-Electron Tomography Data

Cryo-electron tomography can uniquely probe the native cellular environment for macromolecular structures. Tomograms feature complex data with densities of diverse, densely crowded macromolecular complexes, low signal-to-noise, and artifacts such as the missing wedge effect. Post-processing of this data generally involves isolating regions or particles of interest from tomograms, organizing them into related groups, and rendering final structures through subtomogram averaging. Template-matching and reference-based structure determination are popular analysis methods but are vulnerable to biases and can often require significant user input. Most importantly, these approaches cannot identify novel complexes that reside within the imaged cellular environment. To reliably extract and resolve structures of interest, efficient and unbiased approaches are therefore of great value. This review highlights notable computational software and discusses how they contribute to making automated structural pattern discovery a possibility. Perspectives emphasizing the importance of features for user-friendliness and accessibility are also presented.

Efficient manual annotation of cryogenic electron tomograms using IMOD

Annotation highlights and segmentation isolates features in cryogenic electron tomograms to improve visualization and quantification of features (for example, their size and abundance, and spatial relationships with other features), facilitating phenotypic structural analyses of cellular tomograms. Here, we present a manual annotation protocol using the open-source software IMOD and describe segmentation of three types of common cellular features: membranes, large globules, and filaments. IMOD's interpolation function can improve the speed of manual annotation up to an order of magnitude.

Reliable estimation of membrane curvature for cryo-electron tomography

Curvature is a fundamental morphological descriptor of cellular membranes. Cryo-electron tomography (cryo-ET) is particularly well-suited to visualize and analyze membrane morphology in a close-to-native state and molecular resolution. However, current curvature estimation methods cannot be applied directly to membrane segmentations in cryo-ET, as these methods cannot cope with some of the artifacts introduced during image acquisition and membrane segmentation, such as quantization noise and open borders. Here, we developed and implemented a Python package for membrane curvature estimation from tomogram segmentations, which we named PyCurv. From a membrane segmentation, a signed surface (triangle mesh) is first extracted. The triangle mesh is then represented by a graph, which facilitates finding neighboring triangles and the calculation of geodesic distances necessary for local curvature estimation. PyCurv estimates curvature based on tensor voting. Beside curvatures, this algorithm also provides robust estimations of surface normals and principal directions. We tested PyCurv and three well-established methods on benchmark surfaces and biological data. This revealed the superior performance of PyCurv not only for cryo-ET, but also for data generated by other techniques such as light microscopy and magnetic resonance imaging. Altogether, PyCurv is a versatile open-source software to reliably estimate curvature of membranes and other surfaces in a wide variety of applications.

Membrane curvature plays a central role in many cellular processes like cell division, organelle shaping and membrane contact sites. While cryo-electron tomography (cryo-ET) allows the visualization of cellular membranes in 3D at molecular resolution and close-to-native conditions, there is a lack of computational methods to quantify membrane curvature from cryo-ET data. Therefore, we developed a computational procedure for membrane curvature estimation from tomogram segmentations and implemented it in a software package called PyCurv. PyCurv converts a membrane segmentation, i.e. a set of voxels, into a surface, i.e. a mesh of triangles. PyCurv uses the local geometrical information to reliably estimate the local surface orientation, the principal (maximum and minimum) curvatures and their directions. PyCurv outperforms well-established curvature estimation methods, and it can also be applied to data generated by other imaging techniques.

A modified lysosomal organelle mediates nonlytic egress of reovirus

Mammalian orthoreoviruses (reoviruses) are nonenveloped viruses that replicate in cytoplasmic membranous organelles called viral inclusions (VIs) where progeny virions are assembled. To better understand cellular routes of nonlytic reovirus exit, we imaged sites of virus egress in infected, nonpolarized human brain microvascular endothelial cells (HBMECs) and observed one or two distinct egress zones per cell at the basal surface. Transmission electron microscopy and 3D electron tomography (ET) of the egress zones revealed clusters of virions within membrane-bound structures, which we term membranous carriers (MCs), approaching and fusing with the plasma membrane. These virion-containing MCs emerged from larger, LAMP-1-positive membranous organelles that are morphologically compatible with lysosomes. We call these structures sorting organelles (SOs). Reovirus infection induces an increase in the number and size of lysosomes and modifies the pH of these organelles from ∼4.5-5 to ∼6.1 after recruitment to VIs and before incorporation of virions. ET of VI-SO-MC interfaces demonstrated that these compartments are connected by membrane-fusion points, through which mature virions are transported. Collectively, our results show that reovirus uses a previously undescribed, membrane-engaged, nonlytic egress mechanism and highlights a potential new target for therapeutic intervention.

Automatic localization and identification of mitochondria in cellular electron cryo-tomography using faster-RCNN

BACKGROUND

Cryo-electron tomography (cryo-ET) enables the 3D visualization of cellular organization in near-native state which plays important roles in the field of structural cell biology. However, due to the low signal-to-noise ratio (SNR), large volume and high content complexity within cells, it remains difficult and time-consuming to localize and identify different components in cellular cryo-ET. To automatically localize and recognize in situ cellular structures of interest captured by cryo-ET, we proposed a simple yet effective automatic image analysis approach based on Faster-RCNN.

RESULTS

Our experimental results were validated using in situ cyro-ET-imaged mitochondria data. Our experimental results show that our algorithm can accurately localize and identify important cellular structures on both the 2D tilt images and the reconstructed 2D slices of cryo-ET. When ran on the mitochondria cryo-ET dataset, our algorithm achieved Average Precision >0.95. Moreover, our study demonstrated that our customized pre-processing steps can further improve the robustness of our model performance.

CONCLUSIONS

In this paper, we proposed an automatic Cryo-ET image analysis algorithm for localization and identification of different structure of interest in cells, which is the first Faster-RCNN based method for localizing an cellular organelle in Cryo-ET images and demonstrated the high accuracy and robustness of detection and classification tasks of intracellular mitochondria. Furthermore, our approach can be easily applied to detection tasks of other cellular structures as well.

Feature Decomposition Based Saliency Detection in Electron Cryo-Tomograms

Electron Cryo-Tomography (ECT) allows 3D visualization of subcellular structures at the submolecular resolution in close to the native state. However, due to the high degree of structural complexity and imaging limits, the automatic segmentation of cellular components from ECT images is very difficult. To complement and speed up existing segmentation methods, it is desirable to develop a generic cell component segmentation method that is 1) not specific to particular types of cellular components, 2) able to segment unknown cellular components, 3) fully unsupervised and does not rely on the availability of training data. As an important step towards this goal, in this paper, we propose a saliency detection method that computes the likelihood that a subregion in a tomogram stands out from the background. Our method consists of four steps: supervoxel over-segmentation, feature extraction, feature matrix decomposition, and computation of saliency. The method produces a distribution map that represents the regions' saliency in tomograms. Our experiments show that our method can successfully label most salient regions detected by a human observer, and able to filter out regions not containing cellular components. Therefore, our method can remove the majority of the background region, and significantly speed up the subsequent processing of segmentation and recognition of cellular components captured by ECT.

Reovirus σNS and μNS Proteins Remodel the Endoplasmic Reticulum to Build Replication Neo-Organelles

Like most viruses that replicate in the cytoplasm, mammalian reoviruses assemble membranous neo-organelles called inclusions that serve as sites of viral genome replication and particle morphogenesis. Viral inclusion formation is essential for viral infection, but how these organelles form is not well understood. We investigated the biogenesis of reovirus inclusions. Correlative light and electron microscopy showed that endoplasmic reticulum (ER) membranes are in contact with nascent inclusions, which form by collections of membranous tubules and vesicles as revealed by electron tomography. ER markers and newly synthesized viral RNA are detected in inclusion internal membranes. Live-cell imaging showed that early in infection, the ER is transformed into thin cisternae that fragment into small tubules and vesicles. We discovered that ER tubulation and vesiculation are mediated by the reovirus σNS and μNS proteins, respectively. Our results enhance an understanding of how viruses remodel cellular compartments to build functional replication organelles.

Viruses modify cellular structures to build replication organelles. These organelles serve as sites of viral genome replication and particle morphogenesis and are essential for viral infection. However, how these organelles are constructed is not well understood. We found that the replication organelles of mammalian reoviruses are formed by collections of membranous tubules and vesicles derived from extensive remodeling of the peripheral endoplasmic reticulum (ER). We also observed that ER tubulation and vesiculation are triggered by the reovirus σNS and μNS proteins, respectively. Our results enhance an understanding of how viruses remodel cellular compartments to build functional replication organelles and provide functions for two enigmatic reovirus replication proteins. Most importantly, this research uncovers a new mechanism by which viruses form factories for particle assembly.

A convolutional autoencoder approach for mining features in cellular electron cryo-tomograms and weakly supervised coarse segmentation

Cellular electron cryo-tomography enables the 3D visualization of cellular organization in the near-native state and at submolecular resolution. However, the contents of cellular tomograms are often complex, making it difficult to automatically isolate different in situ cellular components. In this paper, we propose a convolutional autoencoder-based unsupervised approach to provide a coarse grouping of 3D small subvolumes extracted from tomograms. We demonstrate that the autoencoder can be used for efficient and coarse characterization of features of macromolecular complexes and surfaces, such as membranes. In addition, the autoencoder can be used to detect non-cellular features related to sample preparation and data collection, such as carbon edges from the grid and tomogram boundaries. The autoencoder is also able to detect patterns that may indicate spatial interactions between cellular components. Furthermore, we demonstrate that our autoencoder can be used for weakly supervised semantic segmentation of cellular components, requiring a very small amount of manual annotation.

RAZA: A Rapid 3D z-crossings algorithm to segment electron tomograms and extract organelles and macromolecules

Resolving the 3D architecture of cells to atomic resolution is one of the most ambitious challenges of cellular and structural biology. Central to this process is the ability to automate tomogram segmentation to identify sub-cellular components, facilitate molecular docking and annotate detected objects with associated metadata. Here we demonstrate that RAZA (Rapid 3D z-crossings algorithm) provides a robust, accurate, intuitive, fast, and generally applicable segmentation algorithm capable of detecting organelles, membranes, macromolecular assemblies and extrinsic membrane protein domains. RAZA defines each continuous contour within a tomogram as a discrete object and extracts a set of 3D structural fingerprints (major, middle and minor axes, surface area and volume), enabling selective, semi-automated segmentation and object extraction. RAZA takes advantage of the fact that the underlying algorithm is a true 3D edge detector, allowing the axes of a detected object to be defined, independent of its random orientation within a cellular tomogram. The selectivity of object segmentation and extraction can be controlled by specifying a user-defined detection tolerance threshold for each fingerprint parameter, within which segmented objects must fall and/or by altering the number of search parameters, to define morphologically similar structures. We demonstrate the capability of RAZA to selectively extract subgroups of organelles (mitochondria) and macromolecular assemblies (ribosomes) from cellular tomograms. Furthermore, the ability of RAZA to define objects and their contours, provides a basis for molecular docking and rapid tomogram annotation.

3D membrane segmentation and quantification of intact thick cells using cryo soft X-ray transmission microscopy: A pilot study

Structural analysis of biological membranes is important for understanding cell and sub-cellular organelle function as well as their interaction with the surrounding environment. Imaging of whole cells in three dimension at high spatial resolution remains a significant challenge, particularly for thick cells. Cryo-transmission soft X-ray microscopy (cryo-TXM) has recently gained popularity to image, in 3D, intact thick cells (∼10μm) with details of sub-cellular architecture and organization in near-native state. This paper reports a new tool to segment and quantify structural changes of biological membranes in 3D from cryo-TXM images by tracking an initial 2D contour along the third axis of the microscope, through a multi-scale ridge detection followed by an active contours-based model, with a subsequent refinement along the other two axes. A quantitative metric that assesses the grayscale profiles perpendicular to the membrane surfaces is introduced and shown to be linearly related to the membrane thickness. Our methodology has been validated on synthetic phantoms using realistic microscope properties and structure dimensions, as well as on real cryo-TXM data. Results demonstrate the validity of our algorithms for cryo-TXM data analysis.

The benefit of thresholding carbon layers in electron tomographic tilt series by intensity downshifting

When performing electron tomography, tilt series of images are often acquired from samples that contain unwanted carbonaceous material, such as an embedding resin, a thin carbon support film or hydrocarbon contamination. The presence of such layers can introduce artefacts in reconstructions, obscuring features of interest. Here, we illustrate the benefit of preprocessing a high-angle annular dark-field tomographic tilt series by thresholding unwanted low-density materials using a simple intensity downshifting procedure. The resulting tomograms have fewer artefacts and segmentation can be performed more accurately. We present two representative examples taken from studies of catalyst nanoparticles and amyloid plaque core material from the human brain.

Hierarchical detection and analysis of macromolecular complexes in cryo-electron tomograms using Pyto software

Molecular complexes, arguably the basic units carrying cellular function, can be visualized directly in their native environment by cryo-electron tomography. Here we describe a procedure for the detection of small, pleomorphic membrane-bound molecular complexes in cryo-tomograms by a hierarchical connectivity segmentation. Validation on phantom and real data showed above 90% true positive rates. This segmentation procedure is implemented in the Pyto software package, together with methods for quantitative characterization and classification of complexes detected by our segmentation procedure and for statistical analysis between experimental conditions. Therefore, the methods presented provide a means for the detection and quantitative interpretation of structures captured in cryo-electron tomograms, as well as for the elucidation of their cellular function.

3D electron tomography of brain tissue unveils distinct Golgi structures that sequester cytoplasmic contents in neurons

Macroautophagy is morphologically characterized by autophagosome formation. Autophagosomes are double-membraned vesicles that sequester cytoplasmic components for further degradation in the lysosome. Basal autophagy is paramount for intracellular quality control in post-mitotic cells but, surprisingly, the number of autophagosomes in post-mitotic neurons is very low, suggesting that alternative degradative structures could exist in neurons. To explore this possibility, we have examined neuronal subcellular architecture by performing three-dimensional (3D) electron tomography analysis of mouse brain tissue that had been preserved through high-pressure freezing. Here, we report that sequestration of neuronal cytoplasmic contents occurs at the Golgi complex in distinct and dynamic structures that coexist with autophagosomes in the brain. These structures are composed of several concentric double-membraned layers that appear to be formed simultaneously by the direct bending and sealing of discrete Golgi stacks. These structures are labelled for proteolytic enzymes, and lysosomes and late endosomes are found in contact with them, leading to the possibility that the sequestered material could be degraded inside them. Our findings highlight the key role that 3D electron tomography, together with tissue rapid-freezing techniques, will have in gaining new knowledge about subcellular architecture.

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.

Three-dimensional imaging of the intracellular assembly of a functional viral RNA replicase complex

Positive-strand RNA viruses, which can be devastating pathogens in humans, animals and plants, replicate their genomes on intracellular membranes. Here, we describe the three-dimensional ultrastructural organization of a tombusvirus replicase in yeast, a valuable model for exploring virus-host interactions. We visualized the intracellular distribution of a viral replicase protein using metal-tagging transmission electron microscopy, a highly sensitive nanotechnology whose full potential remains to be developed. These three-dimensional images show how viral replicase molecules are organized when they are incorporated into the active domains of the intracellular replication compartment. Our approach provides a means to study protein activation mechanisms in cells and to identify targets for new antiviral compounds.

A validated active contour method driven by parabolic arc model for detection and segmentation of mitochondria

Recent studies reveal that mitochondria take substantial responsibility in cellular functions that are closely related to aging diseases caused by degeneration of neurons. These studies emphasize that the membrane and crista morphology of a mitochondrion should receive attention in order to investigate the link between mitochondrial function and its physical structure. Electron microscope tomography (EMT) allows analysis of the inner structures of mitochondria by providing highly detailed visual data from large volumes. Computerized segmentation of mitochondria with minimum manual effort is essential to accelerate the study of mitochondrial structure/function relationships. In this work, we improved and extended our previous attempts to detect and segment mitochondria from transmission electron microcopy (TEM) images. A parabolic arc model was utilized to extract membrane structures. Then, curve energy based active contours were employed to obtain roughly outlined candidate mitochondrial regions. Finally, a validation process was applied to obtain the final segmentation data. 3D extension of the algorithm is also presented in this paper. Our method achieved an average F-score performance of 0.84. Average Dice Similarity Coefficient and boundary error were measured as 0.87 and 14nm respectively.

Removing Contamination-Induced Reconstruction Artifacts from Cryo-electron Tomograms

Imaging of fully hydrated, vitrified biological samples by electron tomography yields structural information about cellular protein complexes in situ. Here we present a computational procedure that removes artifacts of three-dimensional reconstruction caused by contamination present in samples during imaging by electron microscopy. Applying the procedure to phantom data and electron tomograms of cellular samples significantly improved the resolution and the interpretability of tomograms. Artifacts caused by surface contamination associated with thinning by focused ion beam, as well as those arising from gold fiducial markers and from common, lower contrast contamination, could be removed. Our procedure is widely applicable and is especially suited for applications that strive to reach a higher resolution and involve the use of recently developed, state-of-the-art instrumentation.

Structure of a bacterial type III secretion system in contact with a host membrane in situ

Many bacterial pathogens of animals and plants use a conserved type III secretion system (T3SS) to inject virulence effector proteins directly into eukaryotic cells to subvert host functions. Contact with host membranes is critical for T3SS activation, yet little is known about T3SS architecture in this state or the conformational changes that drive effector translocation. Here we use cryo-electron tomography and sub-tomogram averaging to derive the intact structure of the primordial Chlamydia trachomatis T3SS in the presence and absence of host membrane contact. Comparison of the averaged structures demonstrates a marked compaction of the basal body (4 nm) occurs when the needle tip contacts the host cell membrane. This compaction is coupled to a stabilization of the cytosolic sorting platform–ATPase. Our findings reveal the first structure of a bacterial T3SS from a major human pathogen engaged with a eukaryotic host, and reveal striking ‘pump-action' conformational changes that underpin effector injection.

Bacterial type III secretion systems (T3SSs) inject virulence effector proteins into eukaryotic cells and are activated by host membrane contact. Here the authors report the in situ structure of the Chlamydia trachomatis T3SS in the presence or absence of host membrane, and observe compaction of the basal body embedded in the bacterial envelope.

Planctomycetes do possess a peptidoglycan cell wall

Most bacteria contain a peptidoglycan (PG) cell wall, which is critical for maintenance of shape and important for cell division. In contrast, Planctomycetes have been proposed to produce a proteinaceous cell wall devoid of PG. The apparent absence of PG has been used as an argument for the putative planctomycetal ancestry of all bacterial lineages. Here we show, employing multiple bioinformatic methods, that planctomycetal genomes encode proteins required for PG synthesis. Furthermore, we biochemically demonstrate the presence of the sugar and the peptide components of PG in Planctomycetes. In addition, light and electron microscopic experiments reveal planctomycetal PG sacculi that are susceptible to lysozyme treatment. Finally, cryo-electron tomography demonstrates that Planctomycetes possess a typical PG cell wall and that their cellular architecture is thus more similar to that of other Gram-negative bacteria. Our findings shed new light on the cellular architecture and cell division of the maverick Planctomycetes.

Planctomycetes appear to differ from all other bacteria in their cellular organization and their apparent lack of a peptidoglycan (PG) cell wall. Here Jeske et al. show that Planctomycetes do possess a typical PG cell wall and that their cellular architecture resembles that of Gram-negative bacteria.

Removing the effects of the "dark matter" in tomography

Electron tomography (ET) using different imaging modes has been progressively consolidating its position as a key tool in materials science. The fidelity of a tomographic reconstruction, or tomogram, is affected by several experimental factors. Most often, an unrealistic cloud of intensity that does not correspond to a real material phase of the specimen ("dark matter") blurs the tomograms and enhances artefacts arising from the missing wedge (MW). Here we show that by simple preprocessing of the background level of any tomographic tilt series, it is possible to minimise the negative effects of that "dark matter". Iterative reconstruction algorithms converge better, leading to tomograms with fewer streaking artefacts from the MW, more contrast, and increased accuracy. The conclusions are valid irrespective of the imaging mode used, and the methodology improves the segmentation and visualisation of tomograms of both crystalline and amorphous materials. We show examples of HAADF STEM and BF TEM tomography.

Three dimensional reconstruction by electron microscopy in the life sciences: An introduction for cell and tissue biologists

Early applications of transmission electron microscopy (TEM) in the life sciences have contributed tremendously to our current understanding at the subcellular level. Initially limited to two-dimensional representations of three-dimensional (3D) objects, this approach has revolutionized the fields of cellular and structural biology-being instrumental for determining the fine morpho-functional characterization of most cellular structures. Electron microscopy has progressively evolved towards the development of tools that allow for the 3D characterization of different structures. This was done with the aid of a wide variety of techniques, which have become increasingly diverse and highly sophisticated. We start this review by examining the principles of 3D reconstruction of cells and tissues using classical approaches in TEM, and follow with a discussion of the modern approaches utilizing TEM as well as on new scanning electron microscopy-based techniques. 3D reconstruction techniques from serial sections and (cryo) electron-tomography are examined, and the recent applications of focused ion beam-scanning microscopes and serial-block-face techniques for the 3D reconstruction of large volumes are discussed. Alternative low-cost techniques and more accessible approaches using basic transmission or field emission scanning electron microscopes are also examined.

Pathogen-host reorganization during Chlamydia invasion revealed by cryo-electron tomography

Invasion of host cells is a key early event during bacterial infection, but the underlying pathogen–host interactions are yet to be fully visualized in three-dimensional detail. We have captured snapshots of the early stages of bacterial-mediated endocytosis in situ by exploiting the small size of chlamydial elementary bodies (EBs) for whole-cell cryo-electron tomography. Chlamydiae are obligate intracellular bacteria that infect eukaryotic cells and cause sexually transmitted infections and trachoma, the leading cause of preventable blindness. We demonstrate that Chlamydia trachomatis LGV2 EBs are intrinsically polarized. One pole is characterized by a tubular inner membrane invagination, while the other exhibits asymmetric periplasmic expansion to accommodate an array of type III secretion systems (T3SSs). Strikingly, EBs orient with their T3SS-containing pole facing target cells, enabling the T3SSs to directly contact the cellular plasma membrane. This contact induces enveloping macropinosomes, actin-rich filopodia and phagocytic cups to zipper tightly around the internalizing bacteria. Once encapsulated into tight early vacuoles, EB polarity and the T3SSs are lost. Our findings reveal previously undescribed structural transitions in both pathogen and host during the initial steps of chlamydial invasion.

Robust membrane detection based on tensor voting for electron tomography

Electron tomography enables three-dimensional (3D) visualization and analysis of the subcellular architecture at a resolution of a few nanometers. Segmentation of structural components present in 3D images (tomograms) is often necessary for their interpretation. However, it is severely hampered by a number of factors that are inherent to electron tomography (e.g. noise, low contrast, distortion). Thus, there is a need for new and improved computational methods to facilitate this challenging task. In this work, we present a new method for membrane segmentation that is based on anisotropic propagation of the local structural information using the tensor voting algorithm. The local structure at each voxel is then refined according to the information received from other voxels. Because voxels belonging to the same membrane have coherent structural information, the underlying global structure is strengthened. In this way, local information is easily integrated at a global scale to yield segmented structures. This method performs well under low signal-to-noise ratio typically found in tomograms of vitrified samples under cryo-tomography conditions and can bridge gaps present on membranes. The performance of the method is demonstrated by applications to tomograms of different biological samples and by quantitative comparison with standard template matching procedure.

Electron tomography for organelles, cells, and tissues

Electron tomography (ET) is an emerging electron microscopy (EM) technique for three-dimensional (3D) visualization of molecular arrangements and ultrastructural architectures in organelles, cells, and tissues at 2-10 nm resolution. The 3D tomogram is reconstructed from a series of 2D EM images taken from a single specimen at different projecting orientations. The specimen for ET must be specially prepared to meet the ET imaging requirements, i.e., ultrastructural preservation, specimen thickness, tolerance of electron dose and vacuum, and image contrast. In this chapter, the strategies of specimen preparation of organelles, cells, and tissues and the corresponding EM imaging requirements for ET will be described in detail. In addition, the general procedures tomographic reconstruction and tomogram interpretation will be described.

Automated target segmentation and real space fast alignment methods for high-throughput classification and averaging of crowded cryo-electron subtomograms

MOTIVATION

Cryo-electron tomography allows the imaging of macromolecular complexes in near living conditions. To enhance the nominal resolution of a structure it is necessary to align and average individual subtomograms each containing identical complexes. However, if the sample of complexes is heterogeneous, it is necessary to first classify subtomograms into groups of identical complexes. This task becomes challenging when tomograms contain mixtures of unknown complexes extracted from a crowded environment. Two main challenges must be overcomed: First, classification of subtomograms must be performed without knowledge of template structures. However, most alignment methods are too slow to perform reference-free classification of a large number of (e.g. tens of thousands) of subtomograms. Second, subtomograms extracted from crowded cellular environments, contain often fragments of other structures besides the target complex. However, alignment methods generally assume that each subtomogram only contains one complex. Automatic methods are needed to identify the target complexes in a subtomogram even when its shape is unknown.

RESULTS

In this article, we propose an automatic and systematic method for the isolation and masking of target complexes in subtomograms extracted from crowded environments. Moreover, we also propose a fast alignment method using fast rotational matching in real space. Our experiments show that, compared with our previously proposed fast alignment method in reciprocal space, our new method significantly improves the alignment accuracy for highly distorted and especially crowded subtomograms. Such improvements are important for achieving successful and unbiased high-throughput reference-free structural classification of complexes inside whole-cell tomograms.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Cryo-electron tomography: the challenge of doing structural biology in situ

Electron microscopy played a key role in establishing cell biology as a discipline, by producing fundamental insights into cellular organization and ultrastructure. Many seminal discoveries were made possible by the development of new sample preparation methods and imaging modalities. Recent technical advances include sample vitrification that faithfully preserves molecular structures, three-dimensional imaging by electron tomography, and improved image-processing methods. These new techniques have enabled the extraction of high fidelity structural information and are beginning to reveal the macromolecular organization of unperturbed cellular environments.

Viewing Golgi structure and function from a different perspective--insights from electron tomography

Historically, ultrastructural investigations, which have focused on elucidating the biological idiosyncrasies of the Golgi apparatus, have tended towards oversimplified or fallacious hypotheses when postulating how the Golgi apparatus reorganizes itself both structurally and functionally to fulfill the plethora of cellular processes underpinned by this complex organelle. Key questions are still unanswered with regard to how changes in Golgi architecture correlate so reproducibly to changes in its functional priorities under different physiological conditions or experimental perturbations. This fact alone serves to highlight how the technical limitations associated with conventional two-dimensional imaging approaches employed in the past failed to adequately capture the extraordinary complexity of the Golgi's three-dimensional (3D) structure-now a hallmark of this challenging organelle. Consequently, this has hampered progress towards developing a clear understanding of how changes in its structure and function typically occur in parallel. In this chapter, we highlight but a few of the significant new insights regarding variations in the Golgi's structure-function relationships that have been afforded over recent years through advanced electron microscopic techniques for 3D image reconstruction, commonly referred to as electron tomography.