Advances in cryo-electron tomography and subtomogram averaging and classification

Optimizing Contrast in Automated 4D STEM Cryotomography

4D STEM is an emerging approach to electron microscopy. While it was developed principally for high-resolution studies in materials science, the possibility to collect the entire transmitted flux makes it attractive for cryomicroscopy in application to life science and radiation-sensitive materials where dose efficiency is of utmost importance. We present a workflow to acquire tomographic tilt series of 4D STEM data sets using a segmented diode and an ultrafast pixelated detector, demonstrating the methods using a specimen of a T4 bacteriophage. Full integration with the SerialEM platform conveniently provides all the tools for grid navigation and automation of the data collection. Scripts are provided to convert the raw data to mrc format files and further to generate a variety of modes representing both scattering and phase contrasts, including incoherent and annular bright field, integrated center of mass, and parallax decomposition of a simulated integrated differential phase contrast. Principal component analysis of virtual annular detectors proves particularly useful, and axial contrast is improved by 3D deconvolution with an optimized point spread function. Contrast optimization enables visualization of irregular features such as DNA strands and thin filaments of the phage tails, which would be lost upon averaging or imposition of an inappropriate symmetry.

Visualizing the translation landscape in human cells at high resolution

Obtaining comprehensive structural descriptions of macromolecules within their natural cellular context holds immense potential for understanding fundamental biology and improving health. Here, we present the landscape of protein synthesis inside human cells in unprecedented detail obtained using an approach which combines automated cryo-focused ion beam (FIB) milling and in situ single-particle cryo-electron microscopy (cryo-EM). With this in situ cryo-EM approach we resolved a 2.19 Å consensus structure of the human 80S ribosome and unveiled its 21 distinct functional states, nearly all higher than 3 Å resolution. In contrast to in vitro studies, we identified protein factors, including SERBP1, EDF1 and NAC/3, not enriched on purified ribosomes. Most strikingly, we observed that SERBP1 binds to the ribosome in almost all translating and non-translating states to bridge the 60S and 40S ribosomal subunits. These newly observed binding sites suggest that SERBP1 may serve an important regulatory role in translation. We also uncovered a detailed interface between adjacent translating ribosomes which can form the helical polysome structure. Finally, we resolved high-resolution structures from cells treated with homoharringtonine and cycloheximide, and identified numerous polyamines bound to the ribosome, including a spermidine that interacts with cycloheximide bound at the E site of the ribosome, underscoring the importance of high-resolution in situ studies in the complex native environment. Collectively, our work represents a significant advancement in detailed structural studies within cellular contexts.

TomoNet: A streamlined cryogenic electron tomography software pipeline with automatic particle picking on flexible lattices

Cryogenic electron tomography (cryoET) is capable of determining in situ biological structures of molecular complexes at near-atomic resolution by averaging half a million subtomograms. While abundant complexes/particles are often clustered in arrays, precisely locating and seamlessly averaging such particles across many tomograms present major challenges. Here, we developed TomoNet, a software package with a modern graphical user interface to carry out the entire pipeline of cryoET and subtomogram averaging to achieve high resolution. TomoNet features built-in automatic particle picking and three-dimensional (3D) classification functions and integrates commonly used packages to streamline high-resolution subtomogram averaging for structures in 1D, 2D, or 3D arrays. Automatic particle picking is accomplished in two complementary ways: one based on template matching and the other using deep learning. TomoNet's hierarchical file organization and visual display facilitate efficient data management as required for large cryoET datasets. Applications of TomoNet to three types of datasets demonstrate its capability of efficient and accurate particle picking on flexible and imperfect lattices to obtain high-resolution 3D biological structures: virus-like particles, bacterial surface layers within cellular lamellae, and membranes decorated with nuclear egress protein complexes. These results demonstrate TomoNet's potential for broad applications to various cryoET projects targeting high-resolution in situ structures.

STOPGAP: an open-source package for template matching, subtomogram alignment and classification

Cryo-electron tomography (cryo-ET) enables molecular-resolution 3D imaging of complex biological specimens such as viral particles, cellular sections and, in some cases, whole cells. This enables the structural characterization of molecules in their near-native environments, without the need for purification or separation, thereby preserving biological information such as conformational states and spatial relationships between different molecular species. Subtomogram averaging is an image-processing workflow that allows users to leverage cryo-ET data to identify and localize target molecules, determine high-resolution structures of repeating molecular species and classify different conformational states. Here, STOPGAP, an open-source package for subtomogram averaging that is designed to provide users with fine control over each of these steps, is described. In providing detailed descriptions of the image-processing algorithms that STOPGAP uses, this manuscript is also intended to serve as a technical resource to users as well as for further community-driven software development.

Thinner is not always better: Optimizing cryo-lamellae for subtomogram averaging

Cryo-electron tomography (cryo-ET) is a powerful method to elucidate subcellular architecture and to structurally analyze biomolecules in situ by subtomogram averaging, yet data quality critically depends on specimen thickness. Cells that are too thick for transmission imaging can be thinned into lamellae by cryo-focused ion beam (cryo-FIB) milling. Despite being a crucial parameter directly affecting attainable resolution, optimal lamella thickness has not been systematically investigated nor the extent of structural damage caused by gallium ions used for FIB milling. We thus systematically determined how resolution is affected by these parameters. We find that ion-induced damage does not affect regions more than 30 nanometers from either lamella surface and that up to ~180-nanometer lamella thickness does not negatively affect resolution. This shows that there is no need to generate very thin lamellae and lamella thickness can be chosen such that it captures cellular features of interest, thereby opening cryo-ET also for studies of large complexes.

Learning structural heterogeneity from cryo-electron sub-tomograms with tomoDRGN

Cryo-electron tomography (cryo-ET) enables observation of macromolecular complexes in their native, spatially contextualized cellular environment. Cryo-ET processing software to visualize such complexes at nanometer resolution via iterative alignment and averaging are well developed but rely upon assumptions of structural homogeneity among the complexes of interest. Recently developed tools allow for some assessment of structural diversity but have limited capacity to represent highly heterogeneous structures, including those undergoing continuous conformational changes. Here we extend the highly expressive cryoDRGN (Deep Reconstructing Generative Networks) deep learning architecture, originally created for single-particle cryo-electron microscopy analysis, to cryo-ET. Our new tool, tomoDRGN, learns a continuous low-dimensional representation of structural heterogeneity in cryo-ET datasets while also learning to reconstruct heterogeneous structural ensembles supported by the underlying data. Using simulated and experimental data, we describe and benchmark architectural choices within tomoDRGN that are uniquely necessitated and enabled by cryo-ET. We additionally illustrate tomoDRGN's efficacy in analyzing diverse datasets, using it to reveal high-level organization of human immunodeficiency virus (HIV) capsid complexes assembled in virus-like particles and to resolve extensive structural heterogeneity among ribosomes imaged in situ.

Structures and Efflux Mechanisms of the AcrAB-TolC Pump

The global emergence of multidrug resistance (MDR) in gram-negative bacteria has become a matter of worldwide concern. MDR in these pathogens is closely linked to the overexpression of certain efflux pumps, particularly the resistance-nodulation-cell division (RND) efflux pumps. Inhibition of these pumps presents an attractive and promising strategy to combat antibiotic resistance, as the efflux pump inhibitors can effectively restore the potency of existing antibiotics. AcrAB-TolC is one well-studied RND efflux pump, which transports a variety of substrates, therefore providing resistance to a broad spectrum of antibiotics. To develop effective pump inhibitors, a comprehensive understanding of the structural aspect of the AcrAB-TolC efflux pump is imperative. Previous studies on this pump's structure have been limited to individual components or in vitro determination of fully assembled pumps. Recent advancements in cellular cryo-electron tomography (cryo-ET) have provided novel insights into this pump's assembly and functional mechanism within its native cell membrane environment. Here, we present a summary of the structural data regarding the AcrAB-TolC efflux pump, shedding light on its assembly pathway and operational mechanism.

Advanced Techniques for Detecting Protein Misfolding and Aggregation in Cellular Environments

Protein misfolding and aggregation, a key contributor to the progression of numerous neurodegenerative diseases, results in functional deficiencies and the creation of harmful intermediates. Detailed visualization of this misfolding process is of paramount importance for improving our understanding of disease mechanisms and for the development of potential therapeutic strategies. While in vitro studies using purified proteins have been instrumental in delivering significant insights into protein misfolding, the behavior of these proteins in the complex milieu of living cells often diverges significantly from such simplified environments. Biomedical imaging performed in cell provides cellular-level information with high physiological and pathological relevance, often surpassing the depth of information attainable through in vitro methods. This review highlights a variety of methodologies used to scrutinize protein misfolding within biological systems. This includes optical-based methods, strategies leaning on mass spectrometry, in-cell nuclear magnetic resonance, and cryo-electron microscopy. Recent advancements in these techniques have notably deepened our understanding of protein misfolding processes and the features of the resulting misfolded species within living cells. The progression in these fields promises to catalyze further breakthroughs in our comprehension of neurodegenerative disease mechanisms and potential therapeutic interventions.

Computational methods for in situ structural studies with cryogenic electron tomography

Cryo-electron tomography (cryo-ET) plays a critical role in imaging microorganisms in situ in terms of further analyzing the working mechanisms of viruses and drug exploitation, among others. A data processing workflow for cryo-ET has been developed to reconstruct three-dimensional density maps and further build atomic models from a tilt series of two-dimensional projections. Low signal-to-noise ratio (SNR) and missing wedge are two major factors that make the reconstruction procedure challenging. Because only few near-atomic resolution structures have been reconstructed in cryo-ET, there is still much room to design new approaches to improve universal reconstruction resolutions. This review summarizes classical mathematical models and deep learning methods among general reconstruction steps. Moreover, we also discuss current limitations and prospects. This review can provide software and methods for each step of the entire procedure from tilt series by cryo-ET to 3D atomic structures. In addition, it can also help more experts in various fields comprehend a recent research trend in cryo-ET. Furthermore, we hope that more researchers can collaborate in developing computational methods and mathematical models for high-resolution three-dimensional structures from cryo-ET datasets.

Preparing Arabidopsis thaliana root protoplasts for cryo electron tomography

The use of protoplasts in plant biology has become a convenient tool for the application of transient gene expression. This model system has allowed the study of plant responses to biotic and abiotic stresses, protein location and trafficking, cell wall dynamics, and single-cell transcriptomics, among others. Although well-established protocols for isolating protoplasts from different plant tissues are available, they have never been used for studying plant cells using cryo electron microscopy (cryo-EM) and cryo electron tomography (cryo-ET). Here we describe a workflow to prepare root protoplasts from Arabidopsis thaliana plants for cryo-ET. The process includes protoplast isolation and vitrification on EM grids, and cryo-focused ion beam milling (cryo-FIB), with the aim of tilt series acquisition. The whole workflow, from growing the plants to the acquisition of the tilt series, may take a few months. Our protocol provides a novel application to use plant protoplasts as a tool for cryo-ET.

PickYOLO: Fast deep learning particle detector for annotation of cryo electron tomograms

Particle localization (picking) in digital tomograms is a laborious and time-intensive step in cryogenic electron tomography (cryoET) analysis often requiring considerable user involvement, thus becoming a bottleneck for automated cryoET subtomogram averaging (STA) pipelines. In this paper, we introduce a deep learning framework called PickYOLO to tackle this problem. PickYOLO is a super-fast, universal particle detector based on the deep-learning real-time object recognition system YOLO (You Only Look Once), and tested on single particles, filamentous structures, and membrane-embedded particles. After training with the centre coordinates of a few hundred representative particles, the network automatically detects additional particles with high yield and reliability at a rate of 0.24-3.75 s per tomogram. PickYOLO can automatically detect number of particles comparable to those manually selected by experienced microscopists. This makes PickYOLO a valuable tool to substantially reduce the time and manual effort needed to analyse cryoET data for STA, greatly aiding in high-resolution cryoET structure determination.

Advanced fluorescence microscopy in respiratory virus cell biology

Respiratory viruses are a major public health burden across all age groups around the globe, and are associated with high morbidity and mortality rates. They can be transmitted by multiple routes, including physical contact or droplets and aerosols, resulting in efficient spreading within the human population. Investigations of the cell biology of virus replication are thus of utmost importance to gain a better understanding of virus-induced pathogenicity and the development of antiviral countermeasures. Light and fluorescence microscopy techniques have revolutionized investigations of the cell biology of virus infection by allowing the study of the localization and dynamics of viral or cellular components directly in infected cells. Advanced microscopy including high- and super-resolution microscopy techniques available today can visualize biological processes at the single-virus and even single-molecule level, thus opening a unique view on virus infection. We will highlight how fluorescence microscopy has supported investigations on virus cell biology by focusing on three major respiratory viruses: respiratory syncytial virus (RSV), Influenza A virus (IAV) and SARS-CoV-2. We will review our current knowledge of virus replication and highlight how fluorescence microscopy has helped to improve our state of understanding. We will start by introducing major imaging and labeling modalities and conclude the chapter with a perspective discussion on remaining challenges and potential opportunities.

MDTOMO method for continuous conformational variability analysis in cryo electron subtomograms based on molecular dynamics simulations

Cryo electron tomography (cryo-ET) allows observing macromolecular complexes in their native environment. The common routine of subtomogram averaging (STA) allows obtaining the three-dimensional (3D) structure of abundant macromolecular complexes, and can be coupled with discrete classification to reveal conformational heterogeneity of the sample. However, the number of complexes extracted from cryo-ET data is usually small, which restricts the discrete-classification results to a small number of enough populated states and, thus, results in a largely incomplete conformational landscape. Alternative approaches are currently being investigated to explore the continuity of the conformational landscapes that in situ cryo-ET studies could provide. In this article, we present MDTOMO, a method for analyzing continuous conformational variability in cryo-ET subtomograms based on Molecular Dynamics (MD) simulations. MDTOMO allows obtaining an atomic-scale model of conformational variability and the corresponding free-energy landscape, from a given set of cryo-ET subtomograms. The article presents the performance of MDTOMO on a synthetic ABC exporter dataset and an in situ SARS-CoV-2 spike dataset. MDTOMO allows analyzing dynamic properties of molecular complexes to understand their biological functions, which could also be useful for structure-based drug discovery.

Learning structural heterogeneity from cryo-electron sub-tomograms with tomoDRGN

Cryo-electron tomography (cryo-ET) allows one to observe macromolecular complexes in their native, spatially contextualized environment. Tools to visualize such complexes at nanometer resolution via iterative alignment and averaging are well-developed but rely on assumptions of structural homogeneity among the complexes under consideration. Recently developed downstream analysis tools allow for some assessment of macromolecular diversity but have limited capacity to represent highly heterogeneous macromolecules, including those undergoing continuous conformational changes. Here, we extend the highly expressive cryoDRGN deep learning architecture, originally created for cryo-electron microscopy single particle analysis, to sub-tomograms. Our new tool, tomoDRGN, learns a continuous low-dimensional representation of structural heterogeneity in cryo-ET datasets while also learning to reconstruct a large, heterogeneous ensemble of structures supported by the underlying data. Using simulated and experimental data, we describe and benchmark architectural choices within tomoDRGN that are uniquely necessitated and enabled by cryo-ET data. We additionally illustrate tomoDRGN's efficacy in analyzing an exemplar dataset, using it to reveal extensive structural heterogeneity among ribosomes imaged in situ.

Bringing Structure to Cell Biology with Cryo-Electron Tomography

Recent advances in cryo-electron microscopy have marked only the beginning of the potential of this technique. To bring structure into cell biology, the modality of cryo-electron tomography has fast developed into a bona fide in situ structural biology technique where structures are determined in their native environment, the cell. Nearly every step of the cryo-focused ion beam-assisted electron tomography (cryo-FIB-ET) workflow has been improved upon in the past decade, since the first windows were carved into cells, unveiling macromolecular networks in near-native conditions. By bridging structural and cell biology, cryo-FIB-ET is advancing our understanding of structure-function relationships in their native environment and becoming a tool for discovering new biology.

Are extraordinary nucleosome structures more ordinary than we thought?

The nucleosome is a DNA-protein assembly that is the basic unit of chromatin. A nucleosome can adopt various structures. In the canonical nucleosome structure, 145-147 bp of DNA is wrapped around a histone heterooctamer. The strong histone-DNA interactions cause the DNA to be inaccessible for nuclear processes such as transcription. Therefore, the canonical nucleosome structure has to be altered into different, non-canonical structures to increase DNA accessibility. While it is recognised that non-canonical structures do exist, these structures are not well understood. In this review, we discuss both the evidence for various non-canonical nucleosome structures in the nucleus and the factors that are believed to induce these structures. The wide range of non-canonical structures is likely to regulate the amount of accessible DNA, and thus have important nuclear functions.

Manipulation of the Cellular Membrane-Cytoskeleton Network for RNA Virus Replication and Movement in Plants

Viruses infect all cellular life forms and cause various diseases and significant economic losses worldwide. The majority of viruses are positive-sense RNA viruses. A common feature of infection by diverse RNA viruses is to induce the formation of altered membrane structures in infected host cells. Indeed, upon entry into host cells, plant-infecting RNA viruses target preferred organelles of the cellular endomembrane system and remodel organellar membranes to form organelle-like structures for virus genome replication, termed as the viral replication organelle (VRO) or the viral replication complex (VRC). Different viruses may recruit different host factors for membrane modifications. These membrane-enclosed virus-induced replication factories provide an optimum, protective microenvironment to concentrate viral and host components for robust viral replication. Although different viruses prefer specific organelles to build VROs, at least some of them have the ability to exploit alternative organellar membranes for replication. Besides being responsible for viral replication, VROs of some viruses can be mobile to reach plasmodesmata (PD) via the endomembrane system, as well as the cytoskeleton machinery. Viral movement protein (MP) and/or MP-associated viral movement complexes also exploit the endomembrane-cytoskeleton network for trafficking to PD where progeny viruses pass through the cell-wall barrier to enter neighboring cells.

Graph identification of proteins in tomograms (GRIP-Tomo)

In this study, we present a method of pattern mining based on network theory that enables the identification of protein structures or complexes from synthetic volume densities, without the knowledge of predefined templates or human biases for refinement. We hypothesized that the topological connectivity of protein structures is invariant, and they are distinctive for the purpose of protein identification from distorted data presented in volume densities. Three-dimensional densities of a protein or a complex from simulated tomographic volumes were transformed into mathematical graphs as observables. We systematically introduced data distortion or defects such as missing fullness of data, the tumbling effect, and the missing wedge effect into the simulated volumes, and varied the distance cutoffs in pixels to capture the varying connectivity between the density cluster centroids in the presence of defects. A similarity score between the graphs from the simulated volumes and the graphs transformed from the physical protein structures in point data was calculated by comparing their network theory order parameters including node degrees, betweenness centrality, and graph densities. By capturing the essential topological features defining the heterogeneous morphologies of a network, we were able to accurately identify proteins and homo-multimeric complexes from 10 topologically distinctive samples without realistic noise added. Our approach empowers future developments of tomogram processing by providing pattern mining with interpretability, to enable the classification of single-domain protein native topologies as well as distinct single-domain proteins from multimeric complexes within noisy volumes.

Mechanisms of E. coli chemotaxis signaling pathways visualized using cryoET and computational approaches

Chemotaxis signaling pathways enable bacteria to sense and respond to their chemical environment and, in some species, are critical for lifestyle processes such as biofilm formation and pathogenesis. The signal transduction underlying chemotaxis behavior is mediated by large, highly ordered protein complexes known as chemosensory arrays. For nearly two decades, cryo-electron tomography (cryoET) has been used to image chemosensory arrays, providing an increasingly detailed understanding of their structure and function. In this mini-review, we provide an overview of the use of cryoET to study chemosensory arrays, including imaging strategies, key results, and outstanding questions. We further discuss the application of molecular modeling and simulation techniques to complement structure determination efforts and provide insight into signaling mechanisms. We close the review with a brief outlook, highlighting promising future directions for the field.

Recent advances and current trends in cryo-electron microscopy

All steps of cryogenic electron-microscopy (cryo-EM) workflows have rapidly evolved over the last decade. Advances in both single-particle analysis (SPA) cryo-EM and cryo-electron tomography (cryo-ET) have facilitated the determination of high-resolution biomolecular structures that are not tractable with other methods. However, challenges remain. For SPA, these include improved resolution in an additional dimension: time. For cryo-ET, these include accessing difficult-to-image areas of a cell and finding rare molecules. Finally, there is a need for automated and faster workflows, as many projects are limited by throughput. Here, we review current developments in SPA cryo-EM and cryo-ET that push these boundaries. Collectively, these advances are poised to propel our spatial and temporal understanding of macromolecular processes.

Cryomicroscopy in situ: what is the smallest molecule that can be directly identified without labels in a cell?

Electron cryomicroscopy (cryoEM) has made great strides in the last decade, such that the atomic structure of most biological macromolecules can, at least in principle, be determined. Major technological advances - in electron imaging hardware, data analysis software, and cryogenic specimen preparation technology - continue at pace and contribute to the exponential growth in the number of atomic structures determined by cryoEM. It is now conceivable that within the next decade we will have structures for hundreds of thousands of unique protein and nucleic acid molecular complexes. But the answers to many important questions in biology would become obvious if we could identify these structures precisely inside cells with quantifiable error. In the context of an abundance of known structures, it is appropriate to consider the current state of electron cryomicroscopy for frozen specimens prepared directly from cells, and try to answer to the question of the title, both now and in the foreseeable future.

Strategies for picking membrane-associated particles within subtomogram averaging workflows

Cryo-electron tomography (cryo-ET) with subtomogram averaging (STA) has emerged as a key tool for determining macromolecular structure(s) in vitro and in situ. However, processing cryo-ET data with STA currently requires significant user expertise. Recent efforts have streamlined several steps in STA workflows; however, particle picking remains a time-consuming bottleneck for many projects and requires considerable user input. Here, we present several strategies for the time-efficient and accurate picking of membrane-associated particles using the COPII inner coat as a case study. We also discuss a range of particle cleaning solutions to remove both poor quality and false-positive particles from STA datasets. We provide a step-by-step guide and the necessary scripts for users to independently carry out the particle picking and cleaning strategies discussed.

Protein Data Bank: A Comprehensive Review of 3D Structure Holdings and Worldwide Utilization by Researchers, Educators, and Students

The Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB), funded by the United States National Science Foundation, National Institutes of Health, and Department of Energy, supports structural biologists and Protein Data Bank (PDB) data users around the world. The RCSB PDB, a founding member of the Worldwide Protein Data Bank (wwPDB) partnership, serves as the US data center for the global PDB archive housing experimentally-determined three-dimensional (3D) structure data for biological macromolecules. As the wwPDB-designated Archive Keeper, RCSB PDB is also responsible for the security of PDB data and weekly update of the archive. RCSB PDB serves tens of thousands of data depositors (using macromolecular crystallography, nuclear magnetic resonance spectroscopy, electron microscopy, and micro-electron diffraction) annually working on all permanently inhabited continents. RCSB PDB makes PDB data available from its research-focused web portal at no charge and without usage restrictions to many millions of PDB data consumers around the globe. It also provides educators, students, and the general public with an introduction to the PDB and related training materials through its outreach and education-focused web portal. This review article describes growth of the PDB, examines evolution of experimental methods for structure determination viewed through the lens of the PDB archive, and provides a detailed accounting of PDB archival holdings and their utilization by researchers, educators, and students worldwide.

Structure and activity of particulate methane monooxygenase arrays in methanotrophs

Methane-oxidizing bacteria play a central role in greenhouse gas mitigation and have potential applications in biomanufacturing. Their primary metabolic enzyme, particulate methane monooxygenase (pMMO), is housed in copper-induced intracytoplasmic membranes (ICMs), of which the function and biogenesis are not known. We show by serial cryo-focused ion beam (cryoFIB) milling/scanning electron microscope (SEM) volume imaging and lamellae-based cellular cryo-electron tomography (cryoET) that these ICMs are derived from the inner cell membrane. The pMMO trimer, resolved by cryoET and subtomogram averaging to 4.8 Å in the ICM, forms higher-order hexagonal arrays in intact cells. Array formation correlates with increased enzymatic activity, highlighting the importance of studying the enzyme in its native environment. These findings also demonstrate the power of cryoET to structurally characterize native membrane enzymes in the cellular context.

MemBrain: A deep learning-aided pipeline for detection of membrane proteins in Cryo-electron tomograms

BACKGROUND AND OBJECTIVE

Cryo-electron tomography (cryo-ET) is an imaging technique that enables 3D visualization of the native cellular environment at sub-nanometer resolution, providing unpreceded insights into the molecular organization of cells. However, cryo-electron tomograms suffer from low signal-to-noise ratios and anisotropic resolution, which makes subsequent image analysis challenging. In particular, the efficient detection of membrane-embedded proteins is a problem still lacking satisfactory solutions.

METHODS

We present MemBrain - a new deep learning-aided pipeline that automatically detects membrane-bound protein complexes in cryo-electron tomograms. After subvolumes are sampled along a segmented membrane, each subvolume is assigned a score using a convolutional neural network (CNN), and protein positions are extracted by a clustering algorithm. Incorporating rotational subvolume normalization and using a tiny receptive field simplify the task of protein detection and thus facilitate the network training.

RESULTS

MemBrain requires only a small quantity of training labels and achieves excellent performance with only a single annotated membrane (F1 score: 0.88). A detailed evaluation shows that our fully trained pipeline outperforms existing classical computer vision-based and CNN-based approaches by a large margin (F1 score: 0.92 vs. max. 0.63). Furthermore, in addition to protein center positions, MemBrain can determine protein orientations, which has not been implemented by any existing CNN-based method to date. We also show that a pre-trained MemBrain program generalizes to tomograms acquired using different cryo-ET methods and depicting different types of cells.

CONCLUSIONS

MemBrain is a powerful and annotation-efficient tool for the detection of membrane protein complexes in cryo-ET data, with the potential to be used in a wide range of biological studies. It is generalizable to various kinds of tomograms, making it possible to use pretrained models for different tasks. Its efficiency in terms of required annotations also allows rapid training and fine-tuning of models. The corresponding code, pretrained models, and instructions for operating the MemBrain program can be found at: https://github.com/CellArchLab/MemBrain.

Structure and assembly of cargo Rubisco in two native α-carboxysomes

Carboxysomes are a family of bacterial microcompartments in cyanobacteria and chemoautotrophs. They encapsulate Ribulose 1,5-bisphosphate carboxylase/oxygenase (Rubisco) and carbonic anhydrase catalyzing carbon fixation inside a proteinaceous shell. How Rubisco complexes pack within the carboxysomes is unknown. Using cryo-electron tomography, we determine the distinct 3D organization of Rubisco inside two distant α-carboxysomes from a marine α-cyanobacterium Cyanobium sp. PCC 7001 where Rubiscos are organized in three concentric layers, and from a chemoautotrophic bacterium Halothiobacillus neapolitanus where they form intertwining spirals. We further resolve the structures of native Rubisco as well as its higher-order assembly at near-atomic resolutions by subtomogram averaging. The structures surprisingly reveal that the authentic intrinsically disordered linker protein CsoS2 interacts with Rubiscos in native carboxysomes but functions distinctively in the two α-carboxysomes. In contrast to the uniform Rubisco-CsoS2 association in the Cyanobium α-carboxysome, CsoS2 binds only to the Rubiscos close to the shell in the Halo α-carboxysome. Our findings provide critical knowledge of the assembly principles of α-carboxysomes, which may aid in the rational design and repurposing of carboxysome structures for new functions.

High-resolution structure determination using high-throughput electron cryo-tomography

In this article, it is shown that high-throughput strategies for tomographic data acquisition combined with unsupervised techniques for image analysis provide the foundation for closing the resolution gap between the high-resolution strategies used to study molecular assemblies reconstituted in vitro and techniques for in situ structure determination.

Tomographic reconstruction of frozen-hydrated specimens followed by extraction and averaging of sub-tomograms has successfully been used to determine the structure of macromolecules in their native environment at resolutions that are high enough to reveal molecular level interactions. The low throughput characteristic of tomographic data acquisition combined with the complex data-analysis pipeline that is required to obtain high-resolution maps, however, has limited the applicability of this technique to favorable samples or to resolutions that are too low to provide useful mechanistic information. Recently, beam image-shift electron cryo-tomography (BISECT), a strategy to significantly accelerate the acquisition of tilt series without sacrificing image quality, was introduced. The ability to produce thousands of high-quality tilt series during a single microscope session, however, introduces significant bottlenecks in the downstream data analysis, which has so far relied on specialized pipelines. Here, recent advances in accurate estimation of the contrast transfer function and self-tuning exposure-weighting routines that contribute to improving the resolution and streamlining the structure-determination process using sub-volume averaging are reviewed. Ultimately, the combination of automated data-driven techniques for image analysis together with high-throughput strategies for tilt-series acquisition will pave the way for tomography to become the technique of choice for in situ structure determination.

A feature-guided, focused 3D signal permutation method for subtomogram averaging

Advances in electron microscope instrumentation, cryo-electron tomography data collection, and subtomogram averaging have allowed for the in-situ visualization of molecules and their complexes in their native environment. Current data processing pipelines commonly extract subtomograms as a cubic subvolume with the key assumption that the selected object of interest is discrete from its surroundings. However, in instances when the object is in its native environment, surrounding densities may negatively affect the subsequent alignment and refinement processes, leading to loss of information due to misalignment. For example, the strong densities from surrounding membranes may dominate the alignment process for membrane proteins. Here, we developed methods for feature-guided subtomogram alignment and 3D signal permutation for subtomogram averaging. Our 3D signal permutation method randomizes and filters voxels outside a mask of any shape and blurs the boundary of the mask that encapsulates the object of interest. The randomization preserves global statistical properties such as mean density and standard deviation of voxel density values, effectively producing a featureless background surrounding the object of interest. This signal permutation process can be repeatedly applied with intervening alignments of the 3D signal-permuted subvolumes, recentering of the mask, and optional adjustments of the shape of the mask. We have implemented these methods in a new processing pipeline which starts from tomograms, contains feature-guided subtomogram extraction and alignment, 3D signal-permutation, and subtomogram visualization tools. As an example, feature-guided alignment and 3D signal permutation leads to improved subtomogram average maps for a dataset of synaptic protein complexes in their native environment.

Structural parasitology of the malaria parasite Plasmodium falciparum

The difficulty of faithfully recapitulating malarial protein complexes in heterologous expression systems has long impeded structural study for much of the Plasmodium falciparum proteome. However, recent advances in single-particle cryo electron microscopy (cryoEM) now enable structure determination at atomic resolution with significantly reduced requirements for both sample quantity and purity. Combined with recent developments in gene editing, these advances open the door to structure determination and structural proteomics of macromolecular complexes enriched directly from P. falciparum parasites. Furthermore, the combination of cryoEM with the rapidly emerging use of in situ cryo electron tomography (cryoET) to directly visualize ultrastructures and protein complexes in the native cellular context will yield exciting new insights into the molecular machinery underpinning malaria parasite biology and pathogenesis.

High-resolution in situ structure determination by cryo-electron tomography and subtomogram averaging using emClarity

Cryo-electron tomography and subtomogram averaging (STA) has developed rapidly in recent years. It provides structures of macromolecular complexes in situ and in cellular context at or below subnanometer resolution and has led to unprecedented insights into the inner working of molecular machines in their native environment, as well as their functional relevant conformations and spatial distribution within biological cells or tissues. Given the tremendous potential of cryo-electron tomography STA in in situ structural cell biology, we previously developed emClarity, a graphics processing unit-accelerated image-processing software that offers STA and classification of macromolecular complexes at high resolution. However, the workflow remains challenging, especially for newcomers to the field. In this protocol, we describe a detailed workflow, processing and parameters associated with each step, from initial tomography tilt-series data to the final 3D density map, with several features unique to emClarity. We use four different samples, including human immunodeficiency virus type 1 Gag assemblies, ribosome and apoferritin, to illustrate the procedure and results of STA and classification. Following the processing steps described in this protocol, along with a comprehensive tutorial and guidelines for troubleshooting and parameter optimization, one can obtain density maps up to 2.8 Å resolution from six tilt series by cryo-electron tomography STA.

Elusive structure of mammalian DGKs

Mammalian diacylglycerol kinases (DGKs) are a group of enzymes that catalyze the ATP-dependent phosphorylation of diacylglycerol (DAG) to produce phosphatidic acid (PtdOH). In doing so, they modulate the levels of these two important signaling lipids. Currently, ten mammalian DGKs are organized into five classes that vary with respect to domain organization, regulation, and cellular/subcellular distribution. As lipids play critical roles in cells, it is not surprising that there is increasing interest in understanding the mechanism underlying the catalysis and regulation of lipid modulating enzymes such as DGKs. However, there are no solved 3D structures for any of the eukaryotic DGKs. In this review, we summarize what is known and the current challenges in determining the structures of these important enzymes. In addition to gain critical insights into their mechanisms of catalysis and regulation, DGK structures will provide a platform for the design of isoform specific inhibitors.

Structural Analysis of Retrovirus Assembly and Maturation

Retroviruses have a very complex and tightly controlled life cycle which has been studied intensely for decades. After a virus enters the cell, it reverse-transcribes its genome, which is then integrated into the host genome, and subsequently all structural and regulatory proteins are transcribed and translated. The proteins, along with the viral genome, assemble into a new virion, which buds off the host cell and matures into a newly infectious virion. If any one of these steps are faulty, the virus cannot produce infectious viral progeny. Recent advances in structural and molecular techniques have made it possible to better understand this class of viruses, including details about how they regulate and coordinate the different steps of the virus life cycle. In this review we summarize the molecular analysis of the assembly and maturation steps of the life cycle by providing an overview on structural and biochemical studies to understand these processes. We also outline the differences between various retrovirus families with regards to these processes.

Entropy-regularized deconvolution of cellular cryotransmission electron tomograms

Cellular cryo-electron tomography suffers from severely compromised Z resolution due to the missing wedges of information not collected during the acquisition of tilt series. This paper shows that application of entropy-regularized deconvolution to transmission electron tomography substantially fills in this missing information, allowing for improved Z resolution and better interpretation of cellular structures.

Cryo-electron tomography (cryo-ET) allows for the high-resolution visualization of biological macromolecules. However, the technique is limited by a low signal-to-noise ratio (SNR) and variance in contrast at different frequencies, as well as reduced Z resolution. Here, we applied entropy-regularized deconvolution (ER-DC) to cryo-ET data generated from transmission electron microscopy (TEM) and reconstructed using weighted back projection (WBP). We applied deconvolution to several in situ cryo-ET datasets and assessed the results by Fourier analysis and subtomogram analysis (STA).

A structural view of the SARS-CoV-2 virus and its assembly

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Structural biology plays a vital role in SARS-CoV-2 vaccine and treatment.

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High-resolution structures of SARS-CoV-2 proteins and complexes have been obtained.

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In situ structures of SARS-CoV-2 virus and its assembly are visualized by cryoET.

The SARS-CoV-2 pandemic that struck in 2019 has left the world crippled with hundreds of millions of cases and millions of people dead. During this time, we have seen unprecedented support and collaboration amongst scientists to respond to this deadly disease. Advances in the field of structural biology, in particular cryoEM and cryo-electron tomography, have allowed unprecedented structural analysis of SARS-CoV-2. Here, we review the structural work on the SARS-CoV-2 virus and viral components, as well as its cellular assembly process, highlighting some important structural findings that have made significant impact on the protection from and treatment of emerging viral infections.

Computational toolbox for ultrastructural quantitative analysis of filament networks in cryo-ET data

A precise quantitative description of the ultrastructural characteristics underlying biological mechanisms is often key to their understanding. This is particularly true for dynamic extra- and intracellular filamentous assemblies, playing a role in cell motility, cell integrity, cytokinesis, tissue formation and maintenance. For example, genetic manipulation or modulation of actin regulatory proteins frequently manifests in changes of the morphology, dynamics, and ultrastructural architecture of actin filament-rich cell peripheral structures, such as lamellipodia or filopodia. However, the observed ultrastructural effects often remain subtle and require sufficiently large datasets for appropriate quantitative analysis. The acquisition of such large datasets has been enabled by recent advances in high-throughput cryo-electron tomography (cryo-ET) methods. This also necessitates the development of complementary approaches to maximize the extraction of relevant biological information. We have developed a computational toolbox for the semi-automatic quantification of segmented and vectorized filamentous networks from pre-processed cryo-electron tomograms, facilitating the analysis and cross-comparison of multiple experimental conditions. GUI-based components simplify the processing of data and allow users to obtain a large number of ultrastructural parameters describing filamentous assemblies. We demonstrate the feasibility of this workflow by analyzing cryo-ET data of untreated and chemically perturbed branched actin filament networks and that of parallel actin filament arrays. In principle, the computational toolbox presented here is applicable for data analysis comprising any type of filaments in regular (i.e. parallel) or random arrangement. We show that it can ease the identification of key differences between experimental groups and facilitate the in-depth analysis of ultrastructural data in a time-efficient manner.

Challenges and triumphs in cryo-electron tomography

Cryo-electron tomography has stepped fully into the spotlight. Enthusiasm is high. Fortunately for us, this is an exciting time to be a cryotomographer, but there is still a way to go before declaring victory. Despite its potential, cryo-electron tomography possesses many inherent challenges. How do we image through thick cell samples, and possibly even tissue? How do we identify a protein of interest amidst the noisy, crowded environment of the cytoplasm? How do we target specific moments of a dynamic cellular process for tomographic imaging? In this review, we cover the history of cryo-electron tomography and how it came to be, roughly speaking, as well as the many approaches that have been developed to overcome its intrinsic limitations.

Cryo-Electron Microscopy of the Giant Viruses

High resolution study of the giant viruses presents one of the latest challenges in cryo-electron microscopy of viruses. Too small for light microscopy, but too large for easy study at high resolution by electron microscopy, they range in size from ~0.2-2 μm, from high symmetry icosahedral viruses such as Paramecium burseria Chlorella virus 1 to asymmetric forms like Tupanvirus or Pithovirus. To attain high resolution, two strategies exist to study these large viruses by cryo-EM: firstly, increasing the acceleration voltage of the electron microscope to improve sample penetration and overcome the limitations imposed by electro-optical physics at lower voltages, and secondly the method of "block-based reconstruction" pioneered by Michael G. Rossmann and his collaborators, which resolves the latter limitation through an elegant leveraging of high symmetry, but cannot overcome sample penetration limitations. In addition, more recent advances in both computational capacity and image processing also yield assistance in studying the giant viruses. Especially, the inclusion of Ewald sphere correction can provide large improvements in attainable resolutions for 300 kV electron microscopes. Despite this, the study of giant viruses remains a significant challenge.

Structure and dynamics of photoreceptor sensory cilia

The rod and cone photoreceptor cells of the vertebrate retina have highly specialized structures that enable them to carry out their function of light detection over a broad range of illumination intensities with optimized spatial and temporal resolution. Most prominent are their unusually large sensory cilia, consisting of outer segments packed with photosensitive disc membranes, a connecting cilium with many features reminiscent of the primary cilium transition zone, and a pair of centrioles forming a basal body which serves as the platform upon which the ciliary axoneme is assembled. These structures form a highway through which an enormous flux of material moves on a daily basis to sustain the continual turnover of outer segment discs and the energetic demands of phototransduction. After decades of study, the details of the fine structure and distribution of molecular components of these structures are still incompletely understood, but recent advances in cellular imaging techniques and animal models of inherited ciliary defects are yielding important new insights. This knowledge informs our understanding both of the mechanisms of trafficking and assembly and of the pathophysiological mechanisms of human blinding ciliopathies.

Single Cell Cryo-Soft X-ray Tomography Shows That Each Chlamydia Trachomatis Inclusion Is a Unique Community of Bacteria

Chlamydiae are strict intracellular pathogens residing within a specialised membrane-bound compartment called the inclusion. Therefore, each infected cell can, be considered as a single entity where bacteria form a community within the inclusion. It remains unclear as to how the population of bacteria within the inclusion influences individual bacterium. The life cycle of Chlamydia involves transitioning between the invasive elementary bodies (EBs) and replicative reticulate bodies (RBs). We have used cryo-soft X-ray tomography to observe individual inclusions, an approach that combines 40 nm spatial resolution and large volume imaging (up to 16 µm). Using semi-automated segmentation pipeline, we considered each inclusion as an individual bacterial niche. Within each inclusion, we identifyed and classified different forms of the bacteria and confirmed the recent finding that RBs have a variety of volumes (small, large and abnormal). We demonstrate that the proportions of these different RB forms depend on the bacterial concentration in the inclusion. We conclude that each inclusion operates as an autonomous community that influences the characteristics of individual bacteria within the inclusion.

Correlative imaging using super-resolution fluorescence microscopy and soft X-ray tomography at cryogenic temperatures provides a new way to assess virosome solutions for vaccine development

Active virosomes (AVs) are derivatives of viruses, broadly similar to ‘parent’ pathogens, with an outer envelope that contains a bespoke genome coding for four to five viral proteins capable of eliciting an antigenic response. AVs are essentially novel vaccine formulations that present on their surface selected viral proteins as antigens. Once administered, they elicit an initial ‘anti‐viral’ immune response. AVs are also internalised by host cells where their cargo viral genes are used to express viral antigen(s) intracellularly. These can then be transported to the host cell surface resulting in a second wave of antigen exposure and a more potent immuno‐stimulation. A new 3D correlative microscopy approach is used here to provide a robust analytical method for characterisation of Zika‐ and Chikungunya‐derivatised AV populations including vesicle size distribution and variations in antigen loading. Manufactured batches were compared to assess the extent and nature of batch‐to‐batch variations. We also show preliminary results that verify antigen expression on the surface of host cells. We present here a reliable and efficient high‐resolution 3D imaging regime that allows the evaluation of the microstructure and biochemistry of novel vaccine formulations such as AVs.

Cryo-electron tomography provides topological insights into mutant huntingtin exon 1 and polyQ aggregates

Huntington disease (HD) is a neurodegenerative trinucleotide repeat disorder caused by an expanded poly-glutamine (polyQ) tract in the mutant huntingtin (mHTT) protein. The formation and topology of filamentous mHTT inclusions in the brain (hallmarks of HD implicated in neurotoxicity) remain elusive. Using cryo-electron tomography and subtomogram averaging, here we show that mHTT exon 1 and polyQ-only aggregates in vitro are structurally heterogenous and filamentous, similar to prior observations with other methods. Yet, we find filaments in both types of aggregates under ~2 nm in width, thinner than previously reported, and regions forming large sheets. In addition, our data show a prevalent subpopulation of filaments exhibiting a lumpy slab morphology in both aggregates, supportive of the polyQ core model. This provides a basis for future cryoET studies of various aggregated mHTT and polyQ constructs to improve their structure-based modeling as well as their identification in cells without fusion tags.

Locating macromolecular assemblies in cells by 2D template matching with cisTEM

For a more complete understanding of molecular mechanisms, it is important to study macromolecules and their assemblies in the broader context of the cell. This context can be visualized at nanometer resolution in three dimensions (3D) using electron cryo-tomography, which requires tilt series to be recorded and computationally aligned, currently limiting throughput. Additionally, the high-resolution signal preserved in the raw tomograms is currently limited by a number of technical difficulties, leading to an increased false-positive detection rate when using 3D template matching to find molecular complexes in tomograms. We have recently described a 2D template matching approach that addresses these issues by including high-resolution signal preserved in single-tilt images. A current limitation of this approach is the high computational cost that limits throughput. We describe here a GPU-accelerated implementation of 2D template matching in the image processing software cisTEM that allows for easy scaling and improves the accessibility of this approach. We apply 2D template matching to identify ribosomes in images of frozen-hydrated Mycoplasma pneumoniae cells with high precision and sensitivity, demonstrating that this is a versatile tool for in situ visual proteomics and in situ structure determination. We benchmark the results with 3D template matching of tomograms acquired on identical sample locations and identify strengths and weaknesses of both techniques, which offer complementary information about target localization and identity.

Current data processing strategies for cryo-electron tomography and subtomogram averaging

Cryo-electron tomography (cryo-ET) can be used to reconstruct three-dimensional (3D) volumes, or tomograms, from a series of tilted two-dimensional images of biological objects in their near-native states in situ or in vitro. 3D subvolumes, or subtomograms, containing particles of interest can be extracted from tomograms, aligned, and averaged in a process called subtomogram averaging (STA). STA overcomes the low signal to noise ratio within the individual subtomograms to generate structures of the particle(s) of interest. In recent years, cryo-ET with STA has increasingly been capable of reaching subnanometer resolution due to improvements in microscope hardware and data processing strategies. There has also been an increase in the number and quality of software packages available to process cryo-ET data with STA. In this review, we describe and assess the data processing strategies available for cryo-ET data and highlight the recent software developments which have enabled the extraction of high-resolution information from cryo-ET datasets.

The Contribution of Biophysics and Structural Biology to Current Advances in COVID-19

Critical to viral infection are the multiple interactions between viral proteins and host-cell counterparts. The first such interaction is the recognition of viral envelope proteins by surface receptors that normally fulfil other physiological roles, a hijacking mechanism perfected over the course of evolution. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the etiological agent of coronavirus disease 2019 (COVID-19), has successfully adopted this strategy using its spike glycoprotein to dock on the membrane-bound metalloprotease angiotensin-converting enzyme 2 (ACE2). The crystal structures of several SARS-CoV-2 proteins alone or in complex with their receptors or other ligands were recently solved at an unprecedented pace. This accomplishment is partly due to the increasing availability of data on other coronaviruses and ACE2 over the past 18 years. Likewise, other key intervening actors and mechanisms of viral infection were elucidated with the aid of biophysical approaches. An understanding of the various structurally important motifs of the interacting partners provides key mechanistic information for the development of structure-based designer drugs able to inhibit various steps of the infective cycle, including neutralizing antibodies, small organic drugs, and vaccines. This review analyzes current progress and the outlook for future structural studies.

CryoET structures of immature HIV Gag reveal six-helix bundle

Gag is the HIV structural precursor protein which is cleaved by viral protease to produce mature infectious viruses. Gag is a polyprotein composed of MA (matrix), CA (capsid), SP1, NC (nucleocapsid), SP2 and p6 domains. SP1, together with the last eight residues of CA, have been hypothesized to form a six-helix bundle responsible for the higher-order multimerization of Gag necessary for HIV particle assembly. However, the structure of the complete six-helix bundle has been elusive. Here, we determined the structures of both Gag in vitro assemblies and Gag viral-like particles (VLPs) to 4.2 Å and 4.5 Å resolutions using cryo-electron tomography and subtomogram averaging by emClarity. A single amino acid mutation (T8I) in SP1 stabilizes the six-helix bundle, allowing to discern the entire CA-SP1 helix connecting to the NC domain. These structures provide a blueprint for future development of small molecule inhibitors that can lock SP1 in a stable helical conformation, interfere with virus maturation, and thus block HIV-1 infection.

Assessment of scoring functions to rank the quality of 3D subtomogram clusters from cryo-electron tomography

Cryo-electron tomography provides the opportunity for unsupervised discovery of endogenous complexes in situ. This process usually requires particle picking, clustering and alignment of subtomograms to produce an average structure of the complex. When applied to heterogeneous samples, template-free clustering and alignment of subtomograms can potentially lead to the discovery of structures for unknown endogenous complexes. However, such methods require scoring functions to measure and accurately rank the quality of aligned subtomogram clusters, which can be compromised by contaminations from misclassified complexes and alignment errors. Here, we provide the first study to assess the effectiveness of more than 15 scoring functions for evaluating the quality of subtomogram clusters, which differ in the amount of structural misalignments and contaminations due to misclassified complexes. We assessed both experimental and simulated subtomograms as ground truth data sets. Our analysis showed that the robustness of scoring functions varies largely. Most scores were sensitive to the signal-to-noise ratio of subtomograms and often required Gaussian filtering as preprocessing for improved performance. Two scoring functions, Spectral SNR-based Fourier Shell Correlation and Pearson Correlation in the Fourier domain with missing wedge correction, showed a robust ranking of subtomogram clusters without any preprocessing and irrespective of SNR levels of subtomograms. Of these two scoring functions, Spectral SNR-based Fourier Shell Correlation was fastest to compute and is a better choice for handling large numbers of subtomograms. Our results provide a guidance for choosing an accurate scoring function for template-free approaches to detect complexes from heterogeneous samples.

One-Shot Learning With Attention-Guided Segmentation in Cryo-Electron Tomography

Cryo-electron Tomography (cryo-ET) generates 3D visualization of cellular organization that allows biologists to analyze cellular structures in a near-native state with nano resolution. Recently, deep learning methods have demonstrated promising performance in classification and segmentation of macromolecule structures captured by cryo-ET, but training individual deep learning models requires large amounts of manually labeled and segmented data from previously observed classes. To perform classification and segmentation in the wild (i.e., with limited training data and with unseen classes), novel deep learning model needs to be developed to classify and segment unseen macromolecules captured by cryo-ET. In this paper, we develop a one-shot learning framework, called cryo-ET one-shot network (COS-Net), for simultaneous classification of macromolecular structure and generation of the voxel-level 3D segmentation, using only one training sample per class. Our experimental results on 22 macromolecule classes demonstrated that our COS-Net could efficiently classify macromolecular structures with small amounts of samples and produce accurate 3D segmentation at the same time.

The Potential of 19F NMR Application in GPCR Biased Drug Discovery

Although structure-based virtual drug discovery is revolutionizing the conventional high-throughput cell-based screening system, its limitation is obvious, together with other critical challenges. In particular, the resolved static snapshots fail to represent a full free-energy landscape due to homogenization in structural determination processing. The loss of conformational heterogeneity and related functional diversity emphasize the necessity of developing an approach that can fill this space. In this regard, NMR holds undeniable potential. However, outstanding questions regarding the NMR application remain. This review summarizes the limitations of current drug discovery and explores the potential of ¹⁹F NMR in establishing a conformation-guided drug screening system, advancing the cell- and structure-based discovery strategy for G protein-coupled receptor (GPCR) biased drug screening.

Serial cryoFIB/SEM Reveals Cytoarchitectural Disruptions in Leigh Syndrome Patient Cells

The advancement of serial cryoFIB/SEM offers an opportunity to study large volumes of near-native, fully hydrated frozen cells and tissues at voxel sizes of 10 nm and below. We explored this capability for pathologic characterization of vitrified human patient cells by developing and optimizing a serial cryoFIB/SEM volume imaging workflow. We demonstrate profound disruption of subcellular architecture in primary fibroblasts from a Leigh syndrome patient harboring a disease-causing mutation in USMG5 protein responsible for impaired mitochondrial energy production.

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Developed and optimized a serial cryoFIB/SEM volume imaging workflow

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Visualized the 3D structure of an entire cell under native conditions

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Revealed a disruption of cellular structures in primary LS patient fibroblasts

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Demonstrated the potential for clinical phenotyping of pathogenic tissues

Three-dimensional deconvolution processing for STEM cryotomography

The complex environment of biological cells and tissues has motivated development of three-dimensional (3D) imaging in both light and electron microscopies. To this end, one of the primary tools in fluorescence microscopy is that of computational deconvolution. Wide-field fluorescence images are often corrupted by haze due to out-of-focus light, i.e., to cross-talk between different object planes as represented in the 3D image. Using prior understanding of the image formation mechanism, it is possible to suppress the cross-talk and reassign the unfocused light to its proper source post facto. Electron tomography based on tilted projections also exhibits a cross-talk between distant planes due to the discrete angular sampling and limited tilt range. By use of a suitably synthesized 3D point spread function, we show here that deconvolution leads to similar improvements in volume data reconstructed from cryoscanning transmission electron tomography (CSTET), namely a dramatic in-plane noise reduction and improved representation of features in the axial dimension. Contrast enhancement is demonstrated first with colloidal gold particles and then in representative cryotomograms of intact cells. Deconvolution of CSTET data collected from the periphery of an intact nucleus revealed partially condensed, extended structures in interphase chromatin.