Simplified Volumetric Models as an Effective Strategy for Segmenting Actin Networks in Cryo-Electron Tomograms.

Efficient methods for the extraction of features of interest remain one of the biggest challenges for the interpretation of cryo-electron tomograms. Various automated approaches have been proposed, many of which work well for high-contrast datasets where the features of interest can be easily detected and are clearly separated from one another. Our inner ear stereocilia cryo-electron tomographic datasets are characterized by a dense array of hexagonally packed actin filaments that are frequently cross-connected. These features make automated segmentation very challenging, further aggravated by the high-noise environment of cryo-electron tomograms and the high complexity of the densely packed features. Using prior knowledge about the actin bundle organization, we have placed layers of a highly simplified ball-and-stick actin model to first obtain a global fit to the density map, followed by regional and local adjustments of the model. We show that volumetric model building not only allows us to deal with the high complexity, but also provides precise measurements and statistics about the actin bundle. Volumetric models also serve as anchoring points for local segmentation, such as in the case of the actin-actin cross connectors. Volumetric model building, particularly when further augmented by computer-based automated fitting approaches, can be a powerful alternative when conventional automated segmentation approaches are not successful.

Imaging intracellular components in situ using super-resolution cryo-correlative light and electron microscopy.

Super-resolution cryo-correlative light and electron microscopy (SRcryoCLEM) is emerging as a powerful method to enable targeted in situ structural studies of biological samples. By combining the high specificity and localization accuracy of single-molecule localization microscopy (cryoSMLM) with the high resolution of cryo-electron tomography (cryoET), this method enables accurately targeted data acquisition and the observation and identification of biomolecules within their natural cellular context. Despite its potential, the adaptation of SRcryoCLEM has been hindered by the need for specialized equipment and expertise. In this chapter, we outline a workflow for cryoSMLM and cryoET-based SRcryoCLEM, and we demonstrate that, given the right tools, it is possible to incorporate cryoSMLM into an established cryoET workflow. Using Vimentin as an exemplary target of interest, we demonstrate all stages of an SRcryoCLEM experiment: performing cryoSMLM, targeting cryoET acquisition based on single-molecule localization maps, and correlation of cryoSMLM and cryoET datasets using scNodes, a software package dedicated to SRcryoCLEM. By showing how SRcryoCLEM enables the imaging of specific intracellular components in situ, we hope to facilitate adoption of the technique within the field of cryoEM.

Correlative cryo-microscopy pipelines for in situ cellular studies.

Correlative cryo-microscopy pipelines combining light and electron microscopy and tomography in cryogenic conditions (cryoCLEM) on the same sample are powerful methods for investigating the structure of specific cellular targets identified by a fluorescent tag within their unperturbed cellular environment. CryoCLEM approaches circumvent one of the inherent limitations of cryo EM, and specifically cryo electron tomography (cryoET), of identifying the imaged structures in the crowded 3D environment of cells. Whereas several cryoCLEM approaches are based on thinning the sample by cryo FIB milling, here we present detailed protocols of two alternative cryoCLEM approaches for in situ studies of adherent cells at the single-cell level without the need for such cryo-thinning. The first approach is a complete cryogenic pipeline in which both fluorescence and electronic imaging are performed on frozen-hydrated samples, the second is a hybrid cryoCLEM approach in which fluorescence imaging is performed at room temperature, followed by rapid freezing and subsequent cryoEM imaging. We provide a detailed description of the two methods we have employed for imaging fluorescently labeled cellular structures with thickness below 350-500nm, such as cell protrusions and organelles located in the peripheral areas of the cells.

Toward quantitative super-resolution methods for cryo-CLEM.

Cryogenic ultrastructural imaging techniques such as cryo-electron tomography have produced a revolution in how the structure of biological systems is investigated by enabling the determination of structures of protein complexes immersed in a complex biological matrix within vitrified cell and model organisms. However, so far, the portfolio of successes has been mostly limited to highly abundant complexes or to structures that are relatively unambiguous and easy to identify through electron microscopy. In order to realize the full potential of this revolution, researchers would have to be able to pinpoint lower abundance species and obtain functional annotations on the state of objects of interest which would then be correlated to ultrastructural information to build a complete picture of the structure-function relationships underpinning biological processes. Fluorescence imaging at cryogenic conditions has the potential to be able to meet these demands. However, wide-field images acquired at low numeric aperture (NA) using air immersion objective have a low resolving power and cannot provide accurate enough three-dimensional (3D) localization to enable the assignment of functional annotations to individual objects of interest or target sample debulking to ensure the preservation of the structures of interest. It is therefore necessary to develop super-resolved cryo-fluorescence workflows capable of fulfilling this role and enabling new biological discoveries. In this chapter, we present the current state of development of two super-resolution cryogenic fluorescence techniques, superSIL-STORM and astigmatism-based 3D STORM, show their application to a variety of biological systems and discuss their advantages and limitations. We further discuss the future applicability to cryo-CLEM workflows though examples of practical application to the study of membrane protein complexes both in mammalian cells and in Escherichia coli.

High-confidence 3D template matching for cryo-electron tomography.

Visual proteomics attempts to build atlases of the molecular content of cells but the automated annotation of cryo electron tomograms remains challenging. Template matching (TM) and methods based on machine learning detect structural signatures of macromolecules. However, their applicability remains limited in terms of both the abundance and size of the molecular targets. Here we show that the performance of TM is greatly improved by using template-specific search parameter optimization and by including higher-resolution information. We establish a TM pipeline with systematically tuned parameters for the automated, objective and comprehensive identification of structures with confidence 10 to 100-fold above the noise level. We demonstrate high-fidelity and high-confidence localizations of nuclear pore complexes, vaults, ribosomes, proteasomes, fatty acid synthases, lipid membranes and microtubules, and individual subunits inside crowded eukaryotic cells. We provide software tools for the generic implementation of our method that is broadly applicable towards realizing visual proteomics.

Angle between DNA linker and nucleosome core particle regulates array compaction revealed by individual-particle cryo-electron tomography.

The conformational dynamics of nucleosome arrays generate a diverse spectrum of microscopic states, posing challenges to their structural determination. Leveraging cryogenic electron tomography (cryo-ET), we determine the three-dimensional (3D) structures of individual mononucleosomes and arrays comprising di-, tri-, and tetranucleosomes. By slowing the rate of condensation through a reduction in ionic strength, we probe the intra-array structural transitions that precede inter-array interactions and liquid droplet formation. Under these conditions, the arrays exhibite irregular zig-zag conformations with loose packing. Increasing the ionic strength promoted intra-array compaction, yet we do not observe the previously reported regular 30-nanometer fibers. Interestingly, the presence of H1 do not induce array compaction; instead, one-third of the arrays display nucleosomes invaded by foreign DNA, suggesting an alternative role for H1 in chromatin network construction. We also find that the crucial parameter determining the structure adopted by chromatin arrays is the angle between the entry and exit of the DNA and the corresponding tangents to the nucleosomal disc. Our results provide insights into the initial stages of intra-array compaction, a critical precursor to condensation in the regulation of chromatin organization.

Missing Wedge Completion via Unsupervised Learning with Coordinate Networks.

Cryogenic electron tomography (cryoET) is a powerful tool in structural biology, enabling detailed 3D imaging of biological specimens at a resolution of nanometers. Despite its potential, cryoET faces challenges such as the missing wedge problem, which limits reconstruction quality due to incomplete data collection angles. Recently, supervised deep learning methods leveraging convolutional neural networks (CNNs) have considerably addressed this issue; however, their pretraining requirements render them susceptible to inaccuracies and artifacts, particularly when representative training data is scarce. To overcome these limitations, we introduce a proof-of-concept unsupervised learning approach using coordinate networks (CNs) that optimizes network weights directly against input projections. This eliminates the need for pretraining, reducing reconstruction runtime by 3-20× compared to supervised methods. Our in silico results show improved shape completion and reduction of missing wedge artifacts, assessed through several voxel-based image quality metrics in real space and a novel directional Fourier Shell Correlation (FSC) metric. Our study illuminates benefits and considerations of both supervised and unsupervised approaches, guiding the development of improved reconstruction strategies.

Bridging structural and cell biology with cryo-electron microscopy.

Most life scientists would agree that understanding how cellular processes work requires structural knowledge about the macromolecules involved. For example, deciphering the double-helical nature of DNA revealed essential aspects of how genetic information is stored, copied and repaired. Yet, being reductionist in nature, structural biology requires the purification of large amounts of macromolecules, often trimmed off larger functional units. The advent of cryogenic electron microscopy (cryo-EM) greatly facilitated the study of large, functional complexes and generally of samples that are hard to express, purify and/or crystallize. Nevertheless, cryo-EM still requires purification and thus visualization outside of the natural context in which macromolecules operate and coexist. Conversely, cell biologists have been imaging cells using a number of fast-evolving techniques that keep expanding their spatial and temporal reach, but always far from the resolution at which chemistry can be understood. Thus, structural and cell biology provide complementary, yet unconnected visions of the inner workings of cells. Here we discuss how the interplay between cryo-EM and cryo-electron tomography, as a connecting bridge to visualize macromolecules in situ, holds great promise to create comprehensive structural depictions of macromolecules as they interact in complex mixtures or, ultimately, inside the cell itself.

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.

Ghostbuster: A phase retrieval diffraction tomography algorithm for cryo-EM.

Ewald sphere curvature correction, which extends beyond the projection approximation, stretches the shallow depth of field in cryo-EM reconstructions of thick particles. Here we show that even for previously assumed thin particles, reconstruction artifacts which we refer to as ghosts can appear. By retrieving the lost phases of the electron exitwaves and accounting for the first Born approximation scattering within the particle, we show that these ghosts can be effectively eliminated. Our simulations demonstrate how such ghostbusting can improve reconstructions as compared to existing state-of-the-art software. Like ptychographic cryo-EM, our Ghostbuster algorithm uses phase retrieval to improve reconstructions, but unlike the former, we do not need to modify the existing data acquisition pipelines.

Lift-out cryo-FIBSEM and cryo-ET reveal the ultrastructural landscape of extracellular matrix.

The extracellular matrix (ECM) serves as a scaffold for cells and plays an essential role in regulating numerous cellular processes, including cell migration and proliferation. Due to limitations in specimen preparation for conventional room-temperature electron microscopy, we lack structural knowledge on how ECM components are secreted, remodeled, and interact with surrounding cells. We have developed a 3D-ECM platform compatible with sample thinning by cryo-focused ion beam milling, the lift-out extraction procedure, and cryo-electron tomography. Our workflow implements cell-derived matrices (CDMs) grown on EM grids, resulting in a versatile tool closely mimicking ECM environments. This allows us to visualize ECM for the first time in its hydrated, native context. Our data reveal an intricate network of extracellular fibers, their positioning relative to matrix-secreting cells, and previously unresolved structural entities. Our workflow and results add to the structural atlas of the ECM, providing novel insights into its secretion and assembly.

Tomo Live: an on-the-fly reconstruction pipeline to judge data quality for cryo-electron tomography workflows.

Data acquisition and processing for cryo-electron tomography can be a significant bottleneck for users. To simplify and streamline the cryo-ET workflow, Tomo Live, an on-the-fly solution that automates the alignment and reconstruction of tilt-series data, enabling real-time data-quality assessment, has been developed. Through the integration of Tomo Live into the data-acquisition workflow for cryo-ET, motion correction is performed directly after each of the acquired tilt angles. Immediately after the tilt-series acquisition has completed, an unattended tilt-series alignment and reconstruction into a 3D volume is performed. The results are displayed in real time in a dedicated remote web platform that runs on the microscope hardware. Through this web platform, users can review the acquired data (aligned stack and 3D volume) and several quality metrics that are obtained during the alignment and reconstruction process. These quality metrics can be used for fast feedback for subsequent acquisitions to save time. Parameters such as Alignment Accuracy, Deleted Tilts and Tilt Axis Correction Angle are visualized as graphs and can be used as filters to export only the best tomograms (raw data, reconstruction and intermediate data) for further processing. Here, the Tomo Live algorithms and workflow are described and representative results on several biological samples are presented. The Tomo Live workflow is accessible to both expert and non-expert users, making it a valuable tool for the continued advancement of structural biology, cell biology and histology.

Ultrastructure of human brain tissue vitrified from autopsy revealed by cryo-ET with cryo-plasma FIB milling.

Ultrastructure of human brain tissue has traditionally been examined using electron microscopy (EM) following fixation, staining, and sectioning, which limit resolution and introduce artifacts. Alternatively, cryo-electron tomography (cryo-ET) allows higher resolution imaging of unfixed cellular samples while preserving architecture, but it requires samples to be vitreous and thin enough for transmission EM. Due to these requirements, cryo-ET has yet to be employed to investigate unfixed, never previously frozen human brain tissue. Here we present a method for generating lamellae in human brain tissue obtained at time of autopsy that can be imaged via cryo-ET. We vitrify the tissue via plunge-freezing and use xenon plasma focused ion beam (FIB) milling to generate lamellae directly on-grid at variable depth inside the tissue. Lamellae generated in Alzheimer's disease brain tissue reveal intact subcellular structures including components of autophagy and potential pathologic tau fibrils. Furthermore, we reveal intact compact myelin and functional cytoplasmic expansions. These images indicate that plasma FIB milling with cryo-ET may be used to elucidate nanoscale structures within the human brain.

Integrating cellular electron microscopy with multimodal data to explore biology across space and time.

Biology spans a continuum of length and time scales. Individual experimental methods only glimpse discrete pieces of this spectrum but can be combined to construct a more holistic view. In this Review, we detail the latest advancements in volume electron microscopy (vEM) and cryo-electron tomography (cryo-ET), which together can visualize biological complexity across scales from the organization of cells in large tissues to the molecular details inside native cellular environments. In addition, we discuss emerging methodologies for integrating three-dimensional electron microscopy (3DEM) imaging with multimodal data, including fluorescence microscopy, mass spectrometry, single-particle analysis, and AI-based structure prediction. This multifaceted approach fills gaps in the biological continuum, providing functional context, spatial organization, molecular identity, and native interactions. We conclude with a perspective on incorporating diverse data into computational simulations that further bridge and extend length scales while integrating the dimension of time.

Caught in the act: In situ visualization of bacterial secretion systems by cryo-electron tomography.

Bacterial secretion systems, such as the type 3, 4, and 6 are multiprotein nanomachines expressed at the surface of pathogens with Gram-negative like envelopes. They are known to be crucial for virulence and to translocate bacteria-encoded effector proteins into host cells to manipulate cellular functions. This facilitates either pathogen attachment or invasion of the targeted cell. Effector proteins also promote evasion of host immune recognition. Imaging by cryo-electron microscopy in combination with structure determination has become a powerful approach to understand how these nanomachines work. Still, questions on their assembly, the precise secretion mechanisms, and their direct involvement in pathogenicity remain unsolved. Here, we present an overview of the recent developments in in situ cryo-electron microscopy. We discuss its potential for the investigation of the role of bacterial secretion systems during the host-bacterial crosstalk at the molecular level. These in situ studies open new perspectives for our understanding of secretion system structure and function.

In situ cryo-electron tomography: a new method to elucidate cytoplasmic zoning at the molecular level.

Cryo-electron microscopy was developed as a powerful tool for imaging biological specimens in near-native conditions. Nowadays, advances in technology, equipment and computations make it possible to obtain structures of biomolecules with near-atomic resolution. Furthermore, cryo-electron tomography combined with continuous specimen tilting allows structural analysis of heterogeneous biological specimens. In particular, when combined with a cryo-focused ion beam scanning electron microscope, it becomes possible to directly analyse the structure of the biomolecules within cells, a process known as in situ cryo-electron tomography. This technique has the potential to visualize cytoplasmic zoning, involving liquid-liquid phase separation, caused by biomolecular networks in aqueous solutions, which has been the subject of recent debate. Here, we review advances in structural studies of biomolecules to study cytoplasmic zoning by in situ cryo-electron tomography.

A deep learning approach to the automatic detection of alignment errors in cryo-electron tomographic reconstructions.

Electron tomography is an imaging technique that allows for the elucidation of three-dimensional structural information of biological specimens in a very general context, including cellular in situ observations. The approach starts by collecting a set of images at different projection directions by tilting the specimen stage inside the microscope. Therefore, a crucial preliminary step is to precisely define the acquisition geometry by aligning all the tilt images to a common reference. Errors introduced in this step will lead to the appearance of artifacts in the tomographic reconstruction, rendering them unsuitable for the sample study. Focusing on fiducial-based acquisition strategies, this work proposes a deep-learning algorithm to detect misalignment artifacts in tomographic reconstructions by analyzing the characteristics of these fiducial markers in the tomogram. In addition, we propose an algorithm designed to detect fiducial markers in the tomogram with which to feed the classification algorithm in case the alignment algorithm does not provide the location of the markers. This open-source software is available as part of the Xmipp software package inside of the Scipion framework, and also through the command-line in the standalone version of Xmipp.

An introduction to scanning transmission electron microscopy for the study of protozoans.

Since its inception in the 1930s, transmission electron microscopy (TEM) has been a powerful method to explore the cellular structure of parasites. TEM usually requires samples of <100 nm thick and with protozoans being larger than 1 µm, their study requires resin embedding and ultrathin sectioning. During the past decade, several new methods have been developed to improve, facilitate, and speed up the structural characterisation of biological samples, offering new imaging modalities for the study of protozoans. In particular, scanning transmission electron microscopy (STEM) can be used to observe sample sections as thick as 1 µm thus becoming an alternative to conventional TEM. STEM can also be performed under cryogenic conditions in combination with cryo-electron tomography providing access to the study of thicker samples in their native hydrated states in 3D. This method, called cryo-scanning transmission electron tomography (cryo-STET), was first developed in 2014. This review presents the basic concepts and benefits of STEM methods and provides examples to illustrate the potential for new insights into the structure and ultrastructure of protozoans.

Untangling Irregular Actin Cytoskeleton Architectures in Tomograms of the Cell with Struwwel Tracer.

In this work, we established, validated, and optimized a novel computational framework for tracing arbitrarily oriented actin filaments in cryo-electron tomography maps. Our approach was designed for highly complex intracellular architectures in which a long-range cytoskeleton network extends throughout the cell bodies and protrusions. The irregular organization of the actin network, as well as cryo-electron-tomography-specific noise, missing wedge artifacts, and map dimensions call for a specialized implementation that is both robust and efficient. Our proposed solution, Struwwel Tracer, accumulates densities along paths of a specific length in various directions, starting from locally determined seed points. The highest-density paths originating from the seed points form short linear candidate filament segments, which are further scrutinized and classified by users via inspection of a novel pruning map, which visualizes the likelihood of being a part of longer filaments. The pruned linear candidate filament segments are then iteratively fused into continuous, longer, and curved filaments based on their relative orientations, gap spacings, and extendibility. When applied to the simulated phantom tomograms of a Dictyostelium discoideum filopodium under experimental conditions, Struwwel Tracer demonstrated high efficacy, with F1-scores ranging from 0.85 to 0.90, depending on the noise level. Furthermore, when applied to a previously untraced experimental tomogram of mouse fibroblast lamellipodia, the filaments predicted by Struwwel Tracer exhibited a good visual agreement with the experimental map. The Struwwel Tracer framework is highly time efficient and can complete the tracing process in just a few minutes. The source code is publicly available with version 3.2 of the free and open-source Situs software package.