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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Handling Difficult Cryo-ET Samples: A Study with Primary Neurons from Drosophila melanogaster

Cellular neurobiology has benefited from recent advances in the field of cryo-electron tomography (cryo-ET). Numerous structural and ultrastructural insights have been obtained from plunge-frozen primary neurons cultured on electron microscopy grids. With most primary neurons having been derived from rodent sources, we sought to expand the breadth of sample availability by using primary neurons derived from 3rd instar Drosophila melanogaster larval brains. Ultrastructural abnormalities were encountered while establishing this model system for cryo-ET, which were exemplified by excessive membrane blebbing and cellular fragmentation. To optimize neuronal samples, we integrated substrate selection, micropatterning, montage data collection, and chemical fixation. Efforts to address difficulties in establishing Drosophila neurons for future cryo-ET studies in cellular neurobiology also provided insights that future practitioners can use when attempting to establish other cell-based model systems.

Sample Preparation for In Situ Cryotomography of Mammalian Cells

In situ cellular cryotomography is a powerful technique for studying complex objects in their native frozen-hydrated cellular context, making it highly relevant to cellular biology and virology. The potential of combining cryotomography with other microscopy modalities makes it a perfect technique for integrative and correlative imaging. However, sample preparation for in situ cellular tomography is not straightforward, as cells do not readily attach and stretch over the electron microscopy grid. Additionally, the grids themselves are fragile and can break if handled too forcefully, resulting in the loss of imageable areas. The geometry of tissue culture dishes can also pose a challenge when manipulating the grids with tweezers. Here, we describe the tips and tricks to overcome these (and other) challenges and prepare good-quality samples for in situ cellular cryotomography and correlative imaging of adherent mammalian cells. With continued advances in cryomicroscopy technology, this technique holds enormous promise for advancing our understanding of complex biological systems.

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.

Genetically encoded multimeric tags for subcellular protein localization in cryo-EM

Cryo-electron tomography (cryo-ET) allows for label-free high-resolution imaging of macromolecular assemblies in their native cellular context. However, the localization of macromolecules of interest in tomographic volumes can be challenging. Here we present a ligand-inducible labeling strategy for intracellular proteins based on fluorescent, 25-nm-sized, genetically encoded multimeric particles (GEMs). The particles exhibit recognizable structural signatures, enabling their automated detection in cryo-ET data by convolutional neural networks. The coupling of GEMs to green fluorescent protein-tagged macromolecules of interest is triggered by addition of a small-molecule ligand, allowing for time-controlled labeling to minimize disturbance to native protein function. We demonstrate the applicability of GEMs for subcellular-level localization of endogenous and overexpressed proteins across different organelles in human cells using cryo-correlative fluorescence and cryo-ET imaging. We describe means for quantifying labeling specificity and efficiency, and for systematic optimization for rare and abundant protein targets, with emphasis on assessing the potential effects of labeling on protein function.

nextPYP: a comprehensive and scalable platform for characterizing protein variability in situ using single-particle cryo-electron tomography

Single-particle cryo-electron tomography is an emerging technique capable of determining the structure of proteins imaged within the native context of cells at molecular resolution. While high-throughput techniques for sample preparation and tilt-series acquisition are beginning to provide sufficient data to allow structural studies of proteins at physiological concentrations, the complex data analysis pipeline and the demanding storage and computational requirements pose major barriers for the development and broader adoption of this technology. Here, we present a scalable, end-to-end framework for single-particle cryo-electron tomography data analysis from on-the-fly pre-processing of tilt series to high-resolution refinement and classification, which allows efficient analysis and visualization of datasets with hundreds of tilt series and hundreds of thousands of particles. We validate our approach using in vitro and cellular datasets, demonstrating its effectiveness at achieving high-resolution and revealing conformational heterogeneity in situ. The framework is made available through an intuitive and easy-to-use computer application, nextPYP ( http://nextpyp.app ).

Charting the molecular landscape of the cell

Biological function of macromolecules is closely tied to their cellular location, as well as to interactions with other molecules within the native environment of the cell. Therefore, to obtain detailed mechanistic insights into macromolecular functionality, one of the outstanding targets for structural biology is to produce an atomic-level understanding of the cell. One structural biology technique that has already been used to directly derive atomic models of macromolecules from cells, without any additional external information, is electron cryotomography (cryoET). In this perspective article, we discuss possible routes to chart the molecular landscape of the cell by advancing cryoET imaging as well as by embedding cryoET into correlative imaging workflows.

Streamlined structure determination by cryo-electron tomography and subtomogram averaging using TomoBEAR

Structures of macromolecules in their native state provide unique unambiguous insights into their functions. Cryo-electron tomography combined with subtomogram averaging demonstrated the power to solve such structures in situ at resolutions in the range of 3 Angstrom for some macromolecules. In order to be applicable to the structural determination of the majority of macromolecules observable in cells in limited amounts, processing of tomographic data has to be performed in a high-throughput manner. Here we present TomoBEAR-a modular configurable workflow engine for streamlined processing of cryo-electron tomographic data for subtomogram averaging. TomoBEAR combines commonly used cryo-EM packages with reasonable presets to provide a transparent ("white box") approach for data management and processing. We demonstrate applications of TomoBEAR to two data sets of purified macromolecular targets, to an ion channel RyR1 in a membrane, and the tomograms of plasma FIB-milled lamellae and demonstrate the ability to produce high-resolution structures. TomoBEAR speeds up data processing, minimizes human interventions, and will help accelerate the adoption of in situ structural biology by cryo-ET. The source code and the documentation are freely available.

An Improved Method for Growing Primary Neurons on Electron Microscopy Grids Co-Cultured with Astrocytes

With the increasing popularity of cryo-electron tomography (cryo-ET) in recent years, the quest to establish a method for growing primary neurons directly on electron microscopy grids (EM grids) has been ongoing. Here we describe a straightforward way to establish a mature neuronal network on EM grids, which includes formation of synaptic contacts. These synapses were thin enough to allow for direct visualization of small filaments such as SNARE proteins tethering the synaptic vesicle (SV) to the active zone plasma membrane on a Titan Krios without prior focused ion-beam milling.

Gentle ions for cryo-FIB milling

Cryo-EM imaging of vitreous samples is limited to a few hundred nanometers in thickness. Focused ion beams can mill windows into cells and tissues for imaging, but they damage biological samples. In this issue of Structure, Yang et al. (2023) quantitatively describe this damage and suggest ways to minimize it.

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.

Correlative montage parallel array cryo-tomography for in situ structural cell biology

Imaging large fields of view while preserving high-resolution structural information remains a challenge in low-dose cryo-electron tomography. Here we present robust tools for montage parallel array cryo-tomography (MPACT) tailored for vitrified specimens. The combination of correlative cryo-fluorescence microscopy, focused-ion-beam milling, substrate micropatterning, and MPACT supports studies that contextually define the three-dimensional architecture of cells. To further extend the flexibility of MPACT, tilt series may be processed in their entirety or as individual tiles suitable for sub-tomogram averaging, enabling efficient data processing and analysis.

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.

Towards in situ high-resolution imaging of viruses and macromolecular complexes using cryo-electron tomography

Cryo-electron tomography and subtomogram averaging are rising and fast-evolving imaging techniques to study biological events, providing structural information at an unprecedented resolution while preserving spatial correlation in their native contexts. The latest technology and methodology development ranging from sample preparation to data collection and data processing, has enabled significant advancement in its applications to various biological systems. This review provides an overview of the current technology development enabling high-resolution structural study in situ, highlighting the use of a priori information of biological samples to assess the quality of subtomogram averaging pipeline. We exemplify the applications of this technique to understanding viruses and principles of macromolecule assembly using different biological systems, ranging from in vitro to in situ samples, which provide structural information at different resolutions and contexts.

A practical multicellular sample preparation pipeline broadens the application of in situ cryo-electron tomography

The structural studies of macromolecules in their physiological context, particularly in tissue, is constrained by the bottleneck of sample preparation. In this study, we present a practical pipeline for preparing multicellular samples for cryo-electron tomography. The pipeline comprises sample isolation, vitrification, and lift-out-based lamella preparation using commercially available instruments. We demonstrate the efficacy of our pipeline by visualizing pancreatic β cells from mouse islets at the molecular level. This pipeline enables the determination of the properties of insulin crystals in situ for the first time, using unperturbed samples.

Locating cellular contents during cryoFIB milling using cellular secondary-electron imaging

Cryo-electron tomography (cryoET) is a powerful technology that allows in-situ observation of the molecular structure of tissues and cells. Cryo-focused ion beam (cryoFIB) milling plays an important role in the preparation of high-quality thin lamellar samples for cryoET studies, thus, promoting the rapid development of cryoET in recent years. However, locating the regions of interest in a large cell or tissue during cryoFIB milling remains a major challenge limiting cryoET applications on arbitrary biological samples. Here, we report an on-the-fly localization method based on cellular secondary electron imaging (CSEI), which is derived from a basic imaging function of the cryoFIB instruments and enables high-contrast imaging of the cellular contents of frozen-hydrated biological samples. Moreover, CSEI does not require fluorescent labels and additional devices. The present study discusses the imaging principles and settings for optimizing CSEI. Tests on several commercially available cryoFIB instruments demonstrated that CSEI was feasible on mainstream instruments to observe all types of cellular contents and reliable under different milling conditions. We established a simple milling-localization workflow and tested it using the basal body of Chlamydomonas reinhardtii.

Cryo-electron tomography to study viral infection

Developments in cryo-electron microscopy (cryo-EM) have been interwoven with the study of viruses ever since its first applications to biological systems. Following the success of single particle cryo-EM in the last decade, cryo-electron tomography (cryo-ET) is now rapidly maturing as a technology and catalysing great advancement in structural virology as its application broadens. In this review, we provide an overview of the use of cryo-ET to study viral infection biology, discussing the key workflows and strategies used in the field. We highlight the vast body of studies performed on purified viruses and virus-like particles (VLPs), as well as discussing how cryo-ET can characterise host-virus interactions and membrane fusion events. We further discuss the importance of in situ cellular imaging in revealing previously unattainable details of infection and highlight the need for validation of high-resolution findings from purified ex situ systems. We give perspectives for future developments to achieve the full potential of cryo-ET to characterise the molecular processes of viral infection.

High-throughput cryo-ET structural pattern mining by unsupervised deep iterative subtomogram clustering

Cryoelectron tomography directly visualizes heterogeneous macromolecular structures in their native and complex cellular environments. However, existing computer-assisted structure sorting approaches are low throughput or inherently limited due to their dependency on available templates and manual labels. Here, we introduce a high-throughput template-and-label-free deep learning approach, Deep Iterative Subtomogram Clustering Approach (DISCA), that automatically detects subsets of homogeneous structures by learning and modeling 3D structural features and their distributions. Evaluation on five experimental cryo-ET datasets shows that an unsupervised deep learning based method can detect diverse structures with a wide range of molecular sizes. This unsupervised detection paves the way for systematic unbiased recognition of macromolecular complexes in situ.

Quantifying organellar ultrastructure in cryo-electron tomography using a surface morphometrics pipeline

Cellular cryo-electron tomography (cryo-ET) enables three-dimensional reconstructions of organelles in their native cellular environment at subnanometer resolution. However, quantifying ultrastructural features of pleomorphic organelles in three dimensions is challenging, as is defining the significance of observed changes induced by specific cellular perturbations. To address this challenge, we established a semiautomated workflow to segment organellar membranes and reconstruct their underlying surface geometry in cryo-ET. To complement this workflow, we developed an open-source suite of ultrastructural quantifications, integrated into a single pipeline called the surface morphometrics pipeline. This pipeline enables rapid modeling of complex membrane structures and allows detailed mapping of inter- and intramembrane spacing, curvedness, and orientation onto reconstructed membrane meshes, highlighting subtle organellar features that are challenging to detect in three dimensions and allowing for statistical comparison across many organelles. To demonstrate the advantages of this approach, we combine cryo-ET with cryo-fluorescence microscopy to correlate bulk mitochondrial network morphology (i.e., elongated versus fragmented) with membrane ultrastructure of individual mitochondria in the presence and absence of endoplasmic reticulum (ER) stress. Using our pipeline, we demonstrate ER stress promotes adaptive remodeling of ultrastructural features of mitochondria including spacing between the inner and outer membranes, local curvedness of the inner membrane, and spacing between mitochondrial cristae. We show that differences in membrane ultrastructure correlate to mitochondrial network morphologies, suggesting that these two remodeling events are coupled. Our pipeline offers opportunities for quantifying changes in membrane ultrastructure on a single-cell level using cryo-ET, opening new opportunities to define changes in ultrastructural features induced by diverse types of cellular perturbations.

Cryo-electron tomography on focused ion beam lamellae transforms structural cell biology

Cryogenic electron microscopy and data processing enable the determination of structures of isolated macromolecules to near-atomic resolution. However, these data do not provide structural information in the cellular environment where macromolecules perform their native functions, and vital molecular interactions can be lost during the isolation process. Cryogenic focused ion beam (FIB) fabrication generates thin lamellae of cellular samples and tissues, enabling structural studies on the near-native cellular interior and its surroundings by cryogenic electron tomography (cryo-ET). Cellular cryo-ET benefits from the technological developments in electron microscopes, detectors and data processing, and more in situ structures are being obtained and at increasingly higher resolution. In this Review, we discuss recent studies employing cryo-ET on FIB-generated lamellae and the technological developments in ultrarapid sample freezing, FIB fabrication of lamellae, tomography, data processing and correlative light and electron microscopy that have enabled these studies. Finally, we explore the future of cryo-ET in terms of both methods development and biological application.

Determining protein structures in cellular lamella at pseudo-atomic resolution by GisSPA

Cryo-electron tomography is a major tool used to study the structure of protein complexes in situ. However, the throughput of tilt-series image data collection is still quite low. Here, we show that GisSPA, a GPU accelerated program, can translationally and rotationally localize the target protein complex in cellular lamellae, as prepared with a focused ion beam, using single cryo-electron microscopy images without tilt-series, and reconstruct the protein complex at near-atomic resolution. GisSPA allows high-throughput data collection without the acquisition of tilt-series images and reconstruction of the tomogram, which is essential for high-resolution reconstruction of asymmetric or low-symmetry protein complexes. We demonstrate the power of GisSPA with 3.4-Å and 3.9-Å resolutions of resolving phycobilisome and tetrameric photosystem II complex structures in cellular lamellae, respectively. In this work, we present GisSPA as a practical tool that facilitates high-resolution in situ protein structure determination.

Cryo-plasma FIB/SEM volume imaging of biological specimens

Serial focussed ion beam scanning electron microscopy (FIB/SEM) enables imaging and assessment of subcellular structures on the mesoscale (10 nm to 10 µm). When applied to vitrified samples, serial FIB/SEM is also a means to target specific structures in cells and tissues while maintaining constituents' hydration shells for in situ structural biology downstream. However, the application of serial FIB/SEM imaging of non-stained cryogenic biological samples is limited due to low contrast, curtaining, and charging artefacts. We address these challenges using a cryogenic plasma FIB/SEM. We evaluated the choice of plasma ion source and imaging regimes to produce high-quality SEM images of a range of different biological samples. Using an automated workflow we produced three-dimensional volumes of bacteria, human cells, and tissue, and calculated estimates for their resolution, typically achieving 20-50 nm. Additionally, a tag-free localisation tool for regions of interest is needed to drive the application of in situ structural biology towards tissue. The combination of serial FIB/SEM with plasma-based ion sources promises a framework for targeting specific features in bulk-frozen samples (>100 µm) to produce lamellae for cryogenic electron tomography.

ELI trifocal microscope: a precise system to prepare target cryo-lamellae for in situ cryo-ET study

Cryo-electron tomography (cryo-ET) has become a powerful approach to study the high-resolution structure of cellular macromolecular machines in situ. However, the current correlative cryo-fluorescence and electron microscopy lacks sufficient accuracy and efficiency to precisely prepare cryo-lamellae of target locations for subsequent cryo-ET. Here we describe a precise cryogenic fabrication system, ELI-TriScope, which sets electron (E), light (L) and ion (I) beams at the same focal point to achieve accurate and efficient preparation of a target cryo-lamella. ELI-TriScope uses a commercial dual-beam scanning electron microscope modified to incorporate a cryo-holder-based transfer system and embed an optical imaging system just underneath the vitrified specimen. Cryo-focused ion beam milling can be accurately navigated by monitoring the real-time fluorescence signal of the target molecule. Using ELI-TriScope, we prepared a batch of cryo-lamellae of HeLa cells targeting the centrosome with a success rate of ~91% and discovered new in situ structural features of the human centrosome by cryo-ET.

In Situ Structure Determination of Bacterial Surface Nanomachines Using Cryo-Electron Tomography

Bacterial surface nanomachines are often refractory to structural determination in their intact form due to their extensive association with the cell envelope preventing them from being properly purified for traditional structural biology methods. Cryo-electron tomography (cryo-ET) is an emerging branch of cryo-electron microscopy that can visualize supramolecular complexes directly inside frozen-hydrated cells in 3D at nanometer resolution, therefore posing a unique capability to study the intact structures of bacterial surface nanomachines in situ and reveal their molecular association with other cellular components. Furthermore, the resolution of cryo-ET is continually improving alongside methodological advancement. Here, using the type IV pilus machine in Myxococcus xanthus as an example, we describe a step-by-step workflow for in situ structure determination including sample preparation and screening, microscope and camera tuning, tilt series acquisition, data processing and tomogram reconstruction, subtomogram averaging, and structural analysis.

Cryo-Electron Tomography of Reconstituted Biomolecular Condensates

The assembly of membraneless compartments by phase separation has recently been recognized as a mechanism for spatial and temporal organization of biomolecules within the cell. The functions of such mesoscale assemblies, termed biomolecular condensates, depend on networks of multivalent interactions between proteins, their structured and disordered domains, and commonly also include nucleic acids. Cryo-electron tomography is an ideal tool to investigate the three-dimensional architecture of such pleomorphic interaction networks at nanometer resolution and thus form inferences about function. However, preparation of suitable cryo-electron microscopy samples of condensates may be prone to protein denaturation, low retention of material on the sample carrier, and contamination associated with cryo-sample preparation and transfers. Here, we describe a series of protocols designed to obtain high-quality cryo-electron tomography data of biomolecular condensates reconstituted in vitro. These include critical screening by light microscopy, cryo-fixation by plunge freezing, sample loading into an electron microscope operated at liquid nitrogen temperature, data collection, processing of the data into three-dimensional tomograms, and their interpretation.

In Situ Imaging of Virus-Infected Cells by Cryo-Electron Tomography: An Overview

Cryo-electron tomography (cryo-ET) has emerged as a powerful tool in structural biology to study viruses and is undergoing a resolution revolution. Enveloped viruses comprise several RNA and DNA pleomorphic viruses that are pathogens of clinical importance to humans and animals. Considerable efforts in cryogenic correlative light and electron microscopy (cryo-CLEM), cryogenic focused ion beam milling (cryo-FIB), and integrative structural techniques are helping to identify virus structures within cells leading to a rise of in situ discoveries shedding light on how viruses interact with their hosts during different stages of infection. This chapter reviews recent advances in the application of cryo-ET in imaging enveloped viruses and the structural and mechanistic insights revealed studying the viral infection cycle within their eukaryotic cellular hosts, with particular attention to viral entry, replication, assembly, and egress during infection.

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.

Application of super-resolution and correlative double sampling in cryo-electron microscopy

Developments in cryo-EM have allowed atomic or near-atomic resolution structure determination to become routine in single particle analysis (SPA). However, near-atomic resolution structures determined using cryo-electron tomography and sub-tomogram averaging (cryo-ET STA) are much less routine. In this paper, we show that collecting cryo-ET STA data using the same conditions as SPA, with both correlated double sampling (CDS) and the super-resolution mode, allowed apoferritin to be reconstructed out to the physical Nyquist frequency of the images. Even with just two tilt series, STA yields an apoferritin map at 2.9 Å resolution. These results highlight the exciting potential of cryo-ET STA in the future of protein structure determination. While processing SPA data recorded in super-resolution mode may yield structures surpassing the physical Nyquist limit, processing cryo-ET STA data in the super-resolution mode gave no additional resolution benefit. We further show that collecting SPA data in the super-resolution mode, with CDS activated, reduces the estimated B-factor, leading to a reduction in the number of particles required to reach a target resolution without compromising the data size on disk and the area imaged in SerialEM. However, collecting SPA data in CDS does reduce throughput, given that a similar resolution structure, with a slightly larger B-factor, is achievable with optimised parameters for speed in EPU (without CDS).

Towards unsupervised classification of macromolecular complexes in cryo electron tomography: Challenges and opportunities

BACKGROUND AND OBJECTIVES

Cryo electron tomography visualizes native cells at nanometer resolution, but analysis is challenged by noise and artifacts. Recently, supervised deep learning methods have been applied to decipher the 3D spatial distribution of macromolecules. However, in order to discover unknown objects, unsupervised classification techniques are necessary. In this paper, we provide an overview of unsupervised deep learning techniques, discuss the challenges to analyze cryo-ET data, and provide a proof-of-concept on real data.

METHODS

We propose a weakly supervised subtomogram classification method based on transfer learning. We use a deep neural network to learn a clustering friendly representation able to capture 3D shapes in the presence of noise and artifacts. This representation is learned here from a synthetic data set.

RESULTS

We show that when applying k-means clustering given a learning-based representation, it becomes possible to satisfyingly classify real subtomograms according to structural similarity. It is worth noting that no manual annotation is used for performing classification.

CONCLUSIONS

We describe the advantages and limitations of our proof-of-concept and raise several perspectives for improving classification performance.

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 Detection in Three-Dimensional Cellular Cryoelectron Tomograms by Reconstructing Two-Dimensional Annotated Tilt Series

The revolutionary technique cryoelectron tomography (cryo-ET) enables imaging of cellular structure and organization in a near-native environment at submolecular resolution, which is vital to subsequent data analysis and modeling. The conventional structure detection process first reconstructs the three-dimensional (3D) tomogram from a series of two-dimensional (2D) projections and then directly detects subcellular components found within the tomogram. However, this process is challenging due to potential structural information loss during the tomographic reconstruction and the limited scope of existing methods since most major state-of-the-art object detection methods are designed for 2D rather than 3D images. Therefore, in this article, as an alternative approach to complement the conventional process, we propose a novel 2D-to-3D framework that detects structures within 2D projection images before reconstructing the results back to 3D. We implemented the proposed framework as three specific algorithms for three individual tasks: semantic segmentation, edge detection, and object localization. As experimental validation of the 2D-to-3D framework for cryo-ET data, we applied the algorithms to the segmentation of mitochondrial calcium phosphate granules, detection of spherical edges, and localization of mitochondria. Quantitative and qualitative results show better performance for prediction tasks of segmentation on the 2D projections and promising performance on object localization and edge detection, paving the way for future studies in the exploration of cryo-ET for in situ structural biology.

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

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

Cryo-electron Tomography Remote Data Collection and Subtomogram Averaging

Cryo-electron tomography (cryo-ET) has been gaining momentum in recent years, especially since the introduction of direct electron detectors, improved automated acquisition strategies, preparative techniques that expand the possibilities of what the electron microscope can image at high-resolution using cryo-ET and new subtomogram averaging software. Additionally, data acquisition has become increasingly streamlined, making it more accessible to many users. The SARS-CoV-2 pandemic has further accelerated remote cryo-electron microscopy (cryo-EM) data collection, especially for single-particle cryo-EM, in many facilities globally, providing uninterrupted user access to state-of-the-art instruments during the pandemic. With the recent advances in Tomo5 (software for 3D electron tomography), remote cryo-ET data collection has become robust and easy to handle from anywhere in the world. This article aims to provide a detailed walk-through, starting from the data collection setup in the tomography software for the process of a (remote) cryo-ET data collection session with detailed troubleshooting. The (remote) data collection protocol is further complemented with the workflow for structure determination at near-atomic resolution by subtomogram averaging with emClarity, using apoferritin as an example.

Montage electron tomography of vitrified specimens

Cryo-electron tomography provides detailed views of macromolecules in situ. However, imaging a large field of view to provide more cellular context requires reducing magnification during data collection, which in turn restricts the resolution. To circumvent this trade-off between field of view and resolution, we have developed a montage data collection scheme that uniformly distributes the dose throughout the specimen. In this approach, sets of slightly overlapping circular tiles are collected at high magnification and stitched to form a composite projection image at each tilt angle. These montage tilt-series are then reconstructed into massive tomograms with a small pixel size but a large field of view. For proof-of-principle, we applied this method to the thin edge of HeLa cells. Thon rings to better than 10 Å were detected in the montaged tilt-series, and diverse cellular features were observed in the resulting tomograms. These results indicate that the additional dose required by this technique is not prohibitive to performing structural analysis to intermediate resolution across a large field of view. We anticipate that montage tomography will prove particularly useful for lamellae, increase the likelihood of imaging rare cellular events, and facilitate visual proteomics.

Capturing actin assemblies in cells using in situ cryo-electron tomography

Actin contributes to an exceptionally wide range of cellular processes through the assembly and disassembly of highly dynamic and ordered structures. Visualizing these structures in cells can help us understand how the molecular players of the actin machinery work together to produce force-generating systems. In recent years, cryo-electron tomography (cryo-ET) has become the method of choice for structural analysis of the cell interior at the molecular scale. Here we review advances in cryo-ET workflows that have enabled this transformation, especially the automation of sample preparation procedures, data collection, and processing. We discuss new structural analyses of dynamic actin assemblies in cryo-preserved cells, which have provided mechanistic insights into actin assembly and function at the nanoscale. Finally, we highlight the latest visual proteomics studies of actin filaments and their interactors reaching sub-nanometer resolutions in cells.

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.

VP-Detector: A 3D multi-scale dense convolutional neural network for macromolecule localization and classification in cryo-electron tomograms

BACKGROUND AND OBJECTIVE

Cryo-electron tomography (cryo-ET) with subtomogram averaging (STA) is indispensable when studying macromolecule structures and functions in their native environments. Due to the low signal-to-noise ratio, the missing wedge artifacts in tomographic reconstructions, and multiple macromolecules of varied shapes and sizes, macromolecule localization and classification remain challenging. To tackle this bottleneck problem for structural determination by STA, we design an accurate macromolecule localization and classification method named voxelwise particle detector (VP-Detector).

METHODS

VP-Detector is a two-stage particle detection method based on a 3D multiscale dense convolutional neural network (3D MSDNet). The proposed network uses 3D hybrid dilated convolution (3D HDC) to avoid the resolution loss caused by scaling operations. Meanwhile, it uses 3D dense connectivity to encourage the reuse of feature maps to reduce trainable parameters. In addition, the weighted focal loss is proposed to focus more attention on difficult samples and rare classes, which relieves the class imbalance caused by multiple particles of various sizes. The performance of VP-Detector is evaluated on both simulated and real-world tomograms, and it shows that VP-Detector outperforms state-of-the-art methods.

RESULTS

The experiments show that VP-Detector outperforms the state-of-the-art methods on particle localization with an F1-score of 0.951 and a precision of 0.978. In addition, VP-Detector can replace manual particle picking in experiment on the real-world tomograms. Furthermore, it performs well in classifying large-, medium-, and small-weight proteins with accuracies of 1, 0.95, and 0.82, respectively. Finally, ablation studies demonstrate the effectiveness of 3D HDC, 3D dense connectivity, weighted focal loss, and training on small training sets.

CONCLUSIONS

VP-Detector can achieve high accuracy in particle detection with few trainable parameters and support training on small datasets. It can also relieve the class imbalance caused by multiple particles with various shapes and sizes.

Novel transient cytoplasmic rings stabilize assembling bacterial flagellar motors

The process by which bacterial cells build their intricate flagellar motility apparatuses has long fascinated scientists. Our understanding of this process comes mainly from studies of purified flagella from two species, Escherichia coli and Salmonella enterica. Here, we used electron cryo-tomography (cryo-ET) to image the assembly of the flagellar motor in situ in diverse Proteobacteria: Hylemonella gracilis, Helicobacter pylori, Campylobacter jejuni, Pseudomonas aeruginosa, Pseudomonas fluorescens, and Shewanella oneidensis. Our results reveal the in situ structures of flagellar intermediates, beginning with the earliest flagellar type III secretion system core complex (fT3SScc) and MS-ring. In high-torque motors of Beta-, Gamma-, and Epsilon-proteobacteria, we discovered novel cytoplasmic rings that interact with the cytoplasmic torque ring formed by FliG. These rings, associated with the MS-ring, assemble very early and persist until the stators are recruited into their periplasmic ring; in their absence the stator ring does not assemble. By imaging mutants in Helicobacter pylori, we found that the fT3SScc proteins FliO and FliQ are required for the assembly of these novel cytoplasmic rings. Our results show that rather than a simple accretion of components, flagellar motor assembly is a dynamic process in which accessory components interact transiently to assist in building the complex nanomachine.

Statistical spatial analysis for cryo-electron tomography

Cryo-electron tomography (cryo-ET) is uniquely suited to precisely localize macromolecular complexes in situ, that is in a close-to-native state within their cellular compartments, in three-dimensions at high resolution. Point pattern analysis (PPA) allows quantitative characterization of the spatial organization of particles. However, current implementations of PPA functions are not suitable for applications to cryo-ET data because they do not consider the real, typically irregular 3D shape of cellular compartments and molecular complexes. Here, we designed and implemented first and the second-order, uni- and bivariate PPA functions in a Python package for statistical spatial analysis of particles located in three dimensional regions of arbitrary shape, such as those encountered in cellular cryo-ET imaging (PyOrg). To validate the implemented functions, we applied them to specially designed synthetic datasets. This allowed us to find the algorithmic solutions that provide the best accuracy and computational performance, and to evaluate the precision of the implemented functions. Applications to experimental data showed that despite the higher computational demand, the use of the second-order functions is advantageous to the first-order ones, because they allow characterization of the particle organization and statistical inference over a range of distance scales, as well as the comparative analysis between experimental groups comprising multiple tomograms. Altogether, PyOrg is a versatile, precise, and efficient open-source software for reliable quantitative characterization of macromolecular organization within cellular compartments imaged in situ by cryo-ET, as well as to other 3D imaging systems where real-size particles are located within regions possessing complex geometry.

Strain and rupture of HIV-1 capsids during uncoating

SignificanceThe mature capsids of HIV-1 are transiently stable complexes that self-assemble around the viral genome during maturation, and uncoat to release preintegration complexes that archive a double-stranded DNA copy of the virus in the host cell genome. However, a detailed view of how HIV cores rupture remains lacking. Here, we elucidate the physical properties involved in capsid rupture using a combination of large-scale all-atom molecular dynamics simulations and cryo-electron tomography. We find that intrinsic strain on the capsid forms highly correlated patterns along the capsid surface, along which cracks propagate. Capsid rigidity also increases with high strain. Our findings provide fundamental insight into viral capsid uncoating.