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

Recent technical advances in cellular cryo-electron tomography

Understanding the in situ structure, organization, and interactions of macromolecules is essential for elucidating their functions and mechanisms of action. Cellular cryo-electron tomography (cryo-ET) is a cutting-edge technique that reveals in situ molecular-resolution architectures of macromolecules in their lifelike states. It also provides insights into the three-dimensional distribution of macromolecules and their spatial relationships with various subcellular structures. Thus, cellular cryo-ET bridges the gap between structural biology and cell biology. With rapid advancements, this technique achieved substantial improvements in throughput, automation, and resolution. This review presents the fundamental principles and methodologies of cellular cryo-ET, highlighting recent developments in sample preparation, data collection, and image processing. We also discuss emerging trends and potential future directions. As cellular cryo-ET continues to develop, it is set to play an increasingly vital role in structural cell biology.

Expanding insights from in situ cryo-EM

The combination of cryo-electron tomography and subtomogram analysis affords 3D high-resolution views of biological macromolecules in their native cellular environment, or in situ. Streamlined methods for acquiring and processing these data are advancing attainable resolutions into the realm of drug discovery. Yet regardless of resolution, structure prediction driven by artificial intelligence (AI) combined with subtomogram analysis is becoming powerful in understanding macromolecular assemblies. Automated and AI-assisted data mining is increasingly necessary to cope with the growing wealth of tomography data and to maximize the information obtained from them. Leveraging developments from AI and single-particle analysis could be essential in fulfilling the potential of in situ cryo-EM. Here, we highlight new developments for in situ cryo-EM and the emerging potential for AI in this process.

TEX264 drives selective autophagy of DNA lesions to promote DNA repair and cell survival

DNA repair and autophagy are distinct biological processes vital for cell survival. Although autophagy helps maintain genome stability, there is no evidence of its direct role in the repair of DNA lesions. We discovered that lysosomes process topoisomerase 1 cleavage complexes (TOP1cc) DNA lesions in vertebrates. Selective degradation of TOP1cc by autophagy directs DNA damage repair and cell survival at clinically relevant doses of topoisomerase 1 inhibitors. TOP1cc are exported from the nucleus to lysosomes through a transient alteration of the nuclear envelope and independent of the proteasome. Mechanistically, the autophagy receptor TEX264 acts as a TOP1cc sensor at DNA replication forks, triggering TOP1cc processing by the p97 ATPase and mediating the delivery of TOP1cc to lysosomes in an MRE11-nuclease- and ATR-kinase-dependent manner. We found an evolutionarily conserved role for selective autophagy in DNA repair that enables cell survival, protects genome stability, and is clinically relevant for colorectal cancer patients.

Serialized on-grid lift-in sectioning for tomography (SOLIST) enables a biopsy at the nanoscale

Cryo-focused ion beam milling has substantially advanced our understanding of molecular processes by opening windows into cells. However, applying this technique to complex samples, such as tissues, has presented considerable technical challenges. Here we introduce an innovative adaptation of the cryo-lift-out technique, serialized on-grid lift-in sectioning for tomography (SOLIST), addressing these limitations. SOLIST enhances throughput, minimizes ice contamination and improves sample stability for cryo-electron tomography. It thereby facilitates the high-resolution imaging of a wide range of specimens. We illustrate these advantages on reconstituted liquid-liquid phase-separated droplets, brain organoids and native tissues from the mouse brain, liver and heart. With SOLIST, cellular processes can now be investigated at molecular resolution directly in native tissue. Furthermore, our method has a throughput high enough to render cryo-lift-out a competitive tool for structural biology. This opens new avenues for unprecedented insights into cellular function and structure in health and disease, a 'biopsy at the nanoscale'.

An image processing pipeline for electron cryo-tomography in RELION-5

Electron tomography of frozen, hydrated samples allows structure determination of macromolecular complexes that are embedded in complex environments. Provided that the target complexes may be localised in noisy, three-dimensional tomographic reconstructions, averaging images of multiple instances of these molecules can lead to structures with sufficient resolution for de novo atomic modelling. Although many research groups have contributed image processing tools for these tasks, a lack of standardisation and interoperability represents a barrier for newcomers to the field. Here, we present an image processing pipeline for electron tomography data in RELION-5, with functionality ranging from the import of unprocessed movies to the automated building of atomic models in the final maps. Our explicit definition of metadata items that describe the steps of our pipeline has been designed for interoperability with other software tools and provides a framework for further standardisation.

Advances in cryo-ET data processing: meeting the demands of visual proteomics

Cryogenic electron tomography (cryo-ET), a method that enables the viewing of biomolecules in near-native environments at high resolution, is rising in accessibility and applicability. Over the past several years, once slow sample preparation and data collection procedures have seen innovations which enable rapid collection of the large datasets required for attaining high resolution structures. Increased data availability has provided a driving force for exciting improvements in cryo-ET data processing methodologies throughout the entire processing pipeline and the development of accessible graphical user interfaces (GUIs) that enable individuals inexperienced in computational fields to convert raw tilt series into 3D structures. These advances in data processing are enabling cryo-ET to attain higher resolution and extending its applicability to more complex samples.

In situ studies of membrane biology by cryo-electron tomography

Cryo-electron tomography (cryo-ET) allows high resolution 3D imaging of biological samples in near-native environments. Thus, cryo-ET has become the method of choice to analyze the unperturbed organization of cellular membranes. Here, we briefly discuss current cryo-ET workflows and their application to study membrane biology in situ, under basal and pathological conditions.

Molecular architecture of the actin cytoskeleton: From single cells to whole organisms using cryo-electron tomography

Cryo-electron tomography (cryo-ET) has begun to provide intricate views of cellular architecture at unprecedented resolutions. Considerable efforts are being made to further optimize and automate the cryo-ET workflow, from sample preparation to data acquisition and analysis, to enable visual proteomics inside of cells. Here, we will discuss the latest advances in cryo-ET that go hand in hand with their application to the actin cytoskeleton. The development of deep learning tools for automated annotation of tomographic reconstructions and the serial lift-out sample preparation procedure will soon make it possible to perform high-resolution structural biology in a whole new range of samples, from multicellular organisms to organoids and tissues.

ColabSeg: An interactive tool for editing, processing, and visualizing membrane segmentations from cryo-ET data

Cellular cryo-electron tomography (cryo-ET) has emerged as a key method to unravel the spatial and structural complexity of cells in their near-native state at unprecedented molecular resolution. To enable quantitative analysis of the complex shapes and morphologies of lipid membranes, the noisy three-dimensional (3D) volumes must be segmented. Despite recent advances, this task often requires considerable user intervention to curate the resulting segmentations. Here, we present ColabSeg, a Python-based tool for processing, visualizing, editing, and fitting membrane segmentations from cryo-ET data for downstream analysis. ColabSeg makes many well-established algorithms for point-cloud processing easily available to the broad community of structural biologists for applications in cryo-ET through its graphical user interface (GUI). We demonstrate the usefulness of the tool with a range of use cases and biological examples. Finally, for a large Mycoplasma pneumoniae dataset of 50 tomograms, we show how ColabSeg enables high-throughput membrane segmentation, which can be used as valuable training data for fully automated convolutional neural network (CNN)-based segmentation.

The advent of preventive high-resolution structural histopathology by artificial-intelligence-powered cryogenic electron tomography

Advances in cryogenic electron microscopy (cryoEM) single particle analysis have revolutionized structural biology by facilitating the in vitro determination of atomic- and near-atomic-resolution structures for fully hydrated macromolecular complexes exhibiting compositional and conformational heterogeneity across a wide range of sizes. Cryogenic electron tomography (cryoET) and subtomogram averaging are rapidly progressing toward delivering similar insights for macromolecular complexes in situ, without requiring tags or harsh biochemical purification. Furthermore, cryoET enables the visualization of cellular and tissue phenotypes directly at molecular, nanometric resolution without chemical fixation or staining artifacts. This forward-looking review covers recent developments in cryoEM/ET and related technologies such as cryogenic focused ion beam milling scanning electron microscopy and correlative light microscopy, increasingly enhanced and supported by artificial intelligence algorithms. Their potential application to emerging concepts is discussed, primarily the prospect of complementing medical histopathology analysis. Machine learning solutions are poised to address current challenges posed by "big data" in cryoET of tissues, cells, and macromolecules, offering the promise of enabling novel, quantitative insights into disease processes, which may translate into the clinic and lead to improved diagnostics and targeted therapeutics.

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.

Understanding the cell: Future views of structural biology

Determining the structure and mechanisms of all individual functional modules of cells at high molecular detail has often been seen as equal to understanding how cells work. Recent technical advances have led to a flush of high-resolution structures of various macromolecular machines, but despite this wealth of detailed information, our understanding of cellular function remains incomplete. Here, we discuss present-day limitations of structural biology and highlight novel technologies that may enable us to analyze molecular functions directly inside cells. We predict that the progression toward structural cell biology will involve a shift toward conceptualizing a 4D virtual reality of cells using digital twins. These will capture cellular segments in a highly enriched molecular detail, include dynamic changes, and facilitate simulations of molecular processes, leading to novel and experimentally testable predictions. Transferring biological questions into algorithms that learn from the existing wealth of data and explore novel solutions may ultimately unveil how cells work.

Protein nanobarcodes enable single-step multiplexed fluorescence imaging

Multiplexed cellular imaging typically relies on the sequential application of detection probes, as antibodies or DNA barcodes, which is complex and time-consuming. To address this, we developed here protein nanobarcodes, composed of combinations of epitopes recognized by specific sets of nanobodies. The nanobarcodes are read in a single imaging step, relying on nanobodies conjugated to distinct fluorophores, which enables a precise analysis of large numbers of protein combinations. Fluorescence images from nanobarcodes were used as input images for a deep neural network, which was able to identify proteins with high precision. We thus present an efficient and straightforward protein identification method, which is applicable to relatively complex biological assays. We demonstrate this by a multicell competition assay, in which we successfully used our nanobarcoded proteins together with neurexin and neuroligin isoforms, thereby testing the preferred binding combinations of multiple isoforms, in parallel.

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.

Sequential membrane- and protein-bound organelles compartmentalize genomes during phage infection

Eukaryotic viruses assemble compartments required for genome replication, but no such organelles are known to be essential for prokaryotic viruses. Bacteriophages of the family Chimalliviridae sequester their genomes within a phage-generated organelle, the phage nucleus, which is enclosed by a lattice of viral protein ChmA. Using the dRfxCas13d-based knockdown system CRISPRi-ART, we show that ChmA is essential for the E. coli phage Goslar life cycle. Without ChmA, infections are arrested at an early stage in which the injected phage genome is enclosed in a membrane-bound vesicle capable of gene expression but not DNA replication. Not only do we demonstrate that the phage nucleus is essential for genome replication, but we also show that the Chimalliviridae early phage infection (EPI) vesicle is a transcriptionally active, phage-generated organelle.

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.

Rapid Synthesis of Cryo-ET Data for Training Deep Learning Models

Deep learning excels at cryo-tomographic image restoration and segmentation tasks but is hindered by a lack of training data. Here we introduce cryo-TomoSim (CTS), a MATLAB-based software package that builds coarse-grained models of macromolecular complexes embedded in vitreous ice and then simulates transmitted electron tilt series for tomographic reconstruction. We then demonstrate the effectiveness of these simulated datasets in training different deep learning models for use on real cryotomographic reconstructions. Computer-generated ground truth datasets provide the means for training models with voxel-level precision, allowing for unprecedented denoising and precise molecular segmentation of datasets. By modeling phenomena such as a three-dimensional contrast transfer function, probabilistic detection events, and radiation-induced damage, the simulated cryo-electron tomograms can cover a large range of imaging content and conditions to optimize training sets. When paired with small amounts of training data from real tomograms, networks become incredibly accurate at segmenting in situ macromolecular assemblies across a wide range of biological contexts.

Structural investigation of eukaryotic cells: From the periphery to the interior by cryo-electron tomography

Cryo-electron tomography (cryo-ET) combines a close-to-life preservation of the cell with high-resolution three-dimensional (3D) imaging. This allows to study the molecular architecture of the cellular landscape and provides unprecedented views on biological processes and structures. In this review we mainly focus on the application of cryo-ET to visualize and structurally characterize eukaryotic cells - from the periphery to the cellular interior. We discuss strategies that can be employed to investigate the structure of challenging targets in their cellular environment as well as the application of complimentary approaches in conjunction with cryo-ET.