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

What shapes template-matching performance in cryogenic electron tomography in situ?

The detection of specific biological macromolecules in cryogenic electron tomography data is frequently approached by applying cross-correlation-based 3D template matching. To reduce computational cost and noise, high binning is used to aggregate voxels before template matching. This remains a prevalent practice in both practical applications and methods development. Here, the relation between template size, shape and angular sampling is systematically evaluated to identify ribosomes in a ground-truth annotated data set. It is shown that at the commonly used binning, a detailed subtomogram average, a sphere and a heart emoji result in near-identical performance. These findings indicate that with current template-matching practices macromolecules can only be detected with high precision if their shape and size are sufficiently different from the background. Using theoretical considerations, the experimental results are rationalized and it is discussed why primarily low-frequency information remains at high binning and that template matching fails to be accurate because similarly shaped and sized macromolecules have similar low-frequency spectra. These challenges are discussed and potential enhancements for future template-matching methodologies are proposed.

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

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

TOMOMAN: a software package for large scale cryo-electron tomography data preprocessing, community data sharing, and collaborative computing

Cryo-electron tomography (cryo-ET) and subtomogram averaging (STA) are becoming the preferred methodologies for investigating subcellular and macromolecular structures in native or near-native environments. While cryo-ET is amenable to a wide range of biological problems, these problems often have data processing requirements that need to be individually optimized, precluding the notion of a one-size-fits-all processing pipeline. Cryo-ET data processing is also becoming progressively more complex due to an increasing number of packages for each processing step. Though each package has its own strengths and weaknesses, independent development and different data formats makes them difficult to interface with one another. TOMOMAN (TOMOgram MANager) is an extensible package for streamlining the interoperability of packages, enabling users to develop project-specific processing workflows. TOMOMAN does this by maintaining an internal metadata format and wrapping external packages to manage and perform preprocessing, from raw tilt-series data to reconstructed tomograms. TOMOMAN can also export this metadata between various STA packages. TOMOMAN also includes tools for archiving projects to data repositories; allowing subsequent users to download TOMOMAN projects and directly resume processing where it was previously left off. By tracking essential metadata, TOMOMAN streamlines data sharing, which improves reproducibility of published results, reduces computational costs by minimizing reprocessing, and enables distributed cryo-ET projects between multiple groups and institutions. TOMOMAN provides a way for users to test different software packages to develop processing workflows that meet the specific needs of their biological questions and to distribute their results with the broader scientific community.

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.

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.

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.

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

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

Structural insights into the activation mechanism of antimicrobial GBP1

The dynamin-related human guanylate-binding protein 1 (GBP1) mediates host defenses against microbial pathogens. Upon GTP binding and hydrolysis, auto-inhibited GBP1 monomers dimerize and assemble into soluble and membrane-bound oligomers, which are crucial for innate immune responses. How higher-order GBP1 oligomers are built from dimers, and how assembly is coordinated with nucleotide-dependent conformational changes, has remained elusive. Here, we present cryo-electron microscopy-based structural data of soluble and membrane-bound GBP1 oligomers, which show that GBP1 assembles in an outstretched dimeric conformation. We identify a surface-exposed helix in the large GTPase domain that contributes to the oligomerization interface, and we probe its nucleotide- and dimerization-dependent movements that facilitate the formation of an antimicrobial protein coat on a gram-negative bacterial pathogen. Our results reveal a sophisticated activation mechanism for GBP1, in which nucleotide-dependent structural changes coordinate dimerization, oligomerization, and membrane binding to allow encapsulation of pathogens within an antimicrobial protein coat.

Recent advances in infectious disease research using cryo-electron tomography

With the increasing spread of infectious diseases worldwide, there is an urgent need for novel strategies to combat them. Cryogenic sample electron microscopy (cryo-EM) techniques, particularly electron tomography (cryo-ET), have revolutionized the field of infectious disease research by enabling multiscale observation of biological structures in a near-native state. This review highlights the recent advances in infectious disease research using cryo-ET and discusses the potential of this structural biology technique to help discover mechanisms of infection in native environments and guiding in the right direction for future drug discovery.

Cool-contacts: Cryo-Electron Microscopy of Membrane Contact Sites and Their Components

Electron microscopy has played a pivotal role in elucidating the ultrastructure of membrane contact sites between cellular organelles. The advent of cryo-electron microscopy has ushered in the ability to determine atomic models of constituent proteins or protein complexes within sites of membrane contact through single particle analysis. Furthermore, it enables the visualization of the three-dimensional architecture of membrane contact sites, encompassing numerous copies of proteins, whether in vitro reconstituted or directly observed in situ using cryo-electron tomography. Nevertheless, there exists a scarcity of cryo-electron microscopy studies focused on the site of membrane contact and their constitutive proteins. This review provides an overview of the contributions made by cryo-electron microscopy to our understanding of membrane contact sites, outlines the associated limitations, and explores prospects in this field.