Averaging of electron subtomograms and random conical tilt reconstructions through likelihood optimization

AITom: AI-guided cryo-electron tomography image analyses toolkit

Cryo-electron tomography (cryo-ET) is an essential tool in structural biology, uniquely capable of visualizing three-dimensional macromolecular complexes within their native cellular environments, thereby providing profound molecular-level insights. Despite its significant promise, cryo-ET faces persistent challenges in the systematic localization, identification, segmentation, and structural recovery of three-dimensional subcellular components, necessitating the development of efficient and accurate large-scale image analysis methods. In response to these complexities, this paper introduces AITom, an open-source artificial intelligence platform tailored for cryo-ET researchers. AITom integrates a comprehensive suite of public and proprietary algorithms, supporting both traditional template-based and template-free approaches, alongside state-of-the-art deep learning methodologies for cryo-ET data analysis. By incorporating diverse computational strategies, AITom enables researchers to more effectively tackle the complexities inherent in cryo-ET, facilitating precise analysis and interpretation of complex biological structures. Furthermore, AITom provides extensive tutorials for each analysis module, offering valuable guidance to users in utilizing its comprehensive functionalities.

A ternary complex of MIPs in the A-tubule of basal bodies and axonemes depends on RIB22 and the EF-hand domain of RIB72A in Tetrahymena cilia

The lumens of the highly stable microtubules that make up the core of basal bodies, cilia, and flagella are coated with a network of proteins known as MIPs, or microtubule inner proteins. MIPs are hypothesized to enhance the rigidity and stability of these microtubules, but how they assemble and contribute to cilia function is poorly understood. Here we describe a ciliate specific MIP, RIB22, in Tetrahymena thermophila. RIB22 is a calmodulin-like protein found in the A-tubule of doublet and triplet microtubules in cilia and basal bodies. Its localization is dependent on the conserved MIP RIB72. Here we use cryogenic electron tomography (cryoET) to examine RIB22 and its interacting partners in axonemes and basal bodies. RIB22 forms a ternary complex with the C-terminal EF-hand domain of RIB72A and another MIP, FAM166A. Tetrahymena strains lacking RIB22 or the EF-hand domain of RIB72A showed impaired cilia function. CryoET on axonemes from these strains demonstrated an interdependence of the three proteins for stabilization within the structure. Deletion of the RIB72A EF-hand domain resulted in the apparent loss of multiple MIPs in the region. These findings emphasize the intricacy of the MIP network and the importance of understanding MIPs' functions during cilium assembly and regulation.

The structure of basal body inner junctions from Tetrahymena revealed by electron cryo-tomography

The cilium is a microtubule-based eukaryotic organelle critical for many cellular functions. Its assembly initiates at a basal body and continues as an axoneme that projects out of the cell to form a functional cilium. This assembly process is tightly regulated. However, our knowledge of the molecular architecture and the mechanism of assembly is limited. By applying cryo-electron tomography, we obtained structures of the inner junction in three regions of the cilium from Tetrahymena: the proximal, the central core of the basal body, and the axoneme. We identified several protein components in the basal body. While a few proteins are distributed throughout the entire length of the organelle, many are restricted to specific regions, forming intricate local interaction networks in the inner junction and bolstering local structural stability. By examining the inner junction in a POC1 knockout mutant, we found the triplet microtubule was destabilized, resulting in a defective structure. Surprisingly, several axoneme-specific components were found to "infiltrate" into the mutant basal body. Our findings provide molecular insight into cilium assembly at the inner junctions, underscoring its precise spatial regulation.

The Structure of Cilium Inner Junctions Revealed by Electron Cryo-tomography

The cilium is a microtubule-based organelle critical for many cellular functions. Its assembly initiates at a basal body and continues as an axoneme that projects out of the cell to form a functional cilium. This assembly process is tightly regulated. However, our knowledge of the molecular architecture and the mechanism of assembly is limited. By applying electron cryotomography and subtomogram averaging, we obtained subnanometer resolution structures of the inner junction in three distinct regions of the cilium: the proximal region of the basal body, the central core of the basal body, and the flagellar axoneme. The structures allowed us to identify several basal body and axoneme components. While a few proteins are distributed throughout the entire length of the organelle, many are restricted to particular regions of the cilium, forming intricate local interaction networks and bolstering local structural stability. Finally, by knocking out a critical basal body inner junction component Poc1, we found the triplet MT was destabilized, resulting in a defective structure. Surprisingly, several axoneme-specific components were found to "infiltrate" into the mutant basal body. Our findings provide molecular insight into cilium assembly at its inner Junctions, underscoring its precise spatial regulation.

Poc1 bridges basal body inner junctions to promote triplet microtubule integrity and connections

Basal bodies (BBs) are conserved eukaryotic structures that organize cilia. They are comprised of nine, cylindrically arranged, triplet microtubules (TMTs) connected to each other by inter-TMT linkages which stabilize the structure. Poc1 is a conserved protein important for BB structural integrity in the face of ciliary forces transmitted to BBs. To understand how Poc1 confers BB stability, we identified the precise position of Poc1 in the Tetrahymena BB and the effect of Poc1 loss on BB structure. Poc1 binds at the TMT inner junctions, stabilizing TMTs directly. From this location, Poc1 also stabilizes inter-TMT linkages throughout the BB, including the cartwheel pinhead and the inner scaffold. The full localization of the inner scaffold protein Fam161A requires Poc1. As ciliary forces are increased, Fam161A is reduced, indicative of a force-dependent molecular remodeling of the inner scaffold. Thus, while not essential for BB assembly, Poc1 promotes BB interconnections that establish an architecture competent to resist ciliary forces.

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.

Vimentin filaments integrate low-complexity domains in a complex helical structure

Intermediate filaments (IFs) are integral components of the cytoskeleton. They provide cells with tissue-specific mechanical properties and are involved in numerous cellular processes. Due to their intricate architecture, a 3D structure of IFs has remained elusive. Here we use cryo-focused ion-beam milling, cryo-electron microscopy and tomography to obtain a 3D structure of vimentin IFs (VIFs). VIFs assemble into a modular, intertwined and flexible helical structure of 40 α-helices in cross-section, organized into five protofibrils. Surprisingly, the intrinsically disordered head domains form a fiber in the lumen of VIFs, while the intrinsically disordered tails form lateral connections between the protofibrils. Our findings demonstrate how protein domains of low sequence complexity can complement well-folded protein domains to construct a biopolymer with striking mechanical strength and stretchability.

Missing Wedge Completion via Unsupervised Learning with Coordinate Networks

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

Missing Wedge Completion via Unsupervised Learning with Coordinate Networks

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

MDSPACE and MDTOMO Software for Extracting Continuous Conformational Landscapes from Datasets of Single Particle Images and Subtomograms Based on Molecular Dynamics Simulations: Latest Developments in ContinuousFlex Software Package

Cryo electron microscopy (cryo-EM) instrumentation allows obtaining 3D reconstruction of the structure of biomolecular complexes in vitro (purified complexes studied by single particle analysis) and in situ (complexes studied in cells by cryo electron tomography). Standard cryo-EM approaches allow high-resolution reconstruction of only a few conformational states of a molecular complex, as they rely on data classification into a given number of classes to increase the resolution of the reconstruction from the most populated classes while discarding all other classes. Such discrete classification approaches result in a partial picture of the full conformational variability of the complex, due to continuous conformational transitions with many, uncountable intermediate states. In this article, we present the software with a user-friendly graphical interface for running two recently introduced methods, namely, MDSPACE and MDTOMO, to obtain continuous conformational landscapes of biomolecules by analyzing in vitro and in situ cryo-EM data (single particle images and subtomograms) based on molecular dynamics simulations of an available atomic model of one of the conformations. The MDSPACE and MDTOMO software is part of the open-source ContinuousFlex software package (starting from version 3.4.2 of ContinuousFlex), which can be run as a plugin of the Scipion software package (version 3.1 and later), broadly used in the cryo-EM field.

Poc1 is a basal body inner junction protein that promotes triplet microtubule integrity and interconnections

Basal bodies (BBs) are conserved eukaryotic structures that organize motile and primary cilia. The BB is comprised of nine, cylindrically arranged, triplet microtubules (TMTs) that are connected to each other by inter-TMT linkages which maintain BB structure. During ciliary beating, forces transmitted to the BB must be resisted to prevent BB disassembly. Poc1 is a conserved BB protein important for BBs to resist ciliary forces. To understand how Poc1 confers BB stability, we identified the precise position of Poc1 binding in the Tetrahymena BB and the effect of Poc1 loss on BB structure. Poc1 binds at the TMT inner junctions, stabilizing TMTs directly. From this location, Poc1 also stabilizes inter-TMT linkages throughout the BB, including the cartwheel pinhead and the inner scaffold. Moreover, we identify a molecular response to ciliary forces via a molecular remodeling of the inner scaffold, as determined by differences in Fam161A localization. Thus, while not essential for BB assembly, Poc1 promotes BB interconnections that establish an architecture competent to resist ciliary forces.

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.

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

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

Computational Methods Toward Unbiased Pattern Mining and Structure Determination in Cryo-Electron Tomography Data

Cryo-electron tomography can uniquely probe the native cellular environment for macromolecular structures. Tomograms feature complex data with densities of diverse, densely crowded macromolecular complexes, low signal-to-noise, and artifacts such as the missing wedge effect. Post-processing of this data generally involves isolating regions or particles of interest from tomograms, organizing them into related groups, and rendering final structures through subtomogram averaging. Template-matching and reference-based structure determination are popular analysis methods but are vulnerable to biases and can often require significant user input. Most importantly, these approaches cannot identify novel complexes that reside within the imaged cellular environment. To reliably extract and resolve structures of interest, efficient and unbiased approaches are therefore of great value. This review highlights notable computational software and discusses how they contribute to making automated structural pattern discovery a possibility. Perspectives emphasizing the importance of features for user-friendliness and accessibility are also presented.

CryoETGAN: Cryo-Electron Tomography Image Synthesis via Unpaired Image Translation

Cryo-electron tomography (Cryo-ET) has been regarded as a revolution in structural biology and can reveal molecular sociology. Its unprecedented quality enables it to visualize cellular organelles and macromolecular complexes at nanometer resolution with native conformations. Motivated by developments in nanotechnology and machine learning, establishing machine learning approaches such as classification, detection and averaging for Cryo-ET image analysis has inspired broad interest. Yet, deep learning-based methods for biomedical imaging typically require large labeled datasets for good results, which can be a great challenge due to the expense of obtaining and labeling training data. To deal with this problem, we propose a generative model to simulate Cryo-ET images efficiently and reliably: CryoETGAN. This cycle-consistent and Wasserstein generative adversarial network (GAN) is able to generate images with an appearance similar to the original experimental data. Quantitative and visual grading results on generated images are provided to show that the results of our proposed method achieve better performance compared to the previous state-of-the-art simulation methods. Moreover, CryoETGAN is stable to train and capable of generating plausibly diverse image samples.

Electron cryo-tomography structure of axonemal doublet microtubule from Tetrahymena thermophila

Doublet microtubules (DMTs) provide a scaffold for axoneme assembly in motile cilia. Aside from α/β tubulins, the DMT comprises a large number of non-tubulin proteins in the luminal wall of DMTs, collectively named the microtubule inner proteins (MIPs). We used cryoET to study axoneme DMT isolated from Tetrahymena We present the structures of DMT at nanometer and sub-nanometer resolution. The structures confirm that MIP RIB72A/B binds to the luminal wall of DMT by multiple DM10 domains. We found FAP115, an MIP-containing multiple EF-hand domains, located at the interface of four-tubulin dimers in the lumen of A-tubule. It contacts both lateral and longitudinal tubulin interfaces and playing a critical role in DMT stability. We observed substantial structure heterogeneity in DMT in an FAP115 knockout strain, showing extensive structural defects beyond the FAP115-binding site. The defects propagate along the axoneme. Finally, by comparing DMT structures from Tetrahymena and Chlamydomonas, we have identified a number of conserved MIPs as well as MIPs that are unique to each organism. This conservation and diversity of the DMT structures might be linked to their specific functions. Our work provides structural insights essential for understanding the roles of MIPs during motile cilium assembly and function, as well as their relationships to human ciliopathies.

TomoFlow: Analysis of Continuous Conformational Variability of Macromolecules in Cryogenic Subtomograms based on 3D Dense Optical Flow

Cryogenic Electron Tomography (cryo-ET) allows structural and dynamics studies of macromolecules in situ. Averaging different copies of imaged macromolecules is commonly used to obtain their structure at higher resolution and discrete classification to analyze their dynamics. Instrumental and data processing developments are progressively equipping cryo-ET studies with the ability to escape the trap of classification into a complete continuous conformational variability analysis. In this work, we propose TomoFlow, a method for analyzing macromolecular continuous conformational variability in cryo-ET subtomograms based on a three-dimensional dense optical flow (OF) approach. The resultant lower-dimensional conformational space allows generating movies of macromolecular motion and obtaining subtomogram averages by grouping conformationally similar subtomograms. The animations and the subtomogram group averages reveal accurate trajectories of macromolecular motion based on a novel mathematical model that makes use of OF properties. This paper describes TomoFlow with tests on simulated datasets generated using different techniques, namely Normal Mode Analysis and Molecular Dynamics Simulation. It also shows an application of TomoFlow on a dataset of nucleosomes in situ, which provided promising results coherent with previous findings using the same dataset but without imposing any prior knowledge on the analysis of the conformational variability. The method is discussed with its potential uses and limitations.

Proteomic analysis of microtubule inner proteins (MIPs) in Rib72 null Tetrahymena cells reveals functional MIPs

The core structure of motile cilia and flagella, the axoneme, is built from a stable population of doublet microtubules. This unique stability is brought about, at least in part, by a network of microtubule inner proteins (MIPs) that are bound to the luminal side of the microtubule walls. Rib72A and Rib72B were identified as MIPs in the motile cilia of the protist Tetrahymena thermophila. Loss of these proteins leads to ciliary defects and loss of additional MIPs. We performed mass spectrometry coupled with proteomic analysis and bioinformatics to identify the MIPs lost in RIB72A/B knockout Tetrahymena axonemes. We identified a number of candidate MIPs and pursued one, Fap115, for functional characterization. We find that loss of Fap115 results in disrupted cell swimming and aberrant ciliary beating. Cryo-electron tomography reveals that Fap115 localizes to MIP6a in the A-tubule of the doublet microtubules. Overall, our results highlight the complex relationship between MIPs, ciliary structure, and ciliary function.

HEMNMA-3D: Cryo Electron Tomography Method Based on Normal Mode Analysis to Study Continuous Conformational Variability of Macromolecular Complexes

Cryogenic electron tomography (cryo-ET) allows structural determination of biomolecules in their native environment (in situ). Its potential of providing information on the dynamics of macromolecular complexes in cells is still largely unexploited, due to the challenges of the data analysis. The crowded cell environment and continuous conformational changes of complexes make difficult disentangling the data heterogeneity. We present HEMNMA-3D, which is, to the best of our knowledge, the first method for analyzing cryo electron subtomograms in terms of continuous conformational changes of complexes. HEMNMA-3D uses a combination of elastic and rigid-body 3D-to-3D iterative alignments of a flexible 3D reference (atomic structure or electron microscopy density map) to match the conformation, orientation, and position of the complex in each subtomogram. The elastic matching combines molecular mechanics simulation (Normal Mode Analysis of the 3D reference) and experimental, subtomogram data analysis. The rigid-body alignment includes compensation for the missing wedge, due to the limited tilt angle of cryo-ET. The conformational parameters (amplitudes of normal modes) of the complexes in subtomograms obtained through the alignment are processed to visualize the distribution of conformations in a space of lower dimension (typically, 2D or 3D) referred to as space of conformations. This allows a visually interpretable insight into the dynamics of the complexes, by calculating 3D averages of subtomograms with similar conformations from selected (densest) regions and by recording movies of the 3D reference's displacement along selected trajectories through the densest regions. We describe HEMNMA-3D and show its validation using synthetic datasets. We apply HEMNMA-3D to an experimental dataset describing in situ nucleosome conformational variability. HEMNMA-3D software is available freely (open-source) as part of ContinuousFlex plugin of Scipion V3.0 (http://scipion.i2pc.es).

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

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

Few-shot learning for classification of novel macromolecular structures in cryo-electron tomograms

Cryo-electron tomography (cryo-ET) provides 3D visualization of subcellular components in the near-native state and at sub-molecular resolutions in single cells, demonstrating an increasingly important role in structural biology in situ. However, systematic recognition and recovery of macromolecular structures in cryo-ET data remain challenging as a result of low signal-to-noise ratio (SNR), small sizes of macromolecules, and high complexity of the cellular environment. Subtomogram structural classification is an essential step for such task. Although acquisition of large amounts of subtomograms is no longer an obstacle due to advances in automation of data collection, obtaining the same number of structural labels is both computation and labor intensive. On the other hand, existing deep learning based supervised classification approaches are highly demanding on labeled data and have limited ability to learn about new structures rapidly from data containing very few labels of such new structures. In this work, we propose a novel approach for subtomogram classification based on few-shot learning. With our approach, classification of unseen structures in the training data can be conducted given few labeled samples in test data through instance embedding. Experiments were performed on both simulated and real datasets. Our experimental results show that we can make inference on new structures given only five labeled samples for each class with a competitive accuracy (> 0.86 on the simulated dataset with SNR = 0.1), or even one sample with an accuracy of 0.7644. The results on real datasets are also promising with accuracy > 0.9 on both conditions and even up to 1 on one of the real datasets. Our approach achieves significant improvement compared with the baseline method and has strong capabilities of generalizing to other cellular components.

The promise and the challenges of cryo-electron tomography

Structural biologists have traditionally approached cellular complexity in a reductionist manner in which the cellular molecular components are fractionated and purified before being studied individually. This 'divide and conquer' approach has been highly successful. However, awareness has grown in recent years that biological functions can rarely be attributed to individual macromolecules. Most cellular functions arise from their concerted action, and there is thus a need for methods enabling structural studies performed in situ, ideally in unperturbed cellular environments. Cryo-electron tomography (Cryo-ET) combines the power of 3D molecular-level imaging with the best structural preservation that is physically possible to achieve. Thus, it has a unique potential to reveal the supramolecular architecture or 'molecular sociology' of cells and to discover the unexpected. Here, we review state-of-the-art Cryo-ET workflows, provide examples of biological applications, and discuss what is needed to realize the full potential of Cryo-ET.

Ribosome-associated vesicles: A dynamic subcompartment of the endoplasmic reticulum in secretory cells

The endoplasmic reticulum (ER) is a highly dynamic network of membranes. Here, we combine live-cell microscopy with in situ cryo-electron tomography to directly visualize ER dynamics in several secretory cell types including pancreatic β-cells and neurons under near-native conditions. Using these imaging approaches, we identify a novel, mobile form of ER, ribosome-associated vesicles (RAVs), found primarily in the cell periphery, which is conserved across different cell types and species. We show that RAVs exist as distinct, highly dynamic structures separate from the intact ER reticular architecture that interact with mitochondria via direct intermembrane contacts. These findings describe a new ER subcompartment within cells.

Fine-grained alignment of cryo-electron subtomograms based on MPI parallel optimization

BACKGROUND

Cryo-electron tomography (Cryo-ET) is an imaging technique used to generate three-dimensional structures of cellular macromolecule complexes in their native environment. Due to developing cryo-electron microscopy technology, the image quality of three-dimensional reconstruction of cryo-electron tomography has greatly improved. However, cryo-ET images are characterized by low resolution, partial data loss and low signal-to-noise ratio (SNR). In order to tackle these challenges and improve resolution, a large number of subtomograms containing the same structure needs to be aligned and averaged. Existing methods for refining and aligning subtomograms are still highly time-consuming, requiring many computationally intensive processing steps (i.e. the rotations and translations of subtomograms in three-dimensional space).

RESULTS

In this article, we propose a Stochastic Average Gradient (SAG) fine-grained alignment method for optimizing the sum of dissimilarity measure in real space. We introduce a Message Passing Interface (MPI) parallel programming model in order to explore further speedup.

CONCLUSIONS

We compare our stochastic average gradient fine-grained alignment algorithm with two baseline methods, high-precision alignment and fast alignment. Our SAG fine-grained alignment algorithm is much faster than the two baseline methods. Results on simulated data of GroEL from the Protein Data Bank (PDB ID:1KP8) showed that our parallel SAG-based fine-grained alignment method could achieve close-to-optimal rigid transformations with higher precision than both high-precision alignment and fast alignment at a low SNR (SNR=0.003) with tilt angle range ±60∘ or ±40∘. For the experimental subtomograms data structures of GroEL and GroEL/GroES complexes, our parallel SAG-based fine-grained alignment can achieve higher precision and fewer iterations to converge than the two baseline methods.

An integration of fast alignment and maximum-likelihood methods for electron subtomogram averaging and classification

Motivation

Cellular Electron CryoTomography (CECT) is an emerging 3D imaging technique that visualizes subcellular organization of single cells at sub-molecular resolution and in near-native state. CECT captures large numbers of macromolecular complexes of highly diverse structures and abundances. However, the structural complexity and imaging limits complicate the systematic de novo structural recovery and recognition of these macromolecular complexes. Efficient and accurate reference-free subtomogram averaging and classification represent the most critical tasks for such analysis. Existing subtomogram alignment based methods are prone to the missing wedge effects and low signal-to-noise ratio (SNR). Moreover, existing maximum-likelihood based methods rely on integration operations, which are in principle computationally infeasible for accurate calculation.

Results

Built on existing works, we propose an integrated method, Fast Alignment Maximum Likelihood method (FAML), which uses fast subtomogram alignment to sample sub-optimal rigid transformations. The transformations are then used to approximate integrals for maximum-likelihood update of subtomogram averages through expectation–maximization algorithm. Our tests on simulated and experimental subtomograms showed that, compared to our previously developed fast alignment method (FA), FAML is significantly more robust to noise and missing wedge effects with moderate increases of computation cost. Besides, FAML performs well with significantly fewer input subtomograms when the FA method fails. Therefore, FAML can serve as a key component for improved construction of initial structural models from macromolecules captured by CECT.

Availability and implementation

http://www.cs.cmu.edu/mxu1

In situ structure determination by subtomogram averaging

Cryo-tomography and subtomogram averaging are increasingly popular techniques for structural determination of macromolecular complexes in situ. They have the potential to achieve high-resolution views of native complexes, together with the details of their location relative to interacting molecules. The subtomogram averaging (StA) pipelines are well-established, with current developments aiming to optimise each step by reducing manual intervention and user decisions, following similar trends in single-particle approaches that have dramatically increased their popularity. Here, we review the main steps of typical StA workflows. We focus on considerations arising from the fact that the objects of study are embedded within unique crowded environments, and we emphasise those steps where careful decisions need to be made by the user.

Subtomogram averaging from cryo-electron tomograms

Cryo-electron tomography (cryo-ET) allows three-dimensional (3D) visualization of frozen-hydrated biological samples, such as protein complexes and cell organelles, in near-native environments at nanometer scale. Protein complexes that are present in multiple copies in a set of tomograms can be extracted, mutually aligned, and averaged to yield a signal-enhanced 3D structure up to sub-nanometer or even near-atomic resolution. This technique, called subtomogram averaging (StA), is powered by improvements in EM hardware and image processing software. Importantly, StA provides unique biological insights into the structure and function of cellular machinery in close-to-native contexts. In this chapter, we describe the principles and key steps of StA. We briefly cover sample preparation and data collection with an emphasis on image processing procedures related to tomographic reconstruction, subtomogram alignment, averaging, and classification. We conclude by summarizing current limitations and future directions of this technique with a focus on high-resolution StA.

Electron cryo-tomography provides insight into procentriole architecture and assembly mechanism

Centriole is an essential structure with multiple functions in cellular processes. Centriole biogenesis and homeostasis is tightly regulated. Using electron cryo-tomography (cryoET) we present the structure of procentrioles from Chlamydomonas reinhardtii. We identified a set of non-tubulin components attached to the triplet microtubule (MT), many are at the junctions of tubules likely to reinforce the triplet. We describe structure of the A-C linker that bridges neighboring triplets. The structure infers that POC1 is likely an integral component of A-C linker. Its conserved WD40 β-propeller domain provides attachment sites for other A-C linker components. The twist of A-C linker results in an iris diaphragm-like motion of the triplets in the longitudinal direction of procentriole. Finally, we identified two assembly intermediates at the growing ends of procentriole allowing us to propose a model for the procentriole assembly. Our results provide a comprehensive structural framework for understanding the molecular mechanisms underpinning procentriole biogenesis and assembly.

De Novo Structural Pattern Mining in Cellular Electron Cryotomograms

Electron cryotomography enables 3D visualization of cells in a near-native state at molecular resolution. The produced cellular tomograms contain detailed information about a plethora of macromolecular complexes, their structures, abundances, and specific spatial locations in the cell. However, extracting this information in a systematic way is very challenging, and current methods usually rely on individual templates of known structures. Here, we propose a framework called "Multi-Pattern Pursuit" for de novo discovery of different complexes from highly heterogeneous sets of particles extracted from entire cellular tomograms without using information of known structures. These initially detected structures can then serve as input for more targeted refinement efforts. Our tests on simulated and experimental tomograms show that our automated method is a promising tool for supporting large-scale template-free visual proteomics analysis.

Frozen-hydrated chromatin from metaphase chromosomes has an interdigitated multilayer structure

Cryo-electron tomography and small-angle X-ray scattering were used to investigate the chromatin folding in metaphase chromosomes. The tomographic 3D reconstructions show that frozen-hydrated chromatin emanated from chromosomes is planar and forms multilayered plates. The layer thickness was measured accounting for the contrast transfer function fringes at the plate edges, yielding a width of ~ 7.5 nm, which is compatible with the dimensions of a monolayer of nucleosomes slightly tilted with respect to the layer surface. Individual nucleosomes are visible decorating distorted plates, but typical plates are very dense and nucleosomes are not identifiable as individual units, indicating that they are tightly packed. Two layers in contact are ~ 13 nm thick, which is thinner than the sum of two independent layers, suggesting that nucleosomes in the layers interdigitate. X-ray scattering of whole chromosomes shows a main scattering peak at ~ 6 nm, which can be correlated with the distance between layers and between interdigitating nucleosomes interacting through their faces. These observations support a model where compact chromosomes are composed of many chromatin layers stacked along the chromosome axis.

Improved deep learning-based macromolecules structure classification from electron cryo-tomograms

Cellular processes are governed by macromolecular complexes inside the cell. Study of the native structures of macromolecular complexes has been extremely difficult due to lack of data. With recent breakthroughs in Cellular Electron Cryo-Tomography (CECT) 3D imaging technology, it is now possible for researchers to gain accesses to fully study and understand the macro-molecular structures single cells. However, systematic recovery of macromolecular structures from CECT is very difficult due to high degree of structural complexity and practical imaging limitations. Specifically, we proposed a deep learning-based image classification approach for large-scale systematic macromolecular structure separation from CECT data. However, our previous work was only a very initial step toward exploration of the full potential of deep learning-based macromolecule separation. In this paper, we focus on improving classification performance by proposing three newly designed individual CNN models: an extended version of (Deep Small Receptive Field) DSRF3D, donated as DSRF3D-v2, a 3D residual block-based neural network, named as RB3D, and a convolutional 3D (C3D)-based model, CB3D. We compare them with our previously developed model (DSRF3D) on 12 datasets with different SNRs and tilt angle ranges. The experiments show that our new models achieved significantly higher classification accuracies. The accuracies are not only higher than 0.9 on normal datasets, but also demonstrate potentials to operate on datasets with high levels of noises and missing wedge effects presented.

Identification of a Structural Element in HIV-1 Gag Required for Virus Particle Assembly and Maturation

Late in the HIV-1 replication cycle, the viral structural protein Gag is targeted to virus assembly sites at the plasma membrane of infected cells. The capsid (CA) domain of Gag plays a critical role in the formation of the hexameric Gag lattice in the immature virion, and, during particle release, CA is cleaved from the Gag precursor by the viral protease and forms the conical core of the mature virion. A highly conserved Pro-Pro-Ile-Pro (PPIP) motif (CA residues 122 to 125) [PPIP(122-125)] in a loop connecting CA helices 6 and 7 resides at a 3-fold axis formed by neighboring hexamers in the immature Gag lattice. In this study, we characterized the role of this PPIP(122-125) loop in HIV-1 assembly and maturation. While mutations P123A and P125A were relatively well tolerated, mutation of P122 and I124 significantly impaired virus release, caused Gag processing defects, and abolished infectivity. X-ray crystallography indicated that the P122A and I124A mutations induce subtle changes in the structure of the mature CA lattice which were permissive for in vitro assembly of CA tubes. Transmission electron microscopy and cryo-electron tomography demonstrated that the P122A and I124A mutations induce severe structural defects in the immature Gag lattice and abrogate conical core formation. Propagation of the P122A and I124A mutants in T-cell lines led to the selection of compensatory mutations within CA. Our findings demonstrate that the CA PPIP(122-125) loop comprises a structural element critical for the formation of the immature Gag lattice.IMPORTANCE Capsid (CA) plays multiple roles in the HIV-1 replication cycle. CA-CA domain interactions are responsible for multimerization of the Gag polyprotein at virus assembly sites, and in the mature virion, CA monomers assemble into a conical core that encapsidates the viral RNA genome. Multiple CA regions that contribute to the assembly and release of HIV-1 particles have been mapped and investigated. Here, we identified and characterized a Pro-rich loop in CA that is important for the formation of the immature Gag lattice. Changes in this region disrupt viral production and abrogate the formation of infectious, mature virions. Propagation of the mutants in culture led to the selection of second-site compensatory mutations within CA. These results expand our knowledge of the assembly and maturation steps in the viral replication cycle and may be relevant for development of antiviral drugs targeting CA.

DEEP LEARNING BASED SUPERVISED SEMANTIC SEGMENTATION OF ELECTRON CRYO-SUBTOMOGRAMS

Cellular Electron Cryo-Tomography (CECT) is a powerful imaging technique for the 3D visualization of cellular structure and organization at submolecular resolution. It enables analyzing the native structures of macromolecular complexes and their spatial organization inside single cells. However, due to the high degree of structural complexity and practical imaging limitations, systematic macromolecular structural recovery inside CECT images remains challenging. Particularly, the recovery of a macromolecule is likely to be biased by its neighbor structures due to the high molecular crowding. To reduce the bias, here we introduce a novel 3D convolutional neural network inspired by Fully Convolutional Network and Encoder-Decoder Architecture for the supervised segmentation of macromolecules of interest in subtomograms. The tests of our models on realistically simulated CECT data demonstrate that our new approach has significantly improved segmentation performance compared to our baseline approach. Also, we demonstrate that the proposed model has generalization ability to segment new structures that do not exist in training data.

Protocols for Subtomogram Averaging of Membrane Proteins in the Dynamo Software Package

Cryo-electron tomography allows low-resolution three-dimensional (3D) viewing of cellular organelles and macromolecular complexes present as multiple copies within a tomogram. These structures are computationally extracted and averaged in order to obtain high-resolution 3D structures, and provide a map of their spatial distribution and interaction with their biological microenvironment. To do so, we apply the user-friendly Dynamo software package on a tomographic data set. Dynamo acts as a modular toolbox adaptable to different biological scenarios, allowing a custom designed pipeline for subtomogram averaging. Here, we use as a textbook example the mitochondrial docking site of the positive-strand RNA Flock house nodavirus (FHV) to describe how Dynamo coordinates several basic steps in the subtomogram averaging workflow. Our framework covers specific strategies to deal with additional issues in subtomogram averaging as tomographic data management, 3D surface visualization, automatic assignment of asymmetry and inherent loss of Fourier information in presence of preferential views.

Multi-task Learning for Macromolecule Classification, Segmentation and Coarse Structural Recovery in Cryo-Tomography

Cellular Electron Cryo-Tomography (CECT) is a powerful 3D imaging tool for studying the native structure and organization of macromolecules inside single cells. For systematic recognition and recovery of macromolecular structures captured by CECT, methods for several important tasks such as subtomogram classification and semantic segmentation have been developed. However, the recognition and recovery of macromolecular structures are still very difficult due to high molecular structural diversity, crowding molecular environment, and the imaging limitations of CECT. In this paper, we propose a novel multi-task 3D convolutional neural network model for simultaneous classification, segmentation, and coarse structural recovery of macromolecules of interest in subtomograms. In our model, the learned image features of one task are shared and thereby mutually reinforce the learning of other tasks. Evaluated on realistically simulated and experimental CECT data, our multi-task learning model outperformed all single-task learning methods for classification and segmentation. In addition, we demonstrate that our model can generalize to discover, segment and recover novel structures that do not exist in the training data.

A convolutional autoencoder approach for mining features in cellular electron cryo-tomograms and weakly supervised coarse segmentation

Cellular electron cryo-tomography enables the 3D visualization of cellular organization in the near-native state and at submolecular resolution. However, the contents of cellular tomograms are often complex, making it difficult to automatically isolate different in situ cellular components. In this paper, we propose a convolutional autoencoder-based unsupervised approach to provide a coarse grouping of 3D small subvolumes extracted from tomograms. We demonstrate that the autoencoder can be used for efficient and coarse characterization of features of macromolecular complexes and surfaces, such as membranes. In addition, the autoencoder can be used to detect non-cellular features related to sample preparation and data collection, such as carbon edges from the grid and tomogram boundaries. The autoencoder is also able to detect patterns that may indicate spatial interactions between cellular components. Furthermore, we demonstrate that our autoencoder can be used for weakly supervised semantic segmentation of cellular components, requiring a very small amount of manual annotation.

Deep learning-based subdivision approach for large scale macromolecules structure recovery from electron cryo tomograms

Motivation

Cellular Electron CryoTomography (CECT) enables 3D visualization of cellular organization at near-native state and in sub-molecular resolution, making it a powerful tool for analyzing structures of macromolecular complexes and their spatial organizations inside single cells. However, high degree of structural complexity together with practical imaging limitations makes the systematic de novo discovery of structures within cells challenging. It would likely require averaging and classifying millions of subtomograms potentially containing hundreds of highly heterogeneous structural classes. Although it is no longer difficult to acquire CECT data containing such amount of subtomograms due to advances in data acquisition automation, existing computational approaches have very limited scalability or discrimination ability, making them incapable of processing such amount of data.

Results

To complement existing approaches, in this article we propose a new approach for subdividing subtomograms into smaller but relatively homogeneous subsets. The structures in these subsets can then be separately recovered using existing computation intensive methods. Our approach is based on supervised structural feature extraction using deep learning, in combination with unsupervised clustering and reference-free classification. Our experiments show that, compared with existing unsupervised rotation invariant feature and pose-normalization based approaches, our new approach achieves significant improvements in both discrimination ability and scalability. More importantly, our new approach is able to discover new structural classes and recover structures that do not exist in training data.

Availability and Implementation

Source code freely available at http://www.cs.cmu.edu/∼mxu1/software.

Supplementary information

Supplementary data are available at Bioinformatics online.

Cryo-tomography tilt-series alignment with consideration of the beam-induced sample motion

Recent evidence suggests that the beam-induced motion of the sample during tilt-series acquisition is a major resolution-limiting factor in electron cryo-tomography (cryoET). It causes suboptimal tilt-series alignment and thus deterioration of the reconstruction quality. Here we present a novel approach to tilt-series alignment and tomographic reconstruction that considers the beam-induced sample motion through the tilt-series. It extends the standard fiducial-based alignment approach in cryoET by introducing quadratic polynomials to model the sample motion. The model can be used during reconstruction to yield a motion-compensated tomogram. We evaluated our method on various datasets with different sample sizes. The results demonstrate that our method could be a useful tool to improve the quality of tomograms and the resolution in cryoET.

The Fusion Loops of the Initial Prefusion Conformation of Herpes Simplex Virus 1 Fusion Protein Point Toward the Membrane

All enveloped viruses, including herpesviruses, must fuse their envelope with the host membrane to deliver their genomes into target cells, making this essential step subject to interference by antibodies and drugs. Viral fusion is mediated by a viral surface protein that transits from an initial prefusion conformation to a final postfusion conformation. Strikingly, the prefusion conformation of the herpesvirus fusion protein, gB, is poorly understood. Herpes simplex virus (HSV), a model system for herpesviruses, causes diseases ranging from mild skin lesions to serious encephalitis and neonatal infections. Using cryo-electron tomography and subtomogram averaging, we have characterized the structure of the prefusion conformation and fusion intermediates of HSV-1 gB. To this end, we have set up a system that generates microvesicles displaying full-length gB on their envelope. We confirmed proper folding of gB by nondenaturing electrophoresis-Western blotting with a panel of monoclonal antibodies (MAbs) covering all gB domains. To elucidate the arrangement of gB domains, we labeled them by using (i) mutagenesis to insert fluorescent proteins at specific positions, (ii) coexpression of gB with Fabs for a neutralizing MAb with known binding sites, and (iii) incubation of gB with an antibody directed against the fusion loops. Our results show that gB starts in a compact prefusion conformation with the fusion loops pointing toward the viral membrane and suggest, for the first time, a model for gB’s conformational rearrangements during fusion. These experiments further illustrate how neutralizing antibodies can interfere with the essential gB structural transitions that mediate viral entry and therefore infectivity.

The herpesvirus family includes herpes simplex virus (HSV) and other human viruses that cause lifelong infections and a variety of diseases, like skin lesions, encephalitis, and cancers. As enveloped viruses, herpesviruses must fuse their envelope with the host membrane to start an infection. This process is mediated by a viral surface protein that transitions from an initial conformation (prefusion) to a final, more stable, conformation (postfusion). However, the prefusion conformation of the herpesvirus fusion protein (gB) is poorly understood. To elucidate the structure of the prefusion conformation of HSV type 1 gB, we have employed cryo-electron microscopy to study gB molecules expressed on the surface of vesicles. Using different approaches to label gB’s domains allowed us to model the structures of the prefusion and intermediate conformations of gB. Overall, our findings enhance our understanding of HSV fusion and lay the groundwork for the development of new ways to prevent and block HSV infection.

The Dynamo package for tomography and subtomogram averaging: components for MATLAB, GPU computing and EC2 Amazon Web Services

Dynamo is a package for the processing of tomographic data. As a tool for subtomogram averaging, it includes different alignment and classification strategies. Furthermore, its data-management module allows experiments to be organized in groups of tomograms, while offering specialized three-dimensional tomographic browsers that facilitate visualization, location of regions of interest, modelling and particle extraction in complex geometries. Here, a technical description of the package is presented, focusing on its diverse strategies for optimizing computing performance. Dynamo is built upon mbtools (middle layer toolbox), a general-purpose MATLAB library for object-oriented scientific programming specifically developed to underpin Dynamo but usable as an independent tool. Its structure intertwines a flexible MATLAB codebase with precompiled C++ functions that carry the burden of numerically intensive operations. The package can be delivered as a precompiled standalone ready for execution without a MATLAB license. Multicore parallelization on a single node is directly inherited from the high-level parallelization engine provided for MATLAB, automatically imparting a balanced workload among the threads in computationally intense tasks such as alignment and classification, but also in logistic-oriented tasks such as tomogram binning and particle extraction. Dynamo supports the use of graphical processing units (GPUs), yielding considerable speedup factors both for native Dynamo procedures (such as the numerically intensive subtomogram alignment) and procedures defined by the user through its MATLAB-based GPU library for three-dimensional operations. Cloud-based virtual computing environments supplied with a pre-installed version of Dynamo can be publicly accessed through the Amazon Elastic Compute Cloud (EC2), enabling users to rent GPU computing time on a pay-as-you-go basis, thus avoiding upfront investments in hardware and longterm software maintenance.

Progress and Potential of Electron Cryotomography as Illustrated by Its Application to Bacterial Chemoreceptor Arrays

Electron cryotomography (ECT) can produce three-dimensional images of biological samples such as intact cells in a near-native, frozen-hydrated state to macromolecular resolution (∼4 nm). Because one of its first and most common applications has been to bacterial chemoreceptor arrays, ECT's contributions to this field illustrate well its past, present, and future. While X-ray crystallography and nuclear magnetic resonance spectroscopy have revealed the structures of nearly all the individual components of chemoreceptor arrays, ECT has revealed the mesoscale information about how the components are arranged within cells. Receptors assemble into a universally conserved 12-nm hexagonal lattice linked by CheA/CheW rings. Membrane-bound arrays are single layered; cytoplasmic arrays are double layered. Images of in vitro reconstitutions have led to a model of how arrays assemble, and images of native arrays in different states have shown that the conformational changes associated with signal transduction are subtle, constraining models of activation and system cooperativity. Phase plates, better detectors, and more stable stages promise even higher resolution and broader application in the near future.

Structural Study of Heterogeneous Biological Samples by Cryoelectron Microscopy and Image Processing

In living organisms, biological macromolecules are intrinsically flexible and naturally exist in multiple conformations. Modern electron microscopy, especially at liquid nitrogen temperatures (cryo-EM), is able to visualise biocomplexes in nearly native conditions and in multiple conformational states. The advances made during the last decade in electronic technology and software development have led to the revelation of structural variations in complexes and also improved the resolution of EM structures. Nowadays, structural studies based on single particle analysis (SPA) suggests several approaches for the separation of different conformational states and therefore disclosure of the mechanisms for functioning of complexes. The task of resolving different states requires the examination of large datasets, sophisticated programs, and significant computing power. Some methods are based on analysis of two-dimensional images, while others are based on three-dimensional studies. In this review, we describe the basic principles implemented in the various techniques that are currently used in the analysis of structural conformations and provide some examples of successful applications of these methods in structural studies of biologically significant complexes.

A central cavity within the holo-translocon suggests a mechanism for membrane protein insertion

The conserved SecYEG protein-conducting channel and the accessory proteins SecDF-YajC and YidC constitute the bacterial holo-translocon (HTL), capable of protein-secretion and membrane-protein insertion. By employing an integrative approach combining small-angle neutron scattering (SANS), low-resolution electron microscopy and biophysical analyses we determined the arrangement of the proteins and lipids within the super-complex. The results guided the placement of X-ray structures of individual HTL components and allowed the proposal of a model of the functional translocon. Their arrangement around a central lipid-containing pool conveys an unexpected, but compelling mechanism for membrane-protein insertion. The periplasmic domains of YidC and SecD are poised at the protein-channel exit-site of SecY, presumably to aid the emergence of translocating polypeptides. The SecY lateral gate for membrane-insertion is adjacent to the membrane 'insertase' YidC. Absolute-scale SANS employing a novel contrast-match-point analysis revealed a dynamic complex adopting open and compact configurations around an adaptable central lipid-filled chamber, wherein polytopic membrane-proteins could fold, sheltered from aggregation and proteolysis.

A simple and fast approach for missing-wedge invariant classification of subtomograms extracted from filamentous structures

Unsupervised classification of subtomograms extracted from cryo-electron tomograms is often challenging due to the presence of a missing wedge in tomographic data. Here, we propose a simple new approach to classify subtomograms extracted from cryo-electron tomograms of filamentous objects. This unsupervised classification approach uses the 1D projections of the subtomograms for classification and works independently of the orientations of the missing wedge. We applied this approach to subtomograms from eukaryotic cilia and successfully detected heterogeneity including structural polymorphism of dynein molecules.

Cryo-Electron Tomography and Subtomogram Averaging

Cryo-electron tomography (cryo-ET) allows 3D volumes to be reconstructed from a set of 2D projection images of a tilted biological sample. It allows densities to be resolved in 3D that would otherwise overlap in 2D projection images. Cryo-ET can be applied to resolve structural features in complex native environments, such as within the cell. Analogous to single-particle reconstruction in cryo-electron microscopy, structures present in multiple copies within tomograms can be extracted, aligned, and averaged, thus increasing the signal-to-noise ratio and resolution. This reconstruction approach, termed subtomogram averaging, can be used to determine protein structures in situ. It can also be applied to facilitate more conventional 2D image analysis approaches. In this chapter, we provide an introduction to cryo-ET and subtomogram averaging. We describe the overall workflow, including tomographic data collection, preprocessing, tomogram reconstruction, subtomogram alignment and averaging, classification, and postprocessing. We consider theoretical issues and practical considerations for each step in the workflow, along with descriptions of recent methodological advances and remaining limitations.

Alignment algorithms and per-particle CTF correction for single particle cryo-electron tomography

Single particle cryo-electron tomography (cryoSPT) extracts features from cryo-electron tomograms, followed by 3D classification, alignment and averaging to generate improved 3D density maps of such features. Robust methods to correct for the contrast transfer function (CTF) of the electron microscope are necessary for cryoSPT to reach its resolution potential. Many factors can make CTF correction for cryoSPT challenging, such as lack of eucentricity of the specimen stage, inherent low dose per image, specimen charging, beam-induced specimen motions, and defocus gradients resulting both from specimen tilting and from unpredictable ice thickness variations. Current CTF correction methods for cryoET make at least one of the following assumptions: that the defocus at the center of the image is the same across the images of a tiltseries, that the particles all lie at the same Z-height in the embedding ice, and/or that the specimen, the cryo-electron microscopy (cryoEM) grid and/or the carbon support are flat. These experimental conditions are not always met. We have developed a CTF correction algorithm for cryoSPT without making any of the aforementioned assumptions. We also introduce speed and accuracy improvements and a higher degree of automation to the subtomogram averaging algorithms available in EMAN2. Using motion-corrected images of isolated virus particles as a benchmark specimen, recorded with a DE20 direct detection camera, we show that our CTF correction and subtomogram alignment routines can yield subtomogram averages close to 4/5 Nyquist frequency of the detector under our experimental conditions.

Identification of an HIV-1 Mutation in Spacer Peptide 1 That Stabilizes the Immature CA-SP1 Lattice

Upon release of HIV-1 particles from the infected cell, the viral protease cleaves the Gag polyprotein at specific sites, triggering maturation. During this process, which is essential for infectivity, the capsid protein (CA) reassembles into a conical core. Maturation inhibitors (MIs) block HIV-1 maturation by interfering with protease-mediated CA-spacer peptide 1 (CA-SP1) processing, concomitantly stabilizing the immature CA-SP1 lattice; virions from MI-treated cells retain an immature-like CA-SP1 lattice, whereas mutational abolition of cleavage at the CA-SP1 site results in virions in which the CA-SP1 lattice converts to a mature-like form. We previously reported that propagation of HIV-1 in the presence of MI PF-46396 selected for assembly-defective, compound-dependent mutants with amino acid substitutions in the major homology region (MHR) of CA. Propagation of these mutants in the absence of PF-46396 resulted in the acquisition of second-site compensatory mutations. These included a Thr-to-Ile substitution at SP1 residue 8 (T8I), which results in impaired CA-SP1 processing. Thus, the T8I mutation phenocopies PF-46396 treatment in terms of its ability to rescue the replication defect imposed by the MHR mutations and to impede CA-SP1 processing. Here, we use cryo-electron tomography to show that, like MIs, the T8I mutation stabilizes the immature-like CA-SP1 lattice. These results have important implications for the mechanism of action of HIV-1 MIs; they also suggest that T8I may provide a valuable tool for structural definition of the CA-SP1 boundary region, which has thus far been refractory to high-resolution analysis, apparently because of conformational flexibility in this region of Gag.

IMPORTANCE

HIV-1 maturation involves dissection of the Gag polyprotein by the viral protease and assembly of a conical capsid enclosing the viral ribonucleoprotein. Maturation inhibitors (MIs) prevent the final cleavage step at the site between the capsid protein (CA) and spacer peptide 1 (SP1), apparently by binding at this site and denying the protease access. Additionally, MIs stabilize the immature-like CA-SP1 lattice, preventing release of CA into the soluble pool. We previously found that T8I, a mutation in SP1, rescues a PF-46396-dependent CA mutant and blocks CA-SP1 cleavage. In this study, we imaged T8I virions by cryo-electron tomography and showed that T8I mutants, like MI-treated virions, contain an immature CA-SP1 lattice. These results lay the groundwork needed to understand the structure of the CA-SP1 interface region and further illuminate the mechanism of action of MIs.

Single particle tomography in EMAN2

Single particle tomography (SPT or subtomogram averaging) offers a powerful alternative to traditional 2-D single particle reconstruction for studying conformationally or compositionally heterogeneous macromolecules. It can also provide direct observation (without labeling or staining) of complexes inside cells at nanometer resolution. The development of computational methods and tools for SPT remains an area of active research. Here we present the EMAN2.1 SPT toolbox, which offers a full SPT processing pipeline, from particle picking to post-alignment analysis of subtomogram averages, automating most steps. Different algorithm combinations can be applied at each step, providing versatility and allowing for procedural cross-testing and specimen-specific strategies. Alignment methods include all-vs-all, binary tree, iterative single-model refinement, multiple-model refinement, and self-symmetry alignment. An efficient angular search, Graphic Processing Unit (GPU) acceleration and both threaded and distributed parallelism are provided to speed up processing. Finally, automated simulations, per particle reconstruction of subtiltseries, and per-particle Contrast Transfer Function (CTF) correction have been implemented. Processing examples using both real and simulated data are shown for several structures.