De Novo Structural Pattern Mining in Cellular Electron Cryotomograms

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

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

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.

Advances in cellular and molecular predatory biology of Bdellovibrio bacteriovorus six decades after discovery

Since its discovery six decades ago, the predatory bacterium Bdellovibrio bacteriovorus has sparked recent interest as a potential remedy to the antibiotic resistance crisis. Here we give a comprehensive historical overview from discovery to progressive developments in microscopy and molecular mechanisms. Research on B. bacteriovorus has moved from curiosity to a new model organism, revealing over time more details on its physiology and fascinating predatory life cycle with the help of a variety of methods. Based on recent findings in cryo-electron tomography, we recapitulate on the intricate molecular details known in the predatory life cycle including how this predator searches for its prey bacterium, to how it attaches, grows, and divides all from within the prey cell. Finally, the newly developed B. bacteriovorus progeny leave the prey cell remnants in the exit phase. While we end with some unanswered questions remaining in the field, new imaging technologies and quantitative, systematic advances will likely help to unravel them in the next decades.

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.

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

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

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

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

Isotropic reconstruction for electron tomography with deep learning

Cryogenic electron tomography (cryoET) allows visualization of cellular structures in situ. However, anisotropic resolution arising from the intrinsic "missing-wedge" problem has presented major challenges in visualization and interpretation of tomograms. Here, we have developed IsoNet, a deep learning-based software package that iteratively reconstructs the missing-wedge information and increases signal-to-noise ratio, using the knowledge learned from raw tomograms. Without the need for sub-tomogram averaging, IsoNet generates tomograms with significantly reduced resolution anisotropy. Applications of IsoNet to three representative types of cryoET data demonstrate greatly improved structural interpretability: resolving lattice defects in immature HIV particles, establishing architecture of the paraflagellar rod in Eukaryotic flagella, and identifying heptagon-containing clathrin cages inside a neuronal synapse of cultured cells. Therefore, by overcoming two fundamental limitations of cryoET, IsoNet enables functional interpretation of cellular tomograms without sub-tomogram averaging. Its application to high-resolution cellular tomograms should also help identify differently oriented complexes of the same kind for sub-tomogram averaging.

Quantitative Cryo-Electron Tomography

The three-dimensional organization of biomolecules important for the functioning of all living systems can be determined by cryo-electron tomography imaging under native biological contexts. Cryo-electron tomography is continually expanding and evolving, and the development of new methods that use the latest technology for sample thinning is enabling the visualization of ever larger and more complex biological systems, allowing imaging across scales. Quantitative cryo-electron tomography possesses the capability of visualizing the impact of molecular and environmental perturbations in subcellular structure and function to understand fundamental biological processes. This review provides an overview of current hardware and software developments that allow quantitative cryo-electron tomography studies and their limitations and how overcoming them may allow us to unleash the full power of cryo-electron tomography.

FSCC: Few-Shot Learning for Macromolecule Classification Based on Contrastive Learning and Distribution Calibration in Cryo-Electron Tomography

Cryo-electron tomography (Cryo-ET) is an emerging technology for three-dimensional (3D) visualization of macromolecular structures in the near-native state. To recover structures of macromolecules, millions of diverse macromolecules captured in tomograms should be accurately classified into structurally homogeneous subsets. Although existing supervised deep learning–based methods have improved classification accuracy, such trained models have limited ability to classify novel macromolecules that are unseen in the training stage. To adapt the trained model to the macromolecule classification of a novel class, massive labeled macromolecules of the novel class are needed. However, data labeling is very time-consuming and labor-intensive. In this work, we propose a novel few-shot learning method for the classification of novel macromolecules (named FSCC). A two-stage training strategy is designed in FSCC to enhance the generalization ability of the model to novel macromolecules. First, FSCC uses contrastive learning to pre-train the model on a sufficient number of labeled macromolecules. Second, FSCC uses distribution calibration to re-train the classifier, enabling the model to classify macromolecules of novel classes (unseen class in the pre-training). Distribution calibration transfers learned knowledge in the pre-training stage to novel macromolecules with limited labeled macromolecules of novel class. Experiments were performed on both synthetic and real datasets. On the synthetic datasets, compared with the state-of-the-art (SOTA) method based on supervised deep learning, FSCC achieves competitive performance. To achieve such performance, FSCC only needs five labeled macromolecules per novel class. However, the SOTA method needs 1100 ∼ 1500 labeled macromolecules per novel class. On the real datasets, FSCC improves the accuracy by 5% ∼ 16% when compared to the baseline model. These demonstrate good generalization ability of contrastive learning and calibration distribution to classify novel macromolecules with very few labeled macromolecules.

Better, Faster, Cheaper: Recent Advances in Cryo-Electron Microscopy

Cryo-electron microscopy (cryo-EM) continues its remarkable growth as a method for visualizing biological objects, which has been driven by advances across the entire pipeline. Developments in both single-particle analysis and in situ tomography have enabled more structures to be imaged and determined to better resolutions, at faster speeds, and with more scientists having improved access. This review highlights recent advances at each stageof the cryo-EM pipeline and provides examples of how these techniques have been used to investigate real-world problems, including antibody development against the SARS-CoV-2 spike during the recent COVID-19 pandemic.

Harmony: A Generic Unsupervised Approach for Disentangling Semantic Content from Parameterized Transformations

In many real-life image analysis applications, particularly in biomedical research domains, the objects of interest undergo multiple transformations that alters their visual properties while keeping the semantic content unchanged. Disentangling images into semantic content factors and transformations can provide significant benefits into many domain-specific image analysis tasks. To this end, we propose a generic unsupervised framework, Harmony, that simultaneously and explicitly disentangles semantic content from multiple parameterized transformations. Harmony leverages a simple cross-contrastive learning framework with multiple explicitly parameterized latent representations to disentangle content from transformations. To demonstrate the efficacy of Harmony, we apply it to disentangle image semantic content from several parameterized transformations (rotation, translation, scaling, and contrast). Harmony achieves significantly improved disentanglement over the baseline models on several image datasets of diverse domains. With such disentanglement, Harmony is demonstrated to incentivize bioimage analysis research by modeling structural heterogeneity of macromolecules from cryo-ET images and learning transformation-invariant representations of protein particles from single-particle cryo-EM images. Harmony also performs very well in disentangling content from 3D transformations and can perform coarse and fast alignment of 3D cryo-ET subtomograms. Therefore, Harmony is generalizable to many other imaging domains and can potentially be extended to domains beyond imaging as well.

How much metagenome data is needed for protein structure prediction: The advantages of targeted approach from the ecological and evolutionary perspectives

It has been proven that three-dimensional protein structures could be modeled by supplementing homologous sequences with metagenome sequences. Even though a large volume of metagenome data is utilized for such purposes, a significant proportion of proteins remain unsolved. In this review, we focus on identifying ecological and evolutionary patterns in metagenome data, decoding the complicated relationships of these patterns with protein structures, and investigating how these patterns can be effectively used to improve protein structure prediction. First, we proposed the metagenome utilization efficiency and marginal effect model to quantify the divergent distribution of homologous sequences for the protein family. Second, we proposed that the targeted approach effectively identifies homologous sequences from specified biomes compared with the untargeted approach's blind search. Finally, we determined the lower bound for metagenome data required for predicting all the protein structures in the Pfam database and showed that the present metagenome data is insufficient for this purpose. In summary, we discovered ecological and evolutionary patterns in the metagenome data that may be used to predict protein structures effectively. The targeted approach is promising in terms of effectively extracting homologous sequences and predicting protein structures using these patterns.

The Human Proteoform Project: Defining the human proteome

Proteins are the primary effectors of function in biology, and thus, complete knowledge of their structure and properties is fundamental to deciphering function in basic and translational research. The chemical diversity of proteins is expressed in their many proteoforms, which result from combinations of genetic polymorphisms, RNA splice variants, and posttranslational modifications. This knowledge is foundational for the biological complexes and networks that control biology yet remains largely unknown. We propose here an ambitious initiative to define the human proteome, that is, to generate a definitive reference set of the proteoforms produced from the genome. Several examples of the power and importance of proteoform-level knowledge in disease-based research are presented along with a call for improved technologies in a two-pronged strategy to the Human Proteoform Project.

UNSUPERVISED DOMAIN ALIGNMENT BASED OPEN SET STRUCTURAL RECOGNITION OF MACROMOLECULES CAPTURED BY CRYO-ELECTRON TOMOGRAPHY

Cellular cryo-Electron Tomography (cryo-ET) provides three-dimensional views of structural and spatial information of various macromolecules in cells in a near-native state. Subtomogram classification is a key step for recognizing and differentiating these macromolecular structures. In recent years, deep learning methods have been developed for high-throughput subtomogram classification tasks; however, conventional supervised deep learning methods cannot recognize macromolecular structural classes that do not exist in the training data. This imposes a major weakness since most native macromolecular structures in cells are unknown and consequently, cannot be included in the training data. Therefore, open set learning which can recognize unknown macromolecular structures is necessary for boosting the power of automatic subtomogram classification. In this paper, we propose a method called Margin-based Loss for Unsupervised Domain Alignment (MLUDA) for open set recognition problems where only a few categories of interest are shared between cross-domain data. Through extensive experiments, we demonstrate that MLUDA performs well at cross-domain open-set classification on both public datasets and medical imaging datasets. So our method is of practical importance.

Coming of Age: Cryo-Electron Tomography as a Versatile Tool to Generate High-Resolution Structures at Cellular/Biological Interfaces

Over the last few years, cryo electron microscopy has become the most important method in structural biology. While 80% of deposited maps are from single particle analysis, electron tomography has grown to become the second most important method. In particular sub-tomogram averaging has matured as a method, delivering structures between 2 and 5 Å from complexes in cells as well as in vitro complexes. While this resolution range is not standard, novel developments point toward a promising future. Here, we provide a guide for the workflow from sample to structure to gain insight into this emerging field.

An overview of the recent advances in cryo-electron microscopy for life sciences

Cryo-electron microscopy (CryoEM) has superseded X-ray crystallography and NMR to emerge as a popular and effective tool for structure determination in recent times. It has become indispensable for the characterization of large macromolecular assemblies, membrane proteins, or samples that are limited, conformationally heterogeneous, and recalcitrant to crystallization. Besides, it is the only tool capable of elucidating high-resolution structures of macromolecules and biological assemblies in situ. A state-of-the-art electron microscope operable at cryo-temperature helps preserve high-resolution details of the biological sample. The structures can be determined, either in isolation via single-particle analysis (SPA) or helical reconstruction, electron diffraction (ED) or within the cellular environment via cryo-electron tomography (cryoET). All the three streams of SPA, ED, and cryoET (along with subtomogram averaging) have undergone significant advancements in recent times. This has resulted in breaking the boundaries with respect to both the size of the macromolecules/assemblies whose structures could be determined along with the visualization of atomic details at resolutions unprecedented for cryoEM. In addition, the collection of larger datasets combined with the ability to sort and process multiple conformational states from the same sample are providing the much-needed link between the protein structures and their functions. In overview, these developments are helping scientists decipher the molecular mechanism of critical cellular processes, solve structures of macromolecules that were challenging targets for structure determination until now, propelling forward the fields of biology and biomedicine. Here, we summarize recent advances and key contributions of the three cryo-electron microscopy streams of SPA, ED, and cryoET.

Detecting Protein Communities in Native Cell Extracts by Machine Learning: A Structural Biologist's Perspective

Native cell extracts hold great promise for understanding the molecular structure of ordered biological systems at high resolution. This is because higher-order biomolecular interactions, dubbed as protein communities, may be retained in their (near-)native state, in contrast to extensively purifying or artificially overexpressing the proteins of interest. The distinct machine-learning approaches are applied to discover protein-protein interactions within cell extracts, reconstruct dedicated biological networks, and report on protein community members from various organisms. Their validation is also important, e.g., by the cross-linking mass spectrometry or cell biology methods. In addition, the cell extracts are amenable to structural analysis by cryo-electron microscopy (cryo-EM), but due to their inherent complexity, sorting structural signatures of protein communities derived by cryo-EM comprises a formidable task. The application of image-processing workflows inspired by machine-learning techniques would provide improvements in distinguishing structural signatures, correlating proteomic and network data to structural signatures and subsequently reconstructed cryo-EM maps, and, ultimately, characterizing unidentified protein communities at high resolution. In this review article, we summarize recent literature in detecting protein communities from native cell extracts and identify the remaining challenges and opportunities. We argue that the progress in, and the integration of, machine learning, cryo-EM, and complementary structural proteomics approaches would provide the basis for a multi-scale molecular description of protein communities within native cell extracts.

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.

A unified framework for packing deformable and non-deformable subcellular structures in crowded cryo-electron tomogram simulation

BACKGROUND

Cryo-electron tomography is an important and powerful technique to explore the structure, abundance, and location of ultrastructure in a near-native state. It contains detailed information of all macromolecular complexes in a sample cell. However, due to the compact and crowded status, the missing edge effect, and low signal to noise ratio (SNR), it is extremely challenging to recover such information with existing image processing methods. Cryo-electron tomogram simulation is an effective solution to test and optimize the performance of the above image processing methods. The simulated images could be regarded as the labeled data which covers a wide range of macromolecular complexes and ultrastructure. To approximate the crowded cellular environment, it is very important to pack these heterogeneous structures as tightly as possible. Besides, simulating non-deformable and deformable components under a unified framework also need to be achieved.

RESULT

In this paper, we proposed a unified framework for simulating crowded cryo-electron tomogram images including non-deformable macromolecular complexes and deformable ultrastructures. A macromolecule was approximated using multiple balls with fixed relative positions to reduce the vacuum volume. A ultrastructure, such as membrane and filament, was approximated using multiple balls with flexible relative positions so that this structure could deform under force field. In the experiment, 400 macromolecules of 20 representative types were packed into simulated cytoplasm by our framework, and numerical verification proved that our method has a smaller volume and higher compression ratio than the baseline single-ball model. We also packed filaments, membranes and macromolecules together, to obtain a simulated cryo-electron tomogram image with deformable structures. The simulated results are closer to the real Cryo-ET, making the analysis more difficult. The DOG particle picking method and the image segmentation method are tested on our simulation data, and the experimental results show that these methods still have much room for improvement.

CONCLUSION

The proposed multi-ball model can achieve more crowded packaging results and contains richer elements with different properties to obtain more realistic cryo-electron tomogram simulation. This enables users to simulate cryo-electron tomogram images with non-deformable macromolecular complexes and deformable ultrastructures under a unified framework. To illustrate the advantages of our framework in improving the compression ratio, we calculated the volume of simulated macromolecular under our multi-ball method and traditional single-ball method. We also performed the packing experiment of filaments and membranes to demonstrate the simulation ability of deformable structures. Our method can be used to do a benchmark by generating large labeled cryo-ET dataset and evaluating existing image processing methods. Since the content of the simulated cryo-ET is more complex and crowded compared with previous ones, it will pose a greater challenge to existing image processing methods.

The In Situ Structure of Parkinson's Disease-Linked LRRK2

Mutations in leucine-rich repeat kinase 2 (LRRK2) are the most frequent cause of familial Parkinson's disease. LRRK2 is a multi-domain protein containing a kinase and GTPase. Using correlative light and electron microscopy, in situ cryo-electron tomography, and subtomogram analysis, we reveal a 14-Å structure of LRRK2 bearing a pathogenic mutation that oligomerizes as a right-handed double helix around microtubules, which are left-handed. Using integrative modeling, we determine the architecture of LRRK2, showing that the GTPase and kinase are in close proximity, with the GTPase closer to the microtubule surface, whereas the kinase is exposed to the cytoplasm. We identify two oligomerization interfaces mediated by non-catalytic domains. Mutation of one of these abolishes LRRK2 microtubule-association. Our work demonstrates the power of cryo-electron tomography to generate models of previously unsolved structures in their cellular environment.

Towards high-throughput in situ structural biology using electron cryotomography

Electron cryotomography is a rapidly evolving method for imaging macromolecules directly within the native environment of cells and tissues. Combined with sub-tomogram averaging, it allows structural and cell biologists to obtain sub-nanometre resolution structures in situ. However, low throughput in cryo-ET sample preparation and data acquisition, as well as difficulties in target localisation and sub-tomogram averaging image processing, limit its widespread usability. In this review, we discuss new advances in the field that address these throughput and technical problems. We focus on recent efforts made to resolve issues in sample thinning, improvement in data collection speed at the microscope, strategies for localisation of macromolecules using correlated light and electron microscopy and advancements made to improve resolution in sub-tomogram averaging. These advances will considerably decrease the amount of time and effort required for cryo-ET and sub-tomogram averaging, ushering in a new era of structural biology where in situ macromolecular structure determination will be routine.

Gum-Net: Unsupervised Geometric Matching for Fast and Accurate 3D Subtomogram Image Alignment and Averaging

We propose a Geometric unsupervised matching Network (Gum-Net) for finding the geometric correspondence between two images with application to 3D subtomogram alignment and averaging. Subtomogram alignment is the most important task in cryo-electron tomography (cryo-ET), a revolutionary 3D imaging technique for visualizing the molecular organization of unperturbed cellular landscapes in single cells. However, subtomogram alignment and averaging are very challenging due to severe imaging limits such as noise and missing wedge effects. We introduce an end-to-end trainable architecture with three novel modules specifically designed for preserving feature spatial information and propagating feature matching information. The training is performed in a fully unsupervised fashion to optimize a matching metric. No ground truth transformation information nor category-level or instance-level matching supervision information is needed. After systematic assessments on six real and nine simulated datasets, we demonstrate that Gum-Net reduced the alignment error by 40 to 50% and improved the averaging resolution by 10%. Gum-Net also achieved 70 to 110 times speedup in practice with GPU acceleration compared to state-of-the-art subtomogram alignment methods. Our work is the first 3D unsupervised geometric matching method for images of strong transformation variation and high noise level. The training code, trained model, and datasets are available in our open-source software AITom.

A simulated annealing approach for resolution guided homogeneous cryo-electron microscopy image selection

BACKGROUND

Cryo-electron microscopy (Cryo-EM) and tomography (Cryo-ET) have emerged as important imaging techniques for studying structures of macromolecular complexes. In 3D reconstruction of large macromolecular complexes, many 2D projection images of macromolecular complex particles are usually acquired with low signal-to-noise ratio. Therefore, it is meaningful to select multiple images containing the same structure with identical orientation. The selected images are averaged to produce a higher-quality representation of the underlying structure with improved resolution. Existing approaches of selecting such images have limited accuracy and speed.

METHODS

We propose a simulated annealing-based algorithm (SA) to pick the homogeneous image set with best average. Its performance is compared with two baseline methods based on both 2D and 3D datasets. When tested on simulated and experimental 3D Cryo-ET images of Ribosome complex, SA sometimes stopped at a local optimal solution. Restarting is applied to settle this difficulty and significantly improved the performance of SA on 3D datasets.

RESULTS

Experimented on simulated and experimental 2D Cryo-EM images of Ribosome complex datasets respectively with SNR = 10 and SNR = 0.5, our method achieved better accuracy in terms of F-measure, resolution score, and time cost than two baseline methods. Additionally, SA shows its superiority when the proportion of homogeneous images decreases.

CONCLUSIONS

SA is introduced for homogeneous image selection to realize higher accuracy with faster processing speed. Experiments on both simulated and real 2D Cryo-EM and 3D Cryo-ET images demonstrated that SA achieved expressively better performance. This approach serves as an important step for improving the resolution of structural recovery of macromolecular complexes captured by Cryo-EM and Cryo-ET.

Visualizing in Vivo Dynamics of Designer Nanoscaffolds

Enzymes of natural biochemical pathways are routinely subcellularly organized in space and time in order to improve pathway efficacy and control. Designer scaffolding platforms are under development to confer similar benefits upon engineered pathways. Herein, we evaluate bacterial microcompartment shell (pfam0936-domain) proteins as modules for constructing well-defined nanometer scale scaffolds in vivo. We use a suite of visualization techniques to evaluate scaffold assembly and dynamics. We demonstrate recruitment of target cargo molecules onto assembled scaffolds by appending reciprocally interacting adaptor domains. These interactions can be refined by fine-tuning the scaffold expression level. Real-time observation of this system reveals a nucleation-limited step where multiple scaffolds initially form within a cell. Over time, nucleated scaffolds reorganize into a single intracellular assembly, likely due to interscaffold competition for protein subunits. Our results suggest design considerations for using self-assembling proteins as building blocks to construct nanoscaffolds, while also providing a platform to visualize scaffold-cargo dynamics in vivo.

Template-free detection and classification of membrane-bound complexes in cryo-electron tomograms

With faithful sample preservation and direct imaging of fully hydrated biological material, cryo-electron tomography provides an accurate representation of molecular architecture of cells. However, detection and precise localization of macromolecular complexes within cellular environments is aggravated by the presence of many molecular species and molecular crowding. We developed a template-free image processing procedure for accurate tracing of complex networks of densities in cryo-electron tomograms, a comprehensive and automated detection of heterogeneous membrane-bound complexes and an unsupervised classification (PySeg). Applications to intact cells and isolated endoplasmic reticulum (ER) allowed us to detect and classify small protein complexes. This classification provided sufficiently homogeneous particle sets and initial references to allow subsequent de novo subtomogram averaging. Spatial distribution analysis showed that ER complexes have different localization patterns forming nanodomains. Therefore, this procedure allows a comprehensive detection and structural analysis of complexes in situ.

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.

Improved applicability and robustness of fast cryo-electron tomography data acquisition

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Tilt series acquisition in less than 5 min per target.

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Robust compensation of specimen shifts in x, y and z.

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Applicability to new (single-tilt axis) and old (dual-tilt axis) microscope stages.

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Sub-nanometer subtomogram average with data collected in <50 min.

The power of cryo-electron tomography (cryoET) lies in its capability to characterize macromolecules in their cellular context. Structure determination by cryoET, however, is time-consuming compared to single particle approaches. A recent study reported significant acceleration of data acquisition by a fast-incremental single-exposure (FISE) tilt series scheme.

Here we improved the method and evaluated its efficiency and performance. We show that (1) FISE combined with the latest generation of direct electron detectors speeds up collection considerably, (2) previous generation (pre-2017) double-tilt axis Titan Krios holders are also suitable for FISE data acquisition, (3) x, y and z-specimen shifts can be compensated for, and (4) FISE tilt series data can generate averages of sub-nanometer resolution.

These advances will allow for a widespread adoption of cryoET for high-throughput in situ studies and high-resolution structure determination across different biological research disciplines.

Adversarial domain adaptation for cross data source macromolecule in situ structural classification in cellular electron cryo-tomograms

MOTIVATION

Since 2017, an increasing amount of attention has been paid to the supervised deep learning-based macromolecule in situ structural classification (i.e. subtomogram classification) in cellular electron cryo-tomography (CECT) due to the substantially higher scalability of deep learning. However, the success of such supervised approach relies heavily on the availability of large amounts of labeled training data. For CECT, creating valid training data from the same data source as prediction data is usually laborious and computationally intensive. It would be beneficial to have training data from a separate data source where the annotation is readily available or can be performed in a high-throughput fashion. However, the cross data source prediction is often biased due to the different image intensity distributions (a.k.a. domain shift).

RESULTS

We adapt a deep learning-based adversarial domain adaptation (3D-ADA) method to timely address the domain shift problem in CECT data analysis. 3D-ADA first uses a source domain feature extractor to extract discriminative features from the training data as the input to a classifier. Then it adversarially trains a target domain feature extractor to reduce the distribution differences of the extracted features between training and prediction data. As a result, the same classifier can be directly applied to the prediction data. We tested 3D-ADA on both experimental and realistically simulated subtomogram datasets under different imaging conditions. 3D-ADA stably improved the cross data source prediction, as well as outperformed two popular domain adaptation methods. Furthermore, we demonstrate that 3D-ADA can improve cross data source recovery of novel macromolecular structures.

AVAILABILITY AND IMPLEMENTATION

https://github.com/xulabs/projects.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

A primer on resolving the nanoscale structure of the plasma membrane with light and electron microscopy

Taraska reviews the imaging methods that are being used to understand the structure of the plasma membrane at the molecular level.

The plasma membrane separates a cell from its external environment. All materials and signals that enter or leave the cell must cross this hydrophobic barrier. Understanding the architecture and dynamics of the plasma membrane has been a central focus of general cellular physiology. Both light and electron microscopy have been fundamental in this endeavor and have been used to reveal the dense, complex, and dynamic nanoscale landscape of the plasma membrane. Here, I review classic and recent developments in the methods used to image and study the structure of the plasma membrane, particularly light, electron, and correlative microscopies. I will discuss their history and use for mapping the plasma membrane and focus on how these tools have provided a structural framework for understanding the membrane at the scale of molecules. Finally, I will describe how these studies provide a roadmap for determining the nanoscale architecture of other organelles and entire cells in order to bridge the gap between cellular form and function.

A collection of yeast cellular electron cryotomography data

BACKGROUND

Cells are powered by a large set of macromolecular complexes, which work together in a crowded environment. The in situ mechanisms of these complexes are unclear because their 3D distribution, organization, and interactions are largely unknown. Electron cryotomography (cryo-ET) can address these knowledge gaps because it produces cryotomograms-3D images that reveal biological structure at ∼4-nm resolution. Cryo-ET uses no fixation, dehydration, staining, or plastic embedment, so cellular features are visualized in a life-like, frozen-hydrated state. To study chromatin and mitotic machinery in situ, we subjected yeast cells to genetic and chemical perturbations, cryosectioned them, and then imaged the cells by cryo-ET.

FINDINGS

Here we share >1,000 cryo-ET raw datasets of cryosectioned budding yeast Saccharomyces cerevisiaecollected as part of previously published studies. These data will be valuable to cell biologists who are interested in the nanoscale organization of yeasts and of eukaryotic cells in general. All the unpublished tilt series and a subset of corresponding cryotomograms have been deposited in the EMPIAR resource for the community to use freely. To improve tilt series discoverability, we have uploaded metadata and preliminary notes to publicly accessible Google Sheets, EMPIAR, and GigaDB.

CONCLUSIONS

Cellular cryo-ET data can be mined to obtain new cell-biological, structural, and 3D statistical insights in situ. These data contain structures not visible in traditional electron-microscopy data. Template matching and subtomogram averaging of known macromolecular complexes can reveal their 3D distributions and low-resolution structures. Furthermore, these data can serve as testbeds for high-throughput image-analysis pipelines, as training sets for feature-recognition software, for feasibility analysis when planning new structural-cell-biology projects, and as practice data for students.