cryo-ET Imaging Research Articles

Large attachment organelle mediates interaction between Nanobdellota archaeon YN1 and its host.

DPANN archaea are an enigmatic superphylum that are difficult to isolate and culture in the laboratory due to their specific culture conditions and apparent ectosymbiotic lifestyle. Here, we successfully isolated and cultivated a coculture system of a novel Nanobdellota archaeon YN1 and its host Sulfurisphaera ohwakuensis YN1HA. We characterized the coculture system by complementary methods, including metagenomics and metabolic pathway analysis, fluorescence microscopy, and high-resolution electron cryo-tomography (cryoET). We show that YN1 is deficient in essential metabolic processes and requires host resources to proliferate. CryoET imaging revealed an enormous attachment organelle present in the YN1 envelope that forms a direct interaction with the host cytoplasm, bridging the two cells. Together, our results unravel the molecular and structural basis of ectosymbiotic relationship between YN1 and YN1HA. This research broadens our understanding of DPANN biology and the versatile nature oftheir ectosymbiotic relationships.

CryoSamba: self-supervised deep volumetric denoising for cryo-electron tomography data.

Cryogenic electron tomography (cryo-ET) has rapidly advanced as a high-resolution imaging tool for visualizing subcellular structures in 3D with molecular detail. Direct image inspection remains challenging due to inherent low signal-to-noise ratios (SNR). We introduce CryoSamba, a self-supervised deep learning-based model designed for denoising cryo-ET images. CryoSamba enhances single consecutive 2D planes in tomograms by averaging motion-compensated nearby planes through deep learning interpolation, effectively mimicking increased exposure. This approach amplifies coherent signals and reduces high-frequency noise, substantially improving tomogram contrast and SNR. CryoSamba operates on 3D volumes without needing pre-recorded images, synthetic data, labels or annotations, noise models, or paired volumes. CryoSamba suppresses high-frequency information less aggressively than do existing cryo-ET denoising methods, while retaining real information, as shown both by visual inspection and by Fourier shell correlation analysis of icosahedrally symmetric virus particles. Thus, CryoSamba enhances the analytical pipeline for direct 3D tomogram visual interpretation.

Honeycomb gold specimen supports enabling orthogonal focussed ion beam-milling of elongated cells for cryo-ET

Cryo-focussed ion beam (FIB)-milling is a powerful technique that opens up thick, cellular specimens to high-resolution structural analysis by electron cryotomography (cryo-ET). FIB-milled lamellae can be produced from cells on grids, or cut from thicker, high-pressure frozen specimens. However, these approaches can put geometrical constraints on the specimen that may be unhelpful, particularly when imaging structures within the cell that have a very defined orientation. For example, plunge frozen rod-shaped bacteria orient parallel to the plane of the grid, yet the Z-ring, a filamentous structure of the tubulin-like protein FtsZ and the key organiser of bacterial division, runs around the circumference of the cell such that it is perpendicular to the imaging plane. It is therefore difficult or impractical to image many complete rings with current technologies. To circumvent this problem, we have fabricated monolithic gold specimen supports with a regular array of cylindrical wells in a honeycomb geometry, which trap bacteria in a vertical orientation. These supports, which we call “honeycomb gold discs”, replace standard EM grids and when combined with FIB-milling enable the production of lamellae containing cross-sections through cells. The resulting lamellae are more stable and resistant to breakage and charging than conventional lamellae. The design of the honeycomb discs can be modified according to need and so will also enable cryo-ET and cryo-EM imaging of other specimens in otherwise difficult to obtain orientations.

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

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

Visualizing the membrane disruption action of antimicrobial peptides by cryo-electron tomography

The abuse of antibiotics has led to the emergence of multidrug-resistant microbial pathogens, presenting a pressing challenge in global healthcare. Membrane-disrupting antimicrobial peptides (AMPs) combat so-called superbugs via mechanisms different than conventional antibiotics and have good application prospects in medicine, agriculture, and the food industry. However, the mechanism-of-action of AMPs has not been fully characterized at the cellular level due to a lack of high-resolution imaging technologies that can capture cellular-membrane disruption events in the hydrated state. Previously, we reported PepD2M, a de novo-designed AMP with potent and wide-spectrum bactericidal and fungicidal activity. In this study, we use cryo-electron tomography (cryo-ET) and high-speed atomic force microscopy (HS-AFM) to directly visualize the pepD2M-induced disruption of the outer and inner membranes of the Gram-negative bacterium Escherichia coli, and compared with a well-known pore-forming peptide, melittin. Our high-resolution cryo-ET images reveal how pepD2M disrupts the E. coli membrane using a carpet/detergent-like mechanism. Our studies reveal the direct membrane-disrupting consequence of AMPs on the bacterial membrane by cryo-ET, and this information provides critical insights into the mechanisms of this class of antimicrobial agents.

A cryogenic, coincident fluorescence, electron, and ion beam microscope.

Cryogenic electron tomography (cryo-ET) combined with subtomogram averaging, allows in situ visualization and structure determination of macromolecular complexes at subnanometre resolution. Cryogenic focused ion beam (cryo-FIB) micromachining is used to prepare a thin lamella-shaped sample out of a frozen-hydrated cell for cryo-ET imaging, but standard cryo-FIB fabrication is blind to the precise location of the structure or proteins of interest. Fluorescence-guided focused ion beam (FIB) milling at target locations requires multiple sample transfers prone to contamination, and relocation and registration accuracy is often insufficient for 3D targeting. Here, we present in situ fluorescence microscopy-guided FIB fabrication of a frozen-hydrated lamella to address this problem: we built a coincident three-beam cryogenic correlative microscope by retrofitting a compact cryogenic microcooler, custom positioning stage, and an inverted widefield fluorescence microscope (FM) on an existing FIB scanning electron microscope. We show FM controlled targeting at every milling step in the lamella fabrication process, validated with transmission electron microscope tomogram reconstructions of the target regions. The ability to check the lamella during and after the milling process results in a higher success rate in the fabrication process and will increase the throughput of fabrication for lamellae suitable for high-resolution imaging.

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.

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.

Statistical spatial analysis for cryo-electron tomography

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

Knowledge Transfer from Macro-world to Micro-world: Enhancing 3D Cryo-ET Classification through Fine-Tuning Video-based Deep Models.

Deep learning models have achieved remarkable success in a wide range of natural-world tasks, such as vision, language, and speech recognition. These accomplishments are largely attributed to the availability of open-source large-scale datasets. More importantly, pre-trained foundational modellearnings exhibit a surprising degree of transferability to downstream tasks, enabling efficient learning even with limited training examples. However, the application of such natural-domain models to the domain of tiny Cryo-Electron Tomography (Cryo-ET) images has been a relatively unexplored frontier. This research is motivated by the intuition that 3D Cryo-ET voxel data can be conceptually viewed as a sequence of progressively evolving video frames. Leveraging the above insight, we propose a novel approach that involves the utilization of 3D models pre-trained on large-scale video datasets to enhance Cryo-ET subtomogram classification. Our experiments, conducted on both simulated and real Cryo-ET datasets, reveal compelling results. The use of video initialization not only demonstrates improvements in classification accuracy but also substantially reduces training costs. Further analyses provide additional evidence of the value of video initialization in enhancing subtomogram feature extraction. Additionally, we observe that video initialization yields similar positive effects when applied to medical 3D classification tasks, underscoring the potential of cross-domain knowledge transfer from video-based models to advance the state-of-the-art in a wide range of biological and medical data types. https://github.com/xulabs/aitom.

Cryo-shift: reducing domain shift in cryo-electron subtomograms with unsupervised domain adaptation and randomization.

Cryo-Electron Tomography (cryo-ET) is a 3D imaging technology that enables the visualization of subcellular structures in situ at near-atomic resolution. Cellular cryo-ET images help in resolving the structures of macromolecules and determining their spatial relationship in a single cell, which has broad significance in cell and structural biology. Subtomogram classification and recognition constitute a primary step in the systematic recovery of these macromolecular structures. Supervised deep learning methods have been proven to be highly accurate and efficient for subtomogram classification, but suffer from limited applicability due to scarcity of annotated data. While generating simulated data for training supervised models is a potential solution, a sizeable difference in the image intensity distribution in generated data as compared with real experimental data will cause the trained models to perform poorly in predicting classes on real subtomograms. In this work, we present Cryo-Shift, a fully unsupervised domain adaptation and randomization framework for deep learning-based cross-domain subtomogram classification. We use unsupervised multi-adversarial domain adaption to reduce the domain shift between features of simulated and experimental data. We develop a network-driven domain randomization procedure with 'warp' modules to alter the simulated data and help the classifier generalize better on experimental data. We do not use any labeled experimental data to train our model, whereas some of the existing alternative approaches require labeled experimental samples for cross-domain classification. Nevertheless, Cryo-Shift outperforms the existing alternative approaches in cross-domain subtomogram classification in extensive evaluation studies demonstrated herein using both simulated and experimental data. https://github.com/xulabs/aitom. Supplementary data are available at Bioinformatics online.

Integrating on-grid immunogold labeling and cryo-electron tomography to reveal photosystem II structure and spatial distribution in thylakoid membranes

A long-standing challenge in cell biology is elucidating the structure and spatial distribution of individual membrane-bound proteins, protein complexes and their interactions in their native environment. Here, we describe a workflow that combines on-grid immunogold labeling, followed by cryo-electron tomography (cryoET) imaging and structural analyses to identify and characterize the structure of photosystem II (PSII) complexes. Using an antibody specific to a core subunit of PSII, the D1 protein (uniquely found in the water splitting complex in all oxygenic photoautotrophs), we identified PSII complexes in biophysically active thylakoid membranes isolated from a model marine diatom Phaeodactylum tricornutum. Subsequent cryoET analyses of these protein complexes resolved two PSII structures: supercomplexes and dimeric cores. Our integrative approach establishes the structural signature of multimeric membrane protein complexes in their native environment and provides a pathway to elucidate their high-resolution structures.

<i>In situ</i> Alphavirus Assembly and Budding Mechanism Revealed by Cellular CryoET

Chikungunya virus (CHIKV) is an alphavirus and the etiological agent for debilitating arthritogenic disease in humans. Previous studies with purified virions or budding mutants have not resolved the structural mechanism of alphavirus assembly in situ. Here we used cryogenic electron tomography (cryoET) imaging of CHIKV-infected human cells and subvolume classification to resolve distinct assembly intermediate conformations. These structures revealed that particle formation is driven by the spike envelope layer. Additionally, we showed that asymmetric immature nucleocapsids (NCs) provide scaffolds to trigger assembly of the icosahedral spike lattice, which progressively transforms immature NCs into icosahedral cores during virus budding. Further, cryoET of the infected cells treated with neutralizing antibodies (NAbs) showed that NAb-induced blockage of CHIKV assembly was achieved by preventing spike-spike lateral interactions that are required to bend the plasma membrane around NC cores. These findings provide molecular mechanisms for designing antivirals targeting spike-driven assembly/budding of viruses.

A mammalian system for high-resolution imaging of intact cells by cryo-electron tomography

Mammalian cells contain an elaborate network of organelles and molecular machines that orchestrate essential cellular processes. Visualization of this network at a molecular level is vital for understanding these cellular processes. Here we present a model system based on nerve growth factor (NGF)-differentiated PC12 cells (PC12+) and suitable for high resolution imaging of organelles and molecular machines in situ. We detail an optimized imaging pipeline that effectively combines correlative light and electron microscopy (CLEM), cryo-focused ion beam (cryo-FIB), cryo-electron tomography (cryo-ET), and sub-tomogram averaging to produce three-dimensional and molecular resolution snapshots of organelles and molecular machines in near-native cellular environments. Our studies demonstrate that cryo-ET imaging of PC12+ systems provides an accessible and highly efficient avenue for dissecting specific cellular processes in mammalian cells at high resolution.

Preparing samples from whole cells using focused-ion-beam milling for cryo-electron tomography.

Recent advances have made cryogenic (cryo) electron microscopy a key technique to achieve near-atomic-resolution structures of biochemically isolated macromolecular complexes. Cryo-electron tomography (cryo-ET) can give unprecedented insight into these complexes in the context of their natural environment. However, the application of cryo-ET is limited to samples that are thinner than most cells, thereby considerably reducing its applicability. Cryo-focused-ion-beam (cryo-FIB) milling has been used to carve (micromachining) out 100-250-nm-thin regions (called lamella) in the intact frozen cells. This procedure opens a window into the cells for high-resolution cryo-ET and structure determination of biomolecules in their native environment. Further combination with fluorescence microscopy allows users to determine cells or regions of interest for the targeted fabrication of lamellae and cryo-ET imaging. Here, we describe how to prepare lamellae using a microscope equipped with both FIB and scanning electron microscopy modalities. Such microscopes (Aquilos Cryo-FIB/Scios/Helios or CrossBeam) are routinely referred to as dual-beam microscopes, and they are equipped with a cryo-stage for all operations in cryogenic conditions. The basic principle of the described methodologies is also applicable for other types of dual-beam microscopes equipped with a cryo-stage. We also briefly describe how to integrate fluorescence microscopy data for targeted milling and critical considerations for cryo-ET data acquisition of the lamellae. Users familiar with cryo-electron microscopy who get basic training in dual-beam microscopy can complete the protocol within 2-3 d, allowing for several pause points during the procedure.

Fully automated, sequential focused ion beam milling for cryo-electron tomography.

Cryo-electron tomography (cryoET) has become a powerful technique at the interface of structural biology and cell biology, due to its unique ability for imaging cells in their native state and determining structures of macromolecular complexes in their cellular context. A limitation of cryoET is its restriction to relatively thin samples. Sample thinning by cryo-focused ion beam (cryoFIB) milling has significantly expanded the range of samples that can be analyzed by cryoET. Unfortunately, cryoFIB milling is low-throughput, time-consuming and manual. Here, we report a method for fully automated sequential cryoFIB preparation of high-quality lamellae, including rough milling and polishing. We reproducibly applied this method to eukaryotic and bacterial model organisms, and show that the resulting lamellae are suitable for cryoET imaging and subtomogram averaging. Since our method reduces the time required for lamella preparation and minimizes the need for user input, we envision the technique will render previously inaccessible projects feasible.

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

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. 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. 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. 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.

Automated cryo-lamella preparation for high-throughput in-situ structural biology

Cryo-transmission electron tomography (cryo-ET) in association with cryo-focused ion beam (cryo-FIB) milling enables structural biology studies to be performed directly within the cellular environment. Cryo-preserved cells are milled and a lamella with a typical thickness of 200–300 nm provides an electron transparent window suitable for cryo-ET imaging. Cryo-FIB milling is an effective method, but it is a tedious and time-consuming process, which typically results in ~10 lamellae per day. Here, we introduce an automated method to reproducibly prepare cryo-lamellae on a grid and reduce the amount of human supervision. We tested the routine on cryo-preserved Saccharomyces cerevisiae, mammalian 293 T cells, and lysozyme protein crystals. Here we demonstrate that our method allows an increased throughput, achieving a rate of 5 lamellae/hour without the need to supervise the FIB milling. We demonstrate that the quality of the lamellae is consistent throughout the preparation and their compatibility with cryo-ET analyses.

Cryo-ET Workflow for Understanding Ion Channels Localization on the Nodes of Ranvier

The localization of ion channels on myelinated axon is closely related with the saltatory conduction of action potential (AP). Abnormal changes in these channels contribute to multiple mental diseases. The development of cryo-Electron Tomography (cryo-ET) has provided a promising prospect for peering into ion channels in their native environment at high resolution. Previous achievements are reviewed here on cryo-ET. Accordingly, a cryo-ET workflow is designed for understanding ion channels localization in myelinated axon, especially nodes of Ranvier, which are significant for the saltatory conduction involved in the propagation of high-speed AP. The workflow is divided into six parts: the preparation of neural cultures with myelin, antibodies and immunofluorescence staining, frozen-hydrated sample preparation, cryo-ET imaging, cryo-correlative light and electron microscopy (cryo-CLEM) imaging, three-dimensional (3D) reconstruction and refinement. The purpose is to conceive a possible solution for the problems related to ion channel compounds including localization, conformation dynamics, accessory structures of ion channel and transient regulatory factors, and thus provide insights into treating neurological diseases caused by abnormal ion channels activity.

In situ Microfluidic Cryofixation for Cryo Focused Ion Beam Milling and Cryo Electron Tomography

We present a microfluidic platform for studying structure-function relationships at the cellular level by connecting video rate live cell imaging with in situ microfluidic cryofixation and cryo-electron tomography of near natively preserved, unstained specimens. Correlative light and electron microscopy (CLEM) has been limited by the time required to transfer live cells from the light microscope to dedicated cryofixation instruments, such as a plunge freezer or high-pressure freezer. We recently demonstrated a microfluidic based approach that enables sample cryofixation directly in the light microscope with millisecond time resolution, a speed improvement of up to three orders of magnitude. Here we show that this cryofixation method can be combined with cryo-electron tomography (cryo-ET) by using Focused Ion Beam milling at cryogenic temperatures (cryo-FIB) to prepare frozen hydrated electron transparent sections. To make cryo-FIB sectioning of rapidly frozen microfluidic channels achievable, we developed a sacrificial layer technique to fabricate microfluidic devices with a PDMS bottom wall <5 µm thick. We demonstrate the complete workflow by rapidly cryo-freezing Caenorhabditis elegans roundworms L1 larvae during live imaging in the light microscope, followed by cryo-FIB milling and lift out to produce thin, electron transparent sections for cryo-ET imaging. Cryo-ET analysis of initial results show that the structural preservation of the cryofixed C. elegans was suitable for high resolution cryo-ET work. The combination of cryofixation during live imaging enabled by microfluidic cryofixation with the molecular resolution capabilities of cryo-ET offers an exciting avenue to further advance space-time correlative light and electron microscopy (st-CLEM) for investigation of biological processes at high resolution in four dimensions.