Showing papers on "Electron tomography published in 2022"
TL;DR: IsoNet as discussed by the authors is 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.
Abstract: Abstract 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.
38 citations
TL;DR: In this paper , a detailed workflow, processing and parameters associated with each step, from initial tomography tilt-series data to the final 3D density map, with several features unique to emClarity, is described.
Abstract: Cryo-electron tomography and subtomogram averaging (STA) has developed rapidly in recent years. It provides structures of macromolecular complexes in situ and in cellular context at or below subnanometer resolution and has led to unprecedented insights into the inner working of molecular machines in their native environment, as well as their functional relevant conformations and spatial distribution within biological cells or tissues. Given the tremendous potential of cryo-electron tomography STA in in situ structural cell biology, we previously developed emClarity, a graphics processing unit-accelerated image-processing software that offers STA and classification of macromolecular complexes at high resolution. However, the workflow remains challenging, especially for newcomers to the field. In this protocol, we describe a detailed workflow, processing and parameters associated with each step, from initial tomography tilt-series data to the final 3D density map, with several features unique to emClarity. We use four different samples, including human immunodeficiency virus type 1 Gag assemblies, ribosome and apoferritin, to illustrate the procedure and results of STA and classification. Following the processing steps described in this protocol, along with a comprehensive tutorial and guidelines for troubleshooting and parameter optimization, one can obtain density maps up to 2.8 Å resolution from six tilt series by cryo-electron tomography STA.
32 citations
TL;DR: Approaches are described that exploit the increased signal-to-noise ratio in the averaged structure to optimise tilt series alignments, beam-induced motions of the particles throughout the tilt series acquisition, defoci of the individual particles, as well as higher-order optical aberrations of the microscope.
Abstract: We present a new approach for macromolecular structure determination from multiple particles in electron cryo-tomography (cryo-ET) data sets. Whereas existing subtomogram averaging approaches are based on 3D data models, we propose to optimise a regularised likelihood target that approximates a function of the 2D experimental images. In addition, analogous to Bayesian polishing and contrast transfer function (CTF) refinement in single-particle analysis, we describe approaches that exploit the increased signal-to-noise ratio in the averaged structure to optimise tilt series alignments, beam-induced motions of the particles throughout the tilt series acquisition, defoci of the individual particles, as well as higher-order optical aberrations of the microscope. Implementation of our approaches in the open-source software package RELION aims to facilitate their general use, in particular for those researchers who are already familiar with its single-particle analysis tools. We illustrate for three applications that our approaches allow structure determination from cryo-ET data to resolutions sufficient for de novo atomic modelling.
32 citations
TL;DR: In this article , a detailed workflow, processing and parameters associated with each step, from initial tomography tilt-series data to the final 3D density map, with several features unique to emClarity, is described.
Abstract: Cryo-electron tomography and subtomogram averaging (STA) has developed rapidly in recent years. It provides structures of macromolecular complexes in situ and in cellular context at or below subnanometer resolution and has led to unprecedented insights into the inner working of molecular machines in their native environment, as well as their functional relevant conformations and spatial distribution within biological cells or tissues. Given the tremendous potential of cryo-electron tomography STA in in situ structural cell biology, we previously developed emClarity, a graphics processing unit-accelerated image-processing software that offers STA and classification of macromolecular complexes at high resolution. However, the workflow remains challenging, especially for newcomers to the field. In this protocol, we describe a detailed workflow, processing and parameters associated with each step, from initial tomography tilt-series data to the final 3D density map, with several features unique to emClarity. We use four different samples, including human immunodeficiency virus type 1 Gag assemblies, ribosome and apoferritin, to illustrate the procedure and results of STA and classification. Following the processing steps described in this protocol, along with a comprehensive tutorial and guidelines for troubleshooting and parameter optimization, one can obtain density maps up to 2.8 Å resolution from six tilt series by cryo-electron tomography STA.
25 citations
TL;DR: The parallel cryo electron tomography (PACE-tomo) method achieves a throughput faster than 5 min per tilt series and allows the collection of sample areas that were previously unreachable, thus maximising the amount of data from each lamella.
Abstract: In situ cryo electron tomography of cryo focused ion beam milled samples emerged in recent years as a powerful technique for structural studies of macromolecular complexes in their native cellular environment. The lamella-shaped samples, however, have a limited area and are created with a necessary pretilt. This severely limits the possibilities for recording tomographic tilt series in a high-throughput manner. Here, we utilise a geometrical sample model and optical image shift to record tens of tilt series in parallel, thereby saving time and gaining sample areas conventionally used for tracking of specimen movement. The parallel cryo electron tomography (PACE-tomo) method achieves a throughput faster than 5 min per tilt series and allows the collection of sample areas that were previously unreachable, thus maximising the amount of data from each lamella. Performance testing with ribosomes in vitro and in situ on state-of-the-art and general-purpose microscopes demonstrated the high-throughput and high-quality of PACE-tomo.
22 citations
TL;DR: In this article , the authors identified actin filament number, organization, and orientation during clathrin-mediated endocytosis in human SK-MEL-2 cells, showing that force generation is robust despite variance in network organization.
19 citations
TL;DR: In this paper , a regularized likelihood target was proposed to optimise a regularised likelihood target that approximates a function of the 2D experimental images, analogous to Bayesian polishing and contrast transfer function (CTF) refinement in singleparticle analysis.
Abstract: We present a new approach for macromolecular structure determination from multiple particles in electron cryo-tomography (cryo-ET) data sets. Whereas existing subtomogram averaging approaches are based on 3D data models, we propose to optimise a regularised likelihood target that approximates a function of the 2D experimental images. In addition, analogous to Bayesian polishing and contrast transfer function (CTF) refinement in single-particle analysis, we describe the approaches that exploit the increased signal-to-noise ratio in the averaged structure to optimise tilt-series alignments, beam-induced motions of the particles throughout the tilt-series acquisition, defoci of the individual particles, as well as higher-order optical aberrations of the microscope. Implementation of our approaches in the open-source software package RELION aims to facilitate their general use, particularly for those researchers who are already familiar with its single-particle analysis tools. We illustrate for three applications that our approaches allow structure determination from cryo-ET data to resolutions sufficient for de novo atomic modelling.
18 citations
01 Jan 2022
TL;DR: In this article , a super-resolution cryo-correlative light and electron microscopy (cryo-CLEM) method was used to characterize the bacterium Deinococcus radiodurans.
Abstract: Studying bacterial cell envelope architecture with electron microscopy is challenging due to the poor preservation of microbial ultrastructure with traditional methods. Here, we established and validated a super-resolution cryo-correlative light and electron microscopy (cryo-CLEM) method, and combined it with cryo-focused ion beam (cryo-FIB) milling and scanning electron microscopy (SEM) volume imaging to structurally characterize the bacterium Deinococcus radiodurans. Subsequent cryo-electron tomography (cryo-ET) revealed an unusual diderm cell envelope architecture with a thick layer of peptidoglycan (PG) between the inner and outer membranes, an additional periplasmic layer, and a proteinaceous surface S-layer. Cells grew in tetrads, and division septa were formed by invagination of the inner membrane (IM), followed by a thick layer of PG. Cytoskeletal filaments, FtsA and FtsZ, were observed at the leading edges of constricting septa. Numerous macromolecular complexes were found associated with the cytoplasmic side of the IM. Altogether, our study revealed several unique ultrastructural features of D. radiodurans cells, opening new lines of investigation into the physiology and evolution of the bacterium.
16 citations
TL;DR: In this paper , a model for the construction of the abaxial epidermal primary cell wall is proposed, where the cell deposits successive layers of cellulose fibers at -45° and +45° relative to the cell's long axis and secretes the surrounding HG-rich meshing proximal to the plasma membrane, which then migrates to more distal regions of the cell wall.
14 citations
TL;DR: Robust tools for montage electron tomography tailored for vitrified specimens are presented, the integration of correlative cryo-fluorescence microscopy, focused-ion beam milling, and micropatterning produces contextual three-dimensional architecture of cells.
Abstract: Imaging large fields of view while preserving high-resolution structural information remains a challenge in low-dose cryo-electron tomography. Here, we present robust tools for montage electron tomography tailored for vitrified specimens. The integration of correlative cryo-fluorescence microscopy, focused-ion beam milling, and micropatterning produces contextual three-dimensional architecture of cells. Montage tilt series may be processed in their entirety or as individual tiles suitable for sub-tomogram averaging, enabling efficient data processing and analysis.
14 citations
TL;DR: In this paper , a montage data collection scheme that uniformly distributes the dose throughout the specimen is proposed. But the trade-off between field of view and resolution is not addressed.
TL;DR: In this paper , the authors performed in situ cryo-electron tomography of mouse and human sperm, providing the highest-resolution structural information to date, revealing mammalian sperm-specific protein complexes within the microtubules, the radial spokes and nexin-dynein regulatory complexes.
Abstract: The flagella of mammalian sperm display non-planar, asymmetric beating, in contrast to the planar, symmetric beating of flagella from sea urchin sperm and unicellular organisms. The molecular basis of this difference is unclear. Here, we perform in situ cryo-electron tomography of mouse and human sperm, providing the highest-resolution structural information to date. Our subtomogram averages reveal mammalian sperm-specific protein complexes within the microtubules, the radial spokes and nexin-dynein regulatory complexes. The locations and structures of these complexes suggest potential roles in enhancing the mechanical strength of mammalian sperm axonemes and regulating dynein-based axonemal bending. Intriguingly, we find that each of the nine outer microtubule doublets is decorated with a distinct combination of sperm-specific complexes. We propose that this asymmetric distribution of proteins differentially regulates the sliding of each microtubule doublet and may underlie the asymmetric beating of mammalian sperm.
TL;DR: ChromSTEM as mentioned in this paper is a method that utilizes high-angle annular dark-field imaging and tomography in scanning transmission electron microscopy combined with DNA-specific staining for electron microscope to characterize higher-order chromatin structure almost down to the level of the DNA base pair.
Abstract: Chromatin organization over multiple length scales plays a critical role in the regulation of transcription. Deciphering the interplay of these processes requires high-resolution, three-dimensional, quantitative imaging of chromatin structure in vitro. Herein, we introduce ChromSTEM, a method that utilizes high-angle annular dark-field imaging and tomography in scanning transmission electron microscopy combined with DNA-specific staining for electron microscopy. We utilized ChromSTEM for an in-depth quantification of 3D chromatin conformation with high spatial resolution and contrast, allowing for characterization of higher-order chromatin structure almost down to the level of the DNA base pair. Employing mass scaling analysis on ChromSTEM mass density tomograms, we observed that chromatin forms spatially well-defined higher-order domains, around 80 nm in radius. Within domains, chromatin exhibits a polymeric fractal-like behavior and a radially decreasing mass-density from the center to the periphery. Unlike other nanoimaging and analysis techniques, we demonstrate that our unique combination of this high-resolution imaging technique with polymer physics-based analysis enables us to (i) investigate the chromatin conformation within packing domains and (ii) quantify statistical descriptors of chromatin structure that are relevant to transcription. We observe that packing domains have heterogeneous morphological properties even within the same cell line, underlying the potential role of statistical chromatin packing in regulating gene expression within eukaryotic nuclei.
TL;DR: In this paper , a suite of ultrastructural quantifications, integrated into a single pipeline called the surface morphometrics toolkit, is presented, allowing detailed mapping of spacing, curvature, and orientation onto reconstructed membrane meshes, highlighting subtle organellar features that are challenging to detect in 3D.
Abstract: Cellular cryo-electron tomography (cryo-ET) enables 3-dimensional reconstructions of organelles in their native cellular environment at subnanometer resolution. However, quantifying ultrastructural features of pleomorphic organelles in three dimensions is challenging, as is defining the significance of observed changes induced by specific cellular perturbations. To address this challenge, we established a semi-automated workflow to segment organellar membranes and reconstruct their underlying surface geometry in cryo-ET. To complement this workflow, we developed an open source suite of ultrastructural quantifications, integrated into a single pipeline called the surface morphometrics toolkit. This toolkit allows detailed mapping of spacing, curvature, and orientation onto reconstructed membrane meshes, highlighting subtle organellar features that are challenging to detect in three dimensions and allowing for statistical comparison across many organelles. To demonstrate the advantages of this approach, we combine cryo-ET with cryo-fluorescence microscopy to correlate bulk mitochondrial network morphology (i.e., elongated versus fragmented) with membrane ultrastructure of individual mitochondria in the presence and absence of endoplasmic reticulum (ER) stress. Using our toolkit, we demonstrate ER stress promotes adaptive remodeling of ultrastructural features of mitochondria including spacing between the inner and outer membranes, local curvature of the inner membrane, and spacing between mitochondrial cristae. We show that differences in membrane ultrastructure correlate to mitochondrial network morphologies, suggesting that these two remodeling events are coupled. Our toolkit offers opportunities for quantifying changes in organellar architecture on a single-cell level using cryo-ET, opening new opportunities to define changes in ultrastructural features induced by diverse types of cellular perturbations.
TL;DR: In this paper , the authors used 3D reconstruction of the nucleosome core particle at 2.8 A resolution to determine the molecular organization of condensates at various stages of liquid-liquid phase separation.
TL;DR: This work demonstrates how center-of-mass scanning transmission electron microscopy (CoM-STEM) provides an enhanced ability for simultaneous imaging of lithium and heavier element columns in lithium ion conductors and becomes a reliable and facile method for directly probing all elements within energy storage materials at the atomic scale.
Abstract: The performance of energy storage materials is often governed by their structure at the atomic scale. Conventional electron microscopy can provide detailed information about materials at these length scales, but direct imaging of light elements such as lithium presents a challenge. While several recent techniques allow lithium columns to be distinguished, these typically either involve complex contrast mechanisms that make image interpretation difficult or require significant expertise to perform. Here, we demonstrate how center-of-mass scanning transmission electron microscopy (CoM-STEM) provides an enhanced ability for simultaneous imaging of lithium and heavier element columns in lithium ion conductors. Through a combination of experiments and multislice electron scattering calculations, we show that CoM-STEM is straightforward to perform and produces directly interpretable contrast for thin samples, while being more robust to variations in experimental parameters than previously demonstrated techniques. As a result, CoM-STEM is positioned to become a reliable and facile method for directly probing all elements within energy storage materials at the atomic scale.
01 Feb 2022
TL;DR: In this article , the authors present some of the prominent advancements in the field of cryo-electron tomography, illustrated by a subset of structural examples to demonstrate the power of the technique.
Abstract: Advancements in the field of cryo-electron tomography have greatly contributed to our current understanding of prokaryotic cell organization and revealed intracellular structures with remarkable architecture. In this review, we present some of the prominent advancements in cryo-electron tomography, illustrated by a subset of structural examples to demonstrate the power of the technique. More specifically, we focus on technical advances in automation of data collection and processing, sample thinning approaches, correlative cryo-light and electron microscopy, and sub-tomogram averaging methods. In turn, each of these advances enabled new insights into bacterial cell architecture, cell cycle progression, and the structure and function of molecular machines. Taken together, these significant advances within the cryo-electron tomography workflow have led to a greater understanding of prokaryotic biology. The advances made the technique available to a wider audience and more biological questions and provide the basis for continued advances in the near future.
TL;DR: In this paper , the authors describe the application of tomographic principles of data acquisition and reconstruction to frozen-hydrated biological specimens, which combines a close-to-life preservation of cellular structures with the power of high-resolution 3D imaging.
TL;DR: Automated Segmentation of intracellular substructures in Electron Microscopy (ASEM) is described, a new pipeline to train a convolutional network to detect structures of wide range in size and complexity to improve model generalization to different imaging conditions.
Abstract: Three-dimensional electron-microscopy is an important imaging modality in contemporary cell biology. Identification of intracellular structures is laborious and time-consuming, however, and seriously impairs effective use of a potentially powerful tool. Resolving this bottleneck is therefore a critical next step in frontier biomedical imaging. We describe Automated Segmentation of intracellular substructures in Electron Microscopy (ASEM), a new pipeline to train a convolutional network to detect structures of wide range in size and complexity. We obtain for each structure a dedicated model based on a small number of sparsely annotated ground truth annotations from only one or two cells. To improve model generalization to different imaging conditions, we developed a rapid, computationally effective strategy to refine an already trained model by including a few additional annotations. We show the successful automated identification of mitochondria, Golgi apparatus, endoplasmic reticulum, nuclear pore complexes, clathrin coated pits and coated vesicles, and caveolae in cells imaged by focused ion beam scanning electron microscopy with quasi-isotropic resolution. Summary Recent advances in automated segmentation using deep neural network models allow identification of subcellular structures. This study describes a new pipeline to train a convolutional network for rapid and efficient detection of structures of wide range in size and complexity.
TL;DR: In this article , an analysis of the intermediate structures during AuNS synthesis from HEPES, EPPS, and MOPS Good's buffers can provide insight into the formation of seedless AuNS.
Abstract: Good's buffers can act both as nucleating and shape-directing agents during the synthesis of anisotropic gold nanostars (AuNS). Although different Good's buffers can produce AuNS shapes with branches that are oriented along specific crystallographic directions, the mechanism is not fully understood. This paper reports how an analysis of the intermediate structures during AuNS synthesis from HEPES, EPPS, and MOPS Good's buffers can provide insight into the formation of seedless AuNS. Electron tomography of AuNS structures quenched at early times (minutes) was used to characterize the morphology of the incipient seeds, and later times were used to construct the growth maps. Through this approach, we identified how the crystallinity and shape of the first structures synthesized with different Good's buffers determine the final AuNS morphologies.
TL;DR: PyOrg as mentioned in this paper is a Python package for statistical spatial analysis of particles located in 3D regions of arbitrary shape, such as those encountered in cellular cryo-ET imaging (PyOrg).
TL;DR: In this paper , the authors highlight recent research in three emerging areas of bacterial cell biology that have benefited from the cryo-FIB-ET technology - cytoskeletal filament assembly, intracellular organelles, and multicellularity.
TL;DR: The available workflows to produce lamellae by lift- out at cryogenic conditions and recent developments in gas injection system (GIS)-free approaches to the lift-out transfer are described.
Abstract: Cryo-electron tomography (cryo-ET) enables visualization of protein complexes within their native cellular environment at molecular resolution. Most cells and all tissues, however, are too thick to be imaged directly by transmission electron microscopy (TEM). Overcoming this limitation requires the production of thin biological sections called lamellae. The procedure to obtain lamellae of cells, either seeded or grown directly on electron microscopy grids, requires cryo-focused ion beam (cryo-FIB) milling to thin the samples. This method faces an additional challenge when dealing with tissues and multicellular organisms, as these samples must be high-pressure frozen, which embeds the sample in a thick layer of ice. Nonetheless, lamellae can still be prepared from such samples by extracting a small volume and transferring it to a receiver grid for lamella preparation, a process called lift-out. Here, we describe the available workflows to produce lamellae by lift-out at cryogenic conditions and recent developments in gas injection system (GIS)-free approaches to the lift-out transfer. These advances expand the applications of cryo-ET, enabling the investigation of tissues and whole organisms in situ at molecular resolution.
TL;DR: TomoTwin this article is an open source general picking model for cryogenic-electron tomograms based on deep metric learning, embedding tomograms in an information-rich, high-dimensional space that separates macromolecules according to their three-dimensional structure.
Abstract: Abstract Cryogenic-electron tomography enables the visualization of cellular environments in extreme detail, however, tools to analyze the full amount of information contained within these densely packed volumes are still needed. Detailed analysis of macromolecules through subtomogram averaging requires particles to first be localized within the tomogram volume, a task complicated by several factors including a low signal to noise ratio and crowding of the cellular space. Available methods for this task suffer either from being error prone or requiring manual annotation of training data. To assist in this crucial particle picking step, we present TomoTwin: an open source general picking model for cryogenic-electron tomograms based on deep metric learning. By embedding tomograms in an information-rich, high-dimensional space that separates macromolecules according to their three-dimensional structure, TomoTwin allows users to identify proteins in tomograms de novo without manually creating training data or retraining the network to locate new proteins.
27 Jun 2022
TL;DR: TomoTwin this article is a robust, first in class general picking model for cryo-electron tomograms based on deep metric learning, embedding tomograms in an information-rich, high-dimensional space which separates macromolecules according to their 3-dimensional structure.
Abstract: Abstract Cryoelectron tomography enables the visualization of cellular environments in extreme detail through the lens of a benign observer; what remains lacking however are tools to analyze the full amount of information contained within these densely packed volumes. Detailed analysis of macromolecules through subtomogram averaging requires particles to first be localized within the tomogram volume, a task complicated by several factors including a low signal to noise ratio and crowding of the cellular space. Available methods for this task suffer either from being error prone or requiring manual annotation of training data. To assist in this crucial particle picking step, we present TomoTwin: a robust, first in class general picking model for cryo-electron tomograms based on deep metric learning. By embedding tomograms in an information-rich, high-dimensional space which separates macromolecules according to their 3-dimensional structure, TomoTwin allows users to identify proteins in tomograms de novo without manually creating training data or retraining the network each time a new protein is to be located. TomoTwin is open source and available at https://github.com/MPI-Dortmund/tomotwin-cryoet .
TL;DR: In this article , the authors review recent progress made in data collection, new algorithms, and automated electron diffraction analysis, highlighting application examples in materials research and future opportunities based on smart sampling and machine learning are also discussed.
Abstract: Transmission electron diffraction is a powerful and versatile structural probe for the characterization of a broad range of materials, from nanocrystalline thin films to single crystals. With recent developments in fast electron detectors and efficient computer algorithms, it now becomes possible to collect unprecedently large datasets of diffraction patterns (DPs) and process DPs to extract crystallographic information to form images or tomograms based on crystal structural properties, giving rise to data-driven electron microscopy. Critical to this kind of imaging is the type of crystallographic information being collected, which can be achieved with a judicious choice of electron diffraction techniques, and the efficiency and accuracy of DP processing, which requires the development of new algorithms. Here, we review recent progress made in data collection, new algorithms, and automated electron DP analysis. These progresses will be highlighted using application examples in materials research. Future opportunities based on smart sampling and machine learning are also discussed.
TL;DR: Recent trends in volume EM, emerging methods for increasing throughput and implications for sample preparation, image analysis and data management are discussed.
Abstract: Detailed knowledge of biological structure has been key in understanding biology at several levels of organisation, from organs to cells and proteins. Volume electron microscopy (volume EM) provides high resolution 3D structural information about tissues on the nanometre scale. However, the throughput rate of conventional electron microscopes has limited the volume size and number of samples that can be imaged. Recent improvements in methodology are currently driving a revolution in volume EM, making possible the structural imaging of whole organs and small organisms. In turn, these recent developments in image acquisition have created or stressed bottlenecks in other parts of the pipeline, like sample preparation, image analysis and data management. While the progress in image analysis is stunning due to the advent of automatic segmentation and server‐based annotation tools, several challenges remain. Here we discuss recent trends in volume EM, emerging methods for increasing throughput and implications for sample preparation, image analysis and data management.
TL;DR: In this article , a geometrical sample model and optical image shift are used to record tens of tilt series in parallel, thereby saving time and gaining access to sample areas conventionally used for tracking specimen movement.
Abstract: In situ cryo electron tomography of cryo focused ion beam milled samples has emerged in recent years as a powerful technique for structural studies of macromolecular complexes in their native cellular environment. However, the possibilities for recording tomographic tilt series in a high-throughput manner are limited, in part by the lamella-shaped samples. Here we utilize a geometrical sample model and optical image shift to record tens of tilt series in parallel, thereby saving time and gaining access to sample areas conventionally used for tracking specimen movement. The parallel cryo electron tomography (PACE-tomo) method achieves a throughput faster than 5 min per tilt series and allows for the collection of sample areas that were previously unreachable, thus maximizing the amount of data from each lamella. Performance testing with ribosomes in vitro and in situ on state-of-the-art and general-purpose microscopes demonstrated the high throughput and quality of PACE-tomo.
TL;DR: In this paper , the authors demonstrate real-time tomography with dynamic 3D tomographic visualization to enable rapid interpretation of specimen structure immediately as data is collected on an electron microscope, using geometrically complex chiral nanoparticles, and show volumetric interpretation can begin in less than 10 minutes and a high-quality tomogram is available within 30 minutes.
Abstract: Abstract The demand for high-throughput electron tomography is rapidly increasing in biological and material sciences. However, this 3D imaging technique is computationally bottlenecked by alignment and reconstruction which runs from hours to days. We demonstrate real-time tomography with dynamic 3D tomographic visualization to enable rapid interpretation of specimen structure immediately as data is collected on an electron microscope. Using geometrically complex chiral nanoparticles, we show volumetric interpretation can begin in less than 10 minutes and a high-quality tomogram is available within 30 minutes. Real-time tomography is integrated into tomviz, an open-source and cross-platform 3D data analysis tool that contains intuitive graphical user interfaces (GUI), to enable any scientist to characterize biological and material structure in 3D.