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Zeng X, Ding Y, Zhang Y, Uddin MR, Dabouei A, Xu M. DUAL: deep unsupervised simultaneous simulation and denoising for cryo-electron tomography. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.02.583135. [PMID: 38496657 PMCID: PMC10942334 DOI: 10.1101/2024.03.02.583135] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
Recent biotechnological developments in cryo-electron tomography allow direct visualization of native sub-cellular structures with unprecedented details and provide essential information on protein functions/dysfunctions. Denoising can enhance the visualization of protein structures and distributions. Automatic annotation via data simulation can ameliorate the time-consuming manual labeling of large-scale datasets. Here, we combine the two major cryo-ET tasks together in DUAL, by a specific cyclic generative adversarial network with novel noise disentanglement. This enables end-to-end unsupervised learning that requires no labeled data for training. The denoising branch outperforms existing works and substantially improves downstream particle picking accuracy on benchmark datasets. The simulation branch provides learning-based cryo-ET simulation for the first time and generates synthetic tomograms indistinguishable from experimental ones. Through comprehensive evaluations, we showcase the effectiveness of DUAL in detecting macromolecular complexes across a wide range of molecular weights in experimental datasets. The versatility of DUAL is expected to empower cryo-ET researchers by improving visual interpretability, enhancing structural detection accuracy, expediting annotation processes, facilitating cross-domain model adaptability, and compensating for missing wedge artifacts. Our work represents a significant advancement in the unsupervised mining of protein structures in cryo-ET, offering a multifaceted tool that facilitates cryo-ET research.
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Affiliation(s)
- Xiangrui Zeng
- Ray and Stephanie Lane Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA, 15213, USA
| | - Yizhe Ding
- Department of Statistics, The Pennsylvania State University, University Park, PA, 16802, USA
| | - Yueqian Zhang
- School of Electrical and Electronic Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore
| | - Mostofa Rafid Uddin
- Ray and Stephanie Lane Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA, 15213, USA
| | - Ali Dabouei
- Ray and Stephanie Lane Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA, 15213, USA
| | - Min Xu
- Ray and Stephanie Lane Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA, 15213, USA
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Zeng X, Howe G, Xu M. End-to-end robust joint unsupervised image alignment and clustering. PROCEEDINGS. IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION 2021; 2021:3834-3846. [PMID: 35392630 PMCID: PMC8986091 DOI: 10.1109/iccv48922.2021.00383] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Computing dense pixel-to-pixel image correspondences is a fundamental task of computer vision. Often, the objective is to align image pairs from the same semantic category for manipulation or segmentation purposes. Despite achieving superior performance, existing deep learning alignment methods cannot cluster images; consequently, clustering and pairing images needed to be a separate laborious and expensive step. Given a dataset with diverse semantic categories, we propose a multi-task model, Jim-Net, that can directly learn to cluster and align images without any pixel-level or image-level annotations. We design a pair-matching alignment unsupervised training algorithm that selectively matches and aligns image pairs from the clustering branch. Our unsupervised Jim-Net achieves comparable accuracy with state-of-the-art supervised methods on benchmark 2D image alignment dataset PF-PASCAL. Specifically, we apply Jim-Net to cryo-electron tomography, a revolutionary 3D microscopy imaging technique of native subcellular structures. After extensive evaluation on seven datasets, we demonstrate that Jim-Net enables systematic discovery and recovery of representative macromolecular structures in situ, which is essential for revealing molecular mechanisms underlying cellular functions. To our knowledge, Jim-Net is the first end-to-end model that can simultaneously align and cluster images, which significantly improves the performance as compared to performing each task alone.
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Affiliation(s)
- Xiangrui Zeng
- Computational Biology, Carnegie Mellon University, Pittsburgh, PA 15213, USA
| | - Gregory Howe
- Machine Learning, Carnegie Mellon University, Pittsburgh, PA 15213, USA
| | - Min Xu
- Computational Biology, Carnegie Mellon University, Pittsburgh, PA 15213, USA
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Zeng X, Xu M. Gum-Net: Unsupervised Geometric Matching for Fast and Accurate 3D Subtomogram Image Alignment and Averaging. PROCEEDINGS. IEEE COMPUTER SOCIETY CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION 2020; 2020:4072-4082. [PMID: 33716478 PMCID: PMC7955792 DOI: 10.1109/cvpr42600.2020.00413] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Abstract
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.
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Affiliation(s)
- Xiangrui Zeng
- Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA 15213
| | - Min Xu
- Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA 15213
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Lü Y, Zeng X, Zhao X, Li S, Li H, Gao X, Xu M. Fine-grained alignment of cryo-electron subtomograms based on MPI parallel optimization. BMC Bioinformatics 2019; 20:443. [PMID: 31455212 PMCID: PMC6712796 DOI: 10.1186/s12859-019-3003-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2018] [Accepted: 07/19/2019] [Indexed: 11/10/2022] Open
Abstract
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.
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Affiliation(s)
- Yongchun Lü
- University of Chinese Academy of Sciences, Beijing, China
- Institute of Computing Technology of the Chinese Academy of Sciences, Beijing, China
- Key Laboratory of Intelligent Information Processing, CAS, Beijing, China
| | - Xiangrui Zeng
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, USA
| | - Xiaofang Zhao
- University of Chinese Academy of Sciences, Beijing, China
- Institute of Computing Technology of the Chinese Academy of Sciences, Beijing, China
| | - Shirui Li
- Institute of Computing Technology of the Chinese Academy of Sciences, Beijing, China
- Key Laboratory of Intelligent Information Processing, CAS, Beijing, China
| | - Hua Li
- University of Chinese Academy of Sciences, Beijing, China
- Institute of Computing Technology of the Chinese Academy of Sciences, Beijing, China
- Key Laboratory of Intelligent Information Processing, CAS, Beijing, China
| | - Xin Gao
- King Abdullah University of Science and Technology (KAUST), Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, Thuwal, Saudi Arabia
| | - Min Xu
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, USA
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Zhao Y, Zeng X, Guo Q, Xu M. An integration of fast alignment and maximum-likelihood methods for electron subtomogram averaging and classification. Bioinformatics 2019; 34:i227-i236. [PMID: 29949977 PMCID: PMC6022576 DOI: 10.1093/bioinformatics/bty267] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Motivation Cellular Electron CryoTomography (CECT) is an emerging 3D imaging technique that visualizes subcellular organization of single cells at sub-molecular resolution and in near-native state. CECT captures large numbers of macromolecular complexes of highly diverse structures and abundances. However, the structural complexity and imaging limits complicate the systematic de novo structural recovery and recognition of these macromolecular complexes. Efficient and accurate reference-free subtomogram averaging and classification represent the most critical tasks for such analysis. Existing subtomogram alignment based methods are prone to the missing wedge effects and low signal-to-noise ratio (SNR). Moreover, existing maximum-likelihood based methods rely on integration operations, which are in principle computationally infeasible for accurate calculation. Results Built on existing works, we propose an integrated method, Fast Alignment Maximum Likelihood method (FAML), which uses fast subtomogram alignment to sample sub-optimal rigid transformations. The transformations are then used to approximate integrals for maximum-likelihood update of subtomogram averages through expectation–maximization algorithm. Our tests on simulated and experimental subtomograms showed that, compared to our previously developed fast alignment method (FA), FAML is significantly more robust to noise and missing wedge effects with moderate increases of computation cost. Besides, FAML performs well with significantly fewer input subtomograms when the FA method fails. Therefore, FAML can serve as a key component for improved construction of initial structural models from macromolecules captured by CECT. Availability and implementation http://www.cs.cmu.edu/mxu1
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Affiliation(s)
- Yixiu Zhao
- Computational Biology and Computer Science Departments, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Xiangrui Zeng
- Computational Biology and Computer Science Departments, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Qiang Guo
- Department of Molecular Structural Biology, Max Planck Institute of Biochemistry, Martinsried, Germany
| | - Min Xu
- Computational Biology and Computer Science Departments, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA
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Galaz-Montoya JG, Ludtke SJ. The advent of structural biology in situ by single particle cryo-electron tomography. BIOPHYSICS REPORTS 2017; 3:17-35. [PMID: 28781998 PMCID: PMC5516000 DOI: 10.1007/s41048-017-0040-0] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2017] [Accepted: 03/30/2017] [Indexed: 01/06/2023] Open
Abstract
Single particle tomography (SPT), also known as subtomogram averaging, is a powerful technique uniquely poised to address questions in structural biology that are not amenable to more traditional approaches like X-ray crystallography, nuclear magnetic resonance, and conventional cryoEM single particle analysis. Owing to its potential for in situ structural biology at subnanometer resolution, SPT has been gaining enormous momentum in the last five years and is becoming a prominent, widely used technique. This method can be applied to unambiguously determine the structures of macromolecular complexes that exhibit compositional and conformational heterogeneity, both in vitro and in situ. Here we review the development of SPT, highlighting its applications and identifying areas of ongoing development.
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Affiliation(s)
- Jesús G Galaz-Montoya
- National Center for Macromolecular Imaging, Verna and Marrs McLean Department of Biochemistry and Molecular Biology, Baylor College of Medicine, Houston, TX 77030 USA
| | - Steven J Ludtke
- National Center for Macromolecular Imaging, Verna and Marrs McLean Department of Biochemistry and Molecular Biology, Baylor College of Medicine, Houston, TX 77030 USA
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Galaz-Montoya JG, Hecksel CW, Baldwin PR, Wang E, Weaver SC, Schmid MF, Ludtke SJ, Chiu W. Alignment algorithms and per-particle CTF correction for single particle cryo-electron tomography. J Struct Biol 2016; 194:383-94. [PMID: 27016284 DOI: 10.1016/j.jsb.2016.03.018] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2016] [Revised: 03/11/2016] [Accepted: 03/21/2016] [Indexed: 10/22/2022]
Abstract
Single particle cryo-electron tomography (cryoSPT) extracts features from cryo-electron tomograms, followed by 3D classification, alignment and averaging to generate improved 3D density maps of such features. Robust methods to correct for the contrast transfer function (CTF) of the electron microscope are necessary for cryoSPT to reach its resolution potential. Many factors can make CTF correction for cryoSPT challenging, such as lack of eucentricity of the specimen stage, inherent low dose per image, specimen charging, beam-induced specimen motions, and defocus gradients resulting both from specimen tilting and from unpredictable ice thickness variations. Current CTF correction methods for cryoET make at least one of the following assumptions: that the defocus at the center of the image is the same across the images of a tiltseries, that the particles all lie at the same Z-height in the embedding ice, and/or that the specimen, the cryo-electron microscopy (cryoEM) grid and/or the carbon support are flat. These experimental conditions are not always met. We have developed a CTF correction algorithm for cryoSPT without making any of the aforementioned assumptions. We also introduce speed and accuracy improvements and a higher degree of automation to the subtomogram averaging algorithms available in EMAN2. Using motion-corrected images of isolated virus particles as a benchmark specimen, recorded with a DE20 direct detection camera, we show that our CTF correction and subtomogram alignment routines can yield subtomogram averages close to 4/5 Nyquist frequency of the detector under our experimental conditions.
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Affiliation(s)
- Jesús G Galaz-Montoya
- National Center for Macromolecular Imaging, Verna and Marrs McLean Department of Biochemistry and Molecular Biology, Baylor College of Medicine, Houston, TX, USA
| | - Corey W Hecksel
- National Center for Macromolecular Imaging, Verna and Marrs McLean Department of Biochemistry and Molecular Biology, Baylor College of Medicine, Houston, TX, USA; Department of Molecular Virology and Microbiology, Baylor College of Medicine, Houston, TX, USA
| | - Philip R Baldwin
- National Center for Macromolecular Imaging, Verna and Marrs McLean Department of Biochemistry and Molecular Biology, Baylor College of Medicine, Houston, TX, USA; Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
| | - Eryu Wang
- Institute for Human Infections and Immunity and Department of Microbiology and Immunology, University of Texas Medical Branch, Galveston, TX, USA
| | - Scott C Weaver
- Institute for Human Infections and Immunity and Department of Microbiology and Immunology, University of Texas Medical Branch, Galveston, TX, USA
| | - Michael F Schmid
- National Center for Macromolecular Imaging, Verna and Marrs McLean Department of Biochemistry and Molecular Biology, Baylor College of Medicine, Houston, TX, USA
| | - Steven J Ludtke
- National Center for Macromolecular Imaging, Verna and Marrs McLean Department of Biochemistry and Molecular Biology, Baylor College of Medicine, Houston, TX, USA
| | - Wah Chiu
- National Center for Macromolecular Imaging, Verna and Marrs McLean Department of Biochemistry and Molecular Biology, Baylor College of Medicine, Houston, TX, USA; Department of Molecular Virology and Microbiology, Baylor College of Medicine, Houston, TX, USA.
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Turoňová B, Marsalek L, Davidovič T, Slusallek P. Progressive Stochastic Reconstruction Technique (PSRT) for cryo electron tomography. J Struct Biol 2015; 189:195-206. [PMID: 25659894 DOI: 10.1016/j.jsb.2015.01.011] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2014] [Revised: 12/22/2014] [Accepted: 01/20/2015] [Indexed: 10/24/2022]
Abstract
Cryo Electron Tomography (cryoET) plays an essential role in Structural Biology, as it is the only technique that allows to study the structure of large macromolecular complexes in their close to native environment in situ. The reconstruction methods currently in use, such as Weighted Back Projection (WBP) or Simultaneous Iterative Reconstruction Technique (SIRT), deliver noisy and low-contrast reconstructions, which complicates the application of high-resolution protocols, such as Subtomogram Averaging (SA). We propose a Progressive Stochastic Reconstruction Technique (PSRT) - a novel iterative approach to tomographic reconstruction in cryoET based on Monte Carlo random walks guided by Metropolis-Hastings sampling strategy. We design a progressive reconstruction scheme to suit the conditions present in cryoET and apply it successfully to reconstructions of macromolecular complexes from both synthetic and experimental datasets. We show how to integrate PSRT into SA, where it provides an elegant solution to the region-of-interest problem and delivers high-contrast reconstructions that significantly improve template-based localization without any loss of high-resolution structural information. Furthermore, the locality of SA is exploited to design an importance sampling scheme which significantly speeds up the otherwise slow Monte Carlo approach. Finally, we design a new memory efficient solution for the specimen-level interior problem of cryoET, removing all associated artifacts.
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Affiliation(s)
- Beata Turoňová
- Saarland University, Campus E 1.1, 66123 Saarbrücken, Germany; IMPRS-CS, Max-Planck Institute for Informatics, Campus E 1.4, 66123 Saarbrücken, Germany.
| | - Lukas Marsalek
- Saarland University, Campus E 1.1, 66123 Saarbrücken, Germany; Agents and Simulated Reality Group, DFKI GmbH, Campus E 3.4, 66123 Saarbrücken, Germany; Eyen SE, Na Nivách 1043/16, 14100 Prague, Czech Republic
| | - Tomáš Davidovič
- Saarland University, Campus E 1.1, 66123 Saarbrücken, Germany; Intel VCI, Campus E 1.1, 66123 Saarbrücken, Germany
| | - Philipp Slusallek
- Saarland University, Campus E 1.1, 66123 Saarbrücken, Germany; Agents and Simulated Reality Group, DFKI GmbH, Campus E 3.4, 66123 Saarbrücken, Germany; Intel VCI, Campus E 1.1, 66123 Saarbrücken, Germany
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Thalassinos K, Pandurangan AP, Xu M, Alber F, Topf M. Conformational States of macromolecular assemblies explored by integrative structure calculation. Structure 2014; 21:1500-8. [PMID: 24010709 PMCID: PMC3988990 DOI: 10.1016/j.str.2013.08.006] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2013] [Revised: 08/10/2013] [Accepted: 08/12/2013] [Indexed: 12/22/2022]
Abstract
A detailed description of macromolecular assemblies in multiple conformational states can be very valuable for understanding cellular processes. At present, structural determination of most assemblies in different biologically relevant conformations cannot be achieved by a single technique and thus requires an integrative approach that combines information from multiple sources. Different techniques require different computational methods to allow efficient and accurate data processing and analysis. Here, we summarize the latest advances and future challenges in computational methods that help the interpretation of data from two techniques—mass spectrometry and three-dimensional cryo-electron microscopy (with focus on alignment and classification of heterogeneous subtomograms from cryo-electron tomography). We evaluate how new developments in these two broad fields will lead to further integration with atomic structures to broaden our picture of the dynamic behavior of assemblies in their native environment.
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Affiliation(s)
- Konstantinos Thalassinos
- Institute of Structural and Molecular Biology, Division of Biosciences, University College London, London WC1E 6BT, UK
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Xu M, Alber F. Automated target segmentation and real space fast alignment methods for high-throughput classification and averaging of crowded cryo-electron subtomograms. Bioinformatics 2013; 29:i274-82. [PMID: 23812994 PMCID: PMC3694676 DOI: 10.1093/bioinformatics/btt225] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
MOTIVATION Cryo-electron tomography allows the imaging of macromolecular complexes in near living conditions. To enhance the nominal resolution of a structure it is necessary to align and average individual subtomograms each containing identical complexes. However, if the sample of complexes is heterogeneous, it is necessary to first classify subtomograms into groups of identical complexes. This task becomes challenging when tomograms contain mixtures of unknown complexes extracted from a crowded environment. Two main challenges must be overcomed: First, classification of subtomograms must be performed without knowledge of template structures. However, most alignment methods are too slow to perform reference-free classification of a large number of (e.g. tens of thousands) of subtomograms. Second, subtomograms extracted from crowded cellular environments, contain often fragments of other structures besides the target complex. However, alignment methods generally assume that each subtomogram only contains one complex. Automatic methods are needed to identify the target complexes in a subtomogram even when its shape is unknown. RESULTS In this article, we propose an automatic and systematic method for the isolation and masking of target complexes in subtomograms extracted from crowded environments. Moreover, we also propose a fast alignment method using fast rotational matching in real space. Our experiments show that, compared with our previously proposed fast alignment method in reciprocal space, our new method significantly improves the alignment accuracy for highly distorted and especially crowded subtomograms. Such improvements are important for achieving successful and unbiased high-throughput reference-free structural classification of complexes inside whole-cell tomograms. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Min Xu
- University of Southern California, 1050 Childs Way, Los Angeles, CA 90089, USA
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12
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Hsieh C, Schmelzer T, Kishchenko G, Wagenknecht T, Marko M. Practical workflow for cryo focused-ion-beam milling of tissues and cells for cryo-TEM tomography. J Struct Biol 2013; 185:32-41. [PMID: 24211822 DOI: 10.1016/j.jsb.2013.10.019] [Citation(s) in RCA: 64] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2013] [Revised: 10/29/2013] [Accepted: 10/31/2013] [Indexed: 11/30/2022]
Abstract
Vitreous freezing offers a way to study cells and tissue in a near-native state by cryo-transmission electron microscopy (cryo-TEM), which is important when structural information at the macromolecular level is required. Many cells - especially those in tissue - are too thick to study intact in the cryo-TEM. Cryo focused-ion-beam (cryo-FIB) milling is being used in a few laboratories to thin vitreously frozen specimens, thus avoiding the artifacts and difficulties of cryo-ultramicrotomy. However, the technique is challenging because of the need to avoid devitrification and frost accumulation during the entire process, from the initial step of freezing to the final step of loading the specimen into the cryo-TEM. We present a robust workflow that makes use of custom fixtures and devices that can be used for high-pressure-frozen bulk tissue samples as well as for samples frozen on TEM grids.
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Affiliation(s)
- Chyongere Hsieh
- New York State Department of Health, Wadsworth Center, Empire State Plaza, Albany, NY 12201, USA
| | - Thomas Schmelzer
- TGS Technologies, 702 Little Creek Lane, Cranberry Township, PA 16066, USA
| | - Gregory Kishchenko
- New York State Department of Health, Wadsworth Center, Empire State Plaza, Albany, NY 12201, USA
| | - Terence Wagenknecht
- New York State Department of Health, Wadsworth Center, Empire State Plaza, Albany, NY 12201, USA; Department of Biomedical Sciences, School of Public Health, University at Albany, Albany, NY 12203, USA
| | - Michael Marko
- New York State Department of Health, Wadsworth Center, Empire State Plaza, Albany, NY 12201, USA; College of Nanoscale Science and Engineering, University at Albany, 251 Fuller Rd., Albany, NY 12203, USA.
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Shatsky M, Arbelaez P, Glaeser RM, Brenner SE. Optimal and fast rotational alignment of volumes with missing data in Fourier space. J Struct Biol 2013; 184:345-7. [PMID: 23994045 DOI: 10.1016/j.jsb.2013.08.006] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2013] [Revised: 08/12/2013] [Accepted: 08/13/2013] [Indexed: 11/27/2022]
Abstract
Electron tomography of intact cells has the potential to reveal the entire cellular content at a resolution corresponding to individual macromolecular complexes. Characterization of macromolecular complexes in tomograms is nevertheless an extremely challenging task due to the high level of noise, and due to the limited tilt angle that results in missing data in Fourier space. By identifying particles of the same type and averaging their 3D volumes, it is possible to obtain a structure at a more useful resolution for biological interpretation. Currently, classification and averaging of sub-tomograms is limited by the speed of computational methods that optimize alignment between two sub-tomographic volumes. The alignment optimization is hampered by the fact that the missing data in Fourier space has to be taken into account during the rotational search. A similar problem appears in single particle electron microscopy where the random conical tilt procedure may require averaging of volumes with a missing cone in Fourier space. We present a fast implementation of a method guaranteed to find an optimal rotational alignment that maximizes the constrained cross-correlation function (cCCF) computed over the actual overlap of data in Fourier space.
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Affiliation(s)
- Maxim Shatsky
- Physical Biosciences Division, Lawrence Berkeley National Laboratory, CA 94720, USA.
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Shin SH, Comolli LR, Tscheliessnig R, Wang C, Nam KT, Hexemer A, Siegerist CE, De Yoreo JJ, Bertozzi CR. Self-assembly of "S-bilayers", a step toward expanding the dimensionality of S-layer assemblies. ACS NANO 2013; 7:4946-4953. [PMID: 23705800 DOI: 10.1021/nn400263j] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
Protein-based assemblies with ordered nanometer-scale features in three dimensions are of interest as functional nanomaterials but are difficult to generate. Here we report that a truncated S-layer protein assembles into stable bilayers, which we characterized using cryogenic-electron microscopy, tomography, and X-ray spectroscopy. We find that emergence of this supermolecular architecture is the outcome of hierarchical processes; the proteins condense in solution to form 2-D crystals, which then stack parallel to one another to create isotropic bilayered assemblies. Within this bilayered structure, registry between lattices in two layers was disclosed, whereas the intrinsic symmetry in each layer was altered. Comparison of these data to images of wild-type SbpA layers on intact cells gave insight into the interactions responsible for bilayer formation. These results establish a platform for engineering S-layer assemblies with 3-D architecture.
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Affiliation(s)
- Seong-Ho Shin
- Molecular Foundry, Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA
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15
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Fast and accurate reference-free alignment of subtomograms. J Struct Biol 2013; 182:235-45. [DOI: 10.1016/j.jsb.2013.03.002] [Citation(s) in RCA: 61] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2013] [Revised: 03/06/2013] [Accepted: 03/11/2013] [Indexed: 11/17/2022]
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Fernandez JJ. Computational methods for electron tomography. Micron 2012; 43:1010-30. [DOI: 10.1016/j.micron.2012.05.003] [Citation(s) in RCA: 86] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2012] [Revised: 05/08/2012] [Accepted: 05/08/2012] [Indexed: 01/13/2023]
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Xu M, Alber F. High precision alignment of cryo-electron subtomograms through gradient-based parallel optimization. BMC SYSTEMS BIOLOGY 2012; 6 Suppl 1:S18. [PMID: 23046491 PMCID: PMC3403359 DOI: 10.1186/1752-0509-6-s1-s18] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
BACKGROUND Cryo-electron tomography emerges as an important component for structural system biology. It not only allows the structural characterization of macromolecular complexes, but also the detection of their cellular localizations in near living conditions. However, the method is hampered by low resolution, missing data and low signal-to-noise ratio (SNR). To overcome some of these difficulties and enhance the nominal resolution one can align and average a large set of subtomograms. Existing methods for obtaining the optimal alignments are mostly based on an exhaustive scanning of all but discrete relative rigid transformations (i.e. rotations and translations) of one subtomogram with respect to the other. RESULTS In this paper, we propose gradient-guided alignment methods based on two popular subtomogram similarity measures, a real space as well as a Fourier-space constrained score. We also propose a stochastic parallel refinement method that increases significantly the efficiency for the simultaneous refinement of a set of alignment candidates. We estimate that our stochastic parallel refinement is on average about 20 to 40 fold faster in comparison to the standard independent refinement approach. Results on simulated data of model complexes and experimental structures of protein complexes show that even for highly distorted subtomograms and with only a small number of very sparsely distributed initial alignment seeds, our combined methods can accurately recover true transformations with a substantially higher precision than the scanning based alignment methods. CONCLUSIONS Our methods increase significantly the efficiency and accuracy for subtomogram alignments, which is a key factor for the systematic classification of macromolecular complexes in cryo-electron tomograms of whole cells.
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Affiliation(s)
- Min Xu
- Program in Molecular and Computational Biology, University of Southern California, Los Angeles, CA 90089, USA
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Kudryashev M, Stahlberg H, Castaño-Díez D. Assessing the benefits of focal pair cryo-electron tomography. J Struct Biol 2012; 178:88-97. [DOI: 10.1016/j.jsb.2011.10.004] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2011] [Revised: 10/12/2011] [Accepted: 10/19/2011] [Indexed: 01/28/2023]
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19
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High-throughput subtomogram alignment and classification by Fourier space constrained fast volumetric matching. J Struct Biol 2012; 178:152-64. [PMID: 22420977 DOI: 10.1016/j.jsb.2012.02.014] [Citation(s) in RCA: 47] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2011] [Revised: 12/22/2011] [Accepted: 02/27/2012] [Indexed: 11/21/2022]
Abstract
Cryo-electron tomography allows the visualization of macromolecular complexes in their cellular environments in close-to-live conditions. The nominal resolution of subtomograms can be significantly increased when individual subtomograms of the same kind are aligned and averaged. A vital step for such a procedure are algorithms that speedup subtomogram alignment and improve its accuracy to allow reference-free subtomogram classifications. Such methods will facilitate automation of tomography analysis and overall high throughput in the data processing. Building on previous work, here we propose a fast rotational alignment method that uses the Fourier equivalent form of a popular constrained correlation measure that considers missing wedge corrections and density variances in the subtomograms. The fast rotational search is based on 3D volumetric matching, which improves the rotational alignment accuracy in particular for highly distorted subtomograms with low SNR and tilt angle ranges in comparison to fast rotational matching of projected 2D spherical images. We further integrate our fast rotational alignment method in a reference-free iterative subtomogram classification scheme, and propose a local feature enhancement strategy in the classification process. As a proof of principle, we can demonstrate that the automatic method can successfully classify a large number of experimental subtomograms without the need of a reference structure.
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Castaño-Díez D, Kudryashev M, Arheit M, Stahlberg H. Dynamo: a flexible, user-friendly development tool for subtomogram averaging of cryo-EM data in high-performance computing environments. J Struct Biol 2012; 178:139-51. [PMID: 22245546 DOI: 10.1016/j.jsb.2011.12.017] [Citation(s) in RCA: 273] [Impact Index Per Article: 22.8] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2011] [Revised: 12/20/2011] [Accepted: 12/28/2011] [Indexed: 02/07/2023]
Abstract
Dynamo is a new software package for subtomogram averaging of cryo Electron Tomography (cryo-ET) data with three main goals: first, Dynamo allows user-transparent adaptation to a variety of high-performance computing platforms such as GPUs or CPU clusters. Second, Dynamo implements user-friendliness through GUI interfaces and scripting resources. Third, Dynamo offers user-flexibility through a plugin API. Besides the alignment and averaging procedures, Dynamo includes native tools for visualization and analysis of results and data, as well as support for third party visualization software, such as Chimera UCSF or EMAN2. As a demonstration of these functionalities, we studied bacterial flagellar motors and showed automatically detected classes with absent and present C-rings. Subtomogram averaging is a common task in current cryo-ET pipelines, which requires extensive computational resources and follows a well-established workflow. However, due to the data diversity, many existing packages offer slight variations of the same algorithm to improve results. One of the main purposes behind Dynamo is to provide explicit tools to allow the user the insertion of custom designed procedures - or plugins - to replace or complement the native algorithms in the different steps of the processing pipeline for subtomogram averaging without the burden of handling parallelization. Custom scripts that implement new approaches devised by the user are integrated into the Dynamo data management system, so that they can be controlled by the GUI or the scripting capacities. Dynamo executables do not require licenses for third party commercial software. Sources, executables and documentation are freely distributed on http://www.dynamo-em.org.
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Affiliation(s)
- Daniel Castaño-Díez
- Center for Cellular Imaging and Nano Analytics (C-CINA), Biozentrum, University of Basel, Mattenstrasse 26, CH-4058 Basel, Switzerland.
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21
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Hrabe T, Chen Y, Pfeffer S, Cuellar LK, Mangold AV, Förster F. PyTom: a python-based toolbox for localization of macromolecules in cryo-electron tomograms and subtomogram analysis. J Struct Biol 2011; 178:177-88. [PMID: 22193517 DOI: 10.1016/j.jsb.2011.12.003] [Citation(s) in RCA: 155] [Impact Index Per Article: 11.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2011] [Revised: 11/25/2011] [Accepted: 12/05/2011] [Indexed: 10/14/2022]
Abstract
Cryo-electron tomography (CET) is a three-dimensional imaging technique for structural studies of macromolecules under close-to-native conditions. In-depth analysis of macromolecule populations depicted in tomograms requires identification of subtomograms corresponding to putative particles, averaging of subtomograms to enhance their signal, and classification to capture the structural variations among them. Here, we introduce the open-source platform PyTom that unifies standard tomogram processing steps in a python toolbox. For subtomogram averaging, we implemented an adaptive adjustment of scoring and sampling that clearly improves the resolution of averages compared to static strategies. Furthermore, we present a novel stochastic classification method that yields significantly more accurate classification results than two deterministic approaches in simulations. We demonstrate that the PyTom workflow yields faithful results for alignment and classification of simulated and experimental subtomograms of ribosomes and GroEL(14)/GroEL(14)GroES(7), respectively, as well as for the analysis of ribosomal 60S subunits in yeast cell lysate. PyTom enables parallelized processing of large numbers of tomograms, but also provides a convenient, sustainable environment for algorithmic development.
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Affiliation(s)
- Thomas Hrabe
- Max-Planck Institute of Biochemistry, Am Klopferspitz 18, D-82152 Martinsried, Germany
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Xu M, Alber F. Gradient-based high precision alignment of cryo-electron subtomograms. IEEE INTERNATIONAL CONFERENCE ON SYSTEMS BIOLOGY : [PROCEEDINGS]. IEEE INTERNATIONAL CONFERENCE ON SYSTEMS BIOLOGY 2011:279-284. [PMID: 25068871 DOI: 10.1109/isb.2011.6033166] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
Whole cell cryo-electron tomography emerges as an important component for structural system biology approaches. It allows the localization and structural characterization of macromolecular complexes in near living conditions. However, the method is hampered by low resolution, missing data and low signal-to-noise ratio (SNR). To overcome some of these difficulties one can align and average a large set of subtomograms. Existing alignment methods are mostly based on an exhaustive scanning and sampling of all but discrete relative rotations and translations of one subtomogram with respect to the other. In this paper, we propose a gradient-guided alignment method based on two subtomogram similarity measures. We also propose a stochastic parallel optimization that increases significantly the efficiency for the simultaneous refinement of a set of alignment candidates. Results on simulated data of model complexes and experimental structures of protein complexes show that even for highly distorted subtomograms and with only a small number of very sparsely distributed initial alignment seeds, our method can accurately recover true transformations with a significantly higher precision than scanning based alignment methods.
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Affiliation(s)
- Min Xu
- Program in Molecular and Computational Biology University of Southern California, Los Angeles, CA 90089, USA
| | - Frank Alber
- Program in Molecular and Computational Biology University of Southern California, Los Angeles, CA 90089, USA
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Fogarty KH, Zhang W, Grigsby IF, Johnson JL, Chen Y, Mueller JD, Mansky LM. New insights into HTLV-1 particle structure, assembly, and Gag-Gag interactions in living cells. Viruses 2011; 3:770-93. [PMID: 21994753 PMCID: PMC3185773 DOI: 10.3390/v3060770] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2011] [Revised: 05/20/2011] [Accepted: 05/20/2011] [Indexed: 11/16/2022] Open
Abstract
Human T-cell leukemia virus type 1 (HTLV-1) has a reputation for being extremely difficult to study in cell culture. The challenges in propagating HTLV-1 has prevented a rigorous analysis of how these viruses replicate in cells, including the detailed steps involved in virus assembly. The details for how retrovirus particle assembly occurs are poorly understood, even for other more tractable retroviral systems. Recent studies on HTLV-1 using state-of-the-art cryo-electron microscopy and fluorescence-based biophysical approaches explored questions related to HTLV-1 particle size, Gag stoichiometry in virions, and Gag-Gag interactions in living cells. These results provided new and exciting insights into fundamental aspects of HTLV-1 particle assembly-which are distinct from those of other retroviruses, including HIV-1. The application of these and other novel biophysical approaches promise to provide exciting new insights into HTLV-1 replication.
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Affiliation(s)
- Keir H. Fogarty
- Institute for Molecular Virology, University of Minnesota, Minneapolis, 18-242 Moos Tower, 515 Delaware St. SE, Minneapolis, MN 55455, USA; E-Mails: (K.H.F.); (W.Z.); (I.F.G.); (Y.C.); (J.D.M.)
- School of Physics and Astronomy, University of Minnesota, Minneapolis, MN 55455, USA; E-Mail: (J.L.J.)
| | - Wei Zhang
- Institute for Molecular Virology, University of Minnesota, Minneapolis, 18-242 Moos Tower, 515 Delaware St. SE, Minneapolis, MN 55455, USA; E-Mails: (K.H.F.); (W.Z.); (I.F.G.); (Y.C.); (J.D.M.)
- Department of Diagnostic and Biological Sciences, School of Dentistry, University of Minnesota, Minneapolis, MN 55455, USA
| | - Iwen F. Grigsby
- Institute for Molecular Virology, University of Minnesota, Minneapolis, 18-242 Moos Tower, 515 Delaware St. SE, Minneapolis, MN 55455, USA; E-Mails: (K.H.F.); (W.Z.); (I.F.G.); (Y.C.); (J.D.M.)
- Department of Diagnostic and Biological Sciences, School of Dentistry, University of Minnesota, Minneapolis, MN 55455, USA
| | - Jolene L. Johnson
- School of Physics and Astronomy, University of Minnesota, Minneapolis, MN 55455, USA; E-Mail: (J.L.J.)
| | - Yan Chen
- Institute for Molecular Virology, University of Minnesota, Minneapolis, 18-242 Moos Tower, 515 Delaware St. SE, Minneapolis, MN 55455, USA; E-Mails: (K.H.F.); (W.Z.); (I.F.G.); (Y.C.); (J.D.M.)
- School of Physics and Astronomy, University of Minnesota, Minneapolis, MN 55455, USA; E-Mail: (J.L.J.)
| | - Joachim D. Mueller
- Institute for Molecular Virology, University of Minnesota, Minneapolis, 18-242 Moos Tower, 515 Delaware St. SE, Minneapolis, MN 55455, USA; E-Mails: (K.H.F.); (W.Z.); (I.F.G.); (Y.C.); (J.D.M.)
- School of Physics and Astronomy, University of Minnesota, Minneapolis, MN 55455, USA; E-Mail: (J.L.J.)
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN 55455, USA
| | - Louis M. Mansky
- Institute for Molecular Virology, University of Minnesota, Minneapolis, 18-242 Moos Tower, 515 Delaware St. SE, Minneapolis, MN 55455, USA; E-Mails: (K.H.F.); (W.Z.); (I.F.G.); (Y.C.); (J.D.M.)
- Department of Diagnostic and Biological Sciences, School of Dentistry, University of Minnesota, Minneapolis, MN 55455, USA
- Department of Microbiology, Medical School, University of Minnesota, Minneapolis, MN 55455, USA
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Analysis of the intact surface layer of Caulobacter crescentus by cryo-electron tomography. J Bacteriol 2010; 192:5855-65. [PMID: 20833802 DOI: 10.1128/jb.00747-10] [Citation(s) in RCA: 25] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
The surface layers (S layers) of those bacteria and archaea that elaborate these crystalline structures have been studied for 40 years. However, most structural analysis has been based on electron microscopy of negatively stained S-layer fragments separated from cells, which can introduce staining artifacts and allow rearrangement of structures prone to self-assemble. We present a quantitative analysis of the structure and organization of the S layer on intact growing cells of the Gram-negative bacterium Caulobacter crescentus using cryo-electron tomography (CET) and statistical image processing. Instead of the expected long-range order, we observed different regions with hexagonally organized subunits exhibiting short-range order and a broad distribution of periodicities. Also, areas of stacked double layers were found, and these increased in extent when the S-layer protein (RsaA) expression level was elevated by addition of multiple rsaA copies. Finally, we combined high-resolution amino acid residue-specific Nanogold labeling and subtomogram averaging of CET volumes to improve our understanding of the correlation between the linear protein sequence and the structure at the 2-nm level of resolution that is presently available. The results support the view that the U-shaped RsaA monomer predicted from negative-stain tomography proceeds from the N terminus at one vertex, corresponding to the axis of 3-fold symmetry, to the C terminus at the opposite vertex, which forms the prominent 6-fold symmetry axis. Such information will help future efforts to analyze subunit interactions and guide selection of internal sites for display of heterologous protein segments.
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