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Hartley M, Kleywegt GJ, Patwardhan A, Sarkans U, Swedlow JR, Brazma A. The BioImage Archive - Building a Home for Life-Sciences Microscopy Data. J Mol Biol 2022; 434:167505. [PMID: 35189131 DOI: 10.1016/j.jmb.2022.167505] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Revised: 02/04/2022] [Accepted: 02/15/2022] [Indexed: 01/15/2023]
Abstract
Despite the huge impact of data resources in genomics and structural biology, until now there has been no central archive for biological data for all imaging modalities. The BioImage Archive is a new data resource at the European Bioinformatics Institute (EMBL-EBI) designed to fill this gap. In its initial development BioImage Archive accepts bioimaging data associated with publications, in any format, from any imaging modality from the molecular to the organism scale, excluding medical imaging. The BioImage Archive will ensure reproducibility of published studies that derive results from image data and reduce duplication of effort. Most importantly, the BioImage Archive will help scientists to generate new insights through reuse of existing data to answer new biological questions, and provision of training, testing and benchmarking data for development of tools for image analysis. The archive is available at https://www.ebi.ac.uk/bioimage-archive/.
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Affiliation(s)
- Matthew Hartley
- European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK.
| | - Gerard J Kleywegt
- European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Ardan Patwardhan
- European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Ugis Sarkans
- European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Jason R Swedlow
- Division of Computational Biology, Centre for Gene Regulation and Expression, School of Life Sciences, University of Dundee, Dundee, UK
| | - Alvis Brazma
- European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK.
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Zhou Y, Moscovich A, Bartesaghi A. Data-driven determination of number of discrete conformations in single-particle cryo-EM. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 221:106892. [PMID: 35597206 PMCID: PMC10131080 DOI: 10.1016/j.cmpb.2022.106892] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Revised: 05/11/2022] [Accepted: 05/12/2022] [Indexed: 05/20/2023]
Abstract
BACKGROUND AND OBJECTIVE One of the strengths of single-particle cryo-EM compared to other structural determination techniques is its ability to image heterogeneous samples containing multiple molecular species, different oligomeric states or distinct conformations. This is achieved using routines for in-silico 3D classification that are now well established in the field and have successfully been used to characterize the structural heterogeneity of important biomolecules. These techniques, however, rely on expert-user knowledge and trial-and-error experimentation to determine the correct number of conformations, making it a labor intensive, subjective, and difficult to reproduce procedure. METHODS We propose an approach to address the problem of automatically determining the number of discrete conformations present in heterogeneous single-particle cryo-EM datasets. We do this by systematically evaluating all possible partitions of the data and selecting the result that maximizes the average variance of similarities measured between particle images and the corresponding 3D reconstructions. RESULTS Using this strategy, we successfully analyzed datasets of heterogeneous protein complexes, including: 1) in-silico mixtures obtained by combining closely related antibody-bound HIV-1 Env trimers and other important membrane channels, and 2) naturally occurring mixtures from diverse and dynamic protein complexes representing varying degrees of structural heterogeneity and conformational plasticity. CONCLUSIONS The availability of unsupervised strategies for 3D classification combined with existing approaches for fully automatic pre-processing and 3D refinement, represents an important step towards converting single-particle cryo-EM into a high-throughput technique.
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Affiliation(s)
- Ye Zhou
- Department of Computer Science, Duke University, Durham, NC 27708, USA
| | - Amit Moscovich
- Department of Statistics and Operations Research, Tel Aviv University, Tel Aviv, Israel
| | - Alberto Bartesaghi
- Department of Computer Science, Duke University, Durham, NC 27708, USA; Department of Biochemistry, Duke University School of Medicine, Durham, NC 27708, USA; Department of Electrical and Computer Engineering, Duke University, Durham, NC 27708, USA.
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Jiménez de la Morena J, Conesa P, Fonseca YC, de Isidro-Gómez FP, Herreros D, Fernández-Giménez E, Strelak D, Moebel E, Buchholz TO, Jug F, Martinez-Sanchez A, Harastani M, Jonic S, Conesa JJ, Cuervo A, Losana P, Sánchez I, Iceta M, Del Cano L, Gragera M, Melero R, Sharov G, Castaño-Díez D, Koster A, Piccirillo JG, Vilas JL, Otón J, Marabini R, Sorzano COS, Carazo JM. ScipionTomo: Towards cryo-electron tomography software integration, reproducibility, and validation. J Struct Biol 2022; 214:107872. [PMID: 35660516 PMCID: PMC7613607 DOI: 10.1016/j.jsb.2022.107872] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Revised: 05/26/2022] [Accepted: 05/28/2022] [Indexed: 11/25/2022]
Abstract
Image processing in cryogenic electron tomography (cryoET) is currently at a similar state as Single Particle Analysis (SPA) in cryogenic electron microscopy (cryoEM) was a few years ago. Its data processing workflows are far from being well defined and the user experience is still not smooth. Moreover, file formats of different software packages and their associated metadata are not standardized, mainly since different packages are developed by different groups, focusing on different steps of the data processing pipeline. The Scipion framework, originally developed for SPA (de la Rosa-Trevín et al., 2016), has a generic python workflow engine that gives it the versatility to be extended to other fields, as demonstrated for model building (Martínez et al., 2020). In this article, we provide an extension of Scipion based on a set of tomography plugins (referred to as ScipionTomo hereafter), with a similar purpose: to allow users to be focused on the data processing and analysis instead of having to deal with multiple software installation issues and the inconvenience of switching from one to another, converting metadata files, managing possible incompatibilities, scripting (writing a simple program in a language that the computer must convert to machine language each time the program is run), etcetera. Additionally, having all the software available in an integrated platform allows comparing the results of different algorithms trying to solve the same problem. In this way, the commonalities and differences between estimated parameters shed light on which results can be more trusted than others. ScipionTomo is developed by a collaborative multidisciplinary team composed of Scipion team engineers, structural biologists, and in some cases, the developers whose software packages have been integrated. It is open to anyone in the field willing to contribute to this project. The result is a framework extension that combines the acquired knowledge of Scipion developers in close collaboration with third-party developers, and the on-demand design of functionalities requested by beta testers applying this solution to actual biological problems.
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Affiliation(s)
| | - P Conesa
- National Center of Biotechnology (CNB-CSIC), Madrid, Spain
| | - Y C Fonseca
- National Center of Biotechnology (CNB-CSIC), Madrid, Spain
| | | | - D Herreros
- National Center of Biotechnology (CNB-CSIC), Madrid, Spain
| | | | - D Strelak
- National Center of Biotechnology (CNB-CSIC), Madrid, Spain; Masaryk University, Brno, Czech Republic
| | - E Moebel
- Inria Rennes - Bretagne Atlantique, Rennes
| | - T O Buchholz
- Max Planck Institute of Molecular Cell Biology and Genetics (MPI-CBG), Germany; Center for Systems Biology Dresden (CSBD), Germany
| | - F Jug
- Max Planck Institute of Molecular Cell Biology and Genetics (MPI-CBG), Germany; Fondazione Human Technopole, Milan, Italy
| | - A Martinez-Sanchez
- University of Oviedo, Department of Computer Sciences, Oviedo, Spain; Health Research Institute of Asturias (ISPA), Oviedo, Spain
| | - M Harastani
- IMPMC-UMR 7590 CNRS, Sorbonne Université, MNHN, Paris, France
| | - S Jonic
- IMPMC-UMR 7590 CNRS, Sorbonne Université, MNHN, Paris, France
| | - J J Conesa
- National Center of Biotechnology (CNB-CSIC), Madrid, Spain
| | - A Cuervo
- National Center of Biotechnology (CNB-CSIC), Madrid, Spain
| | - P Losana
- National Center of Biotechnology (CNB-CSIC), Madrid, Spain
| | - I Sánchez
- National Center of Biotechnology (CNB-CSIC), Madrid, Spain
| | - M Iceta
- National Center of Biotechnology (CNB-CSIC), Madrid, Spain
| | - L Del Cano
- National Center of Biotechnology (CNB-CSIC), Madrid, Spain
| | - M Gragera
- National Center of Biotechnology (CNB-CSIC), Madrid, Spain
| | - R Melero
- National Center of Biotechnology (CNB-CSIC), Madrid, Spain
| | - G Sharov
- Structural Studies Division, MRC Laboratory of Molecular Biology, Cambridge, United Kingdom
| | - D Castaño-Díez
- BioEM Lab, Biozentrum, University of Basel, Basel, Switzerland
| | - A Koster
- University of Leiden, Ultrastructural and molecular imaging, Leiden, The Netherlands
| | - J G Piccirillo
- National Center of Biotechnology (CNB-CSIC), Madrid, Spain
| | - J L Vilas
- National Center of Biotechnology (CNB-CSIC), Madrid, Spain
| | - J Otón
- Alba Synchrotron - CELLS (ICTS), Barcelona, Spain
| | - R Marabini
- National Center of Biotechnology (CNB-CSIC), Madrid, Spain; Superior Polytechnic School. Univ. Autónoma of Madrid. Madrid, Spain
| | - C O S Sorzano
- National Center of Biotechnology (CNB-CSIC), Madrid, Spain
| | - J M Carazo
- National Center of Biotechnology (CNB-CSIC), Madrid, Spain
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Heymann JB. The progressive spectral signal-to-noise ratio of cryo-electron micrograph movies as a tool to assess quality and radiation damage. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 220:106799. [PMID: 35405434 PMCID: PMC9149132 DOI: 10.1016/j.cmpb.2022.106799] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/28/2021] [Revised: 03/15/2022] [Accepted: 03/31/2022] [Indexed: 06/03/2023]
Abstract
BACKGROUND AND OBJECTIVE The quality of a cryoEM reconstruction is fundamentally a function of the signal-to-noise ratio (SNR) of the original micrographs. The SNR embodies multiple aspects of image formation, including microscope details and alignment, specimen composition and thickness, how it is recorded, and how the specimen degrades during imaging. With the advent of direct electron detectors and the recording of a series of images for each micrograph (a movie), we have an opportunity to count every electron and derive fully quantitative results. After alignment of the movie frames of a micrograph, we can calculate the SNR, or its spatial frequency equivalent, the spectral SNR (SSNR). This SSNR reflects residual movement between frames and the progressive effect of radiation damage. The goal is to develop a quantitative analysis of the SSNR and radiation damage to assess and improve the quality of micrographs. METHODS Several test cases were selected from the EMPIAR database and ten micrograph movies downloaded for each case. The movie frames were aligned as rigid bodies to compensate for stage and support movement. The SSNR for subsets of frames was then calculated to assess the effect of residual movement. The progressive SSNR (PSSNR) was subsequently calculated to determine the decrease in signal accumulation as a result of radiation damage. RESULTS In all cases the alignment of the movie frames compensated for global movement to the extent that the effect on the SSNR is negligible. The subset SSNR can be used as a tool to further confirm the extent of residual movement. The progressive SSNR indicates an increase in value up to an asymptote, consistent with the theory for radiation damage. Fitting these curves gives the inherent SNR before exposure, and the critical dose, which decreases with spatial frequency with an exponential parameter roughly between one and two. CONCLUSIONS The implementation of the PSSNR for movie frames provides a tool for assessing micrograph quality and progression of radiation damage. The estimation of the critical dose further quantifies radiation damage and may shed some light on the mechanisms of damage. These are likely both a function of the specimen composition and the imaging parameters used.
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Affiliation(s)
- J Bernard Heymann
- Laboratory for Structural Biology Research, NIAMS, NIH, Bethesda, MD 20892, USA; National Cryo-EM Program, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Leidos Biomedical Research, Inc., Frederick, MD 21701, USA.
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González-Ruiz V, García-Ortiz JP, Fernández-Fernández MR, Fernández JJ. Optical flow driven interpolation for isotropic FIB-SEM reconstructions. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 221:106856. [PMID: 35544963 DOI: 10.1016/j.cmpb.2022.106856] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Revised: 04/25/2022] [Accepted: 05/03/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND AND OBJECTIVE Focused Ion Beam - Scanning Electron Microscopy (FIB-SEM) allows three-dimensional ultrastructural analysis of cells and tissues at the nanoscale. The technique iteratively removes a section of the sample with a FIB and takes an SEM image from the exposed surface. The section thickness is usually higher than the image pixel size to reduce acquisition time, thus resulting in anisotropic resolution. In this work, we explore novel interpolation methods along the sectioning direction to produce isotropic resolution and facilitate proper interpretation of the FIB-SEM 3D volumes. METHODS Classical interpolation methods are usually applied in this context under the assumption that the changes through successive images are relatively smooth. However, the actual 3D arrangement of the structures in the sample may cause significant changes in the biological features between consecutive images of the FIB-SEM stacks. We have developed a novel interpolation strategy that accounts for this variation by using the Optical Flow (OF) to estimate it. As an intermediate stage, OF-compensated images are produced by aligning the spatial regions of the biological structures. Interpolated images are then generated from these OF-compensated images. The final isotropic stack is assembled by interleaving the interpolated images with the original images of the anisotropic stack. RESULTS OF-driven and classical interpolation methods were compared using an objective assessment based on Pearson Correlation Coefficient (PCC) and a qualitative evaluation based on visual results, using public datasets and representative anisotropy conditions. The objective assessment demonstrated that the OF-driven interpolation always yields higher PCC values, with interpolated images closer to the ground truth. The qualitative evaluation corroborated those results and confirmed that classical interpolation may blur areas with substantial changes between consecutive images whereas OF-driven interpolation provides sharpness. CONCLUSIONS We have developed an OF-driven interpolation approach to generating FIB-SEM stacks with isotropic resolution from experimental anisotropic data. It adapts to the rapid variation of the biological structures observed through the images of the FIB-SEM stack. Our approach outperforms classical interpolation and manages to produce sharp interpolated views in cases where there are significant changes between consecutive experimental images.
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Affiliation(s)
- V González-Ruiz
- University of Almeria, Informatics Department, Agrifood Campus of International Excellence (ceiA3), Ctra. Sacramento, s/n, Almeria, 04120, Spain.
| | - J P García-Ortiz
- University of Almeria, Informatics Department, Agrifood Campus of International Excellence (ceiA3), Ctra. Sacramento, s/n, Almeria, 04120, Spain
| | - M R Fernández-Fernández
- Spanish National Research Council (CINN-CSIC). Health Research Institute of Asturias (ISPA), Av Hospital Universitario s/n, Oviedo, 33011, Spain
| | - J J Fernández
- Spanish National Research Council (CINN-CSIC). Health Research Institute of Asturias (ISPA), Av Hospital Universitario s/n, Oviedo, 33011, Spain.
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Wang Z, Patwardhan A, Kleywegt GJ. Validation analysis of EMDB entries. Acta Crystallogr D Struct Biol 2022; 78:542-552. [PMID: 35503203 PMCID: PMC9063848 DOI: 10.1107/s205979832200328x] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Accepted: 03/23/2022] [Indexed: 11/17/2022] Open
Abstract
The Electron Microscopy Data Bank (EMDB) is the central archive of the electron cryo-microscopy (cryo-EM) community for storing and disseminating volume maps and tomograms. With input from the community, EMDB has developed new resources for the validation of cryo-EM structures, focusing on the quality of the volume data alone and that of the fit of any models, themselves archived in the Protein Data Bank (PDB), to the volume data. Based on recommendations from community experts, the validation resources are developed in a three-tiered system. Tier 1 covers an extensive and evolving set of validation metrics, including tried and tested metrics as well as more experimental ones, which are calculated for all EMDB entries and presented in the Validation Analysis (VA) web resource. This system is particularly useful for cryo-EM experts, both to validate individual structures and to assess the utility of new validation metrics. Tier 2 comprises a subset of the validation metrics covered by the VA resource that have been subjected to extensive testing and are considered to be useful for specialists as well as nonspecialists. These metrics are presented on the entry-specific web pages for the entire archive on the EMDB website. As more experience is gained with the metrics included in the VA resource, it is expected that consensus will emerge in the community regarding a subset that is suitable for inclusion in the tier 2 system. Tier 3, finally, consists of the validation reports and servers that are produced by the Worldwide Protein Data Bank (wwPDB) Consortium. Successful metrics from tier 2 will be proposed for inclusion in the wwPDB validation pipeline and reports. The details of the new resource are described, with an emphasis on the tier 1 system. The output of all three tiers is publicly available, either through the EMDB website (tiers 1 and 2) or through the wwPDB ftp sites (tier 3), although the content of all three will evolve over time (fastest for tier 1 and slowest for tier 3). It is our hope that these validation resources will help the cryo-EM community to obtain a better understanding of the quality and of the best ways to assess the quality of cryo-EM structures in EMDB and PDB.
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Affiliation(s)
- Zhe Wang
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL–EBI), Wellcome Genome Campus, Hinxton CB10 1SD, United Kingdom
| | - Ardan Patwardhan
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL–EBI), Wellcome Genome Campus, Hinxton CB10 1SD, United Kingdom
| | - Gerard J. Kleywegt
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL–EBI), Wellcome Genome Campus, Hinxton CB10 1SD, United Kingdom
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Eldar A, Amos I, Shkolnisky Y. ASOCEM: Automatic Segmentation Of Contaminations in cryo-EM. J Struct Biol 2022; 214:107871. [DOI: 10.1016/j.jsb.2022.107871] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Revised: 05/10/2022] [Accepted: 05/17/2022] [Indexed: 11/25/2022]
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Madeira F, Pearce M, Tivey ARN, Basutkar P, Lee J, Edbali O, Madhusoodanan N, Kolesnikov A, Lopez R. Search and sequence analysis tools services from EMBL-EBI in 2022. Nucleic Acids Res 2022; 50:W276-W279. [PMID: 35412617 PMCID: PMC9252731 DOI: 10.1093/nar/gkac240] [Citation(s) in RCA: 1121] [Impact Index Per Article: 560.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Accepted: 03/28/2022] [Indexed: 12/11/2022] Open
Abstract
The EMBL-EBI search and sequence analysis tools frameworks provide integrated access to EMBL-EBI’s data resources and core bioinformatics analytical tools. EBI Search (https://www.ebi.ac.uk/ebisearch) provides a full-text search engine across nearly 5 billion entries, while the Job Dispatcher tools framework (https://www.ebi.ac.uk/services) enables the scientific community to perform a diverse range of sequence analysis using popular bioinformatics applications. Both allow users to interact through user-friendly web applications, as well as via RESTful and SOAP-based APIs. Here, we describe recent improvements to these services and updates made to accommodate the increasing data requirements during the COVID-19 pandemic.
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Affiliation(s)
- Fábio Madeira
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Matt Pearce
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Adrian R N Tivey
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Prasad Basutkar
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Joon Lee
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Ossama Edbali
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Nandana Madhusoodanan
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Anton Kolesnikov
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Rodrigo Lopez
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
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Gomez-Blanco J, Kaur S, Strauss M, Vargas J. Hierarchical autoclassification of cryo-EM samples and macromolecular energy landscape determination. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 216:106673. [PMID: 35149430 DOI: 10.1016/j.cmpb.2022.106673] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/22/2021] [Revised: 01/10/2022] [Accepted: 01/28/2022] [Indexed: 06/14/2023]
Abstract
BACKGROUND AND OBJECTIVE Cryo-electron microscopy using single particle analysis is a powerful technique for obtaining 3D reconstructions of macromolecules in near native conditions. One of its major advances is its capacity to reveal conformations of dynamic molecular complexes. Most popular and successful current approaches to analyzing heterogeneous complexes are founded on Bayesian inference. However, these 3D classification methods require the tuning of specific parameters by the user and the use of complicated 3D re-classification procedures for samples affected by extensive heterogeneity. Thus, the success of these approaches highly depends on the user experience. We introduce a robust approach to identify many different conformations presented in a cryo-EM dataset based on Bayesian inference through Relion classification methods that does not require tuning of parameters and reclassification strategies. METHODS The algorithm allows both 2D and 3D classification and is based on a hierarchical clustering approach that runs automatically without requiring typical inputs, such as the number of conformations present in the dataset or the required classification iterations. This approach is applied to robustly determine the energy landscapes of macromolecules. RESULTS We tested the performance of the methods proposed here using four different datasets, comprising structurally homogeneous and highly heterogeneous cases. In all cases, the approach provided excellent results. The routines are publicly available as part of the CryoMethods plugin included in the Scipion package. CONCLUSIONS Our results show that the proposed method can be used to align and classify homogeneous and heterogeneous datasets without requiring previous alignment information or any prior knowledge about the number of co-existing conformations. The approach can be used for both 2D and 3D autoclassification and only requires an initial volume. In addition, the approach is robust to the "attractor" problem providing many different conformations/views for samples affected by extensive heterogeneity. The obtained 3D classes can render high resolution 3D structures, while the obtained energy landscapes can be used to determine structural trajectories.
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Affiliation(s)
- J Gomez-Blanco
- Departamento de Óptica, Universidad Complutense de Madrid, Plaza de Ciencias 1, 28040, Spain
| | - S Kaur
- Department of Anatomy and Cell Biology, McGill University, 3640 Rue University, Montréal, QC H3A 0C7, Canada
| | - M Strauss
- Department of Anatomy and Cell Biology, McGill University, 3640 Rue University, Montréal, QC H3A 0C7, Canada
| | - J Vargas
- Departamento de Óptica, Universidad Complutense de Madrid, Plaza de Ciencias 1, 28040, Spain.
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Winfree S. User-Accessible Machine Learning Approaches for Cell Segmentation and Analysis in Tissue. Front Physiol 2022; 13:833333. [PMID: 35360226 PMCID: PMC8960722 DOI: 10.3389/fphys.2022.833333] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Accepted: 01/12/2022] [Indexed: 11/28/2022] Open
Abstract
Advanced image analysis with machine and deep learning has improved cell segmentation and classification for novel insights into biological mechanisms. These approaches have been used for the analysis of cells in situ, within tissue, and confirmed existing and uncovered new models of cellular microenvironments in human disease. This has been achieved by the development of both imaging modality specific and multimodal solutions for cellular segmentation, thus addressing the fundamental requirement for high quality and reproducible cell segmentation in images from immunofluorescence, immunohistochemistry and histological stains. The expansive landscape of cell types-from a variety of species, organs and cellular states-has required a concerted effort to build libraries of annotated cells for training data and novel solutions for leveraging annotations across imaging modalities and in some cases led to questioning the requirement for single cell demarcation all together. Unfortunately, bleeding-edge approaches are often confined to a few experts with the necessary domain knowledge. However, freely available, and open-source tools and libraries of trained machine learning models have been made accessible to researchers in the biomedical sciences as software pipelines, plugins for open-source and free desktop and web-based software solutions. The future holds exciting possibilities with expanding machine learning models for segmentation via the brute-force addition of new training data or the implementation of novel network architectures, the use of machine and deep learning in cell and neighborhood classification for uncovering cellular microenvironments, and the development of new strategies for the use of machine and deep learning in biomedical research.
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Affiliation(s)
- Seth Winfree
- Department of Pathology and Microbiology, University of Nebraska Medical Center, Omaha, NE, United States
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Parlakgül G, Arruda AP, Pang S, Cagampan E, Min N, Güney E, Lee GY, Inouye K, Hess HF, Xu CS, Hotamışlıgil GS. Regulation of liver subcellular architecture controls metabolic homeostasis. Nature 2022; 603:736-742. [PMID: 35264794 DOI: 10.1038/s41586-022-04488-5] [Citation(s) in RCA: 54] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2020] [Accepted: 01/31/2022] [Indexed: 12/28/2022]
Abstract
Cells display complex intracellular organization by compartmentalization of metabolic processes into organelles, yet the resolution of these structures in the native tissue context and their functional consequences are not well understood. Here we resolved the three-dimensional structural organization of organelles in large (more than 2.8 × 105 µm3) volumes of intact liver tissue (15 partial or full hepatocytes per condition) at high resolution (8 nm isotropic pixel size) using enhanced focused ion beam scanning electron microscopy1,2 imaging followed by deep-learning-based automated image segmentation and 3D reconstruction. We also performed a comparative analysis of subcellular structures in liver tissue of lean and obese mice and found substantial alterations, particularly in hepatic endoplasmic reticulum (ER), which undergoes massive structural reorganization characterized by marked disorganization of stacks of ER sheets3 and predominance of ER tubules. Finally, we demonstrated the functional importance of these structural changes by monitoring the effects of experimental recovery of the subcellular organization on cellular and systemic metabolism. We conclude that the hepatic subcellular organization of the ER architecture are highly dynamic, integrated with the metabolic state and critical for adaptive homeostasis and tissue health.
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Affiliation(s)
- Güneş Parlakgül
- Sabri Ülker Center of Metabolic Research and Department of Molecular Metabolism, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Ana Paula Arruda
- Sabri Ülker Center of Metabolic Research and Department of Molecular Metabolism, Harvard T.H. Chan School of Public Health, Boston, MA, USA.,Department of Nutritional Sciences and Toxicology, UC Berkeley, Berkeley, CA, USA
| | - Song Pang
- HHMI Janelia Research Campus, Ashburn, VA, USA
| | - Erika Cagampan
- Sabri Ülker Center of Metabolic Research and Department of Molecular Metabolism, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Nina Min
- Sabri Ülker Center of Metabolic Research and Department of Molecular Metabolism, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Ekin Güney
- Sabri Ülker Center of Metabolic Research and Department of Molecular Metabolism, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Grace Yankun Lee
- Sabri Ülker Center of Metabolic Research and Department of Molecular Metabolism, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Karen Inouye
- Sabri Ülker Center of Metabolic Research and Department of Molecular Metabolism, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | | | - C Shan Xu
- HHMI Janelia Research Campus, Ashburn, VA, USA
| | - Gökhan S Hotamışlıgil
- Sabri Ülker Center of Metabolic Research and Department of Molecular Metabolism, Harvard T.H. Chan School of Public Health, Boston, MA, USA. .,Broad Institute of MIT and Harvard, Cambridge, MA, USA.
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62
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Liedtke J, Depelteau JS, Briegel A. How advances in cryo-electron tomography have contributed to our current view of bacterial cell biology. J Struct Biol X 2022; 6:100065. [PMID: 35252838 PMCID: PMC8894267 DOI: 10.1016/j.yjsbx.2022.100065] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Revised: 02/17/2022] [Accepted: 02/23/2022] [Indexed: 12/13/2022] Open
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.
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Affiliation(s)
- Janine Liedtke
- Department of Microbial Sciences, Institute of Biology, Leiden University, Sylviusweg 72, 2333 BE Leiden, The Netherlands.,Centre for Microbial Cell Biology, Leiden University, Sylviusweg 72, 2333 BE Leiden, The Netherlands
| | - Jamie S Depelteau
- Department of Microbial Sciences, Institute of Biology, Leiden University, Sylviusweg 72, 2333 BE Leiden, The Netherlands.,Centre for Microbial Cell Biology, Leiden University, Sylviusweg 72, 2333 BE Leiden, The Netherlands
| | - Ariane Briegel
- Department of Microbial Sciences, Institute of Biology, Leiden University, Sylviusweg 72, 2333 BE Leiden, The Netherlands.,Centre for Microbial Cell Biology, Leiden University, Sylviusweg 72, 2333 BE Leiden, The Netherlands
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63
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Meschkat M, Steyer AM, Weil MT, Kusch K, Jahn O, Piepkorn L, Agüi-Gonzalez P, Phan NTN, Ruhwedel T, Sadowski B, Rizzoli SO, Werner HB, Ehrenreich H, Nave KA, Möbius W. White matter integrity in mice requires continuous myelin synthesis at the inner tongue. Nat Commun 2022; 13:1163. [PMID: 35246535 PMCID: PMC8897471 DOI: 10.1038/s41467-022-28720-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2020] [Accepted: 01/24/2022] [Indexed: 12/18/2022] Open
Abstract
Myelin, the electrically insulating sheath on axons, undergoes dynamic changes over time. However, it is composed of proteins with long lifetimes. This raises the question how such a stable structure is renewed. Here, we study the integrity of myelinated tracts after experimentally preventing the formation of new myelin in the CNS of adult mice, using an inducible Mbp null allele. Oligodendrocytes survive recombination, continue to express myelin genes, but they fail to maintain compacted myelin sheaths. Using 3D electron microscopy and mass spectrometry imaging we visualize myelin-like membranes failing to incorporate adaxonally, most prominently at juxta-paranodes. Myelinoid body formation indicates degradation of existing myelin at the abaxonal side and the inner tongue of the sheath. Thinning of compact myelin and shortening of internodes result in the loss of about 50% of myelin and axonal pathology within 20 weeks post recombination. In summary, our data suggest that functional axon-myelin units require the continuous incorporation of new myelin membranes. Myelin is formed of proteins of long half-lives. The mechanisms of renewal of such a stable structure are unclear. Here, the authors show that myelin integrity requires continuous myelin synthesis at the inner tongue, contributing to the maintenance of a functional axon-myelin unit.
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Affiliation(s)
- Martin Meschkat
- Department of Neurogenetics, Max Planck Institute of Experimental Medicine, Göttingen, Germany.,Electron Microscopy Core Unit, Max Planck Institute of Experimental Medicine, Göttingen, Germany.,Göttingen Graduate Center for Neurosciences, Biophysics, and Molecular Biosciences (GGNB), Göttingen, Germany.,DFG Research Center for Nanoscale Microscopy and Molecular Physiology of the Brain (CNMPB), Göttingen, Germany.,Abberior Instruments GmbH, Göttingen, Germany
| | - Anna M Steyer
- Department of Neurogenetics, Max Planck Institute of Experimental Medicine, Göttingen, Germany.,Electron Microscopy Core Unit, Max Planck Institute of Experimental Medicine, Göttingen, Germany.,DFG Research Center for Nanoscale Microscopy and Molecular Physiology of the Brain (CNMPB), Göttingen, Germany.,Imaging Centre, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
| | - Marie-Theres Weil
- Department of Neurogenetics, Max Planck Institute of Experimental Medicine, Göttingen, Germany.,Electron Microscopy Core Unit, Max Planck Institute of Experimental Medicine, Göttingen, Germany.,DFG Research Center for Nanoscale Microscopy and Molecular Physiology of the Brain (CNMPB), Göttingen, Germany
| | - Kathrin Kusch
- Department of Neurogenetics, Max Planck Institute of Experimental Medicine, Göttingen, Germany.,Institute for Auditory Neuroscience and InnerEarLab, University Medical Center Göttingen, Göttingen, Germany
| | - Olaf Jahn
- DFG Research Center for Nanoscale Microscopy and Molecular Physiology of the Brain (CNMPB), Göttingen, Germany.,Proteomics Group, Max Planck Institute of Experimental Medicine, Göttingen, Germany.,Translational Neuroproteomics Group, Department of Psychiatry and Psychotherapy, University Medical Center Göttingen, Göttingen, Germany
| | - Lars Piepkorn
- Proteomics Group, Max Planck Institute of Experimental Medicine, Göttingen, Germany.,Translational Neuroproteomics Group, Department of Psychiatry and Psychotherapy, University Medical Center Göttingen, Göttingen, Germany
| | - Paola Agüi-Gonzalez
- Department of Neuro- and Sensory Physiology, University Medical Center Göttingen, Center for Biostructural Imaging of Neurodegeneration, Göttingen, Germany
| | - Nhu Thi Ngoc Phan
- Department of Neuro- and Sensory Physiology, University Medical Center Göttingen, Center for Biostructural Imaging of Neurodegeneration, Göttingen, Germany.,Department of Chemistry and Molecular Biology, University of Gothenburg, Gothenburg, Sweden
| | - Torben Ruhwedel
- Department of Neurogenetics, Max Planck Institute of Experimental Medicine, Göttingen, Germany.,Electron Microscopy Core Unit, Max Planck Institute of Experimental Medicine, Göttingen, Germany
| | - Boguslawa Sadowski
- Department of Neurogenetics, Max Planck Institute of Experimental Medicine, Göttingen, Germany.,Electron Microscopy Core Unit, Max Planck Institute of Experimental Medicine, Göttingen, Germany.,DFG Research Center for Nanoscale Microscopy and Molecular Physiology of the Brain (CNMPB), Göttingen, Germany
| | - Silvio O Rizzoli
- DFG Research Center for Nanoscale Microscopy and Molecular Physiology of the Brain (CNMPB), Göttingen, Germany.,Department of Neuro- and Sensory Physiology, University Medical Center Göttingen, Center for Biostructural Imaging of Neurodegeneration, Göttingen, Germany.,Cluster of Excellence "Multiscale Bioimaging: from Molecular Machines to Networks of Excitable Cells" (MBExC), University of Göttingen, Göttingen, Germany
| | - Hauke B Werner
- Department of Neurogenetics, Max Planck Institute of Experimental Medicine, Göttingen, Germany
| | - Hannelore Ehrenreich
- DFG Research Center for Nanoscale Microscopy and Molecular Physiology of the Brain (CNMPB), Göttingen, Germany.,Clinical Neuroscience, Max Planck Institute of Experimental Medicine, Göttingen, Germany
| | - Klaus-Armin Nave
- Department of Neurogenetics, Max Planck Institute of Experimental Medicine, Göttingen, Germany.,DFG Research Center for Nanoscale Microscopy and Molecular Physiology of the Brain (CNMPB), Göttingen, Germany
| | - Wiebke Möbius
- Department of Neurogenetics, Max Planck Institute of Experimental Medicine, Göttingen, Germany. .,Electron Microscopy Core Unit, Max Planck Institute of Experimental Medicine, Göttingen, Germany. .,DFG Research Center for Nanoscale Microscopy and Molecular Physiology of the Brain (CNMPB), Göttingen, Germany. .,Cluster of Excellence "Multiscale Bioimaging: from Molecular Machines to Networks of Excitable Cells" (MBExC), University of Göttingen, Göttingen, Germany.
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64
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Calcraft T, Rosenthal PB. Cryogenic electron microscopy approaches that combine images and tilt series. Microscopy (Oxf) 2022; 71:i15-i22. [PMID: 35275182 PMCID: PMC8855521 DOI: 10.1093/jmicro/dfab053] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2021] [Revised: 11/29/2021] [Accepted: 01/28/2022] [Indexed: 11/12/2022] Open
Abstract
Cryogenic electron microscopy can be widely applied to biological specimens from the molecular to the cellular scale. In single-particle analysis, 3D structures may be obtained in high resolution by averaging 2D images of single particles in random orientations. For pleomorphic specimens, structures may be obtained by recording the tilt series of a single example of the specimen and calculating tomograms. Where many copies of a single structure such as a protein or nucleic acid assembly are present within the tomogram, averaging of the sub-volumes (subtomogram averaging) has been successfully applied. The choice of data collection method for any given specimen may depend on the structural question of interest and is determined by the radiation sensitivity of the specimen. Here, we survey some recent developments on the use of hybrid methods for recording and analysing data from radiation-sensitive biological specimens. These include single-particle reconstruction from 2D images where additional views are recorded at a single tilt angle of the specimen and methods where image tilt series, initially used for tomogram reconstruction, are processed as individual single-particle images. There is a continuum of approaches now available to maximize structural information obtained from the specimen.
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Affiliation(s)
- Thomas Calcraft
- Structural Biology of Cells and Viruses Laboratory, The Francis Crick Institute, 1 Midland Road, London NW1 1AT, UK
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65
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Huber ST, Sarajlic E, Huijink R, Weis F, Evers WH, Jakobi AJ. Nanofluidic chips for cryo-EM structure determination from picoliter sample volumes. eLife 2022; 11:72629. [PMID: 35060902 PMCID: PMC8786315 DOI: 10.7554/elife.72629] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Accepted: 12/07/2021] [Indexed: 01/25/2023] Open
Abstract
Cryogenic electron microscopy has become an essential tool for structure determination of biological macromolecules. In practice, the difficulty to reliably prepare samples with uniform ice thickness still represents a barrier for routine high-resolution imaging and limits the current throughput of the technique. We show that a nanofluidic sample support with well-defined geometry can be used to prepare cryo-EM specimens with reproducible ice thickness from picoliter sample volumes. The sample solution is contained in electron-transparent nanochannels that provide uniform thickness gradients without further optimisation and eliminate the potentially destructive air-water interface. We demonstrate the possibility to perform high-resolution structure determination with three standard protein specimens. Nanofabricated sample supports bear potential to automate the cryo-EM workflow, and to explore new frontiers for cryo-EM applications such as time-resolved imaging and high-throughput screening.
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Affiliation(s)
- Stefan T Huber
- Department of Bionanoscience, Kavli Institute of Nanoscience, Delft University of Technology
| | | | | | - Felix Weis
- Structural and Computational Biology Unit, European Molecular Biology Laboratory (EMBL)
| | - Wiel H Evers
- Department of Bionanoscience, Kavli Institute of Nanoscience, Delft University of Technology
| | - Arjen J Jakobi
- Department of Bionanoscience, Kavli Institute of Nanoscience, Delft University of Technology
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66
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Cantelli G, Bateman A, Brooksbank C, Petrov AI, Malik-Sheriff R, Ide-Smith M, Hermjakob H, Flicek P, Apweiler R, Birney E, McEntyre J. The European Bioinformatics Institute (EMBL-EBI) in 2021. Nucleic Acids Res 2022; 50:D11-D19. [PMID: 34850134 PMCID: PMC8690175 DOI: 10.1093/nar/gkab1127] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Revised: 10/14/2021] [Accepted: 11/23/2021] [Indexed: 11/28/2022] Open
Abstract
The European Bioinformatics Institute (EMBL-EBI) maintains a comprehensive range of freely available and up-to-date molecular data resources, which includes over 40 resources covering every major data type in the life sciences. This year's service update for EMBL-EBI includes new resources, PGS Catalog and AlphaFold DB, and updates on existing resources, including the COVID-19 Data Platform, trRosetta and RoseTTAfold models introduced in Pfam and InterPro, and the launch of Genome Integrations with Function and Sequence by UniProt and Ensembl. Furthermore, we highlight projects through which EMBL-EBI has contributed to the development of community-driven data standards and guidelines, including the Recommended Metadata for Biological Images (REMBI), and the BioModels Reproducibility Scorecard. Training is one of EMBL-EBI's core missions and a key component of the provision of bioinformatics services to users: this year's update includes many of the improvements that have been developed to EMBL-EBI's online training offering.
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Affiliation(s)
- Gaia Cantelli
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Alex Bateman
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Cath Brooksbank
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Anton I Petrov
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Rahuman S Malik-Sheriff
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Michele Ide-Smith
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Henning Hermjakob
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Paul Flicek
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Rolf Apweiler
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Ewan Birney
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Johanna McEntyre
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
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67
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Zadissa A, Apweiler R. Data Mining, Quality and Management in the Life Sciences. Methods Mol Biol 2022; 2449:3-25. [PMID: 35507257 DOI: 10.1007/978-1-0716-2095-3_1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
With the evermore emphasis put on open science and its invaluable benefits to the scientific community, it is no longer the case where a research project simply ends with a scientific publication. The benefits of data sharing and reproducibility of results have taken the centerpiece within the life science research supported by FAIR principles that firmly underline the importance of open data. The current data-intensive multidisciplinary research has also highlighted the significance of how data is mined and managed. Here we describe some of the features adopted by EMBL-EBI data resources to support data mining, data quality, and data management. We also highlight how EMBL-EBI has responded to the current pandemic through its data resources.
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Affiliation(s)
- Amonida Zadissa
- EMBL-EBI, Wellcome Genome Campus, Hinxton, Cambridgeshire, UK.
| | - Rolf Apweiler
- EMBL-EBI, Wellcome Genome Campus, Hinxton, Cambridgeshire, UK
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68
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Bekker G, Yokochi M, Suzuki H, Ikegawa Y, Iwata T, Kudou T, Yura K, Fujiwara T, Kawabata T, Kurisu G. Protein Data Bank Japan: Celebrating our 20th anniversary during a global pandemic as the Asian hub of three dimensional macromolecular structural data. Protein Sci 2022; 31:173-186. [PMID: 34664328 PMCID: PMC8740847 DOI: 10.1002/pro.4211] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Revised: 10/06/2021] [Accepted: 10/12/2021] [Indexed: 11/25/2022]
Abstract
Protein Data Bank Japan (PDBj), a founding member of the worldwide Protein Data Bank (wwPDB) has accepted, processed and distributed experimentally determined biological macromolecular structures for 20 years. During that time, we have continuously made major improvements to our query search interface of PDBj Mine 2, the BMRBj web interface, and EM Navigator for PDB/BMRB/EMDB entries. PDBj also serves PDB-related secondary database data, original web-based modeling services such as Homology modeling of complex structure (HOMCOS), visualization services and utility tools, which we have continuously enhanced and expanded throughout the years. In addition, we have recently developed several unique archives, BSM-Arc for computational structure models, and XRDa for raw X-ray diffraction images, both of which promote open science in the structural biology community. During the COVID-19 pandemic, PDBj has also started to provide feature pages for COVID-19 related entries across all available archives at PDBj from raw experimental data and PDB structural data to computationally predicted models, while also providing COVID-19 outreach content for high school students and teachers.
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Affiliation(s)
- Gert‐Jan Bekker
- Institute for Protein ResearchOsaka UniversitySuitaOsakaJapan
| | - Masashi Yokochi
- Institute for Protein ResearchOsaka UniversitySuitaOsakaJapan
| | - Hirofumi Suzuki
- School of Advanced Science and EngineeringWaseda UniversityShinjukuTokyoJapan
| | - Yasuyo Ikegawa
- Institute for Protein ResearchOsaka UniversitySuitaOsakaJapan
| | - Takeshi Iwata
- Institute for Protein ResearchOsaka UniversitySuitaOsakaJapan
| | - Takahiro Kudou
- Institute for Protein ResearchOsaka UniversitySuitaOsakaJapan
| | - Kei Yura
- School of Advanced Science and EngineeringWaseda UniversityShinjukuTokyoJapan
- Graduate School of Humanities and Sciences, Ochanoizu UniversityBunkyoTokyoJapan
| | | | - Takeshi Kawabata
- Protein Research FoundationMinohOsakaJapan
- Graduate School of Frontier BiosciencesOsaka UniversitySuitaOsakaJapan
| | - Genji Kurisu
- Institute for Protein ResearchOsaka UniversitySuitaOsakaJapan
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69
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Toro-Nahuelpan M, Plitzko JM, Schüler D, Pfeiffer D. In vivo architecture of the polar organizing protein Z (PopZ) meshwork in the Alphaproteobacteria Magnetospirillum gryphiswaldense and Caulobacter crescentus. J Mol Biol 2021; 434:167423. [PMID: 34971672 DOI: 10.1016/j.jmb.2021.167423] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Revised: 12/17/2021] [Accepted: 12/20/2021] [Indexed: 10/19/2022]
Abstract
The polar organizing protein Z (PopZ) forms a polar microdomain that is inaccessible to larger macromolecules such as ribosomes, and selectively sequesters proteins crucial for cell cycle control and polar morphogenesis in various Alphaproteobacteria. However, the in vivo architecture of this microdomain has remained elusive. Here, we analyzed the three-dimensional ultrastructural organization of the PopZ network in Magnetospirillum gryphiswaldense and Caulobacter crescentus by Volta phase plate Cryo-electron tomography, which provides high spatial resolution and improved image contrast. Our results suggest that PopZ forms a porous network of disordered short, flexible, and branching filaments.
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Affiliation(s)
- Mauricio Toro-Nahuelpan
- Department of Microbiology, University Bayreuth, Germany; Department of Molecular Structural Biology, Max Planck Institute of Biochemistry, Planegg-Martinsried, Germany
| | - Jürgen M Plitzko
- Department of Molecular Structural Biology, Max Planck Institute of Biochemistry, Planegg-Martinsried, Germany
| | - Dirk Schüler
- Department of Microbiology, University Bayreuth, Germany
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70
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Kimanius D, Dong L, Sharov G, Nakane T, Scheres SHW. New tools for automated cryo-EM single-particle analysis in RELION-4.0. Biochem J 2021; 478:4169-4185. [PMID: 34783343 PMCID: PMC8786306 DOI: 10.1042/bcj20210708] [Citation(s) in RCA: 372] [Impact Index Per Article: 124.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Revised: 11/15/2021] [Accepted: 11/16/2021] [Indexed: 12/22/2022]
Abstract
We describe new tools for the processing of electron cryo-microscopy (cryo-EM) images in the fourth major release of the RELION software. In particular, we introduce VDAM, a variable-metric gradient descent algorithm with adaptive moments estimation, for image refinement; a convolutional neural network for unsupervised selection of 2D classes; and a flexible framework for the design and execution of multiple jobs in pre-defined workflows. In addition, we present a stand-alone utility called MDCatch that links the execution of jobs within this framework with metadata gathering during microscope data acquisition. The new tools are aimed at providing fast and robust procedures for unsupervised cryo-EM structure determination, with potential applications for on-the-fly processing and the development of flexible, high-throughput structure determination pipelines. We illustrate their potential on 12 publicly available cryo-EM data sets.
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Affiliation(s)
- Dari Kimanius
- MRC Laboratory of Molecular Biology, Francis Crick Avenue, CB2 0QH Cambridge, UK
| | - Liyi Dong
- MRC Laboratory of Molecular Biology, Francis Crick Avenue, CB2 0QH Cambridge, UK
| | - Grigory Sharov
- MRC Laboratory of Molecular Biology, Francis Crick Avenue, CB2 0QH Cambridge, UK
| | - Takanori Nakane
- MRC Laboratory of Molecular Biology, Francis Crick Avenue, CB2 0QH Cambridge, UK
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71
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Sarkans U, Chiu W, Collinson L, Darrow MC, Ellenberg J, Grunwald D, Hériché JK, Iudin A, Martins GG, Meehan T, Narayan K, Patwardhan A, Russell MRG, Saibil HR, Strambio-De-Castillia C, Swedlow JR, Tischer C, Uhlmann V, Verkade P, Barlow M, Bayraktar O, Birney E, Catavitello C, Cawthorne C, Wagner-Conrad S, Duke E, Paul-Gilloteaux P, Gustin E, Harkiolaki M, Kankaanpää P, Lemberger T, McEntyre J, Moore J, Nicholls AW, Onami S, Parkinson H, Parsons M, Romanchikova M, Sofroniew N, Swoger J, Utz N, Voortman LM, Wong F, Zhang P, Kleywegt GJ, Brazma A. REMBI: Recommended Metadata for Biological Images-enabling reuse of microscopy data in biology. Nat Methods 2021; 18:1418-1422. [PMID: 34021280 PMCID: PMC8606015 DOI: 10.1038/s41592-021-01166-8] [Citation(s) in RCA: 55] [Impact Index Per Article: 18.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Bioimaging data have significant potential for reuse, but unlocking this potential requires systematic archiving of data and metadata in public databases. We propose draft metadata guidelines to begin addressing the needs of diverse communities within light and electron microscopy. We hope this publication and the proposed Recommended Metadata for Biological Images (REMBI) will stimulate discussions about their implementation and future extension.
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Affiliation(s)
- Ugis Sarkans
- European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK.
| | - Wah Chiu
- Department of Bioengineering, Stanford University, Stanford and SLAC National Accelerator Laboratory, Menlo Park, CA, USA
| | | | | | - Jan Ellenberg
- Cell Biology and Biophysics Unit, European Molecular Biology Laboratory, Heidelberg, Germany
| | - David Grunwald
- RNA Therapeutics Institute, University of Massachusetts Medical School, Worcester, MA, USA
| | - Jean-Karim Hériché
- Cell Biology and Biophysics Unit, European Molecular Biology Laboratory, Heidelberg, Germany
| | - Andrii Iudin
- European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK
| | | | - Terry Meehan
- European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK
- Kymab Ltd., Babraham Research Campus, Cambridge, UK
| | - Kedar Narayan
- Center for Molecular Microscopy, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
- Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - Ardan Patwardhan
- European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK
| | | | - Helen R Saibil
- Institute of Structural and Molecular Biology, Birkbeck, University of London, London, UK
| | | | - Jason R Swedlow
- Division of Computational Biology and Centre for Gene Regulation and Expression, University of Dundee, Dundee, UK
| | - Christian Tischer
- Centre for Bioimage Analysis, European Molecular Biology Laboratory, Heidelberg, Germany
| | - Virginie Uhlmann
- European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK
| | - Paul Verkade
- School of Biochemistry, University of Bristol, Bristol, UK
| | - Mary Barlow
- European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK
| | | | - Ewan Birney
- European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK
| | - Cesare Catavitello
- European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK
- Ebury UK, London, UK
| | - Christopher Cawthorne
- Nuclear Medicine and Molecular Imaging, Department of Imaging and Pathology, KU Leuven, Leuven, Belgium
| | | | - Elizabeth Duke
- Diamond Light Source, Harwell Science and Innovation Campus, Harwell, UK
- European Molecular Biology Laboratory, Hamburg, Germany
| | - Perrine Paul-Gilloteaux
- Université de Nantes, CNRS, INSERM, l'institut du thorax, Nantes, France
- Université de Nantes, CHU Nantes, Inserm, CNRS, SFR Santé, Inserm UMS 016, CNRS UMS 3556, Nantes, France
| | - Emmanuel Gustin
- Janssen Pharmaceutical Companies of Johnson & Johnson, Beerse, Belgium
| | - Maria Harkiolaki
- Diamond Light Source, Harwell Science and Innovation Campus, Harwell, UK
| | - Pasi Kankaanpää
- Turku BioImaging, University of Turku and Åbo Akademi University, Turku, Finland
- Euro-BioImaging ERIC, Turku, Finland
| | | | - Jo McEntyre
- European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK
| | - Josh Moore
- Division of Computational Biology and Centre for Gene Regulation and Expression, University of Dundee, Dundee, UK
| | | | - Shuichi Onami
- RIKEN Center for Biosystems Dynamics Research, Kobe, Japan
| | - Helen Parkinson
- European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK
| | - Maddy Parsons
- Randall Centre for Cell and Molecular Biophysics, King's College London, London, UK
| | | | | | - Jim Swoger
- European Molecular Biology Laboratory, Barcelona, Spain
| | - Nadine Utz
- German BioImaging e.V., University of Konstanz, Konstanz, Germany
| | - Lenard M Voortman
- Department of Cell and Chemical Biology, Leiden University Medical Center, Leiden, the Netherlands
| | - Frances Wong
- Division of Computational Biology and Centre for Gene Regulation and Expression, University of Dundee, Dundee, UK
| | - Peijun Zhang
- Diamond Light Source, Harwell Science and Innovation Campus, Harwell, UK
- Division of Structural Biology, Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Gerard J Kleywegt
- European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK.
| | - Alvis Brazma
- European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK.
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72
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Vallat B, Webb B, Fayazi M, Voinea S, Tangmunarunkit H, Ganesan SJ, Lawson CL, Westbrook JD, Kesselman C, Sali A, Berman HM. New system for archiving integrative structures. Acta Crystallogr D Struct Biol 2021; 77:1486-1496. [PMID: 34866606 PMCID: PMC8647179 DOI: 10.1107/s2059798321010871] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Accepted: 10/19/2021] [Indexed: 11/30/2022] Open
Abstract
Structures of many complex biological assemblies are increasingly determined using integrative approaches, in which data from multiple experimental methods are combined. A standalone system, called PDB-Dev, has been developed for archiving integrative structures and making them publicly available. Here, the data standards and software tools that support PDB-Dev are described along with the new and updated components of the PDB-Dev data-collection, processing and archiving infrastructure. Following the FAIR (Findable, Accessible, Interoperable and Reusable) principles, PDB-Dev ensures that the results of integrative structure determinations are freely accessible to everyone.
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Affiliation(s)
- Brinda Vallat
- RCSB PDB, Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, New Jersey, USA
| | - Benjamin Webb
- Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, and California Institute for Quantitative Biosciences, University of California at San Francisco, San Francisco, California, USA
| | - Maryam Fayazi
- RCSB PDB, Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, New Jersey, USA
| | - Serban Voinea
- Information Sciences Institute, Viterbi School of Engineering, University of Southern California, Los Angeles, California, USA
| | - Hongsuda Tangmunarunkit
- Information Sciences Institute, Viterbi School of Engineering, University of Southern California, Los Angeles, California, USA
| | - Sai J. Ganesan
- Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, and California Institute for Quantitative Biosciences, University of California at San Francisco, San Francisco, California, USA
| | - Catherine L. Lawson
- RCSB PDB, Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, New Jersey, USA
| | - John D. Westbrook
- RCSB PDB, Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, New Jersey, USA
| | - Carl Kesselman
- RCSB PDB, Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, New Jersey, USA
| | - Andrej Sali
- Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, and California Institute for Quantitative Biosciences, University of California at San Francisco, San Francisco, California, USA
| | - Helen M. Berman
- Department of Chemistry and Chemical Biology and Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, New Jersey, USA
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73
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Hammer M, Huisman M, Rigano A, Boehm U, Chambers JJ, Gaudreault N, North AJ, Pimentel JA, Sudar D, Bajcsy P, Brown CM, Corbett AD, Faklaris O, Lacoste J, Laude A, Nelson G, Nitschke R, Farzam F, Smith CS, Grunwald D, Strambio-De-Castillia C. Towards community-driven metadata standards for light microscopy: tiered specifications extending the OME model. Nat Methods 2021; 18:1427-1440. [PMID: 34862501 PMCID: PMC9271325 DOI: 10.1038/s41592-021-01327-9] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Rigorous record-keeping and quality control are required to ensure the quality, reproducibility and value of imaging data. The 4DN Initiative and BINA here propose light Microscopy Metadata specifications that extend the OME data model, scale with experimental intent and complexity, and make it possible for scientists to create comprehensive records of imaging experiments.
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Affiliation(s)
- Mathias Hammer
- RNA Therapeutics Institute, UMass Chan Medical School, Worcester, MA, USA
- Department of Biology, Technical University of Darmstadt, Darmstadt, Germany
| | | | - Alessandro Rigano
- Program in Molecular Medicine, UMass Chan Medical School, Worcester, MA, USA
| | - Ulrike Boehm
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - James J Chambers
- Institute for Applied Life Sciences, University of Massachusetts, Amherst, MA, USA
| | | | | | - Jaime A Pimentel
- Laboratorio Nacional de Microscopía Avanzada, Instituto de Biotecnología, Universidad Nacional Autónoma de México, Cuernavaca, Morelos, México
| | - Damir Sudar
- Quantitative Imaging Systems LLC, Portland, OR, USA
| | - Peter Bajcsy
- National Institute of Standards and Technology, Gaithersburg, MD, USA
| | - Claire M Brown
- Advanced BioImaging Facility (ABIF), McGill University, Montreal, Quebec, Canada
| | | | - Orestis Faklaris
- MRI, BCM, University of Montpellier, CNRS, INSERM, Montpellier, France
| | | | - Alex Laude
- Bioimaging Unit, Newcastle University, Newcastle upon Tyne, UK
| | - Glyn Nelson
- Bioimaging Unit, Newcastle University, Newcastle upon Tyne, UK
| | - Roland Nitschke
- Life Imaging Center and Signalling Research Centres CIBSS and BIOSS, University of Freiburg, Freiburg, Germany
| | - Farzin Farzam
- RNA Therapeutics Institute, UMass Chan Medical School, Worcester, MA, USA
| | - Carlas S Smith
- Delft Center for Systems and Control and Department of Imaging Physics, Delft University of Technology, Delft, the Netherlands
| | - David Grunwald
- RNA Therapeutics Institute, UMass Chan Medical School, Worcester, MA, USA
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74
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Seeger C, Dyrhage K, Mahajan M, Odelgard A, Lind SB, Andersson SGE. The Subcellular Proteome of a Planctomycetes Bacterium Shows That Newly Evolved Proteins Have Distinct Fractionation Patterns. Front Microbiol 2021; 12:643045. [PMID: 34745019 PMCID: PMC8567305 DOI: 10.3389/fmicb.2021.643045] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Accepted: 03/25/2021] [Indexed: 11/13/2022] Open
Abstract
The Planctomycetes bacteria have unique cell architectures with heavily invaginated membranes as confirmed by three-dimensional models reconstructed from FIB-SEM images of Tuwongella immobilis and Gemmata obscuriglobus. The subcellular proteome of T. immobilis was examined by differential solubilization followed by LC-MS/MS analysis, which identified 1569 proteins in total. The Tris-soluble fraction contained mostly cytoplasmic proteins, while inner and outer membrane proteins were found in the Triton X-100 and SDS-soluble fractions, respectively. For comparisons, the subcellular proteome of Escherichia coli was also examined using the same methodology. A notable difference in the overall fractionation pattern of the two species was a fivefold higher number of predicted cytoplasmic proteins in the SDS-soluble fraction in T. immobilis. One category of such proteins is represented by innovations in the Planctomycetes lineage, including unique sets of serine/threonine kinases and extracytoplasmic sigma factors with WD40 repeat domains for which no homologs are present in E. coli. Other such proteins are members of recently expanded protein families in which the newly evolved paralog with a new domain structure is recovered from the SDS-soluble fraction, while other paralogs may have similar domain structures and fractionation patterns as the single homolog in E. coli. The expanded protein families in T. immobilis include enzymes involved in replication-repair processes as well as in rRNA and tRNA modification and degradation. These results show that paralogization and domain shuffling have yielded new proteins with distinct fractionation characteristics. Understanding the molecular intricacies of these adaptive changes might aid in the development of a model for the evolution of cellular complexity.
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Affiliation(s)
- Christian Seeger
- Science for Life Laboratory, Molecular Evolution, Department of Cell and Molecular Biology, Biomedical Centre, Uppsala University, Uppsala, Sweden
| | - Karl Dyrhage
- Science for Life Laboratory, Molecular Evolution, Department of Cell and Molecular Biology, Biomedical Centre, Uppsala University, Uppsala, Sweden
| | - Mayank Mahajan
- Science for Life Laboratory, Molecular Evolution, Department of Cell and Molecular Biology, Biomedical Centre, Uppsala University, Uppsala, Sweden
| | - Anna Odelgard
- Science for Life Laboratory, Molecular Evolution, Department of Cell and Molecular Biology, Biomedical Centre, Uppsala University, Uppsala, Sweden
| | - Sara Bergström Lind
- Department of Chemistry-BMC, Analytical Chemistry, Uppsala University, Uppsala, Sweden
| | - Siv G E Andersson
- Science for Life Laboratory, Molecular Evolution, Department of Cell and Molecular Biology, Biomedical Centre, Uppsala University, Uppsala, Sweden
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75
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Hoffmann PC, Giandomenico SL, Ganeva I, Wozny MR, Sutcliffe M, Lancaster MA, Kukulski W. Electron cryo-tomography reveals the subcellular architecture of growing axons in human brain organoids. eLife 2021; 10:e70269. [PMID: 34698018 PMCID: PMC8547956 DOI: 10.7554/elife.70269] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Accepted: 10/08/2021] [Indexed: 12/17/2022] Open
Abstract
During brain development, axons must extend over great distances in a relatively short amount of time. How the subcellular architecture of the growing axon sustains the requirements for such rapid build-up of cellular constituents has remained elusive. Human axons have been particularly poorly accessible to imaging at high resolution in a near-native context. Here, we present a method that combines cryo-correlative light microscopy and electron tomography with human cerebral organoid technology to visualize growing axon tracts. Our data reveal a wealth of structural details on the arrangement of macromolecules, cytoskeletal components, and organelles in elongating axon shafts. In particular, the intricate shape of the endoplasmic reticulum is consistent with its role in fulfilling the high demand for lipid biosynthesis to support growth. Furthermore, the scarcity of ribosomes within the growing shaft suggests limited translational competence during expansion of this compartment. These findings establish our approach as a powerful resource for investigating the ultrastructure of defined neuronal compartments.
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Affiliation(s)
- Patrick C Hoffmann
- MRC Laboratory of Molecular Biology, Francis Crick AvenueCambridgeUnited Kingdom
| | | | - Iva Ganeva
- MRC Laboratory of Molecular Biology, Francis Crick AvenueCambridgeUnited Kingdom
| | - Michael R Wozny
- MRC Laboratory of Molecular Biology, Francis Crick AvenueCambridgeUnited Kingdom
| | - Magdalena Sutcliffe
- MRC Laboratory of Molecular Biology, Francis Crick AvenueCambridgeUnited Kingdom
| | - Madeline A Lancaster
- MRC Laboratory of Molecular Biology, Francis Crick AvenueCambridgeUnited Kingdom
| | - Wanda Kukulski
- MRC Laboratory of Molecular Biology, Francis Crick AvenueCambridgeUnited Kingdom
- Institute of Biochemistry and Molecular Medicine, University of BernBernSwitzerland
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76
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Britt HM, Cragnolini T, Thalassinos K. Integration of Mass Spectrometry Data for Structural Biology. Chem Rev 2021; 122:7952-7986. [PMID: 34506113 DOI: 10.1021/acs.chemrev.1c00356] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
Mass spectrometry (MS) is increasingly being used to probe the structure and dynamics of proteins and the complexes they form with other macromolecules. There are now several specialized MS methods, each with unique sample preparation, data acquisition, and data processing protocols. Collectively, these methods are referred to as structural MS and include cross-linking, hydrogen-deuterium exchange, hydroxyl radical footprinting, native, ion mobility, and top-down MS. Each of these provides a unique type of structural information, ranging from composition and stoichiometry through to residue level proximity and solvent accessibility. Structural MS has proved particularly beneficial in studying protein classes for which analysis by classic structural biology techniques proves challenging such as glycosylated or intrinsically disordered proteins. To capture the structural details for a particular system, especially larger multiprotein complexes, more than one structural MS method with other structural and biophysical techniques is often required. Key to integrating these diverse data are computational strategies and software solutions to facilitate this process. We provide a background to the structural MS methods and briefly summarize other structural methods and how these are combined with MS. We then describe current state of the art approaches for the integration of structural MS data for structural biology. We quantify how often these methods are used together and provide examples where such combinations have been fruitful. To illustrate the power of integrative approaches, we discuss progress in solving the structures of the proteasome and the nuclear pore complex. We also discuss how information from structural MS, particularly pertaining to protein dynamics, is not currently utilized in integrative workflows and how such information can provide a more accurate picture of the systems studied. We conclude by discussing new developments in the MS and computational fields that will further enable in-cell structural studies.
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Affiliation(s)
- Hannah M Britt
- Institute of Structural and Molecular Biology, Division of Biosciences, University College London, London WC1E 6BT, United Kingdom
| | - Tristan Cragnolini
- Institute of Structural and Molecular Biology, Division of Biosciences, University College London, London WC1E 6BT, United Kingdom.,Institute of Structural and Molecular Biology, Birkbeck College, University of London, London WC1E 7HX, United Kingdom
| | - Konstantinos Thalassinos
- Institute of Structural and Molecular Biology, Division of Biosciences, University College London, London WC1E 6BT, United Kingdom.,Institute of Structural and Molecular Biology, Birkbeck College, University of London, London WC1E 7HX, United Kingdom
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77
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Burton-Smith RN, Murata K. Cryo-Electron Microscopy of the Giant Viruses. Microscopy (Oxf) 2021; 70:477-486. [PMID: 34490462 DOI: 10.1093/jmicro/dfab036] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Revised: 08/26/2021] [Accepted: 08/30/2021] [Indexed: 11/12/2022] Open
Abstract
High resolution study of the giant viruses presents one of the latest challenges in cryo-electron microscopy of viruses. Too small for light microscopy, but too large for easy study at high resolution by electron microscopy, they range in size from ~0.2-2 μm, from high symmetry icosahedral viruses such as Paramecium burseria Chlorella virus 1 to asymmetric forms like Tupanvirus or Pithovirus. To attain high resolution, two strategies exist to study these large viruses by cryo-EM: firstly, increasing the acceleration voltage of the electron microscope to improve sample penetration and overcome the limitations imposed by electro-optical physics at lower voltages, and secondly the method of "block-based reconstruction" pioneered by Michael G. Rossmann and his collaborators, which resolves the latter limitation through an elegant leveraging of high symmetry, but cannot overcome sample penetration limitations. In addition, more recent advances in both computational capacity and image processing also yield assistance in studying the giant viruses. Especially, the inclusion of Ewald sphere correction can provide large improvements in attainable resolutions for 300 kV electron microscopes. Despite this, the study of giant viruses remains a significant challenge.
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Affiliation(s)
- Raymond N Burton-Smith
- Exploratory Research Center on Life and Living Systems (ExCELLS), National Institutes of Natural Sciences, Okazaki, Aichi, Japan.,National Institute for Physiological Sciences, National Institutes of Natural Sciences, Okazaki, Aichi, Japan
| | - Kazuyoshi Murata
- Exploratory Research Center on Life and Living Systems (ExCELLS), National Institutes of Natural Sciences, Okazaki, Aichi, Japan.,National Institute for Physiological Sciences, National Institutes of Natural Sciences, Okazaki, Aichi, Japan.,Department of Physiological Sciences, School of Life Science, The Graduate University for Advanced Studies (SOKENDAI), Okazaki, Aichi, Japan
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78
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Sorzano COS, Jiménez-Moreno A, Maluenda D, Ramírez-Aportela E, Martínez M, Cuervo A, Melero R, Conesa JJ, Sánchez-García R, Strelak D, Filipovic J, Fernández-Giménez E, de Isidro-Gómez F, Herreros D, Conesa P, Del Caño L, Fonseca Y, de la Morena JJ, Macías JR, Losana P, Marabini R, Carazo JM. Image Processing in Cryo-Electron Microscopy of Single Particles: The Power of Combining Methods. Methods Mol Biol 2021; 2305:257-289. [PMID: 33950394 DOI: 10.1007/978-1-0716-1406-8_13] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Cryo-electron microscopy has established as a mature structural biology technique to elucidate the three-dimensional structure of biological macromolecules. The Coulomb potential of the sample is imaged by an electron beam, and fast semi-conductor detectors produce movies of the sample under study. These movies have to be further processed by a whole pipeline of image-processing algorithms that produce the final structure of the macromolecule. In this chapter, we illustrate this whole processing pipeline putting in value the strength of "meta algorithms," which are the combination of several algorithms, each one with different mathematical rationale, in order to distinguish correctly from incorrectly estimated parameters. We show how this strategy leads to superior performance of the whole pipeline as well as more confident assessments about the reconstructed structures. The "meta algorithms" strategy is common to many fields and, in particular, it has provided excellent results in bioinformatics. We illustrate this combination using the workflow engine, Scipion.
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Affiliation(s)
| | | | | | | | | | - Ana Cuervo
- National Centre for Biotechnology (CSIC), Madrid, Spain
| | - Robert Melero
- National Centre for Biotechnology (CSIC), Madrid, Spain
| | | | | | - David Strelak
- National Centre for Biotechnology (CSIC), Madrid, Spain
| | | | | | | | | | - Pablo Conesa
- National Centre for Biotechnology (CSIC), Madrid, Spain
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79
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Bäuerlein FJB, Baumeister W. Towards Visual Proteomics at High Resolution. J Mol Biol 2021; 433:167187. [PMID: 34384780 DOI: 10.1016/j.jmb.2021.167187] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Revised: 08/02/2021] [Accepted: 08/02/2021] [Indexed: 11/24/2022]
Abstract
Traditionally, structural biologists approach the complexity of cellular proteomes in a reductionist manner. Proteomes are fractionated, their molecular components purified and studied one-by-one using the experimental methods for structure determination at their disposal. Visual proteomics aims at obtaining a holistic picture of cellular proteomes by studying them in situ, ideally in unperturbed cellular environments. The method that enables doing this at highest resolution is cryo-electron tomography. It allows to visualize cellular landscapes with molecular resolution generating maps or atlases revealing the interaction networks which underlie cellular functions in health and in disease states. Current implementations of cryo ET do not yet realize the full potential of the method in terms of resolution and interpretability. To this end, further improvements in technology and methodology are needed. This review describes the state of the art as well as measures which we expect will help overcoming current limitations.
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Affiliation(s)
- Felix J B Bäuerlein
- Max-Planck-Institute of Biochemistry, Department for Molecular Structural Biology, Am Klopferspitz 18, 82152 Planegg, Germany; Georg-August-University, Institute for Neuropathology, Robert-Koch-Strasse 40, 37075 Göttingen, Germany; Cluster of Excellence "Multiscale Bioimaging: from Molecular Machines to Networks of Excitable Cells" (MBExC), University of Göttingen, Germany.
| | - Wolfgang Baumeister
- Max-Planck-Institute of Biochemistry, Department for Molecular Structural Biology, Am Klopferspitz 18, 82152 Planegg, Germany.
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80
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Chen M, Ludtke SJ. Deep learning-based mixed-dimensional Gaussian mixture model for characterizing variability in cryo-EM. Nat Methods 2021; 18:930-936. [PMID: 34326541 PMCID: PMC8363932 DOI: 10.1038/s41592-021-01220-5] [Citation(s) in RCA: 82] [Impact Index Per Article: 27.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2021] [Accepted: 06/21/2021] [Indexed: 12/15/2022]
Abstract
Structural flexibility and/or dynamic interactions with other molecules is a critical aspect of protein function. CryoEM provides direct visualization of individual macromolecules sampling different conformational and compositional states. While numerous methods are available for computational classification of discrete states, characterization of continuous conformational changes or large numbers of discrete state without human supervision remains challenging. Here we present e2gmm, a machine learning algorithm to determine a conformational landscape for proteins or complexes using a 3-D Gaussian mixture model mapped onto 2-D particle images in known orientations. Using a deep neural network architecture, e2gmm can automatically resolve the structural heterogeneity within the protein complex and map particles onto a small latent space describing conformational and compositional changes. This system presents a more intuitive and flexible representation than other manifold methods currently in use. We demonstrate this method on both simulated data as well as three biological systems, to explore compositional and conformational changes at a range of scales. The software is distributed as part of EMAN2.
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Affiliation(s)
- Muyuan Chen
- Verna Marrs and McLean Department of Biochemistry and Molecular Biology, Baylor College of Medicine, Houston, TX, USA
| | - Steven J Ludtke
- Verna Marrs and McLean Department of Biochemistry and Molecular Biology, Baylor College of Medicine, Houston, TX, USA.
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81
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Wilson SL, Way GP, Bittremieux W, Armache JP, Haendel MA, Hoffman MM. Sharing biological data: why, when, and how. FEBS Lett 2021; 595:847-863. [PMID: 33843054 PMCID: PMC10390076 DOI: 10.1002/1873-3468.14067] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Affiliation(s)
- Samantha L Wilson
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
| | - Gregory P Way
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Wout Bittremieux
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA.,Department of Computer Science, University of Antwerp, Antwerpen, Belgium
| | - Jean-Paul Armache
- Department of Biochemistry & Molecular Biology, The Huck Institutes of Life Sciences, Pennsylvania State University, University Park, PA, USA
| | | | - Michael M Hoffman
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada.,Department of Medical Biophysics, Department of Computer Science, University of Toronto, Toronto, ON, Canada.,Vector Institute, Toronto, ON, Canada
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82
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Harrison PW, Lopez R, Rahman N, Allen SG, Aslam R, Buso N, Cummins C, Fathy Y, Felix E, Glont M, Jayathilaka S, Kadam S, Kumar M, Lauer KB, Malhotra G, Mosaku A, Edbali O, Park YM, Parton A, Pearce M, Estrada Pena JF, Rossetto J, Russell C, Selvakumar S, Sitjà XP, Sokolov A, Thorne R, Ventouratou M, Walter P, Yordanova G, Zadissa A, Cochrane G, Blomberg N, Apweiler R. The COVID-19 Data Portal: accelerating SARS-CoV-2 and COVID-19 research through rapid open access data sharing. Nucleic Acids Res 2021; 49:W619-W623. [PMID: 34048576 PMCID: PMC8218199 DOI: 10.1093/nar/gkab417] [Citation(s) in RCA: 39] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Revised: 04/20/2021] [Accepted: 05/01/2021] [Indexed: 01/07/2023] Open
Abstract
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic will be remembered as one of the defining events of the 21st century. The rapid global outbreak has had significant impacts on human society and is already responsible for millions of deaths. Understanding and tackling the impact of the virus has required a worldwide mobilisation and coordination of scientific research. The COVID-19 Data Portal (https://www.covid19dataportal.org/) was first released as part of the European COVID-19 Data Platform, on April 20th 2020 to facilitate rapid and open data sharing and analysis, to accelerate global SARS-CoV-2 and COVID-19 research. The COVID-19 Data Portal has fortnightly feature releases to continue to add new data types, search options, visualisations and improvements based on user feedback and research. The open datasets and intuitive suite of search, identification and download services, represent a truly FAIR (Findable, Accessible, Interoperable and Reusable) resource that enables researchers to easily identify and quickly obtain the key datasets needed for their COVID-19 research.
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Affiliation(s)
- Peter W Harrison
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Rodrigo Lopez
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Nadim Rahman
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Stefan Gutnick Allen
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Raheela Aslam
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Nicola Buso
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Carla Cummins
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Yasmin Fathy
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Eloy Felix
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Mihai Glont
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Suran Jayathilaka
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Sandeep Kadam
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Manish Kumar
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | | | - Geetika Malhotra
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Abayomi Mosaku
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Ossama Edbali
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Young Mi Park
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Andrew Parton
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Matt Pearce
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Jose Francisco Estrada Pena
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Joseph Rossetto
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Craig Russell
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Sandeep Selvakumar
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | | | - Alexey Sokolov
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Ross Thorne
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Marianna Ventouratou
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Peter Walter
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Galabina Yordanova
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Amonida Zadissa
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Guy Cochrane
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Niklas Blomberg
- ELIXIR, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Rolf Apweiler
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
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83
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Nelson G, Boehm U, Bagley S, Bajcsy P, Bischof J, Brown CM, Dauphin A, Dobbie IM, Eriksson JE, Faklaris O, Fernandez-Rodriguez J, Ferrand A, Gelman L, Gheisari A, Hartmann H, Kukat C, Laude A, Mitkovski M, Munck S, North AJ, Rasse TM, Resch-Genger U, Schuetz LC, Seitz A, Strambio-De-Castillia C, Swedlow JR, Alexopoulos I, Aumayr K, Avilov S, Bakker GJ, Bammann RR, Bassi A, Beckert H, Beer S, Belyaev Y, Bierwagen J, Birngruber KA, Bosch M, Breitlow J, Cameron LA, Chalfoun J, Chambers JJ, Chen CL, Conde-Sousa E, Corbett AD, Cordelieres FP, Nery ED, Dietzel R, Eismann F, Fazeli E, Felscher A, Fried H, Gaudreault N, Goh WI, Guilbert T, Hadleigh R, Hemmerich P, Holst GA, Itano MS, Jaffe CB, Jambor HK, Jarvis SC, Keppler A, Kirchenbuechler D, Kirchner M, Kobayashi N, Krens G, Kunis S, Lacoste J, Marcello M, Martins GG, Metcalf DJ, Mitchell CA, Moore J, Mueller T, Nelson MS, Ogg S, Onami S, Palmer AL, Paul-Gilloteaux P, Pimentel JA, Plantard L, Podder S, Rexhepaj E, Royon A, Saari MA, Schapman D, Schoonderwoert V, Schroth-Diez B, Schwartz S, Shaw M, Spitaler M, Stoeckl MT, Sudar D, Teillon J, Terjung S, Thuenauer R, Wilms CD, Wright GD, Nitschke R. QUAREP-LiMi: A community-driven initiative to establish guidelines for quality assessment and reproducibility for instruments and images in light microscopy. J Microsc 2021; 284:56-73. [PMID: 34214188 PMCID: PMC10388377 DOI: 10.1111/jmi.13041] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2021] [Accepted: 06/16/2021] [Indexed: 11/27/2022]
Abstract
A modern day light microscope has evolved from a tool devoted to making primarily empirical observations to what is now a sophisticated , quantitative device that is an integral part of both physical and life science research. Nowadays, microscopes are found in nearly every experimental laboratory. However, despite their prevalent use in capturing and quantifying scientific phenomena, neither a thorough understanding of the principles underlying quantitative imaging techniques nor appropriate knowledge of how to calibrate, operate and maintain microscopes can be taken for granted. This is clearly demonstrated by the well-documented and widespread difficulties that are routinely encountered in evaluating acquired data and reproducing scientific experiments. Indeed, studies have shown that more than 70% of researchers have tried and failed to repeat another scientist's experiments, while more than half have even failed to reproduce their own experiments. One factor behind the reproducibility crisis of experiments published in scientific journals is the frequent underreporting of imaging methods caused by a lack of awareness and/or a lack of knowledge of the applied technique. Whereas quality control procedures for some methods used in biomedical research, such as genomics (e.g. DNA sequencing, RNA-seq) or cytometry, have been introduced (e.g. ENCODE), this issue has not been tackled for optical microscopy instrumentation and images. Although many calibration standards and protocols have been published, there is a lack of awareness and agreement on common standards and guidelines for quality assessment and reproducibility. In April 2020, the QUality Assessment and REProducibility for instruments and images in Light Microscopy (QUAREP-LiMi) initiative was formed. This initiative comprises imaging scientists from academia and industry who share a common interest in achieving a better understanding of the performance and limitations of microscopes and improved quality control (QC) in light microscopy. The ultimate goal of the QUAREP-LiMi initiative is to establish a set of common QC standards, guidelines, metadata models and tools, including detailed protocols, with the ultimate aim of improving reproducible advances in scientific research. This White Paper (1) summarizes the major obstacles identified in the field that motivated the launch of the QUAREP-LiMi initiative; (2) identifies the urgent need to address these obstacles in a grassroots manner, through a community of stakeholders including, researchers, imaging scientists, bioimage analysts, bioimage informatics developers, corporate partners, funding agencies, standards organizations, scientific publishers and observers of such; (3) outlines the current actions of the QUAREP-LiMi initiative and (4) proposes future steps that can be taken to improve the dissemination and acceptance of the proposed guidelines to manage QC. To summarize, the principal goal of the QUAREP-LiMi initiative is to improve the overall quality and reproducibility of light microscope image data by introducing broadly accepted standard practices and accurately captured image data metrics.
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Affiliation(s)
- Glyn Nelson
- Bioimaging Unit, Newcastle University, Newcastle upon Tyne, UK
| | - Ulrike Boehm
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia, USA
| | - Steve Bagley
- Visualisation, Irradiation & Analysis, Cancer Research UK Manchester Institute, Alderley Park, Macclesfield, UK
| | - Peter Bajcsy
- National Institute of Standards and Technology, Gaithersburg, Maryland, USA
| | | | - Claire M Brown
- Advanced BioImaging Facility (ABIF), McGill University, Montreal, Quebec, Canada
| | - Aurélien Dauphin
- Unité Génétique et Biologie du Développement U934, PICT-IBiSA, Institut Curie/Inserm/CNRS/PSL Research University, Paris, France
| | - Ian M Dobbie
- Department of Biochemistry, University of Oxford, Oxford, Oxon, UK
| | - John E Eriksson
- Turku Bioscience Centre, Euro-Bioimaging ERIC, Turku, Finland
| | | | | | - Alexia Ferrand
- Imaging Core Facility, Biozentrum, University of Basel, Basel, Switzerland
| | - Laurent Gelman
- Friedrich Miescher Institute for Biomedical Research, Basel, Switzerland
| | - Ali Gheisari
- Light Microscopy Facility, CMCB Technology Platform, TU Dresden, Dresden, Germany
| | - Hella Hartmann
- Light Microscopy Facility, CMCB Technology Platform, TU Dresden, Dresden, Germany
| | - Christian Kukat
- FACS & Imaging Core Facility, Max Planck Institute for Biology of Ageing, Cologne, Germany
| | - Alex Laude
- Bioimaging Unit, Newcastle University, Newcastle upon Tyne, UK
| | - Miso Mitkovski
- Light Microscopy Facility, Max Planck Institute of Experimental Medicine, Goettingen, Germany
| | - Sebastian Munck
- VIB BioImaging Core & VIB-KU Leuven Center for Brain and Disease Research & KU Leuven Department for Neuroscience, Leuven, Flanders, Belgium
| | | | - Tobias M Rasse
- Scientific Service Group Microscopy, Max Planck Institute for Heart and Lung Research, Bad Nauheim, Germany
| | - Ute Resch-Genger
- Division Biophotonics, Federal Institute for Materials Research and Testing, Berlin, Germany
| | - Lucas C Schuetz
- European Molecular Biology Laboratory, Advanced Light Microscopy Facility, Heidelberg, Germany
| | - Arne Seitz
- Faculty of Life Sciences, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Vaud, Switzerland
| | | | - Jason R Swedlow
- Divisions of Computational Biology and Gene Regulation and Expression, School of Life Sciences, University of Dundee, Dundee, UK
| | - Ioannis Alexopoulos
- General Instrumentation - Light Microscopy Facility, Faculty of Science, Radboud University, Nijmegen, The Netherlands
| | - Karin Aumayr
- BioOptics Facility, IMP - Research Institute of Molecular Pathology, Vienna, Austria
| | - Sergiy Avilov
- Max Planck Institute of Immunobiology and Epigenetics, Freiburg, Germany
| | - Gert-Jan Bakker
- Department of Cell Biology (route 283), Radboud Institute for Molecular Life Sciences, Nijmegen, The Netherlands
| | | | - Andrea Bassi
- Dipartimento di Fisica, Politecnico di Milano, Milan, Italy
| | - Hannes Beckert
- Microscopy Core Facility, Medizinische Fakultät, Universität Bonn, Bonn, Germany
| | | | - Yury Belyaev
- Microscopy Imaging Center, University of Bern, Bern, Switzerland
| | | | | | - Manel Bosch
- Faculty of Biology, Universitat de Barcelona, Barcelona, Spain
| | | | - Lisa A Cameron
- Light Microscopy Core Facility, Department of Biology, Duke University, Durham, North Carolina, USA
| | - Joe Chalfoun
- National Institute of Standards and Technology, Gaithersburg, Maryland, USA
| | - James J Chambers
- Institute for Applied Life Sciences, University of Massachusetts, Amherst, Massachusetts, USA
| | | | - Eduardo Conde-Sousa
- i3S - Instituto de InvestigaÇão e InovaÇão em Saúde, Universidade do Porto, Porto, Portugal.,INEB - Instituto de Engenharia Biomédica, Universidade do Porto, Porto, Portugal
| | | | | | - Elaine Del Nery
- BioPhenics High-Content Screening Laboratory (PICT-IBiSA), Translational Research Department, Institut Curie - PSL Research University, Paris, France
| | - Ralf Dietzel
- Omicron-Laserage Laserprodukte GmbH, Rodgau, Germany
| | | | | | | | - Hans Fried
- Light Microscope Facility, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | | | - Wah Ing Goh
- A*STAR Microscopy Platform, Research Support Centre, Agency for Science, Technology and Research, Singapore, Singapore
| | - Thomas Guilbert
- Institut Cochin, INSERM (U1016), CNRS (UMR 8104), Université de Paris (UMR-S1016), Paris, France
| | | | - Peter Hemmerich
- Core Facility Imaging, Leibniz Institute on Aging, Jena, Germany
| | | | - Michelle S Itano
- Neuroscience Microscopy Core, University of North Carolina, Chapel Hill, North Carolina, USA
| | | | - Helena K Jambor
- Mildred-Scheel Nachwuchszentrum, Universitätsklinikum Carl Gustav Carus, TU Dresden, Dresden, Germany
| | - Stuart C Jarvis
- Prior Scientific Instruments Limited, Cambridge, Cambridgeshire, UK
| | - Antje Keppler
- EMBL Heidelberg, Global BioImaging, Heidelberg, Germany
| | | | - Marcel Kirchner
- FACS & Imaging Core Facility, Max Planck Institute for Biology of Ageing, Cologne, Germany
| | | | - Gabriel Krens
- Bioimaging Facility, Institute of Science and Technology Austria, Klosterneuburg, Austria
| | - Susanne Kunis
- University Osnabrueck, Biology/Chemistry, Osnabrueck, Germany
| | | | - Marco Marcello
- Institute of Systems, Molecular & Integrative Biology, University of Liverpool, Liverpool, Merseyside, UK
| | - Gabriel G Martins
- Instituto Gulbenkian de Ciencia & Faculdade de Ciencias, University of Lisboa, Oeiras, Portugal
| | | | - Claire A Mitchell
- Warwick Medical School, University of Warwick, Coventry, West Midlands, UK
| | - Joshua Moore
- Divisions of Computational Biology and Gene Regulation and Expression, School of Life Sciences, University of Dundee, Dundee, UK
| | - Tobias Mueller
- Gregor Mendel Institute of Molecular Plant Biology (GMI), Vienna, Austria
| | | | - Stephen Ogg
- Medical Microbiology & Immunology, University of Alberta, Edmonton, Alberta, Canada
| | - Shuichi Onami
- RIKEN Center for Biosystems Dynamics Research, Kobe, Hyogo, Japan
| | | | - Perrine Paul-Gilloteaux
- Université de Nantes, CHU Nantes, Inserm, CNRS, SFR Santé, Inserm UMS 016, CNRS UMS 3556, F-44000 Nantes, France
| | - Jaime A Pimentel
- Instituto de Biotecnología, Universidad Nacional Autónoma de México, Cuernavaca, Morelos, Mexico
| | - Laure Plantard
- Friedrich Miescher Institute for Biomedical Research, Basel, Switzerland
| | - Santosh Podder
- Microscopy Facility, Department of Biology, Indian Institute of Science Education and Research Pune, Pune, India
| | | | | | - Markku A Saari
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
| | - Damien Schapman
- UNIROUEN, INSERM, PRIMACEN, Normandie University, Rouen, France
| | | | - Britta Schroth-Diez
- Light Microscopy Facility, Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany
| | | | - Michael Shaw
- National Physical Laboratory, Teddington, Middlesex, UK
| | - Martin Spitaler
- Imaging Facility, Max Planck Institute of Biochemistry, Martinsried, Munich, Germany
| | | | - Damir Sudar
- Quantitative Imaging Systems, Portland, Oregon, USA
| | - Jeremie Teillon
- Bordeaux Imaging Center, Université de Bordeaux, Bordeaux, Gironde, France
| | - Stefan Terjung
- European Molecular Biology Laboratory, Advanced Light Microscopy Facility, Heidelberg, Germany
| | - Roland Thuenauer
- Technology Platform Microscopy and Image Analysis, Heinrich Pette Institute, Leibniz Institute for Experimental Virology, Hamburg, Germany
| | | | - Graham D Wright
- A*STAR Microscopy Platform, Research Support Centre, Agency for Science, Technology and Research, Singapore, Singapore
| | - Roland Nitschke
- Life Imaging Center and BIOSS Centre for Biological Signaling Studies, Albert-Ludwigs-University Freiburg, Freiburg, Germany
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84
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Giraldo-Barreto J, Ortiz S, Thiede EH, Palacio-Rodriguez K, Carpenter B, Barnett AH, Cossio P. A Bayesian approach to extracting free-energy profiles from cryo-electron microscopy experiments. Sci Rep 2021; 11:13657. [PMID: 34211017 PMCID: PMC8249403 DOI: 10.1038/s41598-021-92621-1] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2021] [Accepted: 06/01/2021] [Indexed: 11/08/2022] Open
Abstract
Cryo-electron microscopy (cryo-EM) extracts single-particle density projections of individual biomolecules. Although cryo-EM is widely used for 3D reconstruction, due to its single-particle nature it has the potential to provide information about a biomolecule's conformational variability and underlying free-energy landscape. However, treating cryo-EM as a single-molecule technique is challenging because of the low signal-to-noise ratio (SNR) in individual particles. In this work, we propose the cryo-BIFE method (cryo-EM Bayesian Inference of Free-Energy profiles), which uses a path collective variable to extract free-energy profiles and their uncertainties from cryo-EM images. We test the framework on several synthetic systems where the imaging parameters and conditions were controlled. We found that for realistic cryo-EM environments and relevant biomolecular systems, it is possible to recover the underlying free energy, with the pose accuracy and SNR as crucial determinants. We then use the method to study the conformational transitions of a calcium-activated channel with real cryo-EM particles. Interestingly, we recover not only the most probable conformation (used to generate a high-resolution reconstruction of the calcium-bound state) but also a metastable state that corresponds to the calcium-unbound conformation. As expected for turnover transitions within the same sample, the activation barriers are on the order of [Formula: see text]. We expect our tool for extracting free-energy profiles from cryo-EM images to enable more complete characterization of the thermodynamic ensemble of biomolecules.
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Affiliation(s)
- Julian Giraldo-Barreto
- Biophysics of Tropical Diseases Max Planck Tandem Group, University of Antioquia UdeA, Calle 70 No. 52-21, Medellín, Colombia
- Magnetism and Simulation Group, University of Antioquia UdeA, Calle 70 No. 52-21, Medellín, Colombia
| | - Sebastian Ortiz
- Biophysics of Tropical Diseases Max Planck Tandem Group, University of Antioquia UdeA, Calle 70 No. 52-21, Medellín, Colombia
| | - Erik H Thiede
- Center for Computational Mathematics, Flatiron Institute, New York City, USA
| | - Karen Palacio-Rodriguez
- Institut de Minéralogie, de Physique des Matériaux et de Cosmochimie, Sorbonne Université, Paris, France
| | - Bob Carpenter
- Center for Computational Mathematics, Flatiron Institute, New York City, USA
| | - Alex H Barnett
- Center for Computational Mathematics, Flatiron Institute, New York City, USA
| | - Pilar Cossio
- Biophysics of Tropical Diseases Max Planck Tandem Group, University of Antioquia UdeA, Calle 70 No. 52-21, Medellín, Colombia.
- Department of Theoretical Biophysics, Max Planck Institute of Biophysics, 60438, Frankfurt am Main, Germany.
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85
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Ohashi M, Hosokawa F, Shinkawa T, Iwasaki K. Evaluation of automated particle picking for cryogenic electron microscopy using high-precision transmission electron microscope simulation based on a multi-slice method. ACTA CRYSTALLOGRAPHICA SECTION D-STRUCTURAL BIOLOGY 2021; 77:966-979. [PMID: 34196622 DOI: 10.1107/s2059798321005106] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/06/2020] [Accepted: 05/13/2021] [Indexed: 11/10/2022]
Abstract
This work describes the GRIPS automated particle-picking software for cryogenic electron microscopy and the evaluation of this software using elbis, a high-precision transmission electron microscope (TEM) image simulator. The goal was to develop a method that can pick particles under a small defocus condition where the particles are not clearly visible or under a condition where the particles are exhibiting preferred orientation. The proposed method handles these issues by repeatedly performing three processes, namely extraction, two-dimensional classification and positioning, and by introducing mask processing to exclude areas with particles that have already been picked. TEM images for evaluation were generated with a high-precision TEM image simulator. TEM images containing both particles and amorphous ice were simulated by randomly placing O atoms in the specimen. The experimental results indicate that the proposed method can be used to pick particles correctly under a relatively small defocus condition. Moreover, the results show that the mask processing introduced in the proposed method is valid for particles exhibiting preferred orientation. It is further shown that the proposed method is applicable to data collected from real samples.
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Affiliation(s)
- Masataka Ohashi
- BioNet Lab. Inc., 2-3-28 Nishiki-cho, Tachikawa, Tokyo 190-0022, Japan
| | - Fumio Hosokawa
- BioNet Lab. Inc., 2-3-28 Nishiki-cho, Tachikawa, Tokyo 190-0022, Japan
| | - Takao Shinkawa
- BioNet Lab. Inc., 2-3-28 Nishiki-cho, Tachikawa, Tokyo 190-0022, Japan
| | - Kenji Iwasaki
- University of Tsukuba, Tsukuba Advanced Research Alliance, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8577, Japan
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86
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Karabağ C, Jones ML, Reyes-Aldasoro CC. Volumetric Semantic Instance Segmentation of the Plasma Membrane of HeLa Cells. J Imaging 2021; 7:93. [PMID: 39080881 PMCID: PMC8321355 DOI: 10.3390/jimaging7060093] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Revised: 05/24/2021] [Accepted: 05/25/2021] [Indexed: 01/10/2023] Open
Abstract
In this work, an unsupervised volumetric semantic instance segmentation of the plasma membrane of HeLa cells as observed with serial block face scanning electron microscopy is described. The resin background of the images was segmented at different slices of a 3D stack of 518 slices with 8192 × 8192 pixels each. The background was used to create a distance map, which helped identify and rank the cells by their size at each slice. The centroids of the cells detected at different slices were linked to identify them as a single cell that spanned a number of slices. A subset of these cells, i.e., the largest ones and those not close to the edges were selected for further processing. The selected cells were then automatically cropped to smaller regions of interest of 2000 × 2000 × 300 voxels that were treated as cell instances. Then, for each of these volumes, the nucleus was segmented, and the cell was separated from any neighbouring cells through a series of traditional image processing steps that followed the plasma membrane. The segmentation process was repeated for all the regions of interest previously selected. For one cell for which the ground truth was available, the algorithm provided excellent results in Accuracy (AC) and the Jaccard similarity Index (JI): nucleus: JI =0.9665, AC =0.9975, cell including nucleus JI =0.8711, AC =0.9655, cell excluding nucleus JI =0.8094, AC =0.9629. A limitation of the algorithm for the plasma membrane segmentation was the presence of background. In samples with tightly packed cells, this may not be available. When tested for these conditions, the segmentation of the nuclear envelope was still possible. All the code and data were released openly through GitHub, Zenodo and EMPIAR.
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Affiliation(s)
- Cefa Karabağ
- giCentre, Department of Computer Science, School of Mathematics, Computer Science and Engineering, City, University of London, London EC1V 0HB, UK;
| | - Martin L. Jones
- Electron Microscopy Science Technology Platform, The Francis Crick Institute, London NW1 1AT, UK;
| | - Constantino Carlos Reyes-Aldasoro
- giCentre, Department of Computer Science, School of Mathematics, Computer Science and Engineering, City, University of London, London EC1V 0HB, UK;
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87
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Heimowitz A, Sharon N, Singer A. Centering Noisy Images with Application to Cryo-EM. SIAM JOURNAL ON IMAGING SCIENCES 2021; 14:689-716. [PMID: 35126803 PMCID: PMC8813033 DOI: 10.1137/20m1365946] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
We target the problem of estimating the center of mass of objects in noisy two-dimensional images. We assume that the noise dominates the image, and thus many standard approaches are vulnerable to estimation errors, e.g., the direct computation of the center of mass and the geometric median which is a robust alternative to the center of mass. In this paper, we define a novel surrogate function to the center of mass. We present a mathematical and numerical analysis of our method and show that it outperforms existing methods for estimating the center of mass of an object in various realistic scenarios. As a case study, we apply our centering method to data from single-particle cryo-electron microscopy (cryo-EM), where the goal is to reconstruct the three-dimensional structure of macromolecules. We show how to apply our approach for a better translational alignment of molecule images picked from experimental data. In this way, we facilitate the succeeding steps of reconstruction and streamline the entire cryo-EM pipeline, saving computational time and supporting resolution enhancement.
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Affiliation(s)
- Ayelet Heimowitz
- Department of Electrical and Electronics Engineering, Ariel University, Ariel, Israel
| | - Nir Sharon
- Department of Applied Mathematics, School of Mathematical Sciences, Tel Aviv University, Tel Aviv, Israel
| | - Amit Singer
- Department of Mathematics, PACM and CSML, Princeton University, NJ 08544 USA
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88
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Croll TI, Diederichs K, Fischer F, Fyfe CD, Gao Y, Horrell S, Joseph AP, Kandler L, Kippes O, Kirsten F, Müller K, Nolte K, Payne AM, Reeves M, Richardson JS, Santoni G, Stäb S, Tronrud DE, von Soosten LC, Williams CJ, Thorn A. Making the invisible enemy visible. Nat Struct Mol Biol 2021; 28:404-408. [PMID: 33972785 DOI: 10.1038/s41594-021-00593-7] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Affiliation(s)
| | | | - Florens Fischer
- Institut für Nanostruktur und Festkörperphysik, Universität Hamburg, Hamburg, Germany.,Rudolf-Virchow-Zentrum, Julius-Maximilians-Universität Würzburg, Würzburg, Germany
| | | | - Yunyun Gao
- Institut für Nanostruktur und Festkörperphysik, Universität Hamburg, Hamburg, Germany.,Rudolf-Virchow-Zentrum, Julius-Maximilians-Universität Würzburg, Würzburg, Germany
| | | | | | - Luise Kandler
- Institut für Nanostruktur und Festkörperphysik, Universität Hamburg, Hamburg, Germany.,Rudolf-Virchow-Zentrum, Julius-Maximilians-Universität Würzburg, Würzburg, Germany
| | - Oliver Kippes
- Institut für Nanostruktur und Festkörperphysik, Universität Hamburg, Hamburg, Germany.,Rudolf-Virchow-Zentrum, Julius-Maximilians-Universität Würzburg, Würzburg, Germany
| | - Ferdinand Kirsten
- Institut für Nanostruktur und Festkörperphysik, Universität Hamburg, Hamburg, Germany.,Rudolf-Virchow-Zentrum, Julius-Maximilians-Universität Würzburg, Würzburg, Germany
| | - Konstantin Müller
- Rudolf-Virchow-Zentrum, Julius-Maximilians-Universität Würzburg, Würzburg, Germany
| | - Kristopher Nolte
- Institut für Nanostruktur und Festkörperphysik, Universität Hamburg, Hamburg, Germany.,Rudolf-Virchow-Zentrum, Julius-Maximilians-Universität Würzburg, Würzburg, Germany
| | | | - Matthew Reeves
- Rudolf-Virchow-Zentrum, Julius-Maximilians-Universität Würzburg, Würzburg, Germany
| | | | | | - Sabrina Stäb
- Institut für Nanostruktur und Festkörperphysik, Universität Hamburg, Hamburg, Germany.,Rudolf-Virchow-Zentrum, Julius-Maximilians-Universität Würzburg, Würzburg, Germany
| | | | - Lea C von Soosten
- Institut für Nanostruktur und Festkörperphysik, Universität Hamburg, Hamburg, Germany.,Rudolf-Virchow-Zentrum, Julius-Maximilians-Universität Würzburg, Würzburg, Germany
| | | | - Andrea Thorn
- Institut für Nanostruktur und Festkörperphysik, Universität Hamburg, Hamburg, Germany. .,Rudolf-Virchow-Zentrum, Julius-Maximilians-Universität Würzburg, Würzburg, Germany.
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89
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Meldal BHM, Pons C, Perfetto L, Del-Toro N, Wong E, Aloy P, Hermjakob H, Orchard S, Porras P. Analysing the yeast complexome-the Complex Portal rising to the challenge. Nucleic Acids Res 2021; 49:3156-3167. [PMID: 33677561 PMCID: PMC8034636 DOI: 10.1093/nar/gkab077] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2020] [Revised: 01/22/2021] [Accepted: 01/27/2021] [Indexed: 02/06/2023] Open
Abstract
The EMBL-EBI Complex Portal is a knowledgebase of macromolecular complexes providing persistent stable identifiers. Entries are linked to literature evidence and provide details of complex membership, function, structure and complex-specific Gene Ontology annotations. Data are freely available and downloadable in HUPO-PSI community standards and missing entries can be requested for curation. In collaboration with Saccharomyces Genome Database and UniProt, the yeast complexome, a compendium of all known heteromeric assemblies from the model organism Saccharomyces cerevisiae, was curated. This expansion of knowledge and scope has led to a 50% increase in curated complexes compared to the previously published dataset, CYC2008. The yeast complexome is used as a reference resource for the analysis of complexes from large-scale experiments. Our analysis showed that genes coding for proteins in complexes tend to have more genetic interactions, are co-expressed with more genes, are more multifunctional, localize more often in the nucleus, and are more often involved in nucleic acid-related metabolic processes and processes where large machineries are the predominant functional drivers. A comparison to genetic interactions showed that about 40% of expanded co-complex pairs also have genetic interactions, suggesting strong functional links between complex members.
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Affiliation(s)
- Birgit H M Meldal
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Carles Pons
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute for Science and Technology, 08028 Barcelona, Catalonia, Spain
| | - Livia Perfetto
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Noemi Del-Toro
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Edith Wong
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305-5477, USA
| | - Patrick Aloy
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute for Science and Technology, 08028 Barcelona, Catalonia, Spain.,Institució Catalana de Recerca i Estudis Avançats (ICREA), 08010 Barcelona, Catalonia, Spain
| | - Henning Hermjakob
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Sandra Orchard
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Pablo Porras
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
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90
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Swedlow JR, Kankaanpää P, Sarkans U, Goscinski W, Galloway G, Malacrida L, Sullivan RP, Härtel S, Brown CM, Wood C, Keppler A, Paina F, Loos B, Zullino S, Longo DL, Aime S, Onami S. A global view of standards for open image data formats and repositories. Nat Methods 2021; 18:1440-1446. [PMID: 33948027 DOI: 10.1038/s41592-021-01113-7] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Jason R Swedlow
- Divisions of Computational Biology and Gene Regulation and Expression, School of Life Sciences, University of Dundee, Dundee, UK.
| | - Pasi Kankaanpää
- Turku BioImaging, Åbo Akademi University and University of Turku, Turku, Finland.,Euro-BioImaging ERIC, Turku, Finland
| | - Ugis Sarkans
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Cambridge, UK
| | - Wojtek Goscinski
- Monash eResearch Centre, Monash University, Melbourne, Victoria, Australia
| | - Graham Galloway
- National Imaging Facility, The University of Queensland, Brisbane, Queensland, Australia
| | - Leonel Malacrida
- Advanced Bioimaging Unit, Institut Pasteur Montevideo and Facultad de Medicina, Universidad de la República, Montevideo, Uruguay
| | - Ryan P Sullivan
- Microscopy Australia, The University of Sydney, Sydney, Australia
| | - Steffen Härtel
- National Center for Health Information Systems (CENS), Center for Medical Informatics and Telemedicine (CIMT), and Biomedical Neuroscience Institute (BNI), Faculty of Medicine, University of Chile, Santiago, Chile
| | - Claire M Brown
- Advanced BioImaging Facility (ABIF), McGill University and Canada BioImaging, Montreal, Quebec, Canada
| | - Christopher Wood
- Laboratorio Nacional de Microscopía Avanzada, Instituto de Biotecnología, Universidad Nacional Autónoma de México, Cuernavaca, México
| | - Antje Keppler
- Euro-BioImaging Bio-Hub, European Molecular Biology Laboratory, Heidelberg, Germany
| | - Federica Paina
- Department of Physiological Sciences, Stellenbosch University, Stellenbosch, South Africa
| | - Ben Loos
- Department of Physiological Sciences, Stellenbosch University, Stellenbosch, South Africa
| | - Sara Zullino
- Molecular Imaging Center, Department of Molecular Biotechnology and Health Sciences, University of Torino, Torino, Italy.,Euro-BioImaging ERIC, Torino, Italy
| | - Dario Livio Longo
- Institute of Biostructures and Bioimaging (IBB), National Research Council of Italy (CNR), Torino, Italy
| | - Silvio Aime
- Molecular Imaging Center, Department of Molecular Biotechnology and Health Sciences, University of Torino, Torino, Italy.,Euro-BioImaging ERIC, Torino, Italy
| | - Shuichi Onami
- RIKEN Center for Biosystems Dynamics Research, Kobe, Japan.
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91
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Grabowski M, Macnar JM, Cymborowski M, Cooper DR, Shabalin IG, Gilski M, Brzezinski D, Kowiel M, Dauter Z, Rupp B, Wlodawer A, Jaskolski M, Minor W. Rapid response to emerging biomedical challenges and threats. IUCRJ 2021; 8:395-407. [PMID: 33953926 PMCID: PMC8086160 DOI: 10.1107/s2052252521003018] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/11/2021] [Accepted: 03/22/2021] [Indexed: 05/13/2023]
Abstract
As part of the global mobilization to combat the present pandemic, almost 100 000 COVID-19-related papers have been published and nearly a thousand models of macromolecules encoded by SARS-CoV-2 have been deposited in the Protein Data Bank within less than a year. The avalanche of new structural data has given rise to multiple resources dedicated to assessing the correctness and quality of structural data and models. Here, an approach to evaluate the massive amounts of such data using the resource https://covid19.bioreproducibility.org is described, which offers a template that could be used in large-scale initiatives undertaken in response to future biomedical crises. Broader use of the described methodology could considerably curtail information noise and significantly improve the reproducibility of biomedical research.
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Affiliation(s)
- Marek Grabowski
- Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, Virginia, USA
| | - Joanna M. Macnar
- Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, Virginia, USA
- College of Inter-Faculty Individual Studies in Mathematics and Natural Sciences, University of Warsaw, Warsaw, Poland
- Faculty of Chemistry, Biological and Chemical Research Center, University of Warsaw, Warsaw, Poland
| | - Marcin Cymborowski
- Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, Virginia, USA
| | - David R. Cooper
- Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, Virginia, USA
| | - Ivan G. Shabalin
- Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, Virginia, USA
| | - Miroslaw Gilski
- Department of Crystallography, Faculty of Chemistry, A. Mickiewicz University, Poznan, Poland
- Center for Biocrystallographic Research, Institute of Bioorganic Chemistry, Polish Academy of Sciences, Poznan, Poland
| | - Dariusz Brzezinski
- Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, Virginia, USA
- Center for Biocrystallographic Research, Institute of Bioorganic Chemistry, Polish Academy of Sciences, Poznan, Poland
- Institute of Computing Science, Poznan University of Technology, Poznan, Poland
| | - Marcin Kowiel
- Center for Biocrystallographic Research, Institute of Bioorganic Chemistry, Polish Academy of Sciences, Poznan, Poland
| | - Zbigniew Dauter
- Center for Structural Biology, National Cancer Institute, Frederick, Maryland, USA
| | - Bernhard Rupp
- k.-k Hofkristallamt, San Diego, California, USA
- Institute of Genetic Epidemiology, Medical University Innsbruck, Innsbruck, Austria
| | - Alexander Wlodawer
- Center for Structural Biology, National Cancer Institute, Frederick, Maryland, USA
| | - Mariusz Jaskolski
- Department of Crystallography, Faculty of Chemistry, A. Mickiewicz University, Poznan, Poland
- Center for Biocrystallographic Research, Institute of Bioorganic Chemistry, Polish Academy of Sciences, Poznan, Poland
| | - Wladek Minor
- Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, Virginia, USA
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92
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Below 3 Å structure of apoferritin using a multipurpose TEM with a side entry cryoholder. Sci Rep 2021; 11:8395. [PMID: 33863933 PMCID: PMC8052451 DOI: 10.1038/s41598-021-87183-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Accepted: 03/22/2021] [Indexed: 12/22/2022] Open
Abstract
Recently, the structural analysis of protein complexes by cryo-electron microscopy (cryo-EM) single particle analysis (SPA) has had great impact as a biophysical method. Many results of cryo-EM SPA are based on data acquired on state-of-the-art cryo-electron microscopes customized for SPA. These are currently only available in limited locations around the world, where securing machine time is highly competitive. One potential solution for this time-competitive situation is to reuse existing multi-purpose equipment, although this comes with performance limitations. Here, a multi-purpose TEM with a side entry cryo-holder was used to evaluate the potential of high-resolution SPA, resulting in a 3 Å resolution map of apoferritin with local resolution extending to 2.6 Å. This map clearly showed two positions of an aromatic side chain. Further, examination of optimal imaging conditions depending on two different multi-purpose electron microscope and camera combinations was carried out, demonstrating that higher magnifications are not always necessary or desirable. Since automation is effectively a requirement for large-scale data collection, and augmenting the multi-purpose equipment is possible, we expanded testing by acquiring data with SerialEM using a β-galactosidase test sample. This study demonstrates the possibilities of more widely available and established electron microscopes, and their applications for cryo-EM SPA.
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93
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Kyrilis FL, Belapure J, Kastritis PL. Detecting Protein Communities in Native Cell Extracts by Machine Learning: A Structural Biologist's Perspective. Front Mol Biosci 2021; 8:660542. [PMID: 33937337 PMCID: PMC8082361 DOI: 10.3389/fmolb.2021.660542] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Accepted: 03/18/2021] [Indexed: 11/13/2022] Open
Abstract
Native cell extracts hold great promise for understanding the molecular structure of ordered biological systems at high resolution. This is because higher-order biomolecular interactions, dubbed as protein communities, may be retained in their (near-)native state, in contrast to extensively purifying or artificially overexpressing the proteins of interest. The distinct machine-learning approaches are applied to discover protein-protein interactions within cell extracts, reconstruct dedicated biological networks, and report on protein community members from various organisms. Their validation is also important, e.g., by the cross-linking mass spectrometry or cell biology methods. In addition, the cell extracts are amenable to structural analysis by cryo-electron microscopy (cryo-EM), but due to their inherent complexity, sorting structural signatures of protein communities derived by cryo-EM comprises a formidable task. The application of image-processing workflows inspired by machine-learning techniques would provide improvements in distinguishing structural signatures, correlating proteomic and network data to structural signatures and subsequently reconstructed cryo-EM maps, and, ultimately, characterizing unidentified protein communities at high resolution. In this review article, we summarize recent literature in detecting protein communities from native cell extracts and identify the remaining challenges and opportunities. We argue that the progress in, and the integration of, machine learning, cryo-EM, and complementary structural proteomics approaches would provide the basis for a multi-scale molecular description of protein communities within native cell extracts.
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Affiliation(s)
- Fotis L. Kyrilis
- Interdisciplinary Research Center HALOmem, Charles Tanford Protein Center, Martin Luther University Halle-Wittenberg, Halle (Saale), Germany
- Institute of Biochemistry and Biotechnology, Martin Luther University Halle-Wittenberg, Halle (Saale), Germany
| | - Jaydeep Belapure
- Interdisciplinary Research Center HALOmem, Charles Tanford Protein Center, Martin Luther University Halle-Wittenberg, Halle (Saale), Germany
| | - Panagiotis L. Kastritis
- Interdisciplinary Research Center HALOmem, Charles Tanford Protein Center, Martin Luther University Halle-Wittenberg, Halle (Saale), Germany
- Institute of Biochemistry and Biotechnology, Martin Luther University Halle-Wittenberg, Halle (Saale), Germany
- Biozentrum, Martin Luther University Halle-Wittenberg, Halle (Saale), Germany
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94
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Conrad R, Narayan K. CEM500K, a large-scale heterogeneous unlabeled cellular electron microscopy image dataset for deep learning. eLife 2021; 10:e65894. [PMID: 33830015 PMCID: PMC8032397 DOI: 10.7554/elife.65894] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Accepted: 03/13/2021] [Indexed: 01/03/2023] Open
Abstract
Automated segmentation of cellular electron microscopy (EM) datasets remains a challenge. Supervised deep learning (DL) methods that rely on region-of-interest (ROI) annotations yield models that fail to generalize to unrelated datasets. Newer unsupervised DL algorithms require relevant pre-training images, however, pre-training on currently available EM datasets is computationally expensive and shows little value for unseen biological contexts, as these datasets are large and homogeneous. To address this issue, we present CEM500K, a nimble 25 GB dataset of 0.5 × 106 unique 2D cellular EM images curated from nearly 600 three-dimensional (3D) and 10,000 two-dimensional (2D) images from >100 unrelated imaging projects. We show that models pre-trained on CEM500K learn features that are biologically relevant and resilient to meaningful image augmentations. Critically, we evaluate transfer learning from these pre-trained models on six publicly available and one newly derived benchmark segmentation task and report state-of-the-art results on each. We release the CEM500K dataset, pre-trained models and curation pipeline for model building and further expansion by the EM community. Data and code are available at https://www.ebi.ac.uk/pdbe/emdb/empiar/entry/10592/ and https://git.io/JLLTz.
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Affiliation(s)
- Ryan Conrad
- Center for Molecular Microscopy, Center for Cancer Research, National Cancer Institute, National Institutes of HealthBethesdaUnited States
- Cancer Research Technology Program, Frederick National Laboratory for Cancer ResearchFrederickUnited States
| | - Kedar Narayan
- Center for Molecular Microscopy, Center for Cancer Research, National Cancer Institute, National Institutes of HealthBethesdaUnited States
- Cancer Research Technology Program, Frederick National Laboratory for Cancer ResearchFrederickUnited States
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95
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Sharov G, Morado DR, Carroni M, de la Rosa-Trevín JM. Using RELION software within the Scipion framework. Acta Crystallogr D Struct Biol 2021; 77:403-410. [PMID: 33825701 PMCID: PMC8025880 DOI: 10.1107/s2059798321001856] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2020] [Accepted: 02/15/2021] [Indexed: 11/10/2022] Open
Abstract
Scipion is a modular image-processing framework that integrates several software packages under a unified interface while taking care of file formats and conversions. Here, new developments and capabilities of the Scipion plugin for the widely used RELION software package are presented and illustrated with an image-processing pipeline for published data. The user interfaces of Scipion and RELION are compared and the key differences are highlighted, allowing this manuscript to be used as a guide for both new and experienced users of this software. Different on-the-fly image-processing options are also discussed, demonstrating the flexibility of the Scipion framework.
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Affiliation(s)
- Grigory Sharov
- MRC Laboratory of Molecular Biology, Cambridge Biomedical Campus, Cambridge, United Kingdom
| | - Dustin R Morado
- Department of Biochemistry and Biophysics, Science for Life Laboratory, Stockholm University, Stockholm, Sweden
| | - Marta Carroni
- Department of Biochemistry and Biophysics, Science for Life Laboratory, Stockholm University, Stockholm, Sweden
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96
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Abstract
Abstract
Deep learning is transforming most areas of science and technology, including electron microscopy. This review paper offers a practical perspective aimed at developers with limited familiarity. For context, we review popular applications of deep learning in electron microscopy. Following, we discuss hardware and software needed to get started with deep learning and interface with electron microscopes. We then review neural network components, popular architectures, and their optimization. Finally, we discuss future directions of deep learning in electron microscopy.
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97
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Chiu W, Schmid MF, Pintilie GD, Lawson CL. Evolution of standardization and dissemination of cryo-EM structures and data jointly by the community, PDB, and EMDB. J Biol Chem 2021; 296:100560. [PMID: 33744287 PMCID: PMC8050867 DOI: 10.1016/j.jbc.2021.100560] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2021] [Revised: 02/08/2021] [Accepted: 03/16/2021] [Indexed: 01/04/2023] Open
Abstract
Cryogenic electron microscopy (cryo-EM) methods began to be used in the mid-1970s to study thin and periodic arrays of proteins. Following a half-century of development in cryo-specimen preparation, instrumentation, data collection, data processing, and modeling software, cryo-EM has become a routine method for solving structures from large biological assemblies to small biomolecules at near to true atomic resolution. This review explores the critical roles played by the Protein Data Bank (PDB) and Electron Microscopy Data Bank (EMDB) in partnership with the community to develop the necessary infrastructure to archive cryo-EM maps and associated models. Public access to cryo-EM structure data has in turn facilitated better understanding of structure–function relationships and advancement of image processing and modeling tool development. The partnership between the global cryo-EM community and PDB and EMDB leadership has synergistically shaped the standards for metadata, one-stop deposition of maps and models, and validation metrics to assess the quality of cryo-EM structures. The advent of cryo-electron tomography (cryo-ET) for in situ molecular cell structures at a broad resolution range and their correlations with other imaging data introduce new data archival challenges in terms of data size and complexity in the years to come.
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Affiliation(s)
- Wah Chiu
- Department of Bioengineering, Stanford University, Stanford, California, USA; Division of CryoEM and Bioimaging, SLAC National Accelerator Laboratory, Stanford University, Menlo Park, California, USA.
| | - Michael F Schmid
- Division of CryoEM and Bioimaging, SLAC National Accelerator Laboratory, Stanford University, Menlo Park, California, USA
| | - Grigore D Pintilie
- Department of Bioengineering, Stanford University, Stanford, California, USA
| | - Catherine L Lawson
- Institute for Quantitative Biomedicine and Research Collaboratory for Structural Bioinformatics, Rutgers, The State University of New Jersey, Piscataway, New Jersey, USA
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98
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Waman VP, Sen N, Varadi M, Daina A, Wodak SJ, Zoete V, Velankar S, Orengo C. The impact of structural bioinformatics tools and resources on SARS-CoV-2 research and therapeutic strategies. Brief Bioinform 2021; 22:742-768. [PMID: 33348379 PMCID: PMC7799268 DOI: 10.1093/bib/bbaa362] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2020] [Revised: 11/06/2020] [Accepted: 11/09/2020] [Indexed: 01/18/2023] Open
Abstract
SARS-CoV-2 is the causative agent of COVID-19, the ongoing global pandemic. It has posed a worldwide challenge to human health as no effective treatment is currently available to combat the disease. Its severity has led to unprecedented collaborative initiatives for therapeutic solutions against COVID-19. Studies resorting to structure-based drug design for COVID-19 are plethoric and show good promise. Structural biology provides key insights into 3D structures, critical residues/mutations in SARS-CoV-2 proteins, implicated in infectivity, molecular recognition and susceptibility to a broad range of host species. The detailed understanding of viral proteins and their complexes with host receptors and candidate epitope/lead compounds is the key to developing a structure-guided therapeutic design. Since the discovery of SARS-CoV-2, several structures of its proteins have been determined experimentally at an unprecedented speed and deposited in the Protein Data Bank. Further, specialized structural bioinformatics tools and resources have been developed for theoretical models, data on protein dynamics from computer simulations, impact of variants/mutations and molecular therapeutics. Here, we provide an overview of ongoing efforts on developing structural bioinformatics tools and resources for COVID-19 research. We also discuss the impact of these resources and structure-based studies, to understand various aspects of SARS-CoV-2 infection and therapeutic development. These include (i) understanding differences between SARS-CoV-2 and SARS-CoV, leading to increased infectivity of SARS-CoV-2, (ii) deciphering key residues in the SARS-CoV-2 involved in receptor-antibody recognition, (iii) analysis of variants in host proteins that affect host susceptibility to infection and (iv) analyses facilitating structure-based drug and vaccine design against SARS-CoV-2.
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Affiliation(s)
| | | | | | - Antoine Daina
- Molecular Modeling Group at SIB, Swiss Institute of Bioinformatics
| | | | - Vincent Zoete
- Department of Fundamental Oncology at the University of Lausanne and Group leader at SIB
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99
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Iudin A, Sarkans U, Koronfel MA, Fish T, Dobbie I, Harkiolaki M, Patwardhan A, Kleywegt GJ. Data-deposition protocols for correlative soft X-ray tomography and super-resolution structured illumination microscopy applications. STAR Protoc 2021; 2:100253. [PMID: 33490973 PMCID: PMC7811169 DOI: 10.1016/j.xpro.2020.100253] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
Abstract
This protocol illustrates the steps necessary to deposit correlated 3D cryo-imaging data from cryo-structured illumination microscopy and cryo-soft X-ray tomography with the BioStudies and EMPIAR deposition databases of the European Bioinformatics Institute. There is currently a real need for a robust method of data deposition to ensure unhindered access to and independent validation of correlative light and X-ray microscopy data to allow use in further comparative studies, educational activities, and data mining. For complete details on the use and execution of this protocol, please refer to Kounatidis et al. (2020).
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Affiliation(s)
- Andrii Iudin
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Ugis Sarkans
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Mohamed A. Koronfel
- Diamond Light Source, Harwell Science and Innovation Campus, Didcot OX11 0DW, UK
| | - Tom Fish
- Diamond Light Source, Harwell Science and Innovation Campus, Didcot OX11 0DW, UK
| | - Ian Dobbie
- Micron, Department of Biochemistry, University of Oxford, Oxford OX1 3QU, UK
| | - Maria Harkiolaki
- Diamond Light Source, Harwell Science and Innovation Campus, Didcot OX11 0DW, UK
| | - Ardan Patwardhan
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Gerard J. Kleywegt
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
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100
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Zhang J, Wang Z, Liu Z, Zhang F. Improve the Resolution and Parallel Performance of the Three-Dimensional Refine Algorithm in RELION Using CUDA and MPI. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021; 18:583-595. [PMID: 31329127 DOI: 10.1109/tcbb.2019.2929171] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
In cryo-electron microscopy, RELION is a powerful tool for high-resolution reconstruction. Due to the complicated imaging procedure and the heterogeneity of particles, some of the selected particle images offer more disturbing information than others. However, in the current RELION, all these particle images are treated equally. In our work, we extend RELION's model with one scalar parameter to score the contribution of a particle depending on the error between the experimental particle and the corresponding reprojection. This scores down weight potentially poor particles, hence accelerating the convergence. Besides, by now there is no sophisticated memory management system for RELION, fragmentation on GPU will increase with iterations, eventually crashing the program. In our work, we designed the stack-based memory management system to guarantee the stability of RELION and to optimize the memory usage condition. Also, to reduce memory usage, we developed a customized compressed data structure for the memory-demanding weight array. In addition, to speed up the GPU version of RELION, we proposed two highly efficient parallel algorithms for weight calculation algorithm and weight selection algorithm. Experiments show that compared with RELION, the optimized three-dimensional refine algorithm can speed up the converge procedure, the memory system can avoid memory fragmentation, and a better speed-up ratio can be obtained.
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