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Theiss M, Hériché JK, Russell C, Helekal D, Soppitt A, Ries J, Ellenberg J, Brazma A, Uhlmann V. Simulating structurally variable nuclear pore complexes for microscopy. Bioinformatics 2023; 39:btad587. [PMID: 37756700 PMCID: PMC10570993 DOI: 10.1093/bioinformatics/btad587] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Revised: 09/08/2023] [Accepted: 09/22/2023] [Indexed: 09/29/2023] Open
Abstract
MOTIVATION The nuclear pore complex (NPC) is the only passageway for macromolecules between nucleus and cytoplasm, and an important reference standard in microscopy: it is massive and stereotypically arranged. The average architecture of NPC proteins has been resolved with pseudoatomic precision, however observed NPC heterogeneities evidence a high degree of divergence from this average. Single-molecule localization microscopy (SMLM) images NPCs at protein-level resolution, whereupon image analysis software studies NPC variability. However, the true picture of this variability is unknown. In quantitative image analysis experiments, it is thus difficult to distinguish intrinsically high SMLM noise from variability of the underlying structure. RESULTS We introduce CIR4MICS ('ceramics', Configurable, Irregular Rings FOR MICroscopy Simulations), a pipeline that synthesizes ground truth datasets of structurally variable NPCs based on architectural models of the true NPC. Users can select one or more N- or C-terminally tagged NPC proteins, and simulate a wide range of geometric variations. We also represent the NPC as a spring-model such that arbitrary deforming forces, of user-defined magnitudes, simulate irregularly shaped variations. Further, we provide annotated reference datasets of simulated human NPCs, which facilitate a side-by-side comparison with real data. To demonstrate, we synthetically replicate a geometric analysis of real NPC radii and reveal that a range of simulated variability parameters can lead to observed results. Our simulator is therefore valuable to test the capabilities of image analysis methods, as well as to inform experimentalists about the requirements of hypothesis-driven imaging studies. AVAILABILITY AND IMPLEMENTATION Code: https://github.com/uhlmanngroup/cir4mics. Simulated data: BioStudies S-BSST1058.
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Affiliation(s)
- Maria Theiss
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, CB10 1SD, United Kingdom
| | - Jean-Karim Hériché
- Cell Biology and Biophysics Unit, European Molecular Biology Laboratory (EMBL), Heidelberg 69117, Germany
| | - Craig Russell
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, CB10 1SD, United Kingdom
| | - David Helekal
- Centre for Doctoral Training in Mathematics for Real-World Systems, University of Warwick, Coventry CV4 7AL, United Kingdom
| | - Alisdair Soppitt
- EPSRC Centre for Doctoral Training in Modelling of Heterogeneous Systems, University of Warwick, Coventry CV4 7AL, United Kingdom
| | - Jonas Ries
- Cell Biology and Biophysics Unit, European Molecular Biology Laboratory (EMBL), Heidelberg 69117, Germany
- Max Perutz Labs, University of Vienna, Department of Structural and Computational Biology, Dr.-Bohr-Gasse 9, Vienna 1030, Austria
| | - Jan Ellenberg
- Cell Biology and Biophysics Unit, European Molecular Biology Laboratory (EMBL), Heidelberg 69117, Germany
| | - Alvis Brazma
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, CB10 1SD, United Kingdom
| | - Virginie Uhlmann
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, CB10 1SD, United Kingdom
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Ray KK, Verma AR, Gonzalez RL, Kinz-Thompson CD. Inferring the shape of data: a probabilistic framework for analysing experiments in the natural sciences. Proc Math Phys Eng Sci 2022; 478:20220177. [PMID: 37767180 PMCID: PMC10521765 DOI: 10.1098/rspa.2022.0177] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2022] [Accepted: 09/26/2022] [Indexed: 09/29/2023] Open
Abstract
A critical step in data analysis for many different types of experiments is the identification of features with theoretically defined shapes in N -dimensional datasets; examples of this process include finding peaks in multi-dimensional molecular spectra or emitters in fluorescence microscopy images. Identifying such features involves determining if the overall shape of the data is consistent with an expected shape; however, it is generally unclear how to quantitatively make this determination. In practice, many analysis methods employ subjective, heuristic approaches, which complicates the validation of any ensuing results-especially as the amount and dimensionality of the data increase. Here, we present a probabilistic solution to this problem by using Bayes' rule to calculate the probability that the data have any one of several potential shapes. This probabilistic approach may be used to objectively compare how well different theories describe a dataset, identify changes between datasets and detect features within data using a corollary method called Bayesian Inference-based Template Search; several proof-of-principle examples are provided. Altogether, this mathematical framework serves as an automated 'engine' capable of computationally executing analysis decisions currently made by visual inspection across the sciences.
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Affiliation(s)
- Korak Kumar Ray
- Department of Chemistry, Columbia University, New York, NY 10027, USA
| | - Anjali R. Verma
- Department of Chemistry, Columbia University, New York, NY 10027, USA
| | - Ruben L. Gonzalez
- Department of Chemistry, Columbia University, New York, NY 10027, USA
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3
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Mendes A, Heil HS, Coelho S, Leterrier C, Henriques R. Mapping molecular complexes with super-resolution microscopy and single-particle analysis. Open Biol 2022; 12:220079. [PMID: 35892200 PMCID: PMC9326279 DOI: 10.1098/rsob.220079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Abstract
Understanding the structure of supramolecular complexes provides insight into their functional capabilities and how they can be modulated in the context of disease. Super-resolution microscopy (SRM) excels in performing this task by resolving ultrastructural details at the nanoscale with molecular specificity. However, technical limitations, such as underlabelling, preclude its ability to provide complete structures. Single-particle analysis (SPA) overcomes this limitation by combining information from multiple images of identical structures and producing an averaged model, effectively enhancing the resolution and coverage of image reconstructions. This review highlights important studies using SRM-SPA, demonstrating how it broadens our knowledge by elucidating features of key biological structures with unprecedented detail.
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Affiliation(s)
| | | | - Simao Coelho
- Instituto Gulbenkian de Ciência, Oeiras, Portugal
| | | | - Ricardo Henriques
- Instituto Gulbenkian de Ciência, Oeiras, Portugal,MRC Laboratory for Molecular Cell Biology, University College London, London, UK
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4
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Sorzano COS, Carazo JM. Cryo-Electron Microscopy: the field of 1,000 + methods. J Struct Biol 2022; 214:107861. [PMID: 35568276 DOI: 10.1016/j.jsb.2022.107861] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Revised: 03/21/2022] [Accepted: 04/21/2022] [Indexed: 01/18/2023]
Abstract
Cryo-Electron Microscopy (CryoEM) is currently a well-established method to elucidate a biological macromolecule's three-dimensional (3D) structure. Its success is due to technological and methodological advances in several fronts: sample preparation, electron optics and detection, image acquisition, image processing, and map interpretation. The first methods started in the late 1960s and, since then, new methods on all fronts have continuously been published, maturating the field as we know it now. In terms of publications, we can distinguish several periods, witnessing a substantial acceleration of methodological publications in recent years, pointing out to an increased interest in the domain. On the other hand, this accelerated increase of methods development may confuse practitioners about which method they should be using (and how) and highlight the importance of paying attention to establishing best practices for methods reporting and usage. In this paper, we analyze the trends identified in over 1,000 methodological papers. Our focus is primarily on computational image processing methods. However, our list also covers some aspects of sample preparation and image acquisition. Several interesting ideas stem out from this study: 1) Single Particle Analysis (SPA) has largely accelerated in the last decade and sample preparation methods in the last five years; 2) Electron Tomography is not yet in a rapidly growing phase, but it is foreseeable that it will soon be; 3) the work horses of SPA are 3D classification, 3D reconstruction, and 3D alignment, and there have been many papers on these topics, which are not considered to be solved yet, but ever improving; and 4) since the resolution revolution, atomic modelling has also caught on as a hot topic.
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Affiliation(s)
- C O S Sorzano
- Natl. Center of Biotechnology, CSIC. c/Darwin, 3. Campus Univ. Autónoma de Madrid. 28049 Madrid, Spain
| | - J M Carazo
- Natl. Center of Biotechnology, CSIC. c/Darwin, 3. Campus Univ. Autónoma de Madrid. 28049 Madrid, Spain
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5
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Heydarian H, Joosten M, Przybylski A, Schueder F, Jungmann R, Werkhoven BV, Keller-Findeisen J, Ries J, Stallinga S, Bates M, Rieger B. 3D particle averaging and detection of macromolecular symmetry in localization microscopy. Nat Commun 2021; 12:2847. [PMID: 33990554 PMCID: PMC8121824 DOI: 10.1038/s41467-021-22006-5] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2021] [Accepted: 02/22/2021] [Indexed: 11/20/2022] Open
Abstract
Single molecule localization microscopy offers in principle resolution down to the molecular level, but in practice this is limited primarily by incomplete fluorescent labeling of the structure. This missing information can be completed by merging information from many structurally identical particles. In this work, we present an approach for 3D single particle analysis in localization microscopy which hugely increases signal-to-noise ratio and resolution and enables determining the symmetry groups of macromolecular complexes. Our method does not require a structural template, and handles anisotropic localization uncertainties. We demonstrate 3D reconstructions of DNA-origami tetrahedrons, Nup96 and Nup107 subcomplexes of the nuclear pore complex acquired using multiple single molecule localization microscopy techniques, with their structural symmetry deducted from the data.
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Affiliation(s)
- Hamidreza Heydarian
- Department of Imaging Physics, Delft University of Technology, Delft, The Netherlands
| | - Maarten Joosten
- Department of Imaging Physics, Delft University of Technology, Delft, The Netherlands
| | - Adrian Przybylski
- Department of NanoBiophotonics, Max Planck Institute for Biophysical Chemistry, Göttingen, Germany
| | - Florian Schueder
- Department of Physics and Center for Nanoscience, Ludwig Maximilian University, Munich, Germany
- Max Planck Institute of Biochemistry, Martinsried, Germany
| | - Ralf Jungmann
- Department of Physics and Center for Nanoscience, Ludwig Maximilian University, Munich, Germany
- Max Planck Institute of Biochemistry, Martinsried, Germany
| | | | - Jan Keller-Findeisen
- Department of NanoBiophotonics, Max Planck Institute for Biophysical Chemistry, Göttingen, Germany
| | - Jonas Ries
- Cell Biology and Biophysics Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
| | - Sjoerd Stallinga
- Department of Imaging Physics, Delft University of Technology, Delft, The Netherlands
| | - Mark Bates
- Department of NanoBiophotonics, Max Planck Institute for Biophysical Chemistry, Göttingen, Germany
| | - Bernd Rieger
- Department of Imaging Physics, Delft University of Technology, Delft, The Netherlands.
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6
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Curd A, Leng J, Hughes RE, Cleasby AJ, Rogers B, Trinh CH, Baird MA, Takagi Y, Tiede C, Sieben C, Manley S, Schlichthaerle T, Jungmann R, Ries J, Shroff H, Peckham M. Nanoscale Pattern Extraction from Relative Positions of Sparse 3D Localizations. NANO LETTERS 2021; 21:1213-1220. [PMID: 33253583 PMCID: PMC7883386 DOI: 10.1021/acs.nanolett.0c03332] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/17/2020] [Revised: 11/24/2020] [Indexed: 05/23/2023]
Abstract
Inferring the organization of fluorescently labeled nanosized structures from single molecule localization microscopy (SMLM) data, typically obscured by stochastic noise and background, remains challenging. To overcome this, we developed a method to extract high-resolution ordered features from SMLM data that requires only a low fraction of targets to be localized with high precision. First, experimentally measured localizations are analyzed to produce relative position distributions (RPDs). Next, model RPDs are constructed using hypotheses of how the molecule is organized. Finally, a statistical comparison is used to select the most likely model. This approach allows pattern recognition at sub-1% detection efficiencies for target molecules, in large and heterogeneous samples and in 2D and 3D data sets. As a proof-of-concept, we infer ultrastructure of Nup107 within the nuclear pore, DNA origami structures, and α-actinin-2 within the cardiomyocyte Z-disc and assess the quality of images of centrioles to improve the averaged single-particle reconstruction.
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Affiliation(s)
- Alistair
P. Curd
- School
of Molecular and Cellular Biology, University
of Leeds, Leeds LS2 9JT, United Kingdom
| | - Joanna Leng
- School
of Computing, University of Leeds, Leeds LS2 9JT, United Kingdom
| | - Ruth E. Hughes
- School
of Molecular and Cellular Biology, University
of Leeds, Leeds LS2 9JT, United Kingdom
| | - Alexa J. Cleasby
- School
of Molecular and Cellular Biology, University
of Leeds, Leeds LS2 9JT, United Kingdom
| | - Brendan Rogers
- School
of Molecular and Cellular Biology, University
of Leeds, Leeds LS2 9JT, United Kingdom
| | - Chi H. Trinh
- School
of Molecular and Cellular Biology, University
of Leeds, Leeds LS2 9JT, United Kingdom
| | - Michelle A. Baird
- Cell
and Developmental Biology Center, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, Maryland 20892, United States
| | - Yasuharu Takagi
- Cell
and Developmental Biology Center, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, Maryland 20892, United States
| | - Christian Tiede
- School
of Molecular and Cellular Biology, University
of Leeds, Leeds LS2 9JT, United Kingdom
| | - Christian Sieben
- Laboratory
of Experimental Biophysics, École
Polytechnique Fédérale de Lausanne, BSP 427 (Cubotron UNIL), Rte de
la Sorge, CH-1015 Lausanne, Switzerland
| | - Suliana Manley
- Laboratory
of Experimental Biophysics, École
Polytechnique Fédérale de Lausanne, BSP 427 (Cubotron UNIL), Rte de
la Sorge, CH-1015 Lausanne, Switzerland
| | - Thomas Schlichthaerle
- Max
Planck Institute of Biochemistry, Am Klopferspitz 18, 82152 Martinsried, Munich, Germany
- Faculty
of Physics and Center for Nanoscience, LMU
Munich, 80539 Munich, Germany
| | - Ralf Jungmann
- Max
Planck Institute of Biochemistry, Am Klopferspitz 18, 82152 Martinsried, Munich, Germany
- Faculty
of Physics and Center for Nanoscience, LMU
Munich, 80539 Munich, Germany
| | - Jonas Ries
- Cell Biology
and Biophysics Unit, European Molecular
Biology Laboratory, 69117 Heidelberg, Germany
| | - Hari Shroff
- Laboratory
of High Resolution Optical Imaging, National Institute of Biomedical
Imaging and Bioengineering, National Institutes
of Health, Bethesda, Maryland 20892, United States
| | - Michelle Peckham
- School
of Molecular and Cellular Biology, University
of Leeds, Leeds LS2 9JT, United Kingdom
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7
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A workflow for sizing oligomeric biomolecules based on cryo single molecule localization microscopy. PLoS One 2021; 16:e0245693. [PMID: 33471861 PMCID: PMC7817001 DOI: 10.1371/journal.pone.0245693] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2020] [Accepted: 01/05/2021] [Indexed: 11/19/2022] Open
Abstract
Single molecule localization microscopy (SMLM) has enormous potential for resolving subcellular structures below the diffraction limit of light microscopy: Localization precision in the low digit nanometer regime has been shown to be achievable. In order to record localization microscopy data, however, sample fixation is inevitable to prevent molecular motion during the rather long recording times of minutes up to hours. Eventually, it turns out that preservation of the sample's ultrastructure during fixation becomes the limiting factor. We propose here a workflow for data analysis, which is based on SMLM performed at cryogenic temperatures. Since molecular dipoles of the fluorophores are fixed at low temperatures, such an approach offers the possibility to use the orientation of the dipole as an additional information for image analysis. In particular, assignment of localizations to individual dye molecules becomes possible with high reliability. We quantitatively characterized the new approach based on the analysis of simulated oligomeric structures. Side lengths can be determined with a relative error of less than 1% for tetramers with a nominal side length of 5 nm, even if the assumed localization precision for single molecules is more than 2 nm.
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8
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Super-resolution reconstruction of real infrared images acquired with unmanned aerial vehicle. PLoS One 2020; 15:e0234775. [PMID: 32555724 PMCID: PMC7299321 DOI: 10.1371/journal.pone.0234775] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2020] [Accepted: 06/02/2020] [Indexed: 11/23/2022] Open
Abstract
Super-resolution (SR) technology provides a far promising computational imaging approach in obtaining a high-resolution (HR) image (or image sequences) from observed multiple low-resolution (LR) images by incorporating complementary information. In this paper, a three-stage SR method is proposed to generate a HR image from infrared (IR) LR Images acquired with Unmanned Aerial Vehicle (UAV). The proposed method integrates a high-level image capturing process and a low-level SR process. In this integrated process, we incorporate UAV path optimization, sub-pixel image registration, and sparseness constraint into a computational imaging framework of a region of interest (ROI). To refine ROI complementary feathers, we design an optimal flight control scheme to acquire adequate image sequences from multi-angles. In particular, a phase correlation approach achieving reliable sub-pixel image feature matching is adapted, on the basis of which an effective sparseness regularization model is built to enhance the fine structures of the IR image. Unlike most traditional multiple-frame SR algorithms that mainly focus on signal processing and achieve good performances when using standard test datasets, the performed experiments with real-life IR sequences indicate the three-stage SR method can also deal with practical LR IR image sequences collected by UAVs. The experimental results demonstrate that the proposed method is capable of generating HR images with good performance in terms of edge preservation and detail enhancement.
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