Curd, AP orcid.org/0000-0002-3949-7523, Leng, J orcid.org/0000-0001-9790-162X, Hughes, RE orcid.org/0000-0001-6167-8286 et al. (13 more authors) (2021) Nanoscale pattern extraction from relative positions of sparse 3D localizations. Nano Letters: a journal dedicated to nanoscience and nanotechnology, 21 (3). pp. 1213-1220. ISSN 1530-6984
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.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2020 American Chemical Society. This is an open access article published under a Creative Commons Attribution (CC-BY) License, which permits unrestricted use, distribution and reproduction in any medium, provided the author and source are cited. |
Keywords: | Super-resolution microscopy; Image analysis; Protein organization; Single molecule localization; Spatial pattern statistics; Nanoscale structures |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) The University of Leeds > Faculty of Biological Sciences (Leeds) > School of Molecular and Cellular Biology (Leeds) > Cell Biology (Leeds) The University of Leeds > Faculty of Biological Sciences (Leeds) > School of Molecular and Cellular Biology (Leeds) > Synthetic Biology (Leed) |
Funding Information: | Funder Grant number MRC (Medical Research Council) MR/K015613/1 Wellcome Trust 091108/Z/10/Z BBSRC (Biotechnology & Biological Sciences Research Council) BB/S015787/1 EPSRC (Engineering and Physical Sciences Research Council) EP/R016372/1 |
Depositing User: | Symplectic Publications |
Date Deposited: | 04 Dec 2020 11:41 |
Last Modified: | 28 Apr 2021 07:32 |
Status: | Published |
Publisher: | American Chemical Society |
Identification Number: | 10.1021/acs.nanolett.0c03332 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:168385 |