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  Nanoscale Pattern Extraction from Relative Positions of Sparse 3D Localizations

Curd, A. P., Leng, J., Hughes, R. E., Cleasby, A. J., Rogers, B., Trinh, C. H., et al. (2021). Nanoscale Pattern Extraction from Relative Positions of Sparse 3D Localizations. Nano Letters, 21(3), 1213-1220. doi:10.1021/acs.nanolett.0c03332.

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Genre: Journal Article

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 Creators:
Curd, Alistair P.1, Author
Leng, Joanna1, Author
Hughes, Ruth E.1, Author
Cleasby, Alexa J.1, Author
Rogers, Brendan1, Author
Trinh, Chi H.1, Author
Baird, Michelle A.1, Author
Takagi, Yasuharu1, Author
Tiede, Christian1, Author
Sieben, Christian1, Author
Manley, Suliana1, Author
Schlichthaerle, Thomas2, Author              
Jungmann, Ralf2, Author              
Ries, Jonas1, Author
Shroff, Hari1, Author
Peckham, Michelle1, Author
Affiliations:
1external, ou_persistent22              
2Jungmann, Ralf / Molecular Imaging and Bionanotechnology, Max Planck Institute of Biochemistry, Max Planck Society, ou_2149679              

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Free keywords: Chemistry; Science & Technology - Other Topics; Materials Science; Physics; Super-resolution microscopy; Image analysis; Protein organization; Single molecule localization; Spatial pattern statistics; Nanoscale structures;
 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 alpha-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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Language(s): eng - English
 Dates: 2021
 Publication Status: Published in print
 Pages: 8
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Degree: -

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Title: Nano Letters
  Abbreviation : Nano Lett.
Source Genre: Journal
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Publ. Info: Washington, DC : American Chemical Society
Pages: - Volume / Issue: 21 (3) Sequence Number: - Start / End Page: 1213 - 1220 Identifier: ISSN: 1530-6984
CoNE: https://pure.mpg.de/cone/journals/resource/110978984570403