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Nanoscale pattern extraction from relative positions of sparse 3D localizations

DOI: 10.1021/acs.nanolett.0c03332 DOI Help

Authors: Alistair P. Curd (University of Leeds) , Joanna Leng (University of Leeds) , Ruth E. Hughes (University of Leeds) , Alexa J. Cleasby (University of Leeds) , Brendan Rogers (University of Leeds) , Chi H. Trinh (University of Leeds) , Michelle A. Baird (National Heart, Lung and Blood Institute, National Institutes of Health) , Yasuharu Takagi (National Heart, Lung and Blood Institute, National Institutes of Health) , Christian Tiede (University of Leeds) , Christian Sieben (École Polytechnique Fédérale de Lausanne) , Suliana Manley (École Polytechnique Fédérale de Lausanne) , Thomas Schlichthaerle (Max Planck Institute of Biochemistry) , Ralf Jungmann (Max Planck Institute of Biochemistry) , Jonas Ries (European Molecular Biology Laboratory) , Hari Shroff (National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health) , Michelle Peckham (University of Leeds)
Co-authored by industrial partner: No

Type: Journal Paper
Journal: Nano Letters

State: Published (Approved)
Published: November 2020
Diamond Proposal Number(s): 15378

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.

Journal Keywords: Super-resolution microscopy; Image analysis; Protein organization; Single molecule localization; Spatial pattern statistics

Subject Areas: Technique Development, Biology and Bio-materials


Instruments: I04-1-Macromolecular Crystallography (fixed wavelength)

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