Nanoscale Pattern Extraction from Relative Positions of Sparse 3D Localizations
Open Access
- 30 November 2020
- journal article
- research article
- Published by American Chemical Society (ACS) in Nano Letters
- Vol. 21 (3), 1213-1220
- https://doi.org/10.1021/acs.nanolett.0c03332
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.Funding Information
- Medical Research Council (MR/K015613/1)
- National Heart, Lung, and Blood Institute
- Deutsche Forschungsgemeinschaft (DFG JU 2957/1-1)
- Center for NanoScience, Ludwig-Maximilians-Universit?t M?nchen
- National Institute of Biomedical Imaging and Bioengineering
- Wellcome Trust
- Max-Planck-Gesellschaft
- H2020 European Research Council (680241)
- Biotechnology and Biological Sciences Research Council (BB/M011151/1, BB/S015787/1)
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