Nanoscale Pattern Extraction from Relative Positions of Sparse 3D Localizations.

Affiliation

Curd AP(1), Leng J(2), Hughes RE(1), Cleasby AJ(1), Rogers B(1), Trinh CH(1), Baird MA(3), Takagi Y(3), Tiede C(1), Sieben C(4), Manley S(4), Schlichthaerle T(5)(6), Jungmann R(5)(6), Ries J(7), Shroff H(8), Peckham M(1).
Author information:
(1)School of Molecular and Cellular Biology, University of Leeds, Leeds LS2 9JT, United Kingdom.
(2)School of Computing, University of Leeds, Leeds LS2 9JT, United Kingdom.
(3)Cell and Developmental Biology Center, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, Maryland 20892, United States.
(4)Laboratory of Experimental Biophysics, École Polytechnique Fédérale de Lausanne, BSP 427
(Cubotron UNIL), Rte de la Sorge, CH-1015 Lausanne, Switzerland.
(5)Max Planck Institute of Biochemistry, Am Klopferspitz 18, 82152 Martinsried, Munich, Germany.
(6)Faculty of Physics and Center for Nanoscience, LMU Munich, 80539 Munich, Germany.
(7)Cell Biology and Biophysics Unit, European Molecular Biology Laboratory, 69117 Heidelberg, Germany.
(8)Laboratory of High Resolution Optical Imaging, National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health, Bethesda, Maryland 20892, United States.

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.