Mohamadreza Fazel, Department of Physics and Astronomy, University of New Mexico, Albuquerque, NM, USA
Michael J. Wester, Department of Physics and Astronomy, University of New Mexico, Albuquerque, NM, USA, Department of Mathematics and Statistics, University of New Mexico, Albuquerque, NM, USA
David J. Schodt, Department of Mathematics and Statistics, University of New Mexico, Albuquerque, NM, USA
Sebastian Restrepo Cruz, Department of Pathology, University of New Mexico Health Science Center, Albuquerque, NM, USA
Sebastian Strauss, Department of Physics and Center for Nanoscience, Ludwig Maximilian University, Munich, Germany,, Max Planck Institute of Biochemistry, Martinsried, Germany
Florian Schueder, Department of Physics and Center for Nanoscience, Ludwig Maximilian University, Munich, Germany,, Max Planck Institute of Biochemistry, Martinsried, Germany
Thomas Schlichthaerle, Department of Physics and Center for Nanoscience, Ludwig Maximilian University, Munich, Germany,, Max Planck Institute of Biochemistry, Martinsried, Germany
Jennifer M. Gillette, Department of Pathology, University of New Mexico Health Science Center, Albuquerque, NM, USA,Comprehensive Cancer Center, University of New Mexico, Albuquerque, NM, USA
Diane S. Lidke, Department of Pathology, University of New Mexico Health Science Center, Albuquerque, NM, USA, Comprehensive Cancer Center, University of New Mexico, Albuquerque, NM, USA
Bernd Rieger, Department of Imaging Physics, Delft University of Technology, Delft, the Netherlands
Ralf Jungmann, Department of Physics and Center for Nanoscience, Ludwig Maximilian University, Munich, Germany,Max Planck Institute of Biochemistry, Martinsried, Germany
Keith A. Lidke, Department of Physics and Astronomy, University of New Mexico, Albuquerque, NM, USA. klidke@unm.edu, Comprehensive Cancer Center, University of New Mexico, Albuquerque, NM, USA. klidke@unm.edu

Document Type

Article

Publication Date

11-22-2022

Abstract

Single-molecule localization microscopy super-resolution methods rely on stochastic blinking/binding events, which often occur multiple times from each emitter over the course of data acquisition. Typically, the blinking/binding events from each emitter are treated as independent events, without an attempt to assign them to a particular emitter. Here, we describe a Bayesian method of inferring the positions of the tagged molecules by exploring the possible grouping and combination of localizations from multiple blinking/binding events. The results are position estimates of the tagged molecules that have improved localization precision and facilitate nanoscale structural insights. The Bayesian framework uses the localization precisions to learn the statistical distribution of the number of blinking/binding events per emitter and infer the number and position of emitters. We demonstrate the method on a range of synthetic data with various emitter densities, DNA origami constructs and biological structures using DNA-PAINT and dSTORM data. We show that under some experimental conditions it is possible to achieve sub-nanometer precision.

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