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High-precision estimation of emitter positions using Bayesian grouping of localizations

Journal Article (2022)
Authors

Mohamadreza Fazel (University of New Mexico)

Michael J. Wester (University of New Mexico)

David J. Schodt (University of New Mexico)

Sebastian Restrepo Cruz (University of New Mexico)

Sebastian Strauss (Ludwig Maximilians University, Max Planck Institute of Biochemistry)

Florian Schueder (Max Planck Institute of Biochemistry, Ludwig Maximilians University)

Thomas Schlichthaerle (Max Planck Institute of Biochemistry, Ludwig Maximilians University)

Keith A. Lidke (University of New Mexico)

B Rieger (ImPhys/Computational Imaging)

G.B. More authors (External organisation)

Research Group
ImPhys/Computational Imaging
Copyright
© 2022 Mohamadreza Fazel, Michael J. Wester, David J. Schodt, Sebastian Restrepo Cruz, Sebastian Strauss, Florian Schueder, Thomas Schlichthaerle, Keith A. Lidke, B. Rieger, More Authors
To reference this document use:
https://doi.org/10.1038/s41467-022-34894-2
More Info
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Publication Year
2022
Language
English
Copyright
© 2022 Mohamadreza Fazel, Michael J. Wester, David J. Schodt, Sebastian Restrepo Cruz, Sebastian Strauss, Florian Schueder, Thomas Schlichthaerle, Keith A. Lidke, B. Rieger, More Authors
Research Group
ImPhys/Computational Imaging
Issue number
1
Volume number
13
DOI:
https://doi.org/10.1038/s41467-022-34894-2
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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.