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

Fazel, M., Wester, M. J., Schodt, D. J., Cruz, S. R., Strauss, S., Schueder, F., et al. (2022). High-precision estimation of emitter positions using Bayesian grouping of localizations. Nature Communications, 13(1): 7152. doi:10.1038/s41467-022-34894-2.

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 Creators:
Fazel, Mohamadreza, Author
Wester, Michael J., Author
Schodt, David J., Author
Cruz, Sebastian Restrepo, Author
Strauss, Sebastian1, Author           
Schueder, Florian1, Author           
Schlichthärle, Thomas1, Author           
Gillette, Jennifer M., Author
Lidke, Diane S., Author
Rieger, Bernd, Author
Jungmann, Ralf1, Author           
Lidke, Keith A., Author
Affiliations:
1Jungmann, Ralf / Molecular Imaging and Bionanotechnology, Max Planck Institute of Biochemistry, Max Planck Society, ou_2149679              

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Free keywords: SINGLE-MOLECULE LOCALIZATION; SUPERRESOLUTION MICROSCOPY; RESOLUTION; DIMERIZATION; PATTERNS; RECEPTOR; PROTEINScience & Technology - Other Topics;
 Abstract: Single-molecule localization microscopy relies on stochastic blinking events, treated as independent events without assignment to a particular emitter. Here, BaGoL takes low precision localizations generated from multiple emitter blinkings during DNAPAINT and dSTORM and finds the underlying emitter positions with high precision.
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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Language(s): eng - English
 Dates: 2022-11-22
 Publication Status: Published online
 Pages: 11
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Degree: -

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Title: Nature Communications
  Abbreviation : Nat. Commun.
Source Genre: Journal
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Publ. Info: London : Nature Publishing Group
Pages: - Volume / Issue: 13 (1) Sequence Number: 7152 Start / End Page: - Identifier: ISSN: 2041-1723
CoNE: https://pure.mpg.de/cone/journals/resource/2041-1723