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

Mohamadreza Fazel, Michael J. Wester, David J. Schodt, Sebastian Restrepo Cruz, Sebastian Strauss, Florian Schueder, Thomas Schlichthaerle, Jennifer M. Gillette, Diane S. Lidke, Bernd Rieger, Ralf Jungmann and Keith A. Lidke ()
Additional contact information
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 Health Science Center
Sebastian Strauss: Ludwig Maximilian University
Florian Schueder: Ludwig Maximilian University
Thomas Schlichthaerle: Ludwig Maximilian University
Jennifer M. Gillette: University of New Mexico Health Science Center
Diane S. Lidke: University of New Mexico Health Science Center
Bernd Rieger: Delft University of Technology
Ralf Jungmann: Ludwig Maximilian University
Keith A. Lidke: University of New Mexico

Nature Communications, 2022, vol. 13, issue 1, 1-11

Abstract: 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.

Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.nature.com/articles/s41467-022-34894-2 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-34894-2

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-022-34894-2

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().