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Bayesian Multiple Emitter Fitting using Reversible Jump Markov Chain Monte Carlo.

Mohamadreza Fazel1, Michael J Wester1,2, Hanieh Mazloom-Farsibaf1

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This study introduces a Bayesian method for super-resolution imaging to precisely locate overlapping single molecules. The approach improves accuracy in dense biological samples, enhancing imaging speed and data quality.

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Area of Science:

  • Biophysics
  • Optical Microscopy
  • Computational Biology

Background:

  • Single-molecule localization microscopy (SMLM) faces challenges with emitter clustering at high densities.
  • Overlapping emitters in raw SMLM data hinder accurate localization and analysis.
  • Existing methods struggle with dense emitter regions, limiting imaging speed and resolution.

Purpose of the Study:

  • To develop a robust method for fitting multiple, overlapping emitters in SMLM data.
  • To improve the accuracy of emitter localization in dense regions.
  • To provide uncertainty quantification for emitter positions and background structure.

Main Methods:

  • Bayesian inference framework utilizing Reversible Jump Markov Chain Monte Carlo (RJMCMC).
  • Incorporation of prior information, including emitter intensity and density.
  • Probabilistic modeling of emitter locations and background.

Main Results:

  • Successful identification and localization of emitters in dense clusters.
  • Generation of posterior probability distributions for emitter locations, accounting for uncertainty.
  • Output of the most probable model with precise coordinates and associated uncertainties.

Conclusions:

  • The Bayesian RJMCMC approach effectively resolves overlapping emitters in SMLM.
  • This method enhances the accuracy and reliability of super-resolution imaging in dense biological systems.
  • The approach offers a powerful tool for analyzing complex, high-density single-molecule data.