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Estimating the localization spread function of static single-molecule localization microscopy images.

Thomas R Shaw1, Frank J Fazekas2, Sumin Kim3

  • 1Program in Biophysics, University of Michigan, Ann Arbor, Michigan; Program in Applied Physics, University of Michigan, Ann Arbor, Michigan.

Biophysical Journal
|July 5, 2022
PubMed
Summary
This summary is machine-generated.

We developed a simple method to estimate the localization spread function (LSF) in single-molecule localization microscopy (SMLM) data. This technique reveals broader LSFs in real images than predicted, improving SMLM resolution assessment.

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Area of Science:

  • Biophysics
  • Microscopy
  • Cell Biology

Background:

  • Single-molecule localization microscopy (SMLM) visualizes cellular structures beyond the diffraction limit.
  • Accurate resolution evaluation is crucial for SMLM image analysis.
  • Existing methods may not fully capture real-world localization uncertainties.

Purpose of the Study:

  • To present a straightforward method for estimating the localization spread function (LSF) from SMLM data.
  • To assess the true resolution of SMLM datasets by accounting for temporal dynamics.
  • To provide an objective evaluation metric for SMLM image quality.

Main Methods:

  • Developed a method to estimate LSF directly from acquired localizations in static SMLM datasets.
  • Utilized correlated emitter dynamics and pair autocorrelation functions (time and space).
  • Validated the method on simulated data, DNA origami, and antibody/fusion protein-labeled cellular structures.

Main Results:

  • Experimentally obtained images exhibit broader LSFs than predicted by localization precision alone.
  • Additional uncertainty arises from localizing molecules imaged over time.
  • The method provides a direct estimation of resolution limitations in SMLM.

Conclusions:

  • The presented method offers a simple and objective way to evaluate SMLM resolution.
  • Temporal dynamics significantly contribute to the overall localization spread.
  • This approach enhances the reliability of SMLM data interpretation for cellular imaging.