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Related Experiment Video

Updated: May 21, 2025

Multi-color Localization Microscopy of Single Membrane Proteins in Organelles of Live Mammalian Cells
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Identifying intermolecular interactions in single-molecule localization microscopy.

Xingchi Yan1,2,3, Polly Y Yu3,4, Arvind Srinivasan1,2

  • 1Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA 02138.

Proceedings of the National Academy of Sciences of the United States of America
|May 12, 2025
PubMed
Summary
This summary is machine-generated.

We developed a new algorithm to accurately count and visualize molecular interactions using single-molecule localization microscopy (SMLM). This method precisely identifies coupled molecules, overcoming previous visualization challenges in cellular functions.

Keywords:
biomolecular interactionsinverse problemprobabilistic modelsingle-molecule

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Area of Science:

  • Biophysics
  • Cell Biology
  • Microscopy

Background:

  • Intermolecular interactions are crucial for cellular functions but challenging to visualize at the single-molecule level.
  • Single-molecule localization microscopy (SMLM) presents a promising technique for studying these interactions.

Purpose of the Study:

  • To develop a probabilistic algorithm for accurate quantification of intermolecular interactions using SMLM.
  • To enable direct visualization and measurement of coupled molecular pairs.

Main Methods:

  • A probabilistic algorithm was created to calculate interaction probabilities between localized molecules.
  • The algorithm identifies the most likely interaction sets and corrects for spurious colocalizations.
  • Benchmarking was performed using simulated molecular localization maps with varying densities and precisions.

Main Results:

  • The algorithm achieved typical errors of only a few percent in identifying correct molecular pairs.
  • With typical SMLM parameters (5-10 molecules/µm², 20-30 nm precision), recall reached approximately 90%.
  • The method effectively differentiated between interacting and non-interacting molecules in simulations and experiments.

Conclusions:

  • The developed algorithm accurately quantifies the number and proportion of coupled molecular pairs.
  • This approach enhances the capability of SMLM for visualizing and measuring intermolecular interactions.
  • It enables the determination of rate constants in dynamic molecular systems.