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Automated single particle detection and tracking for large microscopy datasets.

Rhodri S Wilson1, Lei Yang2, Alison Dun1

  • 1Institute of Biological Chemistry, Biophysics and Bioengineering, Heriot-Watt University, Edinburgh EH14 4AS, UK; Edinburgh Super-Resolution Imaging Consortium, www.esric.org.

Royal Society Open Science
|June 14, 2016
PubMed
Summary

We developed an automated algorithm for analyzing large microscopy datasets, enabling new insights into cellular component dynamics. This tool reveals how membrane proteins behave within nanodomains in living cells.

Keywords:
microscopyparticle detectionphotoactivated localization microscopytracking

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Area of Science:

  • Cellular and Molecular Biology
  • Biophysics
  • Optical Microscopy

Background:

  • Optical microscopy generates large datasets crucial for understanding biological processes.
  • Processing these datasets requires robust algorithms, especially in low signal-to-noise conditions.

Purpose of the Study:

  • To present an automated particle detection and linking algorithm for analyzing large, low signal-to-noise microscopy datasets.
  • To enable quantitative measurements of cellular component dynamics, from organelles to single molecules.

Main Methods:

  • Developed an automated particle detection algorithm for low signal-to-noise fluorescence microscopy.
  • Integrated the algorithm with a particle linking framework for dynamic analysis.
  • Validated the method on synthetic and experimental image data, including ground truth.
  • Applied the algorithm to single-particle-tracking photo-activated localization microscopy (spt-PALM) datasets from living cells.

Main Results:

  • The algorithm successfully handles large datasets in challenging imaging environments.
  • Quantitative measurements of large cohorts (10,000s) of membrane-associated protein molecules were obtained.
  • Analysis revealed that these protein molecules exhibit behavior consistent with being caged within nanodomains.
  • The method provides unprecedented spatial detail and high acquisition rates for single-molecule behavior examination.

Conclusions:

  • The developed algorithm offers a robust and efficient tool for analyzing single-molecule dynamics in living cells.
  • It facilitates the study of cellular component behavior at high spatial and temporal resolutions.
  • The findings suggest nanodomain-based confinement influences membrane protein dynamics.