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Related Experiment Video

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A Protocol for Real-time 3D Single Particle Tracking
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Three-dimensional localization refinement and motion model parameter estimation for confined single particle tracking

Ye Lin1, Fatemeh Sharifi2, Sean B Andersson1,2

  • 1Division of Systems Engineering, Boston University, Boston, MA 02215, USA.

Biomedical Optics Express
|October 25, 2021
PubMed
Summary

Sequential Monte Carlo - Expectation Maximization (SMC-EM) accurately analyzes confined diffusion in cells. This method improves particle tracking and motion model estimation, especially in low signal conditions.

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Area of Science:

  • Biophysics
  • Computational Biology
  • Cellular Dynamics

Background:

  • Confined diffusion models biological macromolecule motion in cellular environments.
  • Single particle tracking (SPT) is crucial for studying these dynamics.
  • Analyzing SPT data is challenging due to complex motion and camera noise.

Purpose of the Study:

  • To enhance the Sequential Monte Carlo - Expectation Maximization (SMC-EM) technique for analyzing confined diffusion.
  • To adapt SMC-EM for the double-helix point spread function (DH-PSF) and scientific CMOS (sCMOS) cameras.
  • To evaluate SMC-EM's performance against standard 'localize-then-estimate' methods, particularly in low signal conditions.

Main Methods:

  • Extended SMC-EM to incorporate DH-PSF for 3D particle localization from 2D images.
  • Accounted for pixel-dependent noise characteristic of sCMOS cameras.
  • Compared SMC-EM with Gaussian fitting, Maximum Likelihood Estimator (MLE), and Mean Squared Displacement (MSD) fitting via simulations.

Main Results:

  • SMC-EM outperformed standard methods in low signal-to-background regimes.
  • SMC-EM and MLE-based methods showed comparable performance at higher signal levels.
  • SMC-EM demonstrated superiority when analyzing motion models dominated by nonlinearities at smaller confinement lengths.

Conclusions:

  • The enhanced SMC-EM method provides a robust approach for analyzing confined diffusion from SPT data.
  • SMC-EM offers significant advantages over traditional methods, especially under challenging low signal and nonlinear motion conditions.
  • This technique advances the study of macromolecule dynamics within crowded cellular environments.