Optimizing experimental parameters for tracking of diffusing particles

TL;DR

This paper provides guidelines for designing single-particle tracking experiments to maximize information about diffusion coefficients, emphasizing the importance of recording more frames over individual frame quality for optimal precision.

Contribution

It offers a theoretical framework for selecting experimental parameters to optimize diffusion coefficient estimation in particle tracking experiments.

Findings

Motion blur has negligible impact on diffusion coefficient estimation in typical experiments.

Optimizing photon counts and recording more frames improves precision.

Designing experiments to maximize frame number enhances data quality for diffusion analysis.

Abstract

We describe how a single-particle tracking experiment should be designed in order for its recorded trajectories to contain the most information about a tracked particle's diffusion coefficient. The precision of estimators for the diffusion coefficient is affected by motion blur, limited photon statistics, and the length of recorded time-series. We demonstrate for a particle undergoing free diffusion that precision is negligibly affected by motion blur in typical experiments, while optimizing photon counts and the number of recorded frames is the key to precision. Building on these results, we describe for a wide range of experimental scenarios how to choose experimental parameters in order to optimize the precision. Generally, one should choose quantity over quality: experiments should be designed to maximize the number of frames recorded in a time-series, even if this means lower information content in individual frames.