Daphne Weihs, Dror Gilad, Moti Seon and Itai Cohen
Particle tracking has become an increasingly useful tool in microfluidics and biophysics, allowing measurement of microrheology, local structure, and flow. We introduce a novel, automated approach to analyze single-particle trajectories with transient elements, based on image-processing approaches and physical analysis of probe motion. In many physical and active biological systems, such as living cells, probe particles experience thermally mediated Brownian motion combined with active transport processes that can lead to transient-trajectories of local diffusion and trapping, punctuated by segments of active transport. Analyzing such a trajectory as a single unit masks the intermittent nature of the motion. Moreover, directly applying the generalized Stokes–Einstein relation in out-of-equilibrium systems is incorrect and returns inaccurate rheological parameters. We present an automated image-processing-based method to identify and segment transient trap-escape trajectories, allowing quantitative analysis of each segment. We define and discuss effects of controlling parameters, such as particle size and camera frame rate. Our algorithm provides a general and automated method to segment and analyze transient elements in trajectories of single particles, which can be applied to many different experiments. Our image-based approach allows identification of trapping segments, unbiased by specific step sizes within those traps or the mechanism driving those steps. As an example, we successfully apply this method to experiments of laser tweezers trapped particles and show that trajectory segmentation allows us to calculate both trap and fluid parameters. We accurately identify a round trap, calculate the trap stiffness at 3.1 pN/μm, and find that significant local heating reduces fluid viscosity.
DOI
Particle tracking has become an increasingly useful tool in microfluidics and biophysics, allowing measurement of microrheology, local structure, and flow. We introduce a novel, automated approach to analyze single-particle trajectories with transient elements, based on image-processing approaches and physical analysis of probe motion. In many physical and active biological systems, such as living cells, probe particles experience thermally mediated Brownian motion combined with active transport processes that can lead to transient-trajectories of local diffusion and trapping, punctuated by segments of active transport. Analyzing such a trajectory as a single unit masks the intermittent nature of the motion. Moreover, directly applying the generalized Stokes–Einstein relation in out-of-equilibrium systems is incorrect and returns inaccurate rheological parameters. We present an automated image-processing-based method to identify and segment transient trap-escape trajectories, allowing quantitative analysis of each segment. We define and discuss effects of controlling parameters, such as particle size and camera frame rate. Our algorithm provides a general and automated method to segment and analyze transient elements in trajectories of single particles, which can be applied to many different experiments. Our image-based approach allows identification of trapping segments, unbiased by specific step sizes within those traps or the mechanism driving those steps. As an example, we successfully apply this method to experiments of laser tweezers trapped particles and show that trajectory segmentation allows us to calculate both trap and fluid parameters. We accurately identify a round trap, calculate the trap stiffness at 3.1 pN/μm, and find that significant local heating reduces fluid viscosity.
DOI
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