Disentangling Random Motion and Flow in a Complex Medium

Abstract

We describe a technique for deconvolving the stochastic motion of particles from large-scale fluid flow in a dynamic environment such as that found in living cells. The method leverages the separation of timescales to subtract out the persistent component of motion from single-particle trajectories. The mean-squared displacement of the resulting trajectories is rescaled so as to enable robust extraction of the diffusion coefficient and subdiffusive scaling exponent of the stochastic motion. We demonstrate the applicability of the method for characterizing both diffusive and fractional Brownian motion overlaid by flow and analytically calculate the accuracy of the method in different parameter regimes. This technique is employed to analyze the motion of lysosomes in motile neutrophil-like cells, showing that the cytoplasm of these cells behaves as a viscous fluid at the timescales examined.

Measurement Error Correction in Particle Tracking Microrheology

“…Noise in single-particle tracking experiments can be categorized roughly into two types. Low-frequency noise, originating primarily from slow drift currents in the fluid itself, is typically removed from particle trajectories by way of various linear detrending methods (e.g., Fong et al, 2013;Rowlands and So, 2013;Koslover et al, 2016;Mellnik et al, 2016). In contrast, high-frequency noise can be due to a variety of reasons: mechanical vibrations of the instrumental setup; particle displacement while the camera shutter is open; noisy estimation of true position from the pixelated microscopy image; error-prone tracking of particle positions when they are out of the camera focal plane.…”