Comparison of particle image velocimetry and the underlying agents dynamics in collectively moving self propelled particles

Abstract

Collective migration of cells is a fundamental behavior in biology. For the quantitative understanding of collective cell migration, live-cell imaging techniques have been used using e.g., phase contrast or fluorescence images. Particle tracking velocimetry (PTV) is a common recipe to quantify cell motility with those image data. However, the precise tracking of cells is not always feasible. Particle image velocimetry (PIV) is an alternative to PTV, corresponding to Eulerian picture of fluid dynamics, which derives the average velocity vector of an aggregate of cells. However, the accuracy of PIV in capturing the underlying cell motility and what values of the parameters should be chosen is not necessarily well characterized, especially for cells that do not adhere to a viscous flow. Here, we investigate the accuracy of PIV by generating images of simulated cells by the Vicsek model using trajectory data of agents at different noise levels. It was found, using an alignment score, that the direction of the PIV vectors coincides with the direction of nearby agents with appropriate choices of PIV parameters. PIV is found to accurately measure the underlying motion of individual agents for a wide range of noise level, and its condition is addressed.

Inferring the roles of individuals in collective systems using information-theoretic measures of influence

“…We conducted a thorough exploration to identify and understand the optimal Particle Image Velocimetry (PIV) parameters specifically tailored for the examination of cell motility. To accomplish this, we employed simulation models to make it possible to compare PIV velocity vector field to the underlying individual agents' motions [25].…”

Imaging actin organisation and dynamics in 3D

“…This can be achieved by tracking either individual particles ( particle tracking velocimetry, PTV) or clusters of particles within an interrogation window ( particle image velocimetry, PIV); the particle displacement divided by the time spanned within subsequent frames gives an estimate of the instantaneous velocity. The choice of PTV over PIV for a given application is normally dictated by the ability of reliably segmenting single particles at an acceptable computational cost, but overall, they tend to lead to comparable results (Basak et al, 2023). Quantitative measurements of actin dynamics have led to insights in network regulation, for example, on how the motion of the leading edge in neuronal growth cone is controlled by random actin polymerisation switches (Betz et al, 2009;Wilson et al, 2010).…”