Deriving Time-varying Cellular Motility Parameters via Wavelet Analysis

TL;DR

This paper introduces a wavelet-based method to analyze and derive time-varying cellular motility parameters from cell migration trajectories, capturing dynamic microenvironment influences.

Contribution

It presents a novel approach combining wavelet denoising, transform, and Lorentzian spectrum to accurately quantify cell motility over time.

Findings

Wavelet analysis effectively captures velocity variations.

Derived parameters reflect microenvironment changes.

Method improves understanding of cell migration dynamics.

Abstract

Cell migration is an indispensable physiological and pathological process for normal tissue development and cancer metastasis, which is greatly regulated by intracellular signal pathways and extracellular microenvironment (ECM). However, there is a lack of adequate tools to analyze the time-varying cell migration characteristics because of the effects of some factors, i.e., the ECM including the time-dependent local stiffness due to microstructural remodeling by migrating cells. Here, we develop an approach to derive the time-dependent motility parameters from cellular trajectories, based on the time-varying persistent random walk model. In particular, we employ the wavelet denoising and wavelet transform to investigate cell migration velocities and obtain the wavelet power spectrum. The time-dependent motility parameters are subsequently derived via Lorentzian power spectrum. Our analysis shows that the combination of wavelet denoising, wavelet transform and Lorentzian power spectrum provides a powerful tool to derive accurately the time-dependent motility parameters, which reflects the time-varying microenvironment characteristics to some extent.