Cell migration, which can be significantly affected by intracellular signaling pathways (ICSP) and extracellular matrix (ECM), plays a crucial role in many physiological and pathological processes. The efficiency of cell migration, which is typically
modeled as a persistent random walk (PRW), depends on two critical motility parameters, i.e., migration speed and persistence. It is generally very challenging to efficiently and accurately extract these key dynamics parameters from noisy experimental data. Here, we employ the normalized Shannon entropy to quantify the deviation of cell migration dynamics from that of diffusive/ballistic motion as well as to derive the persistence of cell migration based on the Fourier power spectrum of migration velocities. Moreover, we introduce the time-varying Shannon entropy based on the wavelet power spectrum of cellular dynamics and demonstrate its superior utility to characterize the time-dependent persistence of cell migration, which is typically resulted from complex and time-varying intra or extra-cellular mechanisms. We employ our approach to analyze trajectory data of in vitro cell migration regulated by distinct intracellular and extracellular mechanisms, exhibiting a rich spectrum of dynamic characteristics. Our analysis indicates that the combination of Shannon entropy and wavelet transform offers a simple and efficient tool to estimate the persistence of cell migration, which may also reflect the real-time effects of ICSP-ECM to some extent.
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 l
ack 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.
