Deciphering migration dynamics of individual cells using a trajectory-based dynamic reconstruction approach

Abstract

Cell migration, which is strictly regulated by intracellular signaling pathways and extracellular microenvironments, is crucial for physiological and pathological processes such as cancer. However, there is a lack of efficient approaches to analyze statistical and time-varying features of target-finding and migration behaviors based on single-cell trajectories. Here, we propose a single-cell trajectory-based dynamic reconstruction approach, which primarily incorporates wavelet transform, power spectrum of OU-process, and fits of the power spectrum to analyze customized metrics related to migration dynamics. Our results reveal diverse relationships between motility parameters (persistence time and migration speed) and dynamic metrics, especially the existence of an optimal parameter domain. Moreover, the analysis shows that the depletion of Arpin protein enhances the migration potential of amoeba D. discoideum and invasive MDA-MB-231 cells, and emphasizes a previously unreported result that the rescued amoeba is distinguishable from the wild-type amoeba. Significantly, periodic phenomena emerge from time-varying dynamic metrics under the combined influence of irregularly changing parameters, which also correlates with migration dynamics. Our analysis suggests that the approach can serve as a powerful tool for estimating time-varying migration potential and statistical features of single-cell trajectories, enabling a better understanding of the relationship between intracellular proteins and modes of cell motility, and providing more insights into the migration dynamics of single cells and cell populations.