Cell migration involves dynamic changes in cell shape. Intricate patterns of cell shape can be analyzed and classified using advanced shape descriptors, including spherical harmonics (SPHARM). Though SPHARM have been used to analyze and classify migrating cells, such classification did not exploit SPHARM spectra in their dynamics. Here, we examine whether additional information from dynamic SPHARM improves classification of cell migration patterns. We combine the static and dynamic SPHARM approach with a support-vector-machine classifier and compare their classification accuracies. We demonstrate that the dynamic SPHARM analysis classifies cell migration patterns more accurately than the static one for both synthetic and experimental data. Furthermore, by comparing the computed accuracies with that of a naive classifier, we can identify the experimental conditions and model parameters that significantly affect cell shape. This capability should – in the future – help to pinpoint factors that play an essential role in cell migration.
A cell’s migration behavior depends on the state of the cell, extracellular environment, and signals from other cells1. We can study the mechanisms of cell migration by, e.g., knocking out a certain gene or altering the structure of the extracellular matrix (ECM) and testing whether these changes affect cell migration patterns, such as cell trajectory, shape, or shape dynamics (Fig. 1). To compare migration patterns in an objective and statistically sound way, they have to be automatically analyzed and quantified2. Whereas both cell trajectories3,4 and cell shape5,6 can be quantified with a multitude of available methods, the analysis of shape dynamics – especially in 3D – received considerably less attention.
Analysis and quantification of migration patterns can help to study cell migration mechanisms. First, a migration experiment is performed, which involves altering the migrating cells (genetic or chemical modification, choosing cells of a different type), altering the extracellular matrix (ECM), or adding chemical signal to the extracellular environment. Next, cell migration patterns are analyzed and quantified. Finally, one or more quantitative measures derived from these patterns are statistically compared between conditions to detect significant differences.