Coordinates in low-dimensional cell shape-space discriminate migration dynamics from single static cell images

TL;DR

This study demonstrates that a single static cell image's shape coordinates in a six-dimensional space can predict its migration behavior, providing a quantitative link between cell morphology and dynamics.

Contribution

The paper introduces a method to use cell shape coordinates in a 6D space to discriminate migration behavior from static images, quantitatively linking morphology to dynamics.

Findings

Cell shape variation space has six dimensions.

Migration behavior can be predicted from shape-space coordinates.

Persistent migration correlates with specific shape distribution signatures.

Abstract

Cell shape has long been used to discern cell phenotypes and states, but the underlying premise has not been quantitatively tested. Here, we show that a single cell image can be used to discriminate its migration behavior by analyzing a large number of cell migration data in vitro. We analyzed a large number of two-dimensional cell migration images over time and found that the cell shape variation space has only six dimensions, and migration behavior can be determined by the coordinates of a single cell image in this 6-dimensional shape-space. We further show that this is possible because persistent cell migration is characterized by spatial-temporally coordinated protrusion and contraction, and a distribution signature in the shape-space. Our findings provide a quantitative underpinning for using cell morphology to differentiate cell dynamical behavior.