  During epithelial-mesenchymal transition (EMT), epithelial cells change their
morphology, disperse, and gain mesenchymal-like characteristics. Usually, cells
are categorized into discrete cell types or states based on gene expression and
other cellular features. Subsequently, EMT is investigated as a dynamical
process where cells jump from one discrete state to another. In the current
work, we moved away from this idea of discrete state transition and
investigated EMT dynamics in a continuous phenotypic space. We used morphology
to define the phenotype of a cell. We used the data from quantitative image
analysis of MDA-MB-468 cells undergoing EGF-induced EMT. We defined the
morphological state space or 'morphospace' using the morphological features
extracted through image analysis. During EMT, as the morphology changed, the
distribution of cells in the morphospace also changed. However, this
morphospace had a very high dimension. We reduced it to a 2-dimensional
"reduced morphospace" and investigated the temporal change in the spatial
distribution of cells in this reduced space. We used proper orthogonal
decomposition to find dominant dynamical features of this spatio-temporal data.
The modal analysis detected key features of EMT in this experimental system -
reversible transition, distinct paths of phenotypic transition during induction
and reversal of EMT, and enhanced diversity of cells during reversal of EMT. We
also provide some intuitive physical meaning of the spatial modes and connect
them to the key molecular event during EMT.
