Morphometrics of complex cell shapes: Lobe Contribution Elliptic Fourier Analysis (LOCO-EFA)

Abstract

Quantifying cell morphology is fundamental to the statistical study of cell populations, and can help us unravel mechanisms underlying cell and tissue morphogenesis. Current methods, however, require extensive human intervention, are highly sensitive to parameter choice, or produce metrics that are difficult to interpret biologically. We therefore developed a novel method, Lobe Contribution Elliptical Fourier Analysis (LOCO-EFA), which generates from digitalised cell outlines meaningful descriptors that can be directly matched to morphological features. We show that LOCO-EFA provides a tool to phenotype efficiently and objectively populations of cells by applying it to the complex shaped pavement cells of Arabidopsis thaliana wild type and speechless leaves. To further validate our method, we analysed computer-generated tissues, where cell shape can be specified in a controlled manner. LOCO-EFA quantifies deviations between the specified shape that an individual in silico cell takes up when in isolation and the resultant shape when they are allowed to interact within a confluent tissue, thereby assessing the role of cell-cell interactions on population cell shape distributions.