Abstract
Cell division and the resulting changes to the cell organization affect the shape and functionality of all tissues. Thus, understanding the determinants of the tissue-wide changes imposed by cell division is a key question in developmental biology. Here, we use a network representation of live cell imaging data from shoot apical meristems (SAMs) in Arabidopsis thaliana to predict cell division events and their consequences at a tissue level. We show that a classifier based on the SAM network properties is predictive of cell division events, with validation accuracy of 82%, on par with that based on cell size alone. Further, we demonstrate that the combination of topological and biological properties, including: cell size, perimeter, distance, and shared cell wall between cells, can further boost the prediction accuracy of resulting changes in topology triggered by cell division. Using our classifiers, we demonstrate the importance of microtubule mediated cell-to-cell growth coordination in influencing tissue-level topology. Altogether, the results from our network-based analysis demonstrates a feedback mechanism between tissue topology and cell division in A. thaliana’s SAMs.
Summary statement we use a network representation of live cell imaging data from SAMs in Arabidopsis thaliana to predict cell division events and their consequences at a tissue level.
Competing Interest Statement
The authors have declared no competing interest.
