Topological properties accurately predict cell division events and organization of shoot apical meristem in Arabidopsis thaliana.

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 the tissue level. We show that a support vector machine classifier based on the SAM network properties is predictive of cell division events, with test accuracy of 76%, which matches that based on cell size alone. Furthermore, 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. Together, the results from our network-based analysis demonstrate a feedback mechanism between tissue topology and cell division in A. thaliana SAMs.

Plant phenotyping relevance

ライブ細胞画像からSAMの細胞分裂イベントと組織トポロジー変化を推定するネットワーク表現・SVM分類法が研究の中心であり、植物の形態・発達状態を定量化している。

Code and data availability

The paper's Data availability statement explicitly deposits the entire code and data to reproduce the SAM cell division prediction analysis (phenotyping measurements, features, and classifiers) in a public GitHub repository.