Deep learning of geometrical cell division rules

TL;DR

This paper introduces a deep learning approach using neural networks to predict cell division patterns from cell geometry, surpassing traditional geometrical rules and offering new insights into plant tissue organization.

Contribution

The study presents a data-driven deep learning method that predicts cell division planes from cell shape, overcoming limitations of rule-based models and revealing complex division patterns.

Findings

Deep neural networks accurately predict division patterns from cell geometry.

Model captures division behaviors previously unexplained by existing rules.

Approach generalizes across diverse cell shapes and division patterns.

Abstract

The positioning of new cellular walls during cell division plays a key role in shaping plant tissue organization. The influence of cell geometry on the positioning of division planes has been previously captured into various geometrical rules. Accordingly, linking cell shape to division orientation has relied on the comparison between observed division patterns and predictions under specific rules. The need to define a priori the tested rules is a fundamental limitation of this hypothesis-driven approach. As an alternative, we introduce a data-based approach to investigate the relation between cell geometry and division plane positioning, exploiting the ability of deep neural network to learn complex relationships across multidimensional spaces. Adopting an image-based cell representation, we show how division patterns can be learned and predicted from mother cell geometry using a UNet architecture modified to operate on cell masks. Using synthetic data and A. thaliana embryo cells, we evaluate the model performances on a wide range of diverse cell shapes and division patterns. We find that the trained model accounted for embryo division patterns that were previously irreconcilable under existing geometrical rules. Our work shows the potential of deep networks to understand cell division patterns and to generate new hypotheses on the control of cell division positioning.