用深度学习从细胞形状预测分裂方向，突破传统几何规则局限

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.