Disentangling blade and vasculature shape in grapevine leaves

Abstract

The leaf blade and vasculature develop together within a shared morphological space. Despite shared molecular patterning pathways, it is unknown if developmental and evolutionary variation affect these tissues separately or together in a coordinated way. Grapevine leaves have a morphometric history and abundant data measuring the shape of the blade and vasculature together. Using a combination of topological data analysis and deep learning, we perform reciprocal semantic segmentation of leaf blade and vasculature. Each tissue contains sufficient information to predict the other. We hypothesize that this is due to a one-to-one relationship between blade and vein. Using thin plate splines to swap and warp different combinations of blade and vein shapes, we show that a set of leaves with a many-to-one relationship of blade and vein are distinguishable from true leaves. We also swap blade and vein across the developmental series and between species and show that only reversing the developmental series disrupts the relationship between blade and vasculature. We end by discussing the evolutionary and developmental implications that there is a unique, one-to-one mapping between blade and vein that allows each to be predicted from the other. Author summary Leaves are made of two closely connected parts: the flat blade that captures light and the network of veins that transports water, nutrients, and developmental signals. Although these tissues grow together and share common molecular patterning pathways, it has remained unclear whether a particular blade shape is uniquely linked to a specific vein pattern. In this study, we use grapevine leaves as a model system and combine mathematical shape analysis with deep learning to examine this relationship. We show that the shape of the blade alone can accurately predict the vein network, and that the vein network can likewise predict the blade. This finding suggests a near one-to-one relationship between these two tissues. To test this idea, we created artificial leaves in which blade and vein shapes were deliberately mismatched. Although these synthetic leaves appeared realistic at a global level, a neural network was able to distinguish them from real leaves based on subtle differences. We further show that this tight coupling is maintained by the developmental sequence of leaf growth rather than by species identity, revealing a conserved constraint linking leaf form and internal structure.

Plant phenotyping relevance

葉身と葉脈の形状を深層学習による相互セグメンテーションと形状解析で抽出・予測する方法が研究の中心であり、植物形態表現型の方法開発に該当する。

Code and data availability

The Data Availability Statement explicitly lists three public Zenodo deposits containing data and code to reproduce the paper's analyses: reciprocal U-Net blade/vein prediction, the two-tower CNN 1:1 vein:blade relationship, and interspecies/intraspecies developmental series swaps. All URLs are in allowed_urls and the