High-throughput characterization of cortical microtubule arrays response to anisotropic tensile stress.

Abstract

Background Plants can perceive and respond to mechanical signals. For instance, cortical microtubule (CMT) arrays usually reorganize following the predicted maximal tensile stress orientation at the cell and tissue level. While research in the last few years has started to uncover some of the mechanisms mediating these responses, much remains to be discovered, including in most cases the actual nature of the mechanosensors. Such discovery is hampered by the absence of adequate quantification tools that allow the accurate and sensitive detection of phenotypes, along with high throughput and automated handling of large datasets that can be generated with recent imaging devices. Results Here we describe an image processing workflow specifically designed to quantify CMT arrays response to tensile stress in time-lapse datasets following an ablation in the epidermis - a simple and robust method to change mechanical stress pattern. Our Fiji-based workflow puts together several plugins and algorithms under the form of user-friendly macros that automate the analysis process and remove user bias in the quantification. One of the key aspects is also the implementation of a simple geometry-based proxy to estimate stress patterns around the ablation site and compare it with the actual CMT arrays orientation. Testing our workflow on well-established reporter lines and mutants revealed subtle differences in the response over time, as well as the possibility to uncouple the anisotropic and orientational response. Conclusion This new workflow opens the way to dissect with unprecedented detail the mechanisms controlling microtubule arrays re-organization, and potentially uncover the still largely elusive plant mechanosensors.

Plant phenotyping relevance

植物細胞の微小管配向応答を定量化する画像解析ワークフローを開発し、自動化・バイアス低減・応力パターン推定まで扱うため、植物フェノタイピング手法が研究の中心である。

Code and data availability

The paper deposits its authors' Fiji/ImageJ analysis workflow code on GitHub (with a Zenodo code archive), the raw confocal microscopy time-lapse data at the Swedish National Data Service, and all intermediate processed data (projections, ROIs, quantifications) on Zenodo. All are paper-specific, public, and directly re