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Robotic Assay for Drought (RoAD): An Automated Phenotyping System for Brassinosteroid and Drought Response
bioRxiv - Plant Biology Pub Date : 2020-06-02 , DOI: 10.1101/2020.06.01.128199
Lirong Xiang , Trevor M. Nolan , Yin Bao , Mitch Elmore , Taylor Tuel , Jingyao Gai , Dylan Shah , Nicole M. Huser , Ashley M. Hurd , Sean A. McLaughlin , Stephen H. Howell , Justin W. Walley , Yanhai Yin , Lie Tang

Brassinosteroids (BRs) are a group of plant steroid hormones involved in regulating growth, development, and stress responses. Many components of the BR pathway have previously been identified and characterized. However, BR phenotyping experiments are typically performed on petri plates and/or in a low-throughput manner. Additionally, the BR pathway has extensive crosstalk with drought responses, but drought experiments are time-consuming and difficult to control. Thus, we developed Robotic Assay for Drought (RoAD) to perform BR and drought response experiments in soil-grown Arabidopsis plants. RoAD is equipped with a bench scale, a precisely controlled watering system, an RGB camera, and a laser profilometer. It performs daily weighing, watering, and imaging tasks and is capable of administering BR response assays by watering plants with Propiconazole (PCZ), a BR biosynthesis inhibitor. We developed image processing algorithms for both plant segmentation and phenotypic trait extraction in order to accurately measure traits in 2-dimensional (2D) and 3-dimensional (3D) spaces including plant surface area, leaf length, and leaf width. We then applied machine learning algorithms that utilized the extracted phenotypic parameters to identify image-derived traits that can distinguish control, drought, and PCZ-treated plants. We carried out PCZ and drought experiments on a set of BR mutants and Arabidopsis accessions with altered BR responses. Finally, we extended the RoAD assays to perform BR response assays using PCZ in Zea mays (maize) plants. This study establishes an automated and non-invasive robotic imaging system as a tool to accurately measure morphological and growth-related traits of Arabidopsis and maize plants, providing insights into the BR-mediated control of plant growth and stress responses.

中文翻译:

机器人干旱分析(RoAD):油菜素类固醇和干旱反应的自动表型分析系统

油菜素类固醇(BRs)是一组植物类固醇激素,参与调节生长,发育和应激反应。BR途径的许多组成部分先前已经被鉴定和表征。然而,BR表型实验通常在培养皿上和/或以低通量方式进行。此外,BR途径与干旱响应之间存在广泛的串扰,但干旱实验耗时且难以控制。因此,我们开发了针对干旱的机器人分析(RoAD),以在土壤生长的拟南芥植物中进行BR和干旱响应实验。RoAD配备了台式秤,精确控制的浇水系统,RGB摄像头和激光轮廓仪。它每天执行称重,浇水,和成像任务,并能够通过使用BR生物合成抑制剂Propiconazole(PCZ)给植物浇水来进行BR反应测定。我们开发了用于植物分割和表型性状提取的图像处理算法,以便准确地测量2维(2D)和3维(3D)空间中的性状,包括植物表面积,叶长和叶宽。然后,我们应用了机器学习算法,该算法利用提取的表型参数来识别可区分对照,干旱和PCZ处理植物的图像衍生性状。我们对一组BR突变体和BR响应有所改变的拟南芥种进行了PCZ和干旱试验。最后,我们将RoAD分析扩展为在玉米(玉米)植物中使用PCZ进行BR反应分析。
更新日期:2020-06-02
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