Robotic Assay for Drought (RoAD): an automated phenotyping system for brassinosteroid and drought responses
Robotic Assay for Drought (RoAD): an automated phenotyping system for brassinosteroid and drought responses
复制标题
干旱机器人检测 (RoAD)：油菜素类固醇和干旱反应的自动表型分析系统
DOI：
10.1111/tpj.15401
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发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Hurd, Ashley M.
中科院分区：
文献类型：
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作者：
Xiang, Lirong;Nolan, Trevor M.;Bao, Yin;Elmore, Mitch;Tuel, Taylor;Gai, Jingyao;Shah, Dylan;Wang, Ping;Huser, Nicole M.;Hurd, Ashley M.
油菜素类固醇(BRs)是一组参与调节生长、发育和逆境反应的植物类固醇激素。BR途径的许多成分以前已经被鉴定和表征。然而，BR表型实验通常以低通量的方式进行，例如在培养板上。此外，BR途径会影响干旱反应，但干旱实验既耗时又难以控制。为了缓解这些问题并提高产量，我们开发了机器人干旱分析(ROAD)系统，以在土壤种植的拟南芥植物中进行BR和干旱响应实验。Road配备了一个机械臂、一个漫游车、一个台秤、一个精确控制的浇水系统、一个RGB摄像头和一个激光轮廓仪。它执行日常称重、浇水和成像任务，并能够通过用BR生物合成抑制剂丙环唑(PCZ)浇灌植物来进行BR反应分析。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，以准确测量包括植物面积、植株体积、叶长和叶宽在内的性状。然后，我们应用机器学习算法，利用提取的表型参数来识别图像衍生的特征，这些特征可以区分对照、干旱处理和PCZ处理的植物。我们在一组BR突变体和BR反应改变的拟南芥材料上进行了PCZ和干旱实验。最后，我们将道路试验扩展到在玉米植株上使用PCZ进行BR反应分析。本研究建立了一个自动化和非侵入性的机器人成像系统，作为一种工具，可以准确地在3D中测量拟南芥和玉米植株的形态和生长相关性状，为深入了解BR介导的植物生长和胁迫响应提供深入的了解。
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 in a low‐throughput manner, such as on Petri plates. Additionally, the BR pathway affects drought responses, but drought experiments are time consuming and difficult to control. To mitigate these issues and increase throughput, we developed the Robotic Assay for Drought (RoAD) system to perform BR and drought response experiments in soil‐grown Arabidopsis plants. RoAD is equipped with a robotic arm, a rover, 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 to accurately measure traits including plant area, plant volume, leaf length, and leaf width. We then applied machine learning algorithms that utilize the extracted phenotypic parameters to identify image‐derived traits that can distinguish control, drought‐treated, 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 inZea 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 in 3D, providing insights into the BR‐mediated control of plant growth and stress responses.