Le Ru A, Ibarcq G, Boniface M- C, Baussart A, Muños S, Chabaud M
FRAIB, Castanet-Tolosan, France.
LIPME, Université de Toulouse, INRAE, CNRS, Castanet-Tolosan, France.
Plant Methods. 2021 Jul 21;17(1):80. doi: 10.1186/s13007-021-00779-6.
The parasitic plant Orobanche cumana is one of the most important threats to sunflower crops in Europe. Resistant sunflower varieties have been developed, but new O. cumana races have evolved and have overcome introgressed resistance genes, leading to the recurrent need for new resistance methods. Screening for resistance requires the phenotyping of thousands of sunflower plants to various O. cumana races. Most phenotyping experiments have been performed in fields at the later stage of the interaction, requiring time and space. A rapid phenotyping screening method under controlled conditions would need less space and would allow screening for resistance of many sunflower genotypes. Our study proposes a phenotyping tool for the sunflower/O. cumana interaction under controlled conditions through image analysis for broomrape tubercle analysis at early stages of the interaction.
We optimized the phenotyping of sunflower/O. cumana interactions by using rhizotrons (transparent Plexiglas boxes) in a growth chamber to control culture conditions and Orobanche inoculum. We used a Raspberry Pi computer with a picamera for acquiring images of inoculated sunflower roots 3 weeks post inoculation. We set up a macro using ImageJ free software for the automatic counting of the number of tubercles. This phenotyping tool was named RhizOSun. We evaluated five sunflower genotypes inoculated with two O. cumana races and showed that automatic counting of the number of tubercles using RhizOSun was highly correlated with manual time-consuming counting and could be efficiently used for screening sunflower genotypes at the tubercle stage.
This method is rapid, accurate and low-cost. It allows rapid imaging of numerous rhizotrons over time, and it enables image tracking of all the data with time kinetics. This paves the way toward automatization of phenotyping in rhizotrons that could be used for other root phenotyping, such as symbiotic nodules on legumes.
寄生植物向日葵列当是欧洲向日葵作物面临的最重要威胁之一。已培育出抗向日葵列当的品种，但新的向日葵列当小种不断进化，已克服了渐渗的抗性基因，因此不断需要新的抗性方法。抗性筛选需要对数千株向日葵植株针对不同向日葵列当小种进行表型分析。大多数表型分析实验是在相互作用后期的田间进行，需要时间和空间。在可控条件下的快速表型筛选方法所需空间较小，且能对多种向日葵基因型进行抗性筛选。我们的研究提出了一种用于向日葵/向日葵列当相互作用在可控条件下的表型分析工具，通过图像分析在相互作用早期对列当瘤进行分析。
我们通过在生长室中使用根箱（透明有机玻璃箱）来控制培养条件和向日葵列当接种物，优化了向日葵/向日葵列当相互作用的表型分析。接种3周后，我们使用配备了摄像头的树莓派计算机采集接种向日葵根系的图像。我们使用ImageJ免费软件设置了一个宏程序来自动计数瘤的数量。这个表型分析工具被命名为RhizOSun。我们对接种了两个向日葵列当小种的五个向日葵基因型进行了评估，结果表明使用RhizOSun自动计数瘤的数量与人工耗时计数高度相关，并且可以有效地用于在瘤阶段筛选向日葵基因型。
该方法快速、准确且成本低。它允许随着时间对大量根箱进行快速成像，并能对所有数据进行时间动力学的图像跟踪。这为根箱表型分析的自动化铺平了道路，该自动化可用于其他根系表型分析，如豆科植物上的共生根瘤。
