Kranse Olaf Prosper, Ko Itsuhiro, Healey Roberta, Sonawala Unnati, Wei Siyuan, Senatori Beatrice, De Batté Francesco, Zhou Ji, Eves-van den Akker Sebastian
Department of Plant Sciences, The Crop Science Centre, University of Cambridge, Cambridge, CB2 3EA, UK.
Plant Pathology Department, Washington State University, Pullman, WA, 99164, USA.
Plant Methods. 2022 Dec 12;18(1):134. doi: 10.1186/s13007-022-00963-2.
Cyst nematodes are one of the major groups of plant-parasitic nematode, responsible for considerable crop losses worldwide. Improving genetic resources, and therefore resistant cultivars, is an ongoing focus of many pest management strategies. One of the major bottlenecks in identifying the plant genes that impact the infection, and thus the yield, is phenotyping. The current available screening method is slow, has unidimensional quantification of infection limiting the range of scorable parameters, and does not account for phenotypic variation of the host. The ever-evolving field of computer vision may be the solution for both the above-mentioned issues. To utilise these tools, a specialised imaging platform is required to take consistent images of nematode infection in quick succession.
Here, we describe an open-source, easy to adopt, imaging hardware and trait analysis software method based on a pre-existing nematode infection screening method in axenic culture. A cost-effective, easy-to-build and -use, 3D-printed imaging device was developed to acquire images of the root system of Arabidopsis thaliana infected with the cyst nematode Heterodera schachtii, replacing costly microscopy equipment. Coupling the output of this device to simple analysis scripts allowed the measurement of some key traits such as nematode number and size from collected images, in a semi-automated manner. Additionally, we used this combined solution to quantify an additional trait, root area before infection, and showed both the confounding relationship of this trait on nematode infection and a method to account for it.
Taken together, this manuscript provides a low-cost and open-source method for nematode phenotyping that includes the biologically relevant nematode size as a scorable parameter, and a method to account for phenotypic variation of the host. Together these tools highlight great potential in aiding our understanding of nematode parasitism.
孢囊线虫是植物寄生线虫的主要类群之一，在全球范围内造成了相当大的作物损失。改善遗传资源，进而培育抗性品种，是许多害虫管理策略的持续重点。鉴定影响感染从而影响产量的植物基因的主要瓶颈之一是表型分析。目前可用的筛选方法速度慢，对感染的量化是一维的，限制了可评分参数的范围，并且没有考虑宿主的表型变异。不断发展的计算机视觉领域可能是解决上述两个问题的方法。为了利用这些工具，需要一个专门的成像平台来快速连续地拍摄线虫感染的一致图像。
在此，我们基于无菌培养中现有的线虫感染筛选方法，描述了一种开源、易于采用的成像硬件和性状分析软件方法。开发了一种经济高效、易于构建和使用的3D打印成像设备，用于获取被孢囊线虫甜菜孢囊线虫感染的拟南芥根系图像，取代了昂贵的显微镜设备。将该设备的输出与简单的分析脚本相结合，可以以半自动方式从采集的图像中测量一些关键性状，如线虫数量和大小。此外，我们使用这种组合解决方案来量化另一个性状，即感染前的根面积，并展示了该性状与线虫感染之间的混杂关系以及一种解决方法。
综上所述，本手稿提供了一种低成本、开源的线虫表型分析方法，该方法将生物学上相关的线虫大小作为一个可评分参数，以及一种考虑宿主表型变异的方法。这些工具共同凸显了在帮助我们理解线虫寄生方面的巨大潜力。
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