Paauw Misha, Hardeman Gerrit, Taks Nanne W, Lambalk Lennart, Berg Jeroen A, Pfeilmeier Sebastian, van den Burg Harrold A
Molecular Plant Pathology, Faculty of Science, Swammerdam Institute for Life Sciences (SILS), University of Amsterdam, Science Park 904, Amsterdam, 1098 XH, The Netherlands.
Technologie Centrum FNWI, Faculty of Science, University of Amsterdam, Science Park 904, Amsterdam, 1098 XH, The Netherlands.
Plant Methods. 2024 May 31;20(1):80. doi: 10.1186/s13007-024-01213-3.
Plants are known to be infected by a wide range of pathogenic microbes. To study plant diseases caused by microbes, it is imperative to be able to monitor disease symptoms and microbial colonization in a quantitative and objective manner. In contrast to more traditional measures that use manual assignments of disease categories, image processing provides a more accurate and objective quantification of plant disease symptoms. Besides monitoring disease symptoms, computational image processing provides additional information on the spatial localization of pathogenic microbes in different plant tissues.
Here we report on an image analysis tool called ScAnalyzer to monitor disease symptoms and bacterial spread in Arabidopsis thaliana leaves. Thereto, detached leaves are assembled in a grid and scanned, which enables automated separation of individual samples. A pixel color threshold is used to segment healthy (green) from chlorotic (yellow) leaf areas. The spread of luminescence-tagged bacteria is monitored via light-sensitive films, which are processed in a similar manner as the leaf scans. We show that this tool is able to capture previously identified differences in susceptibility of the model plant A. thaliana to the bacterial pathogen Xanthomonas campestris pv. campestris. Moreover, we show that the ScAnalyzer pipeline provides a more detailed assessment of bacterial spread within plant leaves than previously used methods. Finally, by combining the disease symptom values with bacterial spread values from the same leaves, we show that bacterial spread precedes visual disease symptoms.
Taken together, we present an automated script to monitor plant disease symptoms and microbial spread in A. thaliana leaves. The freely available software ( https://github.com/MolPlantPathology/ScAnalyzer ) has the potential to standardize the analysis of disease assays between different groups.
已知植物会受到多种致病微生物的感染。为了研究由微生物引起的植物病害，必须能够以定量和客观的方式监测病害症状和微生物定殖情况。与使用病害类别手动赋值的更传统方法相比，图像处理能更准确、客观地对植物病害症状进行量化。除了监测病害症状外，计算机图像处理还能提供有关致病微生物在不同植物组织中空间定位的额外信息。
在此，我们报告一种名为ScAnalyzer的图像分析工具，用于监测拟南芥叶片中的病害症状和细菌传播情况。为此，将离体叶片排列在网格中并进行扫描，从而实现单个样本的自动分离。使用像素颜色阈值将健康（绿色）叶片区域与褪绿（黄色）叶片区域区分开来。通过对感光胶片进行处理来监测发光标记细菌的传播，感光胶片的处理方式与叶片扫描类似。我们表明，该工具能够捕捉到模式植物拟南芥对细菌病原体野油菜黄单胞菌野油菜致病变种的先前已确定的易感性差异。此外，我们表明，与先前使用的方法相比，ScAnalyzer流程能对细菌在植物叶片内的传播进行更详细的评估。最后，通过将病害症状值与同一片叶子的细菌传播值相结合，我们表明细菌传播先于可见的病害症状出现。
综上所述，我们提出了一个用于监测拟南芥叶片中植物病害症状和微生物传播的自动化脚本。该免费软件（https://github.com/MolPlantPathology/ScAnalyzer ）有潜力使不同研究小组之间的病害分析标准化。
