PDF(Pubmed)
Abstract：
BACKGROUND: 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.
RESULTS: 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.
CONCLUSIONS: 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.
RESULTS: 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.
CONCLUSIONS: 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.
摘要：
背景：已知植物被广泛的病原微生物感染。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疾病症状和微生物定植。与使用手动分配疾病类别的更传统措施相反，图像处理提供了更准确和客观的植物病害症状的量化。除了监测疾病症状，计算图像处理提供了有关病原微生物在不同植物组织中的空间定位的额外信息。
结果：在这里，我们报告了一种称为ScAnalyzer的图像分析工具，用于监测拟南芥叶片中的疾病症状和细菌传播。除此之外，分离的叶子被组装在一个网格中并扫描，这使得单个样品的自动分离。像素颜色阈值用于将健康（绿色）与褪绿（黄色）叶区分开。通过光敏薄膜监测发光标记细菌的传播，以与叶子扫描类似的方式进行处理。我们表明，该工具能够捕获先前确定的模型植物拟南芥对细菌病原体黄单胞菌pv的敏感性差异。Campestris.此外，我们表明，与以前使用的方法相比，ScAnalyzer管道提供了更详细的植物叶片内细菌传播评估。最后,通过将疾病症状值与来自同一片叶子的细菌传播值相结合，我们表明细菌传播先于视觉疾病症状。
结论：综合来看，我们提出了一个自动脚本来监测植物疾病症状和微生物在拟南芥叶片中的传播。免费提供的软件（https://github.com/MolPlantPathology/ScAnalyzer）具有标准化不同群体之间疾病测定分析的潜力。
结果：在这里，我们报告了一种称为ScAnalyzer的图像分析工具，用于监测拟南芥叶片中的疾病症状和细菌传播。除此之外，分离的叶子被组装在一个网格中并扫描，这使得单个样品的自动分离。像素颜色阈值用于将健康（绿色）与褪绿（黄色）叶区分开。通过光敏薄膜监测发光标记细菌的传播，以与叶子扫描类似的方式进行处理。我们表明，该工具能够捕获先前确定的模型植物拟南芥对细菌病原体黄单胞菌pv的敏感性差异。Campestris.此外，我们表明，与以前使用的方法相比，ScAnalyzer管道提供了更详细的植物叶片内细菌传播评估。最后,通过将疾病症状值与来自同一片叶子的细菌传播值相结合，我们表明细菌传播先于视觉疾病症状。
结论：综合来看，我们提出了一个自动脚本来监测植物疾病症状和微生物在拟南芥叶片中的传播。免费提供的软件（https://github.com/MolPlantPathology/ScAnalyzer）具有标准化不同群体之间疾病测定分析的潜力。
