ScAnalyzer: an image processing tool to monitor plant disease symptoms and pathogen spread in Arabidopsis thaliana leaves.

IF 4.7 2区生物学 Q1 BIOCHEMICAL RESEARCH METHODS

Plant Methods Pub Date : 2024-05-31 DOI:10.1186/s13007-024-01213-3

Misha Paauw, Gerrit Hardeman, Nanne W Taks, Lennart Lambalk, Jeroen A Berg, Sebastian Pfeilmeier, Harrold A van den Burg

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引用次数: 0

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.

Conclusion: 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.

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ScAnalyzer：监测拟南芥叶片植物病害症状和病原体传播的图像处理工具。

背景：众所周知，植物会受到多种病原微生物的感染。要研究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植情况。与使用人工分配病害类别的传统方法相比，图像处理能更准确、更客观地量化植物病害症状。除了监测病害症状，计算图像处理还能提供病原微生物在不同植物组织中空间定位的额外信息：结果：我们在此报告一种名为 ScAnalyzer 的图像分析工具，用于监测拟南芥叶片的病害症状和细菌扩散情况。为此，将分离的叶片组合成网格并进行扫描，这样就能自动分离单个样本。使用像素颜色阈值来分割健康（绿色）和萎蔫（黄色）的叶片区域。通过感光胶片监测发光标记细菌的扩散情况，这些胶片的处理方式与叶片扫描类似。我们的研究表明，这一工具能够捕捉到先前发现的模式植物 A. thaliana 对细菌病原体野油菜黄单胞菌 pv. campestris 的敏感性差异。此外，我们还表明，与以前使用的方法相比，ScAnalyzer 管道能更详细地评估细菌在植物叶片内的传播情况。最后，通过将同一叶片的病害症状值与细菌扩散值相结合，我们发现细菌扩散先于视觉病害症状：综上所述，我们提出了一种自动脚本，用于监测植物病害症状和微生物在大丽花叶片中的扩散。免费提供的软件 ( https://github.com/MolPlantPathology/ScAnalyzer ) 有可能使不同小组之间的病害分析标准化。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Plant Methods 生物-植物科学

CiteScore

9.20

自引率

3.90%

发文量

121

审稿时长

2 months

期刊介绍： Plant Methods is an open access, peer-reviewed, online journal for the plant research community that encompasses all aspects of technological innovation in the plant sciences. There is no doubt that we have entered an exciting new era in plant biology. The completion of the Arabidopsis genome sequence, and the rapid progress being made in other plant genomics projects are providing unparalleled opportunities for progress in all areas of plant science. Nevertheless, enormous challenges lie ahead if we are to understand the function of every gene in the genome, and how the individual parts work together to make the whole organism. Achieving these goals will require an unprecedented collaborative effort, combining high-throughput, system-wide technologies with more focused approaches that integrate traditional disciplines such as cell biology, biochemistry and molecular genetics. Technological innovation is probably the most important catalyst for progress in any scientific discipline. Plant Methods’ goal is to stimulate the development and adoption of new and improved techniques and research tools and, where appropriate, to promote consistency of methodologies for better integration of data from different laboratories.