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

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 an 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, it 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. 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 diseased (yellow) leaf area. The spread of luminescence-tagged bacteria is monitored via light-sensitive films, which are processed in a similar way 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.

Plant phenotyping relevance

植物葉の病徴面積と病原体拡散を画像解析で自動定量するソフトウェアを開発・提示しており、植物表現型の取得・抽出が研究の中心である。

Code and data availability

The preprint states that all code and raw images generated during the study are available at the authors' GitHub repository (https://github.com/MolPlantPathology/ScAnalyzer), which contains the ScAnalyzer Python/R analysis pipeline; the repository also hosts the printable leaf-sampling grid (grid.pdf) used as the phenp