Molecular Plant-Microbe Interactions (2016) 29, 919-924

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Bradley Laflamme, Maggie Middleton, Timothy Lo, Darrell Desveaux and David S. Guttman (2016)
Image-based quantification of plant immunity and disease
Molecular Plant-Microbe Interactions 29 (12), 919-924
Abstract: Measuring the extent and severity of disease is a critical component of plant pathology research and crop breeding. Unfortunately, existing visual scoring systems are qualitative, subjective, and the results are difficult to transfer between research groups, while existing quantitative methods can be quite laborious. Here, we present plant immunity and disease image-based quantification (PIDIQ), a quantitative, semi-automated system to rapidly and objectively measure disease symptoms in a biologically relevant context. PIDIQ applies an ImageJ-based macro to plant photos in order to distinguish healthy tissue from tissue that has yellowed due to disease. It can process a directory of images in an automated manner and report the relative ratios of healthy to diseased leaf area, thereby providing a quantitative measure of plant health that can be statistically compared with appropriate controls. We used the Arabidopsis thalianaPseudomonas syringae model system to show that PIDIQ is able to identify both enhanced plant health associated with effector-triggered immunity as well as elevated disease symptoms associated with effector-triggered susceptibility. Finally, we show that the quantitative results provided by PIDIQ correspond to those obtained via traditional in planta pathogen growth assays. PIDIQ provides a simple and effective means to nondestructively quantify disease from whole plants and we believe it will be equally effective for monitoring disease on excised leaves and stems.
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Research topic(s) for pests/diseases/weeds:
resistance/tolerance/defence of host


Pest and/or beneficial records:

Beneficial Pest/Disease/Weed Crop/Product Country Quarant.
Pseudomonas syringae