Deep phenotyping platform for microscopic plant-pathogen interactions

Abstract

1O_LIThe initial phases of plant-pathogen interactions are critical since they are often decisive for the successful infection. However, these early stages of interaction are typically microscopic, making it challenging to study on a large scale. C_LIO_LIFor this reason, using the powdery mildew fungi of cereals as a model, we have developed an automated microscopy pipeline coupled with deep learning-based image analysis for the high-throughput phenotyping of plant-pathogen interactions. C_LIO_LIThe system can quantify fungal microcolony count and density, the precise area of the secondary hyphae of each colony, and different morphological parameters. Moreover, the high throughput and sensitivity allow quantifying rare microscopic phenotypes in a large sample size. One of these phenotypes is the cryptic infection of non-adapted pathogens, marking the hidden transition stages of pathogen adaptation and breaking the nonhost barrier. Thus, our tool opens the nonhost resistance phenomenon to genetics and genomics studies. C_LIO_LIWe have developed an open-source high-throughput automated microscopy system for phenotyping the initial stages of plant-pathogen interactions, extendable to other microscopic phenotypes and hardware platforms. Furthermore, we have validated the systems performance in disease resistance screens of genetically diverse barley material and performed Genome-wide associations scans (GWAS), discovering several resistance-associated loci, including conferring nonhost resistance. C_LI

Plant phenotyping relevance

植物病原体相互作用の顕微鏡画像を自動取得・深層学習解析し、感染関連の形態・状態を定量化する高スループット表現型解析基盤を開発・検証しており、方法が研究の中心である。

Code and data availability

The supplied blocks describe the BluVision Micro phenotyping platform, training datasets, and GWAS analysis, but contain no data or code availability statement, no public repository, and no authors' URL for datasets, images, models, or scripts. Only generic library names (OpenCV, Keras, etc.) are mentioned.

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