Deep phenotyping platform for microscopic plant-pathogen interactions

Abstract

The increasing availability of genetic and genomic resources has underscored the need for automated microscopic phenotyping in plant-pathogen interactions to identify genes involved in disease resistance. Building on accumulated experience and leveraging automated microscopy and software, we developed BluVision Micro, a modular, machine learning-aided system designed for high-throughput microscopic phenotyping. This system is adaptable to various image data types and extendable with modules for additional phenotypes and pathogens. BluVision Micro was applied to screen 196 genetically diverse barley genotypes for interactions with powdery mildew fungi, delivering accurate, sensitive, and reproducible results. This enabled the identification of novel genetic loci and marker-trait associations in the barley genome. The system also facilitated high-throughput studies of labor-intensive phenotypes, such as precise colony area measurement. Additionally, BluVision's open-source software supports the development of specific modules for various microscopic phenotypes, including high-throughput transfection assays for disease resistance-related genes.

Keywords

BluVision; automated microscopy; barley; deep learning; microphenomics; neuronal networks; pathogens; powdery mildew

Published in

Frontiers in Plant Science
2025, volume: 16, article number: 1462694
Publisher: FRONTIERS MEDIA SA

SLU Authors

• Bourras, Salim

  • Department of Plant Biology, Swedish University of Agricultural Sciences
    

UKÄ Subject classification

Botany

Publication identifier

• DOI: https://doi.org/10.3389/fpls.2025.1462694

Permanent link to this page (URI)

https://res.slu.se/id/publ/140933