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Accelerated Haustoria Segmentation Enables Rapid Gene Function Analysis in Cereal-Powdery Mildew Pathosystems.

Stefanie Lück¹, Deniz Demirhan¹, Laura Agsten¹

¹Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), 06466 Seeland OT Gatersleben, Germany.

Molecular Plant-Microbe Interactions : MPMI

|September 22, 2025

Summary

A new pipeline automates the detection of fungal infections in plants using deep learning. This tool accurately quantifies powdery mildew in barley and wheat, speeding up gene function studies.

Keywords:

BluVision GUS barley cereal–pathogen phenotyping deep learning haustorium high-throughput slide imaging microphenomics powdery mildew wheat

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Area of Science:

Plant Pathology
Computational Biology
Genetics

Background:

Quantifying early fungal infections in plants is essential for genetic studies but is currently labor-intensive.
Powdery mildew infection in cereals like barley and wheat requires efficient analysis methods.

Purpose of the Study:

To develop and validate an automated pipeline for high-throughput quantification of fungal infection events.
To enable rapid functional validation screens and large-scale phenotyping of cereal-powdery mildew interactions.

Main Methods:

An openly available pipeline was developed using deep learning models (You Only Look Once network and segmentation model).
The pipeline analyzes whole-slide images of GUS-stained epidermal cells and intracellular haustoria in barley and wheat leaves.
Automatic focus-layer selection was implemented to preserve fine structural details.

Main Results:

The pipeline accurately detects β-glucuronidase (GUS)-stained cells and haustoria in both barley and wheat.
Automated counts showed near-perfect agreement with manual counts, demonstrating robust cross-species transferability.
The workflow processes slides in minutes per workstation with minimal user input.

Conclusions:

The developed pipeline significantly reduces labor intensity in quantifying fungal infections.
This automated system enables rapid functional validation and large-scale phenotyping of plant-fungal interactions.
The pipeline supports efficient research in cereal-powdery mildew interactions.