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

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.