Non-Invasive, Bioluminescence-Based Visualisation and Quantification of Bacterial Infections in Arabidopsis Over Time

Area of Science:

• Plant pathology
• Microbiology
• Bioimaging

Background:

• Plant-pathogenic bacteria utilize natural openings and wounds for host colonization.
• Vascular pathogens like Xanthomonas campestris pv. campestris (Xcc) infect hosts via hydathodes and progress into xylem vessels.
• Non-invasive in planta imaging is crucial for understanding bacterial infection mechanisms.

Purpose of the Study:

• To develop and validate a phenotyping setup and Python image analysis pipeline for parallel, non-invasive monitoring of bacterial infections in Arabidopsis thaliana.
• To enable early detection and quantification of bacterial progression in planta.

Main Methods:

• A phenotyping setup combining RGB and ultrasensitive CCD cameras for simultaneous disease symptom and bioluminescence imaging.
• A Python image analysis pipeline for quantifying bacterial growth in parallel across 16 independent infections.
• Utilizing bioluminescence imaging to track bacterial movement and growth within plant tissues.

Main Results:

• The method reliably quantified bacterial growth for both vascular (Xcc) and mesophyll (Pseudomonas syringae pv. tomato) pathogens.
• Xcc imaging was achieved in hydathodes, providing reproducible data for early infection stages.
• The image analysis pipeline yielded robust data, validated previous findings, and required fewer samples than alternative methods.
• Bioluminescence detection within 5 minutes offered a significant time advantage.

Conclusions:

• The developed method provides a robust, non-invasive approach for quantifying plant-pathogenic bacterial growth in planta.
• This technique is suitable for high-throughput phenotypic screening of Arabidopsis thaliana accessions and mutant lines against various bacterial strains.
• The rapid detection and parallel processing capabilities accelerate the study of plant-bacterial interactions.