Assembly and Evaluation of a Confocal Microscopy Image Analysis Pipeline Useful in Revealing the Secrets of Plant-Fungal Interactions

• Published in: Molecular plant-microbe interactions 2024-12, Vol.37 (12), p.804-813
• Main Authors: Nelson, Ashley C, Kariyawasam, Gayan K, Wyatt, Nathan A, Li, Jinling, Haueisen, Janine, Stukenbrock, Eva H, Borowicz, Pawel, Liu, Zhaohui, Friesen, Timothy L
• Format: Article
• Language: English
• Subjects: Ascomycota - genetics; Ascomycota - physiology; Beta vulgaris - microbiology; confocal microscopy; fungal pathogens; Fungi - physiology; Hordeum - microbiology; Host-Pathogen Interactions; Image Processing, Computer-Assisted - methods; machine learning; Microscopy, Confocal; Plant Diseases - microbiology; Plant Leaves - microbiology; Propidium - chemistry; Propidium - metabolism; Triticum - microbiology
• ISSN: 0894-0282; 1943-7706
• DOI: 10.1094/MPMI-08-24-0090-TA

The ability of laser scanning confocal microscopy to generate high-contrast 2D and 3D images has become essential in studying plant-fungal interactions. Techniques such as visualization of native fluorescence, fluorescent protein tagging of microbes, green fluorescent protein (GFP)/red fluorescent protein (RFP)-fusion proteins, and fluorescent labeling of plant and fungal proteins have been widely used to aid in these investigations. Use of fluorescent proteins has several pitfalls, including variability of expression in planta and the requirement of gene transformation. Here, we used the unlabeled pathogens , f. , and infecting wheat, barley, and sugar beet, respectively, to show the utility of a staining and imaging pipeline that uses propidium iodide (PI), which stains RNA and DNA, and wheat germ agglutinin labeled with fluorescein isothiocyanate (WGA-FITC), which stains chitin, to visualize fungal colonization of plants. This pipeline relies on the use of KOH to remove the cutin layer of the leaf, increasing its permeability, allowing the different stains to penetrate and effectively bind to their targets, resulting in a consistent visualization of cellular structures. To expand the utility of this pipeline, we used the staining techniques in conjunction with machine learning to analyze fungal biomass through volume analysis, as well as quantifying nuclear breakdown, an early indicator of programmed cell death (PCD). This pipeline is simple to use, robust, consistent across host and fungal species, and can be applied to most plant-fungal interactions. Therefore, this pipeline can be used to characterize model systems as well as nonmodel interactions where transformation is not routine. [Formula: see text] The author(s) have dedicated the work to the public domain under the Creative Commons CC0 "No Rights Reserved" license by waiving all of his or her rights to the work worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law, 2024.