Assembly and Evaluation of a Confocal Microscopy Image Analysis Pipeline Useful in Revealing the Secrets of Plant-Fungal Interactions
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 p...
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| Format: | Article |
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The American Phytopathological Society
2024-12-01
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| Series: | Molecular Plant-Microbe Interactions |
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| Online Access: | https://apsjournals.apsnet.org/doi/10.1094/MPMI-08-24-0090-TA |
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| author | Ashley C. Nelson Gayan K. Kariyawasam Nathan A. Wyatt Jinling Li Janine Haueisen Eva H. Stukenbrock Pawel Borowicz Zhaohui Liu Timothy L. Friesen |
| author_facet | Ashley C. Nelson Gayan K. Kariyawasam Nathan A. Wyatt Jinling Li Janine Haueisen Eva H. Stukenbrock Pawel Borowicz Zhaohui Liu Timothy L. Friesen |
| author_sort | Ashley C. Nelson |
| collection | DOAJ |
| description | 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 Parastagonospora nodorum, Pyrenophora teres f. teres, and Cercospora beticola 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. [Figure: 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. |
| format | Article |
| id | doaj-art-a9c4cef16ba643598bfb6c80b37eb319 |
| institution | OA Journals |
| issn | 0894-0282 1943-7706 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | The American Phytopathological Society |
| record_format | Article |
| series | Molecular Plant-Microbe Interactions |
| spelling | doaj-art-a9c4cef16ba643598bfb6c80b37eb3192025-08-20T01:57:59ZengThe American Phytopathological SocietyMolecular Plant-Microbe Interactions0894-02821943-77062024-12-01371280481310.1094/MPMI-08-24-0090-TAAssembly and Evaluation of a Confocal Microscopy Image Analysis Pipeline Useful in Revealing the Secrets of Plant-Fungal InteractionsAshley C. Nelson0Gayan K. Kariyawasam1Nathan A. Wyatt2Jinling Li3Janine Haueisen4Eva H. Stukenbrock5Pawel Borowicz6Zhaohui Liu7Timothy L. Friesen8Department of Plant Pathology, North Dakota State University, Fargo, ND 58102, U.S.A.Department of Plant Pathology, North Dakota State University, Fargo, ND 58102, U.S.A.Department of Plant Pathology, North Dakota State University, Fargo, ND 58102, U.S.A.Department of Plant Pathology, North Dakota State University, Fargo, ND 58102, U.S.A.Max Planck Institute for Evolutionary Biology, Plön 24306, GermanyMax Planck Institute for Evolutionary Biology, Plön 24306, GermanyDepartment of Animal Sciences, North Dakota State University, Fargo, ND 58102, U.S.A.Department of Plant Pathology, North Dakota State University, Fargo, ND 58102, U.S.A.Department of Plant Pathology, North Dakota State University, Fargo, ND 58102, U.S.A.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 Parastagonospora nodorum, Pyrenophora teres f. teres, and Cercospora beticola 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. [Figure: 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.https://apsjournals.apsnet.org/doi/10.1094/MPMI-08-24-0090-TAconfocal microscopyfungal pathogensmachine learning |
| spellingShingle | Ashley C. Nelson Gayan K. Kariyawasam Nathan A. Wyatt Jinling Li Janine Haueisen Eva H. Stukenbrock Pawel Borowicz Zhaohui Liu Timothy L. Friesen Assembly and Evaluation of a Confocal Microscopy Image Analysis Pipeline Useful in Revealing the Secrets of Plant-Fungal Interactions Molecular Plant-Microbe Interactions confocal microscopy fungal pathogens machine learning |
| title | Assembly and Evaluation of a Confocal Microscopy Image Analysis Pipeline Useful in Revealing the Secrets of Plant-Fungal Interactions |
| title_full | Assembly and Evaluation of a Confocal Microscopy Image Analysis Pipeline Useful in Revealing the Secrets of Plant-Fungal Interactions |
| title_fullStr | Assembly and Evaluation of a Confocal Microscopy Image Analysis Pipeline Useful in Revealing the Secrets of Plant-Fungal Interactions |
| title_full_unstemmed | Assembly and Evaluation of a Confocal Microscopy Image Analysis Pipeline Useful in Revealing the Secrets of Plant-Fungal Interactions |
| title_short | Assembly and Evaluation of a Confocal Microscopy Image Analysis Pipeline Useful in Revealing the Secrets of Plant-Fungal Interactions |
| title_sort | assembly and evaluation of a confocal microscopy image analysis pipeline useful in revealing the secrets of plant fungal interactions |
| topic | confocal microscopy fungal pathogens machine learning |
| url | https://apsjournals.apsnet.org/doi/10.1094/MPMI-08-24-0090-TA |
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