Deep phenotyping platform for microscopic plant-pathogen interactions
The increasing availability of genetic and genomic resources has underscored the need for automated microscopic phenotyping in plant-pathogen interactions to identify genes involved in disease resistance. Building on accumulated experience and leveraging automated microscopy and software, we develop...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-02-01
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| Series: | Frontiers in Plant Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpls.2025.1462694/full |
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| author | Stefanie Lück Salim Bourras Dimitar Douchkov |
| author_facet | Stefanie Lück Salim Bourras Dimitar Douchkov |
| author_sort | Stefanie Lück |
| collection | DOAJ |
| description | The increasing availability of genetic and genomic resources has underscored the need for automated microscopic phenotyping in plant-pathogen interactions to identify genes involved in disease resistance. Building on accumulated experience and leveraging automated microscopy and software, we developed BluVision Micro, a modular, machine learning-aided system designed for high-throughput microscopic phenotyping. This system is adaptable to various image data types and extendable with modules for additional phenotypes and pathogens. BluVision Micro was applied to screen 196 genetically diverse barley genotypes for interactions with powdery mildew fungi, delivering accurate, sensitive, and reproducible results. This enabled the identification of novel genetic loci and marker-trait associations in the barley genome. The system also facilitated high-throughput studies of labor-intensive phenotypes, such as precise colony area measurement. Additionally, BluVision’s open-source software supports the development of specific modules for various microscopic phenotypes, including high-throughput transfection assays for disease resistance-related genes. |
| format | Article |
| id | doaj-art-8464f8a24dca4d0384116ef392231d09 |
| institution | Kabale University |
| issn | 1664-462X |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Plant Science |
| spelling | doaj-art-8464f8a24dca4d0384116ef392231d092025-02-03T17:40:07ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2025-02-011610.3389/fpls.2025.14626941462694Deep phenotyping platform for microscopic plant-pathogen interactionsStefanie Lück0Salim Bourras1Dimitar Douchkov2Department of Breeding Research, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Seeland, GermanyDepartment of Plant Biology, Swedish University of Agricultural Sciences (SLU), Uppsala, SwedenDepartment of Breeding Research, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Seeland, GermanyThe increasing availability of genetic and genomic resources has underscored the need for automated microscopic phenotyping in plant-pathogen interactions to identify genes involved in disease resistance. Building on accumulated experience and leveraging automated microscopy and software, we developed BluVision Micro, a modular, machine learning-aided system designed for high-throughput microscopic phenotyping. This system is adaptable to various image data types and extendable with modules for additional phenotypes and pathogens. BluVision Micro was applied to screen 196 genetically diverse barley genotypes for interactions with powdery mildew fungi, delivering accurate, sensitive, and reproducible results. This enabled the identification of novel genetic loci and marker-trait associations in the barley genome. The system also facilitated high-throughput studies of labor-intensive phenotypes, such as precise colony area measurement. Additionally, BluVision’s open-source software supports the development of specific modules for various microscopic phenotypes, including high-throughput transfection assays for disease resistance-related genes.https://www.frontiersin.org/articles/10.3389/fpls.2025.1462694/fullBluVisionautomated microscopybarleydeep learningmicrophenomicsneuronal networks |
| spellingShingle | Stefanie Lück Salim Bourras Dimitar Douchkov Deep phenotyping platform for microscopic plant-pathogen interactions Frontiers in Plant Science BluVision automated microscopy barley deep learning microphenomics neuronal networks |
| title | Deep phenotyping platform for microscopic plant-pathogen interactions |
| title_full | Deep phenotyping platform for microscopic plant-pathogen interactions |
| title_fullStr | Deep phenotyping platform for microscopic plant-pathogen interactions |
| title_full_unstemmed | Deep phenotyping platform for microscopic plant-pathogen interactions |
| title_short | Deep phenotyping platform for microscopic plant-pathogen interactions |
| title_sort | deep phenotyping platform for microscopic plant pathogen interactions |
| topic | BluVision automated microscopy barley deep learning microphenomics neuronal networks |
| url | https://www.frontiersin.org/articles/10.3389/fpls.2025.1462694/full |
| work_keys_str_mv | AT stefanieluck deepphenotypingplatformformicroscopicplantpathogeninteractions AT salimbourras deepphenotypingplatformformicroscopicplantpathogeninteractions AT dimitardouchkov deepphenotypingplatformformicroscopicplantpathogeninteractions |