Nf-Root: A Best-Practice Pipeline for Deep-Learning-Based Analysis of Apoplastic pH in Microscopy Images of Developmental Zones in Plant Root Tissue
Hormonal mechanisms associated with cell elongation play a vital role in the development and growth of plants. Here, we report Nextflow-root (nf-root), a novel best-practice pipeline for deep-learning-based analysis of fluorescence microscopy images of plant root tissue from A. thaliana. This bioinf...
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| Format: | Article |
| Language: | English |
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Cambridge University Press
2024-01-01
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| Series: | Quantitative Plant Biology |
| Online Access: | https://www.cambridge.org/core/product/identifier/S2632882824000110/type/journal_article |
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| _version_ | 1841526543192424448 |
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| author | Julian Wanner Luis Kuhn Cuellar Luiselotte Rausch Kenneth W. Berendzen Friederike Wanke Gisela Gabernet Klaus Harter Sven Nahnsen |
| author_facet | Julian Wanner Luis Kuhn Cuellar Luiselotte Rausch Kenneth W. Berendzen Friederike Wanke Gisela Gabernet Klaus Harter Sven Nahnsen |
| author_sort | Julian Wanner |
| collection | DOAJ |
| description | Hormonal mechanisms associated with cell elongation play a vital role in the development and growth of plants. Here, we report Nextflow-root (nf-root), a novel best-practice pipeline for deep-learning-based analysis of fluorescence microscopy images of plant root tissue from A. thaliana. This bioinformatics pipeline performs automatic identification of developmental zones in root tissue images. This also includes apoplastic pH measurements, which is useful for modeling hormone signaling and cell physiological responses. We show that this nf-core standard-based pipeline successfully automates tissue zone segmentation and is both high-throughput and highly reproducible. In short, a deep-learning module deploys deterministically trained convolutional neural network models and augments the segmentation predictions with measures of prediction uncertainty and model interpretability, while aiming to facilitate result interpretation and verification by experienced plant biologists. We observed a high statistical similarity between the manually generated results and the output of the nf-root. |
| format | Article |
| id | doaj-art-fdda108587d448c4ac91736c5558cae1 |
| institution | Kabale University |
| issn | 2632-8828 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Cambridge University Press |
| record_format | Article |
| series | Quantitative Plant Biology |
| spelling | doaj-art-fdda108587d448c4ac91736c5558cae12025-01-16T21:47:32ZengCambridge University PressQuantitative Plant Biology2632-88282024-01-01510.1017/qpb.2024.11Nf-Root: A Best-Practice Pipeline for Deep-Learning-Based Analysis of Apoplastic pH in Microscopy Images of Developmental Zones in Plant Root TissueJulian Wanner0Luis Kuhn Cuellar1https://orcid.org/0000-0002-6950-6929Luiselotte Rausch2Kenneth W. Berendzen3Friederike Wanke4Gisela Gabernet5Klaus Harter6Sven Nahnsen7Quantitative Biology Center (QBiC), University of Tübingen, Tübingen, Germany Hasso Plattner Institute, University of Potsdam, Germany Finnish Institute for Molecular Medicine (FIMM), University of Helsinki, Helsinki, FinlandQuantitative Biology Center (QBiC), University of Tübingen, Tübingen, GermanyCenter for Plant Molecular Biology (ZMBP), University of Tübingen, Tübingen, GermanyCenter for Plant Molecular Biology (ZMBP), University of Tübingen, Tübingen, GermanyCenter for Plant Molecular Biology (ZMBP), University of Tübingen, Tübingen, GermanyQuantitative Biology Center (QBiC), University of Tübingen, Tübingen, GermanyCenter for Plant Molecular Biology (ZMBP), University of Tübingen, Tübingen, GermanyQuantitative Biology Center (QBiC), University of Tübingen, Tübingen, GermanyHormonal mechanisms associated with cell elongation play a vital role in the development and growth of plants. Here, we report Nextflow-root (nf-root), a novel best-practice pipeline for deep-learning-based analysis of fluorescence microscopy images of plant root tissue from A. thaliana. This bioinformatics pipeline performs automatic identification of developmental zones in root tissue images. This also includes apoplastic pH measurements, which is useful for modeling hormone signaling and cell physiological responses. We show that this nf-core standard-based pipeline successfully automates tissue zone segmentation and is both high-throughput and highly reproducible. In short, a deep-learning module deploys deterministically trained convolutional neural network models and augments the segmentation predictions with measures of prediction uncertainty and model interpretability, while aiming to facilitate result interpretation and verification by experienced plant biologists. We observed a high statistical similarity between the manually generated results and the output of the nf-root.https://www.cambridge.org/core/product/identifier/S2632882824000110/type/journal_article |
| spellingShingle | Julian Wanner Luis Kuhn Cuellar Luiselotte Rausch Kenneth W. Berendzen Friederike Wanke Gisela Gabernet Klaus Harter Sven Nahnsen Nf-Root: A Best-Practice Pipeline for Deep-Learning-Based Analysis of Apoplastic pH in Microscopy Images of Developmental Zones in Plant Root Tissue Quantitative Plant Biology |
| title | Nf-Root: A Best-Practice Pipeline for Deep-Learning-Based Analysis of Apoplastic pH in Microscopy Images of Developmental Zones in Plant Root Tissue |
| title_full | Nf-Root: A Best-Practice Pipeline for Deep-Learning-Based Analysis of Apoplastic pH in Microscopy Images of Developmental Zones in Plant Root Tissue |
| title_fullStr | Nf-Root: A Best-Practice Pipeline for Deep-Learning-Based Analysis of Apoplastic pH in Microscopy Images of Developmental Zones in Plant Root Tissue |
| title_full_unstemmed | Nf-Root: A Best-Practice Pipeline for Deep-Learning-Based Analysis of Apoplastic pH in Microscopy Images of Developmental Zones in Plant Root Tissue |
| title_short | Nf-Root: A Best-Practice Pipeline for Deep-Learning-Based Analysis of Apoplastic pH in Microscopy Images of Developmental Zones in Plant Root Tissue |
| title_sort | nf root a best practice pipeline for deep learning based analysis of apoplastic ph in microscopy images of developmental zones in plant root tissue |
| url | https://www.cambridge.org/core/product/identifier/S2632882824000110/type/journal_article |
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