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Non-destructive, high-content analysis of wheat grain traits using X-ray micro computed tomography

BACKGROUND: Wheat is one of the most widely grown crop in temperate climates for food and animal feed. In order to meet the demands of the predicted population increase in an ever-changing climate, wheat production needs to dramatically increase. Spike and grain traits are critical determinants of f...

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Autores principales: Hughes, Aoife, Askew, Karen, Scotson, Callum P., Williams, Kevin, Sauze, Colin, Corke, Fiona, Doonan, John H., Nibau, Candida
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5664813/
https://www.ncbi.nlm.nih.gov/pubmed/29118820
http://dx.doi.org/10.1186/s13007-017-0229-8
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author Hughes, Aoife
Askew, Karen
Scotson, Callum P.
Williams, Kevin
Sauze, Colin
Corke, Fiona
Doonan, John H.
Nibau, Candida
author_facet Hughes, Aoife
Askew, Karen
Scotson, Callum P.
Williams, Kevin
Sauze, Colin
Corke, Fiona
Doonan, John H.
Nibau, Candida
author_sort Hughes, Aoife
collection PubMed
description BACKGROUND: Wheat is one of the most widely grown crop in temperate climates for food and animal feed. In order to meet the demands of the predicted population increase in an ever-changing climate, wheat production needs to dramatically increase. Spike and grain traits are critical determinants of final yield and grain uniformity a commercially desired trait, but their analysis is laborious and often requires destructive harvest. One of the current challenges is to develop an accurate, non-destructive method for spike and grain trait analysis capable of handling large populations. RESULTS: In this study we describe the development of a robust method for the accurate extraction and measurement of spike and grain morphometric parameters from images acquired by X-ray micro-computed tomography (μCT). The image analysis pipeline developed automatically identifies plant material of interest in μCT images, performs image analysis, and extracts morphometric data. As a proof of principle, this integrated methodology was used to analyse the spikes from a population of wheat plants subjected to high temperatures under two different water regimes. Temperature has a negative effect on spike height and grain number with the middle of the spike being the most affected region. The data also confirmed that increased grain volume was correlated with the decrease in grain number under mild stress. CONCLUSIONS: Being able to quickly measure plant phenotypes in a non-destructive manner is crucial to advance our understanding of gene function and the effects of the environment. We report on the development of an image analysis pipeline capable of accurately and reliably extracting spike and grain traits from crops without the loss of positional information. This methodology was applied to the analysis of wheat spikes can be readily applied to other economically important crop species. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13007-017-0229-8) contains supplementary material, which is available to authorized users.
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spelling pubmed-56648132017-11-08 Non-destructive, high-content analysis of wheat grain traits using X-ray micro computed tomography Hughes, Aoife Askew, Karen Scotson, Callum P. Williams, Kevin Sauze, Colin Corke, Fiona Doonan, John H. Nibau, Candida Plant Methods Research BACKGROUND: Wheat is one of the most widely grown crop in temperate climates for food and animal feed. In order to meet the demands of the predicted population increase in an ever-changing climate, wheat production needs to dramatically increase. Spike and grain traits are critical determinants of final yield and grain uniformity a commercially desired trait, but their analysis is laborious and often requires destructive harvest. One of the current challenges is to develop an accurate, non-destructive method for spike and grain trait analysis capable of handling large populations. RESULTS: In this study we describe the development of a robust method for the accurate extraction and measurement of spike and grain morphometric parameters from images acquired by X-ray micro-computed tomography (μCT). The image analysis pipeline developed automatically identifies plant material of interest in μCT images, performs image analysis, and extracts morphometric data. As a proof of principle, this integrated methodology was used to analyse the spikes from a population of wheat plants subjected to high temperatures under two different water regimes. Temperature has a negative effect on spike height and grain number with the middle of the spike being the most affected region. The data also confirmed that increased grain volume was correlated with the decrease in grain number under mild stress. CONCLUSIONS: Being able to quickly measure plant phenotypes in a non-destructive manner is crucial to advance our understanding of gene function and the effects of the environment. We report on the development of an image analysis pipeline capable of accurately and reliably extracting spike and grain traits from crops without the loss of positional information. This methodology was applied to the analysis of wheat spikes can be readily applied to other economically important crop species. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13007-017-0229-8) contains supplementary material, which is available to authorized users. BioMed Central 2017-11-01 /pmc/articles/PMC5664813/ /pubmed/29118820 http://dx.doi.org/10.1186/s13007-017-0229-8 Text en © The Author(s) 2017 https://creativecommons.org/licenses/by/4.0/ Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) ), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research
Hughes, Aoife
Askew, Karen
Scotson, Callum P.
Williams, Kevin
Sauze, Colin
Corke, Fiona
Doonan, John H.
Nibau, Candida
Non-destructive, high-content analysis of wheat grain traits using X-ray micro computed tomography
title Non-destructive, high-content analysis of wheat grain traits using X-ray micro computed tomography
title_full Non-destructive, high-content analysis of wheat grain traits using X-ray micro computed tomography
title_fullStr Non-destructive, high-content analysis of wheat grain traits using X-ray micro computed tomography
title_full_unstemmed Non-destructive, high-content analysis of wheat grain traits using X-ray micro computed tomography
title_short Non-destructive, high-content analysis of wheat grain traits using X-ray micro computed tomography
title_sort non-destructive, high-content analysis of wheat grain traits using x-ray micro computed tomography
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5664813/
https://www.ncbi.nlm.nih.gov/pubmed/29118820
http://dx.doi.org/10.1186/s13007-017-0229-8
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