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Histological quantification of maize stem sections from FASGA-stained images

BACKGROUND: Crop species are of increasing interest both for cattle feeding and for bioethanol production. The degradability of the plant material largely depends on the lignification of the tissues, but it also depends on histological features such as the cellular morphology or the relative amount...

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Autores principales: Legland, David, El-Hage, Fadi, Méchin, Valérie, Reymond, Matthieu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5664815/
https://www.ncbi.nlm.nih.gov/pubmed/29118822
http://dx.doi.org/10.1186/s13007-017-0225-z
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author Legland, David
El-Hage, Fadi
Méchin, Valérie
Reymond, Matthieu
author_facet Legland, David
El-Hage, Fadi
Méchin, Valérie
Reymond, Matthieu
author_sort Legland, David
collection PubMed
description BACKGROUND: Crop species are of increasing interest both for cattle feeding and for bioethanol production. The degradability of the plant material largely depends on the lignification of the tissues, but it also depends on histological features such as the cellular morphology or the relative amount of each tissue fraction. There is therefore a need for high-throughput phenotyping systems that quantify the histology of plant sections. RESULTS: We developed custom image processing and an analysis procedure for quantifying the histology of maize stem sections coloured with FASGA staining and digitalised with whole microscopy slide scanners. The procedure results in an automated segmentation of the input images into distinct tissue regions. The size and the fraction area of each tissue region can be quantified, as well as the average coloration within each region. The measured features can discriminate contrasted genotypes and identify changes in histology induced by environmental factors such as water deficit. CONCLUSIONS: The simplicity and the availability of the software will facilitate the elucidation of the relationships between the chemical composition of the tissues and changes in plant histology. The tool is expected to be useful for the study of large genetic populations, and to better understand the impact of environmental factors on plant histology. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13007-017-0225-z) contains supplementary material, which is available to authorized users.
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spelling pubmed-56648152017-11-08 Histological quantification of maize stem sections from FASGA-stained images Legland, David El-Hage, Fadi Méchin, Valérie Reymond, Matthieu Plant Methods Methodology BACKGROUND: Crop species are of increasing interest both for cattle feeding and for bioethanol production. The degradability of the plant material largely depends on the lignification of the tissues, but it also depends on histological features such as the cellular morphology or the relative amount of each tissue fraction. There is therefore a need for high-throughput phenotyping systems that quantify the histology of plant sections. RESULTS: We developed custom image processing and an analysis procedure for quantifying the histology of maize stem sections coloured with FASGA staining and digitalised with whole microscopy slide scanners. The procedure results in an automated segmentation of the input images into distinct tissue regions. The size and the fraction area of each tissue region can be quantified, as well as the average coloration within each region. The measured features can discriminate contrasted genotypes and identify changes in histology induced by environmental factors such as water deficit. CONCLUSIONS: The simplicity and the availability of the software will facilitate the elucidation of the relationships between the chemical composition of the tissues and changes in plant histology. The tool is expected to be useful for the study of large genetic populations, and to better understand the impact of environmental factors on plant histology. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13007-017-0225-z) contains supplementary material, which is available to authorized users. BioMed Central 2017-11-01 /pmc/articles/PMC5664815/ /pubmed/29118822 http://dx.doi.org/10.1186/s13007-017-0225-z Text en © The Author(s) 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://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/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Methodology
Legland, David
El-Hage, Fadi
Méchin, Valérie
Reymond, Matthieu
Histological quantification of maize stem sections from FASGA-stained images
title Histological quantification of maize stem sections from FASGA-stained images
title_full Histological quantification of maize stem sections from FASGA-stained images
title_fullStr Histological quantification of maize stem sections from FASGA-stained images
title_full_unstemmed Histological quantification of maize stem sections from FASGA-stained images
title_short Histological quantification of maize stem sections from FASGA-stained images
title_sort histological quantification of maize stem sections from fasga-stained images
topic Methodology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5664815/
https://www.ncbi.nlm.nih.gov/pubmed/29118822
http://dx.doi.org/10.1186/s13007-017-0225-z
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