Histological quantification of maize stem sections from FASGA-stained images
2017
Legland, David | El-Hage, Fadi | Méchin, Valérie | Reymond, Matthieu | Unité de recherche sur les Biopolymères, Interactions Assemblages (BIA) ; Institut National de la Recherche Agronomique (INRA) | Institut Jean-Pierre Bourgin (IJPB) ; Institut National de la Recherche Agronomique (INRA)-AgroParisTech | Université Paris Saclay (COmUE) | French Government Grants (LabEx Saclay Plant Sciences-SPS) [ANR-10-LABX-0040-SPS, ANR-11-BTBR-0006 BIOMASS]; French National Research Agency under an "Investments for the Future" programme [ANR-11-IDEX-0003-02] | ANR-11-BTBR-0006,BFF,Biomasse pour le futur(2011) | ANR-11-IDEX-0003,IPS,Idex Paris-Saclay(2011) | ANR-10-LABX-0040,SPS,Saclay Plant Sciences(2010)
International audience
Mostrar más [+] Menos [-]Inglés. 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.
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