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. 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. 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.
Histological quantification of maize stem sections from FASGA-stained images
D. Legland,Fadi El-Hage,V. Méchin,M. Reymond
Published 2017 in Plant Methods
ABSTRACT
PUBLICATION RECORD
- Publication year
2017
- Venue
Plant Methods
- Publication date
2017-11-01
- Fields of study
Biology, Medicine, Materials Science, Environmental Science
- Identifiers
- External record
- Source metadata
Semantic Scholar, PubMed
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CLAIMS
- Histological features including size, fraction area, and average coloration within each tissue region can discriminate contrasted genotypes and identify changes induced by environmental factors such as water deficit.All you need is Python (5d7gwfm5zu) extraction뀨 (7c402c1b98) review배휘동 (872fa5d2c9) reviewmexicorea (qjvnbu8xg3) review
CONCEPTS
- automated image segmentation
A computational procedure that partitions digitalized microscopy images of stained sections into distinct tissue regions without manual intervention.
Aliases: automated segmentation
All you need is Python (5d7gwfm5zu) extraction뀨 (7c402c1b98) review배휘동 (872fa5d2c9) reviewmexicorea (qjvnbu8xg3) review - fasga staining
A histological staining method used to color maize stem sections, enabling visual differentiation of tissue types.
Aliases: FASGA
All you need is Python (5d7gwfm5zu) extraction뀨 (7c402c1b98) review배휘동 (872fa5d2c9) reviewmexicorea (qjvnbu8xg3) review - genotype discrimination
The ability to differentiate between genetic varieties of maize based on quantified histological features.
All you need is Python (5d7gwfm5zu) extraction뀨 (7c402c1b98) review배휘동 (872fa5d2c9) reviewmexicorea (qjvnbu8xg3) review - maize stem sections
Cross-sectional slices of maize stems analyzed for cellular morphology and tissue composition.
Aliases: maize stem section
All you need is Python (5d7gwfm5zu) extraction뀨 (7c402c1b98) review배휘동 (872fa5d2c9) reviewmexicorea (qjvnbu8xg3) review - tissue region quantification
Measurement of the size, fraction area, and average coloration within each segmented tissue region of maize stem sections.
All you need is Python (5d7gwfm5zu) extraction뀨 (7c402c1b98) review배휘동 (872fa5d2c9) reviewmexicorea (qjvnbu8xg3) review - water deficit
An environmental stress factor that induces measurable changes in plant histology.
Aliases: drought, water stress
All you need is Python (5d7gwfm5zu) extraction뀨 (7c402c1b98) review배휘동 (872fa5d2c9) reviewmexicorea (qjvnbu8xg3) review - whole-slide microscopy scanning
Digitalization of microscopy slides using whole-slide scanners to produce high-resolution images for computational analysis.
Aliases: whole microscopy slide scanning
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REFERENCES
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