Histological quantification of maize stem sections from FASGA-stained images

Abstract

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.

Plant phenotyping relevance

トウモロコシ茎切片の組織形態を画像処理で自動分割・定量する手法を開発しており、植物表現型の取得・抽出が研究の中心である。

Code and data availability

The paper's image segmentation/quantification workflow is publicly released as an ImageJ/Fiji plugin (QuantifFasga) on GitHub, and the authors' in-house Matlab statistical analysis library (MatStats) is also publicly available on GitHub. The phenotype measurement data themselves are only available upon request.