A robust and efficient automatic method to segment maize FASGA stained stem cross section images to accurately quantify histological profile

Abstract

Background Grasses internodes are made of distinct tissues such as vascular bundles, epidermis, rind and pith. The histology of grasses stem was largely revisited recently taking advantage of the development of microscopy combined with the development of computer-automated image analysis workflows. However, the diversity and complexity of the histological profile complicates quantification. Accurate and automated analysis of histological images thus remains challenging. Results Herein, we present a workflow that automatically segments maize internode cross section images into 40 distinct tissues: two tissues in the epidermis, 19 tissues in the rind, 14 tissues in the pith and 5 tissues in the bundles. This level of segmentation is achieved by combining the Hue, Saturation and Value properties of each pixel and the location of each pixel in FASGA stained cross sectiona. This workflow is likewise able to highlight significant and subtle histological genotypic variations between maize internodes. The grain of precision provided by the workflow also makes it possible to demonstrate different levels of sensitivity to digestion by enzymatic cocktails of the tissues in the pith. The precision and strength of the workflow is all the more impressive because it is preserved on cross section images of other grasses such as miscanthus or sorghum. Conclusions The fidelity of this tool and its capacity to automatically identify variations of a large number of histological profiles among different genotypes pave the way for its use to identify genotypes of interest and to study the underlying genetic bases of variations in histological profiles in maize or other species.

Cell wall digestibility outperforms ear in maintaining digestibility even under severe combined water and heat stress

“…While we had previously identified such bands, albeit to a lesser extent, in hybrids, the WD 2022 plants are the first commercial hybrids in our studies to replicate these results from inbred lines. This blue ring is reduced under humid conditions, but the use of the new FASGA image segmentation plugin (Lopez-Marnet et al, 2022) has further permitted the identification of a smaller red ring in these conditions. This red ring appears adjacent to the space between the nonlignified cortical parenchyma cells and the rind, progressively becoming more prominent under humid conditions.…”

“…These represented no more than 30 samples out of an original 390 image set. Images were then automatically segmented utilising the ImageJ plugin developed by Lopez-Marnet et al ., 2022. Briefly, this plugin, originally established for segmenting FASGA-stained maize internodes, segments 44 different tissues (alongside a few summary variables) in raw pixel amounts.…”

Precise control of water stress in the field reveals different response thresholds for forage yield and digestibility of maize hybrids

“…New targets or combinations of targets need to be found. These could be new biochemical traits such as p -coumaric acids or histological traits ( Méchin et al., 2005 ; Zhang et al., 2011 ; El Hage et al., 2021 ; Zhang, 2021 ; Lopez-Marnet et al., 2022 ) to study the localization of lignified tissue by FASGA staining of internode cross section.…”

Maize Internode Autofluorescence at the Macroscopic Scale: Image Representation and Principal Component Analysis of a Series of Large Multispectral Images

“…Corcel et al (2017) [48] applied k-means clustering to autofluorescence multispectral images of 10 maize stems of the same genotype and identified up to 17 classes with different autofluorescence profiles. Lopez-Marnet et al (2022) [30] segmented maize colour images of internode cross-sections stained with FASGA into 40 classes according to the hue, saturation, value and localisation of each pixel.…”

“…The imaging method also needs to be able to quantify variations in tissue composition. Staining methods such as FASGA, Weisner or Maule staining have been used to locate and quantify lignin variations in lignocellulosic samples [30][31][32]. The interpretation is not straightforward, and staining is an added step in the sample preparation workflow that can have an effect on repeatability.…”