Parametric mapping of cellular morphology in plant tissue sections by gray level granulometry

Automated quantification of fluorescence and morphological changes in pretreated wood cells by fluorescence macroscopy

BACKGROUND

Lignocellulosic biomass is a complex network of polysaccharides and lignin that requires a pretreatment step to overcome recalcitrance and optimize valorisation into biobased products. Pretreatment of biomass induces chemical and morphological changes. Quantification of these changes is critical to understand biomass recalcitrance and to predict lignocellulose reactivity. In this study, we propose an automated method for the quantification of chemical and morphological parameters through fluorescence macroscopy, which was applied on wood samples (spruce, beechwood) pretreated with steam explosion.

RESULTS

Results in fluorescence macroscopy highlighted the impact of steam explosion on spruce and beechwood: fluorescence intensity of samples was highly altered, especially for the most severe conditions. Morphological changes were also revealed: shrinkage of cells and deformation of cell walls manifested as the loss of rectangularity or circular shape, for tracheids in spruce and vessels in beechwood respectively. Quantification of fluorescence intensity of cell walls and quantification of morphological parameters related to cell lumens were carried out accurately by applying the automated method onto the macroscopic images. The results showed that lumens area and circularity could be considered as complementary markers of cell deformation, and that fluorescence intensity of the cell walls could be related to morphological changes and to the conditions of pretreatment.

CONCLUSIONS

The developed procedure allows simultaneous and effective quantification of morphological parameters and fluorescence intensity of the cell walls. This approach can be applied to fluorescence macroscopy as well as other imaging techniques and provides encouraging results towards the understanding of biomass architecture.

Darkfield and Fluorescence Macrovision of a Series of Large Images to Assess Anatomical and Chemical Tissue Variability in Whole Cross-Sections of Maize Stems

The proportion and composition of plant tissues in maize stems vary with genotype and agroclimatic factors and may impact the final biomass use. In this manuscript, we propose a quantitative histology approach without any section labelling to estimate the proportion of different tissues in maize stem sections as well as their chemical characteristics. Macroscopic imaging was chosen to observe the entire section of a stem. Darkfield illumination was retained to visualise the whole stem cellular structure. Multispectral autofluorescence images were acquired to detect cell wall phenolic compounds after UV and visible excitations. Image analysis was implemented to extract morphological features and autofluorescence pseudospectra. By assimilating the internode to a cylinder, the relative proportions of tissues in the internode were estimated from their relative areas in the sections. The approach was applied to study a series of 14 maize inbred lines. Considerable variability was revealed among the 14 inbred lines for both anatomical and chemical traits. The most discriminant morphological descriptors were the relative amount of rind and parenchyma tissues together with the density and size of the individual bundles, the area of stem and the parenchyma cell diameter. The rind, as the most lignified tissue, showed strong visible-induced fluorescence which was line-dependant. The relative amount of para-coumaric acid was associated with the UV-induced fluorescence intensity in the rind and in the parenchyma near the rind, while ferulic acid amount was significantly correlated mainly with the parenchyma near the rind. The correlation between lignin and the tissue pseudospectra showed that a global higher amount of lignin resulted in a higher level of lignin fluorescence whatever the tissues. We demonstrated here the potential of darkfield and autofluorescence imaging coupled with image analysis to quantify histology of maize stem and highlight variability between different lines.