Simple, Fast and Efficient Methods for Analysing the Structural, Ultrastructural and Cellular Components of the Cell Wall

Imaging plant cell walls using fluorescent stains: The beauty is in the details

Plants continuously face various environmental stressors throughout their lifetime. To be able to grow and adapt in different environments, they developed specialized tissues that allowed them to maintain a protected yet interconnected body. These tissues undergo specific primary and secondary cell wall modifications that are essential to ensure normal plant growth, adaptation and successful land colonization. The composition of cell walls can vary among different plant species, organs and tissues. The ability to remodel their cell walls is fundamental for plants to be able to cope with multiple biotic and abiotic stressors. A better understanding of the changes taking place in plant cell walls may help identify and develop new strategies as well as tools to enhance plants' survival under environmental stresses or prevent pathogen attack. Since the invention of microscopy, numerous imaging techniques have been developed to determine the composition and dynamics of plant cell walls during normal growth and in response to environmental stimuli. In this review, we discuss the main advances in imaging plant cell walls, with a particular focus on fluorescent stains for different cell wall components and their compatibility with tissue clearing techniques. Lay Description: Plants are continuously subjected to various environmental stresses during their lifespan. They evolved specialized tissues that thrive in different environments, enabling them to maintain a protected yet interconnected body. Such tissues undergo distinct primary and secondary cell wall alterations essential to normal plant growth, their adaptability and successful land colonization. Cell wall composition may differ among various plant species, organs and even tissues. To deal with various biotic and abiotic stresses, plants must have the capacity to remodel their cell walls. Gaining insight into changes that take place in plant cell walls will help identify and create novel tools and strategies to improve plants' ability to withstand environmental challenges. Multiple imaging techniques have been developed since the introduction of microscopy to analyse the composition and dynamics of plant cell walls during growth and in response to environmental changes. Advancements in plant tissue cleaning procedures and their compatibility with cell wall stains have significantly enhanced our ability to perform high-resolution cell wall imaging. At the same time, several factors influence the effectiveness of cleaning and staining plant specimens, as well as the time necessary for the process, including the specimen's size, thickness, tissue complexity and the presence of autofluorescence. In this review, we will discuss the major advances in imaging plant cell walls, with a particular emphasis on fluorescent stains for diverse cell wall components and their compatibility with tissue clearing techniques. We hope that this review will assist readers in selecting the most appropriate stain or combination of stains to highlight specific cell wall components of interest.

Hyperspectral and Chlorophyll Fluorescence Analyses of Comparative Leaf Surfaces Reveal Cellular Influences on Leaf Optical Properties in Tradescantia Plants

The differential effects of cellular and ultrastructural characteristics on the optical properties of adaxial and abaxial leaf surfaces in the genus Tradescantia highlight the intricate relationships between cellular arrangement and pigment distribution in the plant cells. We examined hyperspectral and chlorophyll a fluorescence (ChlF) kinetics using spectroradiometers and optical and electron microscopy techniques. The leaves were analysed for their spectral properties and cellular makeup. The biochemical compounds were measured and correlated with the biophysical and ultrastructural features. The main findings showed that the top and bottom leaf surfaces had different amounts and patterns of pigments, especially anthocyanins, flavonoids, total phenolics, chlorophyll-carotenoids, and cell and organelle structures, as revealed by the hyperspectral vegetation index (HVI). These differences were further elucidated by the correlation coefficients, which influence the optical signatures of the leaves. Additionally, ChlF kinetics varied between leaf surfaces, correlating with VIS-NIR-SWIR bands through distinct cellular structures and pigment concentrations in the hypodermis cells. We confirmed that the unique optical properties of each leaf surface arise not only from pigmentation but also from complex cellular arrangements and structural adaptations. Some of the factors that affect how leaves reflect light are the arrangement of chloroplasts, thylakoid membranes, vacuoles, and the relative size of the cells themselves. These findings improve our knowledge of the biophysical and biochemical reasons for leaf optical diversity, and indicate possible implications for photosynthetic efficiency and stress adaptation under different environmental conditions in the mesophyll cells of Tradescantia plants.

A Comprehensive Molecular, Biochemical, Histochemical, and Spectroscopic Characterization of Early and Medium Duration Rice Genotypes Investigating Dry Matter Accumulation Efficiencies

Investigation on accumulation of cell wall components over critical growth stages will surely provide a new insight into dry matter accumulation studies in rice. An elevated biomass production provides an alternative strategy of yield improvement, which in turn maneuvers the species concerned as potential dual-purpose crop. On that note, present study was carried on 33 early and 39 medium duration rice genotypes. The average cellulose accumulation was 6.51% and 8.17% in early and medium duration genotypes, respectively, at flowering stage, which later on dipped to 1.43% and 3.46%, respectively, at physiological maturity. The gene specific marker MDgsp-5.a exhibited highest estimate of polymorphic information content (PIC), i.e., 0.685, closely followed by MDgsp-6.a with polymorphic information content (PIC) of 0.683. The control genotypes, i.e., Pratap and Mandakini, are grouped under the same cluster, i.e., Cluster-I.A, indicating their inherent genetic divergence from that of potential accumulators pertaining to cellulose accumulation. Pratap and Mandakini failed to produce peaks of conspicuous form at 3342 cm-1 and 1635 cm-1, bearing out by their low performance pertaining to cellulose and lignin accumulation at the later stages of development, respectively. From histochemistry studies, it was observed that the cell walls of sclerenchyma, peripheral vascular bundles, and parenchyma of the culm transections in control genotypes stained lightly than that of prolific accumulator cell walls, thus corroborating the findings of compositional analysis. The variation in cell wall thickening is primarily accounted due to altered carbohydrate accumulation across the growth stages as explored under scanning electron micrograph.

A Novel Method for Estimating Chlorophyll and Carotenoid Concentrations in Leaves: A Two Hyperspectral Sensor Approach

Leaf optical properties can be used to identify environmental conditions, the effect of light intensities, plant hormone levels, pigment concentrations, and cellular structures. However, the reflectance factors can affect the accuracy of predictions for chlorophyll and carotenoid concentrations. In this study, we tested the hypothesis that technology using two hyperspectral sensors for both reflectance and absorbance data would result in more accurate predictions of absorbance spectra. Our findings indicated that the green/yellow regions (500-600 nm) had a greater impact on photosynthetic pigment predictions, while the blue (440-485 nm) and red (626-700 nm) regions had a minor impact. Strong correlations were found between absorbance (R2 = 0.87 and 0.91) and reflectance (R2 = 0.80 and 0.78) for chlorophyll and carotenoids, respectively. Carotenoids showed particularly high and significant correlation coefficients using the partial least squares regression (PLSR) method (R2C = 0.91, R2cv = 0.85, and R2P = 0.90) when associated with hyperspectral absorbance data. Our hypothesis was supported, and these results demonstrate the effectiveness of using two hyperspectral sensors for optical leaf profile analysis and predicting the concentration of photosynthetic pigments using multivariate statistical methods. This method for two sensors is more efficient and shows better results compared to traditional single sensor techniques for measuring chloroplast changes and pigment phenotyping in plants.

Classification and Prediction by Pigment Content in Lettuce (Lactuca sativa L.) Varieties Using Machine Learning and ATR-FTIR Spectroscopy

Green or purple lettuce varieties produce many secondary metabolites, such as chlorophylls, carotenoids, anthocyanins, flavonoids, and phenolic compounds, which is an emergent search in the field of biomolecule research. The main objective of this study was to use multivariate and machine learning algorithms on Attenuated Total Reflectance Fourier Transform Infrared Spectroscopy (ATR-FTIR)-based spectra to classify, predict, and categorize chemometric attributes. The cluster heatmap showed the highest efficiency in grouping similar lettuce varieties based on pigment profiles. The relationship among pigments was more significant than the absolute contents. Other results allow classification based on ATR-FTIR fingerprints of inflections associated with structural and chemical components present in lettuce, obtaining high accuracy and precision (>97%) by using principal component analysis and discriminant analysis (PCA-LDA)-associated linear LDA and SVM machine learning algorithms. In addition, PLSR models were capable of predicting Chla, Chlb, Chla+b, Car, AnC, Flv, and Phe contents, with R2P and RPDP values considered very good (0.81−0.88) for Car, Anc, and Flv and excellent (0.91−0.93) for Phe. According to the RPDP metric, the models were considered excellent (>2.10) for all variables estimated. Thus, this research shows the potential of machine learning solutions for ATR-FTIR spectroscopy analysis to classify, estimate, and characterize the biomolecules associated with secondary metabolites in lettuce.

Rapid Quantification Method for Yield, Calorimetric Energy and Chlorophyll a Fluorescence Parameters in Nicotiana tabacum L. Using Vis-NIR-SWIR Hyperspectroscopy

High-throughput and large-scale data are part of a new era of plant remote sensing science. Quantification of the yield, energetic content, and chlorophyll a fluorescence (ChlF) remains laborious and is of great interest to physiologists and photobiologists. We propose a new method that is efficient and applicable for estimating photosynthetic performance and photosystem status using remote sensing hyperspectroscopy with visible, near-infrared and shortwave spectroscopy (Vis-NIR-SWIR) based on rapid multivariate partial least squares regression (PLSR) as a tool to estimate biomass production, calorimetric energy content and chlorophyll a fluorescence parameters. The results showed the presence of typical inflections associated with chemical and structural components present in plants, enabling us to obtain PLSR models with R2P and RPDP values greater than >0.82 and 3.33, respectively. The most important wavelengths were well distributed into 400 (violet), 440 (blue), 550 (green), 670 (red), 700−750 (red edge), 1330 (NIR), 1450 (SWIR), 1940 (SWIR) and 2200 (SWIR) nm operating ranges of the spectrum. Thus, we report a methodology to simultaneously determine fifteen attributes (i.e., yield (biomass), ΔH°area, ΔH°mass, Fv/Fm, Fv’/Fm’, ETR, NPQ, qP, qN, ΦPSII, P, D, SFI, PI(abs), D.F.) with high accuracy and precision and with excellent predictive capacity for most of them. These results are promising for plant physiology studies and will provide a better understanding of photosystem dynamics in tobacco plants when a large number of samples must be evaluated within a short period and with remote acquisition data.