Multivariate Raman mapping for phenotypic characterization in plant tissue sections

Deposition of silica in sorghum root endodermis modifies the chemistry of associated lignin

Silica aggregates at the endodermis of sorghum roots. Aggregation follows a spotted pattern of locally deposited lignin at the inner tangential cell walls. Autofluorescence microscopy suggests that non-silicified (-Si) lignin spots are composed of two distinct concentric regions of varied composition. To highlight variations in lignin chemistry, we used Raman microspectroscopy to map the endodermal cell wall and silica aggregation sites in sorghum roots grown hydroponically with or without Si amendment. In +Si samples, the aggregate center was characterized by typical lignin monomer bands surrounded by lignin with a low level of polymerization. Farther from the spot, polysaccharide concentration increased and soluble silicic acid was detected in addition to silica bands. In -Si samples, the main band at the spot center was assigned to lignin radicals and highly polymerized lignin. Both +Si and -Si loci were enriched by aromatic carbonyls. We propose that at silica aggregation sites, carbonyl rich lignin monomers are locally exported to the apoplast. These monomers are radicalized and polymerized into short lignin polymers. In the presence of silicic acid, bonds typically involved in lignin extension, bind to silanols and nucleate silica aggregates near the monomer extrusion loci. This process inhibits further polymerization of lignin. In -Si samples, the monomers diffuse farther in the wall and crosslink with cell wall polymers, forming a ring of dense lignified cell wall around their export sites.

Raman developmental markers in root cell walls are associated with lodging tendency in tef

MAIN CONCLUSION

Using Raman micro-spectroscopy on tef roots, we could monitor cell wall maturation in lines with varied genetic lodging tendency. We describe the developing cell wall composition in root endodermis and cylinder tissue. Tef [Eragrostis tef (Zucc.) Trotter] is an important staple crop in Ethiopia and Eritrea, producing gluten-free and protein-rich grains. However, this crop is not adapted to modern farming practices due to high lodging susceptibility, which prevents the application of mechanical harvest. Lodging describes the displacement of roots (root lodging) or fracture of culms (stem lodging), forcing plants to bend or fall from their vertical position, causing significant yield losses. In this study, we aimed to understand the microstructural properties of crown roots, underlining tef tolerance/susceptibility to lodging. We analyzed plants at 5 and 10 weeks after emergence and compared trellised to lodged plants. Root cross sections from different tef genotypes were characterized by scanning electron microscopy, micro-computed tomography, and Raman micro-spectroscopy. Lodging susceptible genotypes exhibited early tissue maturation, including developed aerenchyma, intensive lignification, and lignin with high levels of crosslinks. A comparison between trellised and lodged plants suggested that lodging itself does not affect the histology of root tissue. Furthermore, cell wall composition along plant maturation was typical to each of the tested genotypes independently of trellising. Our results suggest that it is possible to select lines that exhibit slow maturation of crown roots. Such lines are predicted to show reduction in lodging and facilitate mechanical harvest.

Physicochemical understanding of biomineralization by molecular vibrational spectroscopy: From mechanism to nature

The process and mechanism of biomineralization and relevant physicochemical properties of mineral crystals are remarkably sophisticated multidisciplinary fields that include biology, chemistry, physics, and materials science. The components of the organic matter, structural construction of minerals, and related mechanical interaction, etc., could help to reveal the unique nature of the special mineralization process. Herein, the paper provides an overview of the biomineralization process from the perspective of molecular vibrational spectroscopy, including the physicochemical properties of biomineralized tissues, from physiological to applied mineralization. These physicochemical characteristics closely to the hierarchical mineralization process include biological crystal defects, chemical bonding, atomic doping, structural changes, and content changes in organic matter, along with the interface between biocrystals and organic matter as well as the specific mechanical effects for hardness and toughness. Based on those observations, the special physiological properties of mineralization for enamel and bone, as well as the possible mechanism of pathological mineralization and calcification such as atherosclerosis, tumor micro mineralization, and urolithiasis are also reviewed and discussed. Indeed, the clearly defined physicochemical properties of mineral crystals could pave the way for studies on the mechanisms and applications.

Optical imaging spectroscopy coupled with machine learning for detecting heavy metal of plants: A review

Heavy metal elements, which inhibit plant development by destroying cell structure and wilting leaves, are easily absorbed by plants and eventually threaten human health via the food chain. Recently, with the increasing precision and refinement of optical instruments, optical imaging spectroscopy has gradually been applied to the detection and reaction of heavy metals in plants due to its in-situ, real-time, and simple operation compared with traditional chemical analysis methods. Moreover, the emergence of machine learning helps improve detection accuracy, making optical imaging spectroscopy comparable to conventional chemical analysis methods in some situations. This review (a): summarizes the progress of advanced optical imaging spectroscopy techniques coupled with artificial neural network algorithms for plant heavy metal detection over ten years from 2012-2022; (b) briefly describes and compares the principles and characteristics of spectroscopy and traditional chemical techniques applied to plants heavy metal detection, and the advantages of artificial neural network techniques including machine learning and deep learning techniques in combination with spectroscopy; (c) proposes the solutions such as coupling with other analytical and detection methods, portability, to address the challenges of unsatisfactory sensitivity of optical imaging spectroscopy and expensive instruments.