ArticleFrontiers in plant science2022
A Spatial-Temporal Analysis of Cellular Biopolymers on Leaf Blight-Infected Tea Plants Using Confocal Raman Microspectroscopy.
Article in Frontiers in plant science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed, 21 citations in OpenAlex.
- Uncovering the key miRNA-target network of tea plants in resistance to sooty mold disease.BMC plant biology · 2025Article
- Early detection of soybean mosaic virus using portable Raman spectroscopy coupled with machine learning.Frontiers in plant science · 2025Article
- Integrative hyperspectral, transcriptomic, and metabolomic analysis reveals the mechanism of tea plants in response to sooty mold disease.BMC plant biology · 2024Article
- Harnessing Raman spectroscopy for the analysis of plant diversity.Scientific reports · 2024Article
- Rapid diagnosis of membranous nephropathy based on serum and urine Raman spectroscopy combined with deep learning methods.Scientific reports · 2023Article
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6 authors at 3 institutions in 1 country.
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Abstract
The objective of the present study was to characterize the temporal and spatial variation of biopolymers in cells infected by the tea leaf blight using confocal Raman microspectroscopy. We investigated the biopolymers on serial sections of the infection part, and four sections corresponding to different stages of infection were obtained for analysis. Raman spectra extracted from four selected regions (circumscribing the vascular bundle) were analyzed in detail to enable a semi-quantitative comparison of biopolymers on a micron-scale. As the infection progressed, lignin and other phenolic compounds decreased in the vascular bundle, while they increased in both the walls of the bundle sheath cells as well as their intracellular components. The amount of cellulose and other polysaccharides increased in all parts as the infection developed. The variations in the content of lignin and cellulose in different tissues of an individual plant may be part of the reason for the plant's disease resistance. Through wavelet-based data mining, two-dimensional chemical images of lignin, cellulose and all biopolymers were quantified by integrating the characteristic spectral bands ranging from 1,589 to 1,607 cm
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