Principal component analysis facilitated fast and noninvasive Raman spectroscopic imaging of plant cell wall pectin distribution and interaction with enzymatic hydrolysis

Abstract

The plant cell wall is a complex network of polysaccharides. A better understanding of the plant cell wall polysaccharide content, distribution, and interactions among them could be instrumental for our further understanding of plant cell wall in general. Confocal Raman microscopy (CRM) is a powerful tool that could reveal details about chemical landscape of the plant cell wall with micrometer resolution. However, the low signal-to-noise (S/N) ratio of Raman spectral signal led to low throughput of Raman imaging, which limited its application in plant cell wall study. In this study, the interaction between the cell wall polysaccharides and the pectin enzyme endo-polygalacturonase (EPG) was characterized by analyzing Raman images collected before and after EPG treatment at high scanning speed. The obtained low S/N ratio Raman spectra were processed with principal component analysis (PCA) and hierarchical clustering analysis (HCA) to recover information-rich signature changes in Raman signal dataset that reflected the changes in the polysaccharides caused specifically by the enzymatic hydrolysis. The PCA reconstruction technique significantly improved the S/N ratio of the spectra dataset while kept the Raman signal intact. Further analysis of principal components (PCs) revealed the pectin distribution and its interaction with the enzyme, which provided organizational details of pectin inside the onion plant cell wall. The technique could be used to reveal polysaccharide organization and distribution inside plant cell walls.

Cuticle architecture and mechanical properties: a functional relationship delineated through correlated multimodal imaging

“…Cosmic rays were removed from Raman spectra using W ire 4.23 software (Renishaw, UK). To improve the signal‐to‐noise ratio and the specificity of Raman signals, an extended multiplicative scatter correction method (Kerr & Hennelly, 2016) combined with principal components analysis (PCA) noise filtering (He et al ., 2020) was used. Preprocessing was executed in Q uasar software (v.1.5.0, https://quasar.codes), and spectra were further analyzed in R software.…”

Cuticle architecture and mechanical properties: a functional relationship delineated through correlated multimodal imaging

“…Cosmic rays were removed from Raman spectra using the WiRE 4.23 software (Renishaw, UK). To improve the signal to noise ratio and the specificity of Raman signals, an Extended Multiplicative Scattered Correction (EMSC) method (Kerr & Hennelly, 2016) combined with Principal Components Analysis (PCA) noise filtering PCA (He et al ., 2020) was used. Preprocessing was executed in Quasar software (version 1.5.0, https://quasar.codes) and spectra further analysed in R software.…”

Imaging tools for plant nanobiotechnology

“…Several reports have shown the use of CRM for imaging mammalian cells and visualizing the uptake of drug vectors and nanomaterials ( Kann et al, 2015 ). Currently, the application of CRM in plants focuses mainly on analysis of plant cell wall, regarding to molecular composition, structure and spatial arrangement ( He et al, 2020 ). We believe CRM could be employed as a promising tool in plant nanotechnology, especially in analyzing interaction process of nanomaterial with plant cell wall Figure 1 .…”