Multivariate Raman mapping for phenotypic characterization in plant tissue sections

Spectrochim Acta A Mol Biomol Spectrosc. 2021 Apr 15:251:119418. doi: 10.1016/j.saa.2020.119418. Epub 2021 Jan 2.

Abstract

Identifying and characterizing the biochemical variation in plant tissues is an important task in many research fields. Small spectral differences of the plant cell wall that are caused by genetic or environmental influences may be superimposed by individual variation as well as by a microscopic heterogeneity in molecular composition and structure of different histological substructures. A set of 56 samples from Cucumis sativus (cucumber) plants, comprising a total of ~168,000 spectra from tissue sections of leaf, stem, and roots was investigated by Raman microspectroscopic mapping excited at 532 nm. A multivariate analysis was carried out in order to assess the variation of the spectra with respect to origin of the tissue, the histological (cell wall) substructures, and the possibility to discriminate the spectra obtained from different individuals that had been subjected to two different conditions during growth. Combining the results of principal component analysis (PCA) based classification with the original spatial information in the maps of 23 sections of leaf xylem, variation in cell wall composition is found for four different individuals that also includes a discrimination of tissue grown in the presence and absence of additional silicic acid in the irrigation water of the plants. The spectral data point to differences in a contribution by carotenoids, as well as by hydroxycinnamic acids to the spectra. The results give new insight into the chemical heterogeneity of plant tissues and may be useful for elucidating biochemical processes associated with biomineralization by vibrational spectroscopy.

Keywords: Cucumis sativus (cucumber); Raman microspectroscopy; hierarchical cluster analysis (HCA); plant cell wall; principal component analysis (PCA); silica; tissue sections; xylem.

MeSH terms

  • Allergens
  • Cucumis sativus*
  • Humans
  • Multivariate Analysis
  • Principal Component Analysis
  • Spectrum Analysis, Raman*

Substances

  • Allergens