A Guide to Elucidate the Hidden Multicomponent Layered Structure of Plant Cuticles by Raman Imaging

The cuticle covers almost all plant organs as the outermost layer and serves as a transpiration barrier, sunscreen, and first line of defense against pathogens. Waxes, fatty acids, and aromatic components build chemically and structurally diverse layers with different functionality. So far, electron microscopy has elucidated structure, while isolation, extraction, and analysis procedures have revealed chemistry. With this method paper, we close the missing link by demonstrating how Raman microscopy gives detailed information about chemistry and structure of the native cuticle on the microscale. We introduce an optimized experimental workflow, covering the whole process of sample preparation, Raman imaging experiment, data analysis, and interpretation and show the versatility of the approach on cuticles of a spruce needle, a tomato peel, and an Arabidopsis stem. We include laser polarization experiments to deduce the orientation of molecules and multivariate data analysis to separate cuticle layers and verify their molecular composition. Based on the three investigated cuticles, we discuss the chemical and structural diversity and validate our findings by comparing models based on our spectroscopic data with the current view of the cuticle. We amend the model by adding the distribution of cinnamic acids and flavonoids within the cuticle layers and their transition to the epidermal layer. Raman imaging proves as a non-destructive and fast approach to assess the chemical and structural variability in space and time. It might become a valuable tool to tackle knowledge gaps in plant cuticle research.

Automated summary

This article introduces how to study the chemistry and structure of plant cuticles using Raman microscopy, and provides an optimized experimental workflow. By investigating different plant cuticles, the study reveals the diversity in chemical and structural composition and validates this finding through data analysis.