Plant biomass is the most abundant renewable resource on Earth; is mainly composed of lignin, cellulose, and hemicellulose; and is considered an attractive source of bioenergy and bio-based chemicals1. Unfortunately, it can resist degradation and confer hydrolytic stability or structural robustness to the plant cell wall. Such resistance is attributable to the accessible surface area, biomass particle size, degree of polymerization, cellulose crystallinity, and protective lignin2. A comprehensive understanding of the structural and chemical nature of the plant cell wall is thus significant from the viewpoint of plant biology and chemistry, as well as from that of commercial utilization. Commonly used wet chemistry analyses, such as chromatography, mass spectrometry, and nuclear magnetic resonance spectroscopy, only provide average compositional data of the measured sample. Furthermore, these methods are invasive and destroy the original structure of the plant tissue3.
The Raman imaging technique is a powerful tool for the nondestructive visualization of spatially resolved chemical information4. It uses a laser light to cause inelastic scattering with a photon and relies on changes in polarizability arising from the molecular vibrations. In this case, water causes weak Raman scattering, which makes this approach suitable for in situ investigations of biological samples5. The application of the Raman imaging technique to the plant cell wall can elucidate the structure and composition of plant cell walls in their native state, with the resolution on the scale of the single cell and even of the cell wall layers6. A typical Raman imaging analysis of a plant cell wall generally consists of three steps: 1) sample preparation, 2) spectral acquisition, and 3) data processing.
Although one of the major advantages of Raman imaging is the ability to achieve label-free and non-destructive spectra with minimal sample preparation, physical sample sectioning is still necessary to expose the surface of interest. This process should be performed carefully to obtain a flat surface, since the technique depends on maintaining optical focus7. Spectral acquisition requires a balance between image quality and extensive acquisition times8. Data processing aims to effectively extract the chemical information from the image data, especially for the components with similar chemical structures, such as cellulose and hemicellulose. Due to the strong spectral overlap, the exact spectra are difficult to discern. In this case, multivariate analysis is a straightforward approach to effectively uncover the hiding structural and chemical information9. This work presents a general protocol describing the use of Raman imaging to visualize the main components in plant cell walls, including lignin, cellulose, and hemicellulose.
Protocol
样品制备从植物样品( 例如,杨树茎)切下一个小的组织块(约3 mm x 3 mm x 5 mm)。 将组织浸入沸腾的去离子水中30分钟。立即将其在室温(RT)下转移到去离子水中30分钟。重复此步骤,直到组织沉入容器的底部,表明组织中的空气已经被去除并且组织已经软化。 注意:对于在此步骤之前沉入底部的样品,通常重复此循环3-5次。 在去离子水中制备20,50,70,90%(…
Representative Results
图1给出了用于植物细胞壁的拉曼成像的典型的微拉曼系统的概述。例如,杨树( Populus nigra L.)的原始拉曼光谱具有显着的基线漂移和峰值( 图2a )。在执行拉曼成像数据集(APRI)的自动预处理方法之后,这两个光谱污染物被成功地去除( 图2b )。 图3中显示了杨树的典型?…
Zhang, X., Chen, S., Xu, F. Combining Raman Imaging and Multivariate Analysis to Visualize Lignin, Cellulose, and Hemicellulose in the Plant Cell Wall. J. Vis. Exp. (124), e55910, doi:10.3791/55910 (2017).