Summary

结合拉曼成像和多变量分析可视化植物细胞壁中的木质素,纤维素和半纤维素

Published: June 10, 2017
doi:

Summary

该方案旨在提出使用拉曼成像和多变量分析在植物细胞壁中可视化木质素,纤维素和半纤维素的一般方法。

Abstract

拉曼成像对植物生物量的应用正在增加,因为它可以提供水溶液的空间和组成信息。分析通常不需要大量样品制备;结构和化学信息可以在没有标记的情况下获得。然而,每个拉曼图像包含数千个光谱;这在提取隐藏信息时尤其是具有类似化学结构的组件提出了困难。这项工作引入了一个多变量分析来解决这个问题。该方案建立了可视化植物细胞壁内主要成分(包括木质素,纤维素和半纤维素)的一般方法。在该协议中,描述了样品制备,光谱采集和数据处理的过程。它在样品制备和数据分析方面高度依赖操作员的技能。通过使用这种方法,可以由非专业用户执行拉曼调查以获得high质量数据和植物细胞壁分析的有意义的结果。

Introduction

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?…

Discussion

植物细胞壁是组织成几层的复合材料,包括细胞角(CC),次生壁(SW,具有S1,S2和S3层)和复合中间片(CML,中间片加上相邻的原始细胞壁),这使得在样品制备期间难以获得平坦的表面。因此,具有比木材更复杂结构的植物样品,特别是草,通常需要被固化以允许精细切片。 PEG是一种用于切割和拉曼研究的理想硬质基质,因为它可溶于水。用去离子水漂洗可以很容易地去除。用于嵌入的PEG具?…

Divulgazioni

The authors have nothing to disclose.

Acknowledgements

我们感谢中国科技部(2016YDF0600803)的财政支持。

Materials

Microtome Thermo Scientific Microm HM430
Confocal Raman microscope Horiba Jobin Yvon Xplora
Oven Shanghai ZHICHENG ZXFD-A5040

Riferimenti

  1. Gonzalo, G. D., et al. Bacterial Enzymes Involved in Lignin Degradation. J. Biotechnol. 236, 110-119 (2016).
  2. Rosatella, A. A., Afonso, C. A. M. Chapter 2. Ionic Liquids in the Biorefinery Concept: Challenges and Perspectives. , 38-64 (2016).
  3. Sun, L., et al. Understanding tissue specific compositions of bioenergy feedstocks Through hyperspectral Raman imaging. Bio. 108 (2), 286-295 (2009).
  4. Tolstik, T., et al. Classification and prediction of HCC tissues by Raman imaging with identification of fatty acids as potential lipid biomarkers. J. Cancer. Res. Clin. Oncol. 141 (3), 407-418 (2015).
  5. Schrader, B. . Infrared and Raman spectroscopy: methods and applications. , (2008).
  6. Gierlinger, N., et al. Imaging of plant cell walls by confocal Raman microscopy. Nat. Protoc. 7 (9), 1694-1708 (2012).
  7. Luca, A. C. D., et al. Online fluorescence suppression in modulated Raman spectroscopy. Anal. Chem. 82 (2), 738-745 (2009).
  8. Schlücker, S., et al. Raman microspectroscopy: a comparison of point, line, and wide-field imaging methodologies. Anal. Chem. 75 (16), 4312-4318 (2003).
  9. Cooper, J. B. Chemometric analysis of Raman spectroscopic data for process control applications. Chemometr. Intell. Lab. Syst. 46 (2), 231-247 (1999).
  10. Cheng, H. J., Hsiau, S. S. The study of granular agglomeration mechanism. Powder Technol. 199 (3), 272-283 (2010).
  11. Zhang, X., et al. Method for removing spectral contaminants to improve analysis of Raman imaging data. Sci. Rep. 6, 39891 (2016).
  12. Shinzawa, H., et al. Multivariate data analysis for Raman spectroscopic imaging. J. Raman Spectrosc. 40 (12), 1720-1725 (2009).
  13. Lawton, W. H., Sylvestre, E. A. Self modeling curve resolution. Technometrics. 13, 617-633 (1971).
  14. Zhang, X., et al. Method for automatically identifying spectra of different wood cell wall layers in Raman imaging data set. Anal. Chem. 87 (2), 1344-1350 (2015).
  15. Kudelski, A. Analytical application of Raman spectroscopy. Talanta. 76 (1), 1-8 (2008).
check_url/it/55910?article_type=t

Play Video

Citazione di questo articolo
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).

View Video