ラマンイメージングと多変量解析を組み合わせて、植物細胞壁のリグニン、セルロース、およびヘミセルロースを視覚化する
Summary
このプロトコルは、ラマンイメージングおよび多変量解析を用いて、植物細胞壁のリグニン、セルロースおよびヘミセルロースを視覚化するための一般的な方法を提示することを目的とする。
Abstract
プラントバイオマスへのラマンイメージングの適用は、水溶液について空間的および組成的情報を提供することができるので、増加している。分析には通常、大量の試料調製は必要ありません。ラベルを付けることなく構造的および化学的情報を得ることができる。しかしながら、各ラマン像は数千のスペクトルを含む。これは、特に類似の化学構造を有する構成要素について、隠された情報を抽出する際に困難を生じさせる。この研究では、この問題に対処するために多変量解析を導入しています。このプロトコルは、植物細胞壁内のリグニン、セルロースおよびヘミセルロースを含む主成分を視覚化するための一般的な方法を確立する。このプロトコールでは、試料調製、スペクトル取得、およびデータ処理のための手順が記載されている。これは、試料調製およびデータ分析における操作者の技能に大きく依存する。このアプローチを使用することにより、非専門家によるラマン調査を行い、HIを得ることができる植物細胞壁分析のための高品質データおよび有意な結果を提供する。
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
Representative Results
Discussion
植物細胞壁は、細胞コーナー（CC）、二次壁（S1、S2、およびS3層を有するSW）および化合物中間薄層（CML、中間薄層および隣接する一次層）を含むいくつかの層に組織化された複合体である壁面）であり、試料調製中に平坦な表面を得ることが困難になる。したがって、木材より複雑な構造を有する植物サンプル、特に草は、しばしば細かい切片化を可能にするために凝固する必要がある。 PE…
Disclosures
The authors have nothing to disclose.
Acknowledgements
私たちは財政的支援のために中国科学技術省（2016YDF0600803）に感謝します。
Materials
| Microtome | Thermo Scientific | Microm HM430 | |
| Confocal Raman microscope | Horiba Jobin Yvon | Xplora | |
| Oven | Shanghai ZHICHENG | ZXFD-A5040 |
References
- Gonzalo, G. D., et al. Bacterial Enzymes Involved in Lignin Degradation. J. Biotechnol. 236, 110-119 (2016).
- Rosatella, A. A., Afonso, C. A. M. Chapter 2. Ionic Liquids in the Biorefinery Concept: Challenges and Perspectives. , 38-64 (2016).
- Sun, L., et al. Understanding tissue specific compositions of bioenergy feedstocks Through hyperspectral Raman imaging. Bio. 108 (2), 286-295 (2009).
- 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).
- Schrader, B. . Infrared and Raman spectroscopy: methods and applications. , (2008).
- Gierlinger, N., et al. Imaging of plant cell walls by confocal Raman microscopy. Nat. Protoc. 7 (9), 1694-1708 (2012).
- Luca, A. C. D., et al. Online fluorescence suppression in modulated Raman spectroscopy. Anal. Chem. 82 (2), 738-745 (2009).
- Schlücker, S., et al. Raman microspectroscopy: a comparison of point, line, and wide-field imaging methodologies. Anal. Chem. 75 (16), 4312-4318 (2003).
- Cooper, J. B. Chemometric analysis of Raman spectroscopic data for process control applications. Chemometr. Intell. Lab. Syst. 46 (2), 231-247 (1999).
- Cheng, H. J., Hsiau, S. S. The study of granular agglomeration mechanism. Powder Technol. 199 (3), 272-283 (2010).
- Zhang, X., et al. Method for removing spectral contaminants to improve analysis of Raman imaging data. Sci. Rep. 6, 39891 (2016).
- Shinzawa, H., et al. Multivariate data analysis for Raman spectroscopic imaging. J. Raman Spectrosc. 40 (12), 1720-1725 (2009).
- Lawton, W. H., Sylvestre, E. A. Self modeling curve resolution. Technometrics. 13, 617-633 (1971).
- 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).
- Kudelski, A. Analytical application of Raman spectroscopy. Talanta. 76 (1), 1-8 (2008).
