Name: combining raman imaging and multivariate analysis to visualize lignin, cellulose, and hemicellulose in the plant cell wall
Uploaded: 2017-06-10T14:00:00
Description: Watch this Scientific Journal Video about Combining Raman Imaging and Multivariate Analysis to Visualize Lignin, Cellulose, and Hemicellulose in the Plant Cell Wall at JoVE.com

Agradecemos ao Ministério da Ciência e Tecnologia da China (2016YDF0600803) o apoio financeiro.

1. Preparação da amostra <ol><li> Corte um pequeno bloco de tecido (cerca de 3 mm x 3 mm x 5 mm) da amostra da planta ( por exemplo, uma haste de álamo). </li><li> Mergulhe o tecido em água desionizada fervente durante 30 min. Imediatamente transferi-lo para água desionizada à temperatura ambiente (RT) durante 30 min. Repita este passo até o tecido escorrer no fundo do recipiente, indicando que o ar no tecido foi removido e que o tecido se amaciou. Nota: Para as amostras que afundam no fundo ant...

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M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Chapter+2.">Chapter 2.</a> Ionic Liquids in the Biorefinery Concept: Challenges and Perspectives. , 38-64 (2016).</li><li>Sun, L., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Understanding+tissue+specific+compositions+of+bioenergy+feedstocks+Through+hyperspectral+Raman+imaging.">Understanding tissue specific compositions of bioenergy feedstocks Through hyperspectral Raman imaging.</a> Bio. 108 (2), 286-295 (2009).</li><li>Tolstik, T., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Classification+and+prediction+of+HCC+tissues+by+Raman+imaging+with+identification+of+fatty+acids+as+potential+lipid+biomarkers.">Classification and prediction of HCC tissues by Raman imaging with identification of fatty acids as potential lipid biomarkers.</a> J. 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A parede celular da planta é um composto que é organizado em várias camadas, incluindo o canto da célula (CC), a parede secundária (SW, com as camadas S1, S2 e S3) e a lamela intermediária composta (LMC, lamela média mais a primária adjacente Parede), o que dificulta a obtenção de uma superfície plana durante a preparação da amostra. Assim, as amostras de plantas, especialmente a grama, que tem uma estrutura mais complicada do que a madeira, muitas vezes precisam ser solidificadas para permitir a separaçã...

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.

<table border="0" cellpadding="0" cellspacing="0" width="597">
	<tbody>
		<tr height="20">
			<td height="20" style="height:20px;width:199px;">Microtome</td>
			<td style="width:136px;">Thermo Scientific</td>
			<td style="width:199px;">Microm HM430</td>
			<td style="width:64px;"></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Confocal Raman microscope</td>
			<td>Horiba Jobin Yvon</td>
			<td>Xplora</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Oven</td>
			<td>Shanghai ZHICHENG</td>
			<td>ZXFD-A5040</td>
			<td></td>
		</tr>
	</tbody>
</table>

combining raman imaging and multivariate analysis to visualize lignin, cellulose, and hemicellulose in the plant cell wall

A aplicação da imagem de Raman para planta de biomassa está aumentando porque pode oferecer informações espaciais e de composição sobre soluções aquosas. A análise geralmente não requer preparação extensiva de amostras; As informações estruturais e químicas podem ser obtidas sem rotulagem. No entanto, cada imagem Raman contém milhares de espectros; Isso levanta dificuldades ao extrair informações ocultas, especialmente para componentes com estruturas químicas semelhantes. Este trabalho apresenta uma análise multivariada para resolver esta questão. O protocolo estabelece um método geral para visualizar os principais componentes, incluindo lignina, celulose e hemicelulose dentro da parede celular da planta. Neste protocolo, são descritos os procedimentos para preparação de amostras, aquisição espectral e processamento de dados. É altamente dependente da habilidade do operador na preparação da amostra e análise de dados. Ao usar essa abordagem, uma investigação Raman pode ser realizada por um usuário não especialista para adquirirDados de qualidade h e resultados significativos para análise de parede celular vegetal.

A Figura 1 apresenta uma visão geral de um sistema típico de micro-Raman para a imagem de Raman de uma parede celular de planta. Por exemplo, os espectros de Raman originais do álamo ( Populus nigra L.) têm derivações e espigas de linha de base significativas ( Figura 2a ). Depois de executar o método de pré-processamento automático para o conjunto de dados de imagem Raman (APRI), esses dois contaminantes espec...

Watch this Scientific Journal Video about Combining Raman Imaging and Multivariate Analysis to Visualize Lignin, Cellulose, and Hemicellulose in the Plant Cell Wall at JoVE.com

Combining Raman Imaging and Multivariate Analysis to Visualize Lignin, Cellulose, and Hemicellulose in the Plant Cell Wall

Este protocolo visa apresentar um método geral para visualizar lignina, celulose e hemicelulose em paredes celulares de plantas usando imagem de Raman e análise multivariada.

Combinando imagem Raman e análise multivariada para visualizar Lignina, celulose e hemicelulose no muro celular da planta

The application of Raman imaging to plant biomass is increasing because it can offer spatial and compositional information on aqueous solutions. The ...

Research

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Cells are known to be inherently heterogeneous in their responses to drugs. Therefore, it is essential that single-cell heterogeneity is accounted for in ...

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MicroRNAs (miRNAs) are a class of endogenously expressed non-coding, ~21 nt small RNAs involved in the regulation of gene expression in both plants and ...