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

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1. Préparation de l&#39;échantillon <ol><li> Couper un petit bloc de tissu (environ 3 mm x 3 mm x 5 mm) de l&#39;échantillon de la plante ( p. Ex. Une tige de peuplier). </li><li> Immerger le tissu dans de l&#39;eau désionisée bouillante pendant 30 min. Immédiatement, transférez-le à de l&#39;eau désionisée à température ambiante (RT) pendant 30 minutes. Répétez cette étape jusqu&#39;à ce que le tissu s&#39;enfonce au fond du conteneur, ce qui indique que l&#39;air dans le tissu a été enlev?...

<ol>
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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. Cancer. Res. Clin. Oncol. 141 (3), 407-418 (2015).</li><li>Schrader, B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Infrared and Raman spectroscopy: methods and applications. , (2008).</li><li>Gierlinger, N., 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=Imaging+of+plant+cell+walls+by+confocal+Raman+microscopy.">Imaging of plant cell walls by confocal Raman microscopy.</a> Nat. Protoc. 7 (9), 1694-1708 (2012).</li><li>Luca, A. C. D., 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=Online+fluorescence+suppression+in+modulated+Raman+spectroscopy.">Online fluorescence suppression in modulated Raman spectroscopy.</a> Anal. 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La paroi de la cellule végétale est composée en plusieurs couches, y compris le coin cellulaire (CC), la paroi secondaire (SW, avec les couches S1, S2 et S3) et les lamelles intermédiaires composées (LML, la lamelle intermédiaire plus la primaire adjacente Mur), ce qui rend difficile l&#39;obtention d&#39;une surface plane lors de la préparation de l&#39;échantillon. Ainsi, les échantillons de plantes, en particulier l&#39;herbe, qui ont une structure plus compliquée que le bois, doivent souvent être solidifi...

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

L&#39;application de l&#39;imagerie Raman à la biomasse végétale augmente car elle peut offrir des informations spatiales et de composition sur des solutions aqueuses. L&#39;analyse ne nécessite généralement pas une préparation approfondie des échantillons; Des informations structurelles et chimiques peuvent être obtenues sans étiquetage. Cependant, chaque image Raman contient des milliers de spectres; Cela soulève des difficultés lors de l&#39;extraction d&#39;informations cachées, en particulier pour les composants ayant des structures chimiques similaires. Ce travail présente une analyse multivariée pour résoudre ce problème. Le protocole établit une méthode générale pour visualiser les principaux composants, y compris la lignine, la cellulose et l&#39;hémicellulose dans la paroi cellulaire de la plante. Dans ce protocole, des procédures pour la préparation des échantillons, l&#39;acquisition spectrale et le traitement des données sont décrites. Il dépend fortement des compétences de l&#39;opérateur lors de la préparation des échantillons et de l&#39;analyse des données. En utilisant cette approche, une enquête Raman peut être effectuée par un utilisateur non spécialisé pour acquérirDonnées de qualité h et résultats significatifs pour l&#39;analyse de la paroi cellulaire végétale.

La figure 1 présente un aperçu d&#39;un système typique de micro-Raman pour l&#39;imagerie Raman d&#39;une paroi cellulaire végétale. À titre d&#39;exemple, les spectres Raman originaux du peuplier ( Populus nigra L.) ont des dérives et des pointes de base significatives ( Figure 2a ). Après avoir effectué la méthode de prétraitement automatique pour le jeu de données d&#39;imagerie Raman (APRI), ces deux co...

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

Ce protocole vise à présenter une méthode générale pour visualiser la lignine, la cellulose et l&#39;hémicellulose dans les parois des cellules végétales en utilisant l&#39;imagerie Raman et l&#39;analyse multivariée.

Combinaison d&#39;imagerie Raman et analyse multivariée pour visualiser la lignine, la cellulose et l&#39;hémicellulose dans le mur de cellules végétales

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

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