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

Vi takker Kina Ministeriet for Videnskab og Teknologi (2016YDF0600803) for den økonomiske støtte.

1. Prøveforberedelse <ol><li> Skær en lille vævsblok (ca. 3 mm x 3 mm x 5 mm) fra planteprøven ( f.eks. En poppelstamme). </li><li> Dyp vævet i kogende deioniseret vand i 30 minutter. Overfør straks det til deioniseret vand ved stuetemperatur (RT) i 30 minutter. Gentag dette trin, indtil vævet synker til bunden af ​​beholderen, hvilket indikerer at luften i vævet er fjernet, og at vævet er blødgjort. Bemærk: For de prøver, der synker til bunden forud for dette trin, gentages denne cyklu...

<ol>
	<li>Gonzalo, G. 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=Bacterial+Enzymes+Involved+in+Lignin+Degradation.">Bacterial Enzymes Involved in Lignin Degradation.</a> J. Biotechnol. 236, 110-119 (2016).</li><li>Rosatella, A. A., Afonso, C. A. 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. Chem. 82 (2), 738-745 (2009).</li><li>Schl&#252;cker, S., 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=Raman+microspectroscopy:+a+comparison+of+point,+line,+and+wide-field+imaging+methodologies.">Raman microspectroscopy: a comparison of point, line, and wide-field imaging methodologies.</a> Anal. Chem. 75 (16), 4312-4318 (2003).</li><li>Cooper, J. B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Chemometric+analysis+of+Raman+spectroscopic+data+for+process+control+applications.">Chemometric analysis of Raman spectroscopic data for process control applications.</a> Chemometr. Intell. Lab. Syst. 46 (2), 231-247 (1999).</li><li>Cheng, H. J., Hsiau, S. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+study+of+granular+agglomeration+mechanism.">The study of granular agglomeration mechanism.</a> Powder Technol. 199 (3), 272-283 (2010).</li><li>Zhang, X., 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=Method+for+removing+spectral+contaminants+to+improve+analysis+of+Raman+imaging+data.">Method for removing spectral contaminants to improve analysis of Raman imaging data.</a> Sci. Rep. 6, 39891 (2016).</li><li>Shinzawa, H., 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=Multivariate+data+analysis+for+Raman+spectroscopic+imaging.">Multivariate data analysis for Raman spectroscopic imaging.</a> J. Raman Spectrosc. 40 (12), 1720-1725 (2009).</li><li>Lawton, W. H., Sylvestre, E. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Self+modeling+curve+resolution.">Self modeling curve resolution.</a> Technometrics. 13, 617-633 (1971).</li><li>Zhang, X., 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=Method+for+automatically+identifying+spectra+of+different+wood+cell+wall+layers+in+Raman+imaging+data+set.">Method for automatically identifying spectra of different wood cell wall layers in Raman imaging data set.</a> Anal. Chem. 87 (2), 1344-1350 (2015).</li><li>Kudelski, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Analytical+application+of+Raman+spectroscopy.">Analytical application of Raman spectroscopy.</a> Talanta. 76 (1), 1-8 (2008).</li></ol>

Planten cellevæg er en sammensætning, der er organiseret i flere lag, herunder cellehjørne (CC), sekundærvæg (SW, med S1, S2 og S3 lag), og sammensatte midter lameller (CML, midter lamell plus tilstødende primær Væg), hvilket gør det vanskeligt at opnå en flad overflade under prøvefremstilling. Således skal planteprøver, især græs, som har en mere kompliceret struktur end træ, ofte størkne for at muliggøre fint snitning. PEG er en ideel hård matrix til skæring og Raman undersøgelse, da den er opløs...

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

Anvendelsen af ​​Raman-billeddannelse til plantebiomasse er stigende, fordi den kan tilbyde rumlige og sammensatte oplysninger om vandige opløsninger. Analysen kræver normalt ikke omfattende prøvepræparation; Strukturelle og kemiske oplysninger kan opnås uden mærkning. Imidlertid indeholder hvert Raman-billede tusindvis af spektre; Dette rejser vanskeligheder ved udtrækning af skjulte oplysninger, især for komponenter med lignende kemiske strukturer. Dette arbejde introducerer en multivariabel analyse for at løse dette problem. Protokollen etablerer en generel metode til visualisering af hovedkomponenterne, herunder lignin, cellulose og hemicellulose inden i plantecellevæggen. I denne protokol beskrives procedurer til prøveudarbejdelse, spektraloptagelse og databehandling. Det er stærkt afhængig af operatør færdigheder ved prøve forberedelse og data analyse. Ved at anvende denne fremgangsmåde kan en Raman-undersøgelse udføres af en ikke-specialist bruger til at erhverve higH-kvalitetsdata og meningsfulde resultater til analyse af plantecellevægge.

Figur 1 viser et overblik over et typisk mikro-Raman-system til Raman-billeddannelsen af ​​en plantecellevæg. Som et eksempel har de oprindelige Ramanspektre af poppel ( Populus nigra L.) betydelige baseline drift og pigge ( figur 2a ). Efter at have udført den automatiske forbehandlingsteknik for Raman-billeddatasæt (APRI) fjernes disse to spektrale forureninger med succes ( figur 2b</strong...

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

Denne protokol har til formål at fremlægge en generel metode til visualisering af lignin, cellulose og hemicellulose i plantecellevægge ved anvendelse af Raman-billeddannelse og multivariatanalyse.

Kombination af Raman Imaging og Multivariate Analysis til Visualisering af Lignin, Cellulose og Hemicellulose i Plant Cell Wall

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

Research

JoVE Journal

Biochemistry

Covalent Binding of Antibodies to Cellulose Paper Discs and Their Applications in Naked-eye Colorimetric Immunoassays

Kovalent binding af antistoffer til cellulosepapir diske og deres anvendelser i Naked-eye Farvemåletekniske Immunoassays

This report presents two methods for the covalent immobilization of capture antibodies on cellulose filter paper grade No. 1 (medium-flow filter paper) ...

Structural Characterization of Mannan Cell Wall Polysaccharides in Plants Using PACE

Strukturel karakterisering af Mannan cellevæg polysaccharider i anlaeg, der anvender PACE

Plant cell wall polysaccharides are notoriously difficult to analyze, and most methods require expensive equipment, skilled operators, and large amounts ...

Isolation and Respiratory Measurements of Mitochondria from Arabidopsis thaliana

Isolation and Respiratory Measurements of Mitochondria from Arabidopsis thaliana

Isolation og respiratoriske målinger af mitokondrier fra Arabidopsis thaliana

Mitochondria are essential organelles involved in numerous metabolic pathways in plants, most notably the production of adenosine triphosphate (ATP) from ...

2 in 1: One-step Affinity Purification for the Parallel Analysis of Protein-Protein and Protein-Metabolite Complexes

2 i 1: et-trins affinitet rensning for den parallelle analyse af Protein-Protein og Protein-metabolit komplekser

Cellular processes are regulated by interactions between biological molecules such as proteins, metabolites, and nucleic acids. While the investigation of ...

Using In Vitro Fluorescence Resonance Energy Transfer to Study the Dynamics Of Protein Complexes at a Millisecond Time Scale

Bruger i Vitro fluorescens resonans energioverførsel at studere dynamikken i proteinkomplekser på en millisekund tidsskala

Proteins are the primary operators of biological systems, and they usually interact with other macro- or small molecules to carry out their biological ...

In Vivo Calcium Imaging in C. elegans Body Wall Muscles

In Vivo Calcium Imaging in C. elegans Body Wall Muscles

In vivo calcium Imaging i C. elegans krop væg muskler

The model organism C. elegans provides an excellent system to perform in vivo calcium imaging. Its transparent body and genetic manipulability allow for ...

Study on the Metabolism of Six Systemic Insecticides in a Newly Established Cell Suspension Culture Derived from Tea (Camellia Sinensis L.) Leaves

Study on the Metabolism of Six Systemic Insecticides in a Newly Established Cell Suspension Culture Derived from Tea Camellia Sinensis L. Leaves

Undersøgelse af metabolismen af seks systemiske insekticider i en nyetableret celle affjedrings kultur afledt af te (Camellia sinensis L.) Blade

A platform for studying insecticide metabolism using in vitro tissues of tea plant was developed. Leaves from sterile tea plantlets were induced to form ...

Measuring Interactions between Fluorescent Probes and Lignin in Plant Sections by sFLIM Based on Native Autofluorescence

Måling af interaktioner mellem fluorescerende sonder og lignin i plante sektioner af sFLIM baseret på naturlig Auto Luorescens

In lignocellulosic biomass (LB), the activity of enzymes is limited by the appearance of non-specific interactions with lignin during the hydrolysis ...

An Integrated Raman Spectroscopy and Mass Spectrometry Platform to Study Single-Cell Drug Uptake, Metabolism, and Effects

En integreret Raman spektroskopi og massespektrometri platform til at studere enkelt-celle stof optagelse, metabolisme, og effekter

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

RNA Blot Analysis for the Detection and Quantification of Plant MicroRNAs

RNA Blot analyse til påvisning og kvantificering af plantemikrorna'er

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 ...