We thank the China Ministry of Science and Technology (2016YDF0600803) for the financial support.

1. Sample Preparation
<ol>
	<li>Cut a small tissue block (about 3 mm x 3 mm x 5 mm) from the plant sample (e.g., a poplar stem).</li>
	<li>Immerse the tissue in boiling deionized water for 30 min. Immediately transfer it to deionized water at room temperature (RT) for 30 min. Repeat this step until the tissue sinks to the bottom of the container, indicating that the air in tissue has been removed and that the tissue has softened. 
	Note: For the samples that sink to the bottom prior to this step, generally repeat this cycle 3-5 times.</li>
	<li>Prepare 20, 50, 70, 90% (v/v) aliquots of polyethylene glycol (PEG) in deionized water, as well as pure PEG, and keep the solutions at 65 &#176;C.</li>
	<li>Incubate the tissue in a series of graded PEG baths to displace the water and allow the PEG to infiltrate. 
	Note: Typically, PEG with a molecular weight 2,000 (PEG2000) is used for embedding.
	<ol>
		<li>Process the tissue with graded PEG in a drying oven as follows: (a) 20% PEG for 1 h, (b) 50% PEG for 1.5 h, (c) 70% PEG for 2 h, (d) 90% PEG for 2 h, and (e) 100% PEG for 10 h.</li>
	</ol>
	</li>
	<li>Pre-warm a cassette at 65 &#176;C in an oven. Pour the PEG containing the block into the cassette and then place the tissue block in a desired position using pre-warmed tweezers or needles.</li>
	<li>Slowly cool down the cassette and store the tissue at RT until use.</li>
	<li>Dissect the PEG block containing the target tissue into a small block (about 1 cm x 1 cm x 2 cm) using a sharp razor blade and mount it on the microtome.</li>
	<li>Cut thin sections from the PEG block (typically 3-10 &#181;m).</li>
	<li>Rinse the section with deionized water in a watch glass 10 times to remove the PEG from the tissue.</li>
	<li>Immerse the sections with toluene/ethanol (2:1, v/v) for 6 h to remove the extractives. Prepare the reaction liquid by mixing 65 mL of deionized water, 0.5 mL of acetic acid, and 0.6 g of sodium chlorite in a beaker. Add one section and 3 mL of reaction liquid to a 5 mL vial. Screw on the top of the vial.</li>
	<li>Heat the vial in water bath at 75 &#176;C for 2 h to remove the lignin of the tissue. Rinse the section with deionized water in a watch glass 10 times.</li>
	<li>Transfer the untreated/delignified section to a glass microscope slide. Unfold the section carefully using brushes or needles. Remove the extra deionized water with tissue paper.</li>
	<li>Immerse the section into D2O. Cover the sample with a glass cover slip. Seal the cover slip with nail polish to prevent the evaporation of the D2O.</li>
</ol>
2. Spectral Acquisition
<ol>
	<li>Open the instrument operating software of the confocal Raman microscope. Turn on the laser (wavelength = 532 nm), focus on the surface of the crystalline silicon with a 100X microscope objective, and click the calibration button to calibrate the instrument.</li>
	<li>Switch the instrument to the optical microscope mode and turn on the microscope lamp. Mount the microscope slide on the stage, with the cover slip facing the objective.</li>
	<li>View the sample with a 20X microscope objective and locate the area of interest. Apply immersion oil to the coverslip and switch to the immersion microscope objective (60X, numerical aperture NA = 1.35). Focus on the surface of the sample.</li>
	<li>Switch the instrument to the Raman testing mode and turn off the microscope lamp.</li>
	<li>Choose a mapping area by using a rectangular tool. Alter the step size to determine the number of obtained spectra. 
	Note: Be aware of the step size (usually larger than the spot diameter calculated by the numerical aperture of the objective; theoretically 1.22&#955;/NA). Sizes below this will result in oversampling.</li>
	<li>Set the optimum spectral parameters to obtain the best signal-to-noise ratio (SNR) and spectral quality in an appropriate acquisition time (generally &#60; 8 h), depending on the sample suitability. Generally, input the imaging parameters in the instrument software as follows: laser (532 nm), filter (100%), hole (300), slit (100), spectrometer (1,840 cm-1), grooves (1200t), objective (60X oil), and acquisition time (2 s).</li>
	<li>Save the spectral data before data processing and convert them to a universal format (e.g., TXT files).</li>
</ol>
3. Data Analysis
<ol>
	<li>Load the spectral data (TXT files) into the data analysis software (e.g., Matlab). Apply a noise-reduction technique on the data set to improve the SNR (e.g., the Savitzsky-Golay algorithm or the wavelet algorithm)</li>
	<li>Use untreated samples to produce the images of lignin and polysaccharides. For lignin imaging, consider the spectral peak around 1,600 cm-1 due to the aromatic ring symmetric stretching vibration. For polysaccharide imaging (including cellulose and hemicellulose), use the spectral peak around 2,889 cm-1 because of the CH and CH2 stretches.</li>
	<li>Use delignified samples to generate the images of cellulose and hemicellulose. Perform self-modeling curve resolution (SMCR) on the acquired spectra to discriminate the spectra of cellulose and hemicellulose and to image their distributions.</li>
	<li>Perform principal component analysis (PCA) and clustering analysis on the acquired data to distinguish the Raman spectra from different cell wall layers.</li>
</ol>

<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>

The authors have nothing to disclose.

The plant cell wall is a composite that is organized into several layers, including cell corner (CC), secondary wall (SW, with the S1, S2, and S3 layers), and compound middle lamella (CML, middle lamella plus the adjacent primary wall), which makes it difficult to obtain a flat surface during sample preparation. Thus, plant samples, especially grass, which has a more complicated structure than wood, often need to be solidified to allow for fine sectioning. PEG is an ideal hard matrix for cutting and Raman investigation, since it is soluble in water. It can be easily removed by rinsing with deionized water. PEG used for embedding has different molecular weights, varying from 1,000-20,000. The higher the molecular weight of the PEG is, the lower the penetration ability10. D2O will help to reduce the fluorescence of lignin and has a marked peak at 2,490 cm-1, but it will not eliminate the fluorescence interference. Two major noise signals spill over into the channels, along with the actual signals: 1) sample and background fluorescence, as well as thermal fluctuations of CCD, can result in baseline drifts and 2) cosmic rays can markedly affect the sensitive detectors, which manifest in spectra as narrow-bandwidth spikes. The APRI method was developed to address these issues11. APRI includes the adaptive iteratively reweighted penalized least-squares (airPLS) and the principal component analysis (PCA) to eliminate the baseline drifts and cosmic spikes by using the spectral features themselves.
To achieve distribution images, peak intensity/integration and whole-spectra linear fitting by multivariate methods should be selected. The former is suitable to the components with specific Raman peaks (e.g., lignin), while the latter is appropriate to the components with strong spectral overlap (e.g., cellulose and hemicellulose). The distribution images of lignin and polysaccharides are available by integrating the specific peaks around 1,600 cm-1 and 2,889 cm-1, respectively (Figure 4). However, the images of cellulose and hemicellulose are difficult to directly generate by peak integration due to the strong spectral overlap. Multivariate analysis allows the convoluted information content to be sorted according to the hypothesis that the original data is reconstructed from a limited number of significant factors12. It thus can be applied to discriminate their spectra and to produce corresponding Raman images. For plant samples, the presence of extractives and lignin interferes with the ability to reconstruct the spectra of cellulose and hemicellulose. It is necessary to remove these interferences prior to the SMCR analysis of the imaging data. SMCR was first introduced by Lawton and Sylvester as a multivariate technique specifically developed for resolving a pure component from a set of spectra, without any recourse to a spectral library, to analyze the imaging data13. Spectral classification is important for further understanding the structural and chemical nature of the plant cell wall. Here, the imaging data have been subjected to principal component analysis (PCA) and clustering analysis to distinguish Raman spectra from different cell wall layers14.
However, this technique has two limitations. First, the Raman effect is weak&#8212;the typical total Raman scattering cross-section is ~10-29 cm2 per molecule&#8212;which makes it vulnerable to intense fluorescence15. A possible way to improve the defect is by preparing the sections as thinly as possible and applying a baseline correction algorithm, but fluorescent signals are difficult to remove. Second, chemical treatment is a significant procedure when achieving Raman images of cellulose and hemicellulose, and it may increase the risk of changing the original chemical content. Therefore, it is best to remove the extractives and lignin as much as possible, without the cell wall structure looking too different from the untreated one.
In conclusion, this protocol is suitable for studying the distribution of lignin, cellulose, and hemicellulose within plant cell wall. Using Raman imaging to obtain the desired information is highly dependent upon the operator skill at sample preparation and data analysis. Good sample preparation is essential to collect high-quality Raman spectra. Appropriate data analysis provides insights into the large-scale spectra and extracts the hidden information from the image. As a fundamental method, this protocol can be used to trace the dynamic changes of major components during chemical, physical, or biological treatment at the micro-level.

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><tbody><tr><td>Microtome</td><td>Thermo Scientific</td><td>Microm HM430</td><td></td></tr><tr><td>Confocal Raman microscope</td><td>Horiba Jobin Yvon</td><td>Xplora</td><td></td></tr><tr><td>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

The application of Raman imaging to plant biomass is increasing because it can offer spatial and compositional information on aqueous solutions. The analysis does not usually require extensive sample preparation; structural and chemical information can be obtained without labeling. However, each Raman image contains thousands of spectra; this raises difficulties when extracting hidden information, especially for components with similar chemical structures. This work introduces a multivariate analysis to address this issue. The protocol establishes a general method to visualize the main components, including lignin, cellulose, and hemicellulose within the plant cell wall. In this protocol, procedures for sample preparation, spectral acquisition, and data processing are described. It is highly dependent upon operator skill at sample preparation and data analysis. By using this approach, a Raman investigation can be performed by a non-specialist user to acquire high-quality data and meaningful results for plant cell wall analysis.

Figure 1 presents an overview of a typical micro-Raman system for the Raman imaging of a plant cell wall. As an example, the original Raman spectra of poplar (Populus nigra L.) have significant baseline drifts and spikes (Figure 2a). After performing the automatic pre-processing method for Raman imaging data set (APRI), these two spectral contaminants are successfully removed (Figure 2b). A typical Raman spectrum of poplar is displayed in Figure 3, and its band assignments are listed in Table 1. The Raman image of lignin is generated by the integration of the spectral region from 1,550-1,650 cm-1, which is attributed to the aromatic ring structure (Figure 4a). Figure 4d displays the Raman image of polysaccharides, which is achieved by integrating the peak at 2,889 cm-1. To image cellulose and hemicellulose, SMCR is performed on the Raman imaging data of a delignified sample. The corresponding spectra and images are shown in Figure 5. Figure 6 presents the results achieved by PCA and clustering analysis.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/55910/55910fig1.jpg" /> 
Figure 1: A Schematic of Raman Imaging for the Plant Cell Wall. The cross-section sample (poplar as an example) is measured by a micro-Raman system that couples an optical microscope to a Raman high-resolution spectrometer with a charge coupled device (CCD) detector. In the bright-field image, the plant cell wall is organized into the cell corner (CC), compound middle lamella (CML), and secondary wall (SW). Conventionally, a Raman image is generated by single-peak integration or intensity. Two spectral contaminants (i.e., baseline drifts and cosmic spikes) are found in the original data. <a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/55910/55910fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/55910/55910fig2.jpg" /> 
Figure 2: The Raman Spectra Before (a) and After (b) Pre-processing by APRI. Two spectral contaminants are successfully removed. <a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/55910/55910fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/55910/55910fig3.jpg" /> 
Figure 3: A Typical Raman Spectrum of the Poplar Cell Wall. The typical Raman spectrum is from the secondary wall. The band assignments are shown in Table 1. <a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/55910/55910fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/55910/55910fig4.jpg" /> 
Figure 4: Raman Images of Lignin (a) and Polysaccharides (b) within the Poplar Cell Wall. The lignin image is generated by integrating the peak around 1,600 cm-1. The polysaccharide image is produced by integrating the peak around 2,889 cm-1. <a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/55910/55910fig4large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/55910/55910fig5.jpg" /> 
Figure 5: Raman Images of Cellulose (a) and Hemicellulose (b) within the Poplar Cell Wall. SMCR is performed on the Raman imaging data of a delignified sample. Delignification contributes to the exposure of the spectral characteristics of cellulose and hemicellulose. Cellulose is mostly concentrated in the SW, whilst the distribution of hemicellulose is almost uniform throughout the poplar cell wall. <a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/55910/55910fig5large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 6" class="xfigimg" src="/files/ftp_upload/55910/55910fig6.jpg" /> 
Figure 6: PCA and Clustering Analysis Results for Automatically Identifying Different Cell Wall Layers in the Poplar Cell Wall. By using PCA and clustering analysis, the spectra are divided into four parts corresponding to the cell lumen, CC, CML and SW. The average spectra of these layers are given below. The results revealed that the lignin is concentrated along the CML and in the CC, while the polysaccharides are mostly concentrated in the SW. <a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/55910/55910fig6large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Wavenumbers (cm-1)</td>
			<td>Components</td>
			<td>Assignments</td>
		</tr>
		<tr>
			<td>1,095</td>
			<td>Cellulose, Hemicellulose</td>
			<td>heavy atom CC and CO stretching vibration</td>
		</tr>
		<tr>
			<td>1,123</td>
			<td>Cellulose, Hemicellulose</td>
			<td>heavy atom CC and CO stretching vibration</td>
		</tr>
		<tr>
			<td>1,163</td>
			<td>Cellulose, Hemicellulose</td>
			<td>heavy atom CC and CO stretching vibration plus HCC and HCO bending vibration</td>
		</tr>
		<tr>
			<td>1,275</td>
			<td>Lignin</td>
			<td>aryl-O of aryl OH and aryl O&#8722;CH3; guaiacyl ring with C=O group</td>
		</tr>
		<tr>
			<td>1,331</td>
			<td>Lignin, Cellulose, Hemicellulose</td>
			<td>HCC and HCO bending vibration</td>
		</tr>
		<tr>
			<td>1,378</td>
			<td>Cellulose, Hemicellulose</td>
			<td>HCC, HCO, and HOC bending vibration</td>
		</tr>
		<tr>
			<td>1,460</td>
			<td>Lignin, Cellulose, Hemicellulose</td>
			<td>HCH and HOC bending vibration</td>
		</tr>
		<tr>
			<td>1,603</td>
			<td>Lignin</td>
			<td>aryl ring stretching vibration, symmetrical vibration</td>
		</tr>
		<tr>
			<td>1,656</td>
			<td>Lignin</td>
			<td>ring conjugated C=C stretching vibration of coniferyl alcohol; C=O stretching vibration of coniferaldehyde</td>
		</tr>
		<tr>
			<td>2,889</td>
			<td>C, H</td>
			<td>CH and CH2 stretching vibration</td>
		</tr>
		<tr>
			<td>2,940</td>
			<td>L, C, H</td>
			<td>CH stretching vibration in OCH3 asymmetric vibration&#160;</td>
		</tr>
	</tbody>
</table>
Table 1: Raman Peak Positions and Band Assignments

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

This protocol aims to present a general method to visualize lignin, cellulose, and hemicellulose in plant cell walls using Raman imaging and multivariate analysis.

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

combining-raman-imaging-multivariate-analysis-to-visualize-lignin

Nanyang Technological University

Research

JoVE Journal

Biochemistry

11.8K Views.  Beijing Forestry University.  Dit protocol beoogt een algemene methode te presenteren om lignine, cellulose en hemicellulose in plantencelwanden te visualiseren met behulp van Raman imaging en multivariate analyse. 

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

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Plant cell wall structure and chemistry traits are evaluated to identify ideal feedstocks for biofuels and bio-materials. Standard methods have limitations when applied to large data sets. These high-throughput pretreatment, enzyme saccharification, and pyrolysis-molecular beam mass spectrometry methods compare large numbers of biomass samples with decreased experimental time and cost.

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This protocol shows how uniformly 13C, 15N-labeled fungal materials can be produced and how these soft materials should be proceeded for solid-state NMR ...

Visualizing Lignification Dynamics in Plants with Click Chemistry: Dual Labeling is BLISS!

Lignin is one of the most prevalent biopolymers on the planet and a major component of lignocellulosic biomass. This phenolic polymer plays a vital structural and protective role in the development and life of higher plants. Although the intricate mechanisms regulating lignification processes in vivo strongly impact the industrial valorization of many plant-derived products, the scientific community still has a long way to go to decipher them. In a simple three-step workflow, the dual labeling protocol presented herein enables bioimaging studies of actively lignifying zones of plant tissues. The first step consists in the metabolic incorporation of two independent chemical reporters, surrogates of the two native monolignols that give rise to lignin H- and G-units. After incorporation into growing lignin polymers, each reporter is then specifically labeled with its own fluorescent probe via a sequential combination of bioorthogonal SPAAC/CuAAC click reactions. Combined with lignin autofluorescence, this approach leads to the generation of three-color localization maps of lignin within plant cell walls by confocal fluorescence microscopy and provides precise spatial information on the presence or absence of active lignification machinery at the scale of plant tissues, cells and different cell wall layers.

BLISS, a dual labeling protocol for studying lignification dynamics, was developed. Using synthetic monolignol reporters and a sequential combination of SPAAC and CuAAC bioorthogonal click reactions, this methodology paves the way to in-depth analysis of the factors that regulate the biogenesis of lignins in planta.

Lignin is one of the most prevalent biopolymers on the planet and a major component of lignocellulosic biomass. This phenolic polymer plays a vital ...

Chemistry

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

In lignocellulosic biomass (LB), the activity of enzymes is limited by the appearance of non-specific interactions with lignin during the hydrolysis process, which maintains enzymes far from their substrate. Characterization of these complex interactions is thus a challenge in complex substrates such as LB. The method here measures molecular interactions between fluorophore-tagged molecules and native autofluorescent lignin, to be revealed by F&#246;rster resonance energy transfer (FRET). Contrary to FRET measurements in living cells using two exogenous fluorophores, FRET measurements in plants using lignin is not trivial due to its complex autofluorescence. We have developed an original acquisition and analysis pipeline with correlated observation of two complementary properties of fluorescence: fluorescence emission and lifetime. sFLIM (spectral and fluorescent lifetime imaging microscopy) provides the quantification of these interactions with high sensitivity, revealing different interaction levels between biomolecules and lignin.

This protocol describes an original setup combining spectral and fluorescence lifetime measurements to evaluate F&#246;rster resonance energy transfer (FRET) between rhodamine-based fluorescent probes and lignin polymer directly in thick plant sections.

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

Comprehensive Compositional Analysis of Plant Cell Walls (Lignocellulosic biomass) Part I: Lignin

The need for renewable, carbon neutral, and sustainable raw materials for industry and society has become one of the most pressing issues for the 21st century. This has rekindled interest in the use of plant products as industrial raw materials for the production of liquid fuels for transportation1 and other products such as biocomposite materials7. Plant biomass remains one of the greatest untapped reserves on the planet4. It is mostly comprised of cell walls that are composed of energy rich polymers including cellulose, various hemicelluloses (matrix polysaccharides, and the polyphenol lignin6 and thus sometimes termed lignocellulosics. However, plant cell walls have evolved to be recalcitrant to degradation as walls provide tensile strength to cells and the entire plants, ward off pathogens, and allow water to be transported throughout the plant; in the case of trees up to more the 100 m above ground level. Due to the various functions of walls, there is an immense structural diversity within the walls of different plant species and cell types within a single plant4. Hence, depending of what crop species, crop variety, or plant tissue is used for a biorefinery, the processing steps for depolymerization by chemical/enzymatic processes and subsequent fermentation of the various sugars to liquid biofuels need to be adjusted and optimized. This fact underpins the need for a thorough characterization of plant biomass feedstocks. Here we describe a comprehensive analytical methodology that enables the determination of the composition of lignocellulosics and is amenable to a medium to high-throughput analysis. In this first part we focus on the analysis of the polyphenol lignin (Figure 1). The method starts of with preparing destarched cell wall material. The resulting lignocellulosics are then split up to determine its lignin content by acetylbromide solubilization3, and its lignin composition in terms of its syringyl, guaiacyl- and p-hydroxyphenyl units5. The protocol for analyzing the carbohydrates in lignocellulosic biomass including cellulose content and matrix polysaccharide composition is discussed in Part II2.

Comprehensive Compositional Analysis of Plant Cell Walls Lignocellulosic biomass Part I: Lignin

Plant biomass is a major carbon-neutral renewable resource that could be used for the production of biofuels. Plant biomass consists mainly of cell walls, a structurally complex composite material termed lignocellulosics. Here we describe a protocol for a comprehensive analysis of the content and composition of the polyphenolic lignin.

The need for renewable, carbon neutral, and sustainable raw materials for industry and society has become one of the most pressing issues for the 21st ...

Biology

Comprehensive Compositional Analysis of Plant Cell Walls (Lignocellulosic biomass) Part II: Carbohydrates

The need for renewable, carbon neutral, and sustainable raw materials for industry and society has become one of the most pressing issues for the 21st century. This has rekindled interest in the use of plant products as industrial raw materials for the production of liquid fuels for transportation2 and other products such as biocomposite materials6. Plant biomass remains one of the greatest untapped reserves on the planet4. It is mostly comprised of cell walls that are composed of energy rich polymers including cellulose, various hemicelluloses, and the polyphenol lignin5 and thus sometimes termed lignocellulosics. However, plant cell walls have evolved to be recalcitrant to degradation as walls contribute extensively to the strength and structural integrity of the entire plant. Despite its necessary rigidity, the cell wall is a highly dynamic entity that is metabolically active and plays crucial roles in numerous cell activities such as plant growth and differentiation5. Due to the various functions of walls, there is an immense structural diversity within the walls of different plant species and cell types within a single plant4. Hence, depending of what crop species, crop variety, or plant tissue is used for a biorefinery, the processing steps for depolymerisation by chemical/enzymatic processes and subsequent fermentation of the various sugars to liquid biofuels need to be adjusted and optimized. This fact underpins the need for a thorough characterization of plant biomass feedstocks. Here we describe a comprehensive analytical methodology that enables the determination of the composition of lignocellulosics and is amenable to a medium to high-throughput analysis (Figure 1). The method starts of with preparing destarched cell wall material. The resulting lignocellulosics are then split up to determine its monosaccharide composition of the hemicelluloses and other matrix polysaccharides1, and its content of crystalline cellulose7. The protocol for analyzing the lignin components in lignocellulosic biomass is discussed in Part I3.

Comprehensive Compositional Analysis of Plant Cell Walls Lignocellulosic biomass Part II: Carbohydrates

Plant biomass is a major carbon-neutral renewable resource that could be used for the production of biofuels. Plant biomass consists mainly of cell walls, a structurally complex composite material termed lignocellulosics. Here we describe a protocol for a comprehensive analysis of the content and composition of wall derived carbohydrates.

Ultrafast Lignin Extraction from Unusual Mediterranean Lignocellulosic Residues

Pretreatment is still the most expensive step in lignocellulosic biorefinery processes. It must be made cost-effective by minimizing chemical requirements as well as power and heat consumption and by using environment-friendly solvents. Deep eutectic solvents (DESs) are key, green, and low-cost solvents in sustainable biorefineries. They are transparent mixtures characterized by low freezing points resulting from at least one hydrogen bond donor and one hydrogen bond acceptor. Although DESs are promising solvents, it is necessary to combine them with an economic heating technology, such as microwave irradiation, for competitive profitability. Microwave irradiation is a promising strategy to shorten the heating time and boost fractionation because it can rapidly attain the appropriate temperature. The aim of this study was to develop a one-step, rapid method for biomass fractionation and lignin extraction using a low-cost and biodegradable solvent. 
In this study, a microwave-assisted DES pretreatment was conducted for 60 s at 800 W, using three kinds of DESs. The DES mixtures were facilely prepared from choline chloride (ChCl) and three hydrogen-bond donors (HBDs): a monocarboxylic acid (lactic acid), a dicarboxylic acid (oxalic acid), and urea. This pretreatment was used for biomass fractionation and lignin recovery from marine residues (Posidonia leaves and aegagropile), agri-food byproducts (almond shells and olive pomace), forest residues (pinecones), and perennial lignocellulosic grasses (Stipa tenacissima). Further analyses were conducted to determine the yield, purity, and molecular weight distribution of the recovered lignin. In addition, the effect of DESs on the chemical functional groups in the extracted lignin was determined by Fourier-transform infrared (FTIR) spectroscopy. The results indicate that the ChCl-oxalic acid mixture affords the highest lignin purity and the lowest yield. The present study demonstrates that the DES-microwave process is an ultrafast, efficient, and cost-competitive technology for lignocellulosic biomass fractionation.

Deep eutectic solvent-based, microwave-assisted pretreatment is a green, fast, and efficient process for lignocellulosic fractionation and high-purity lignin recovery.

Pretreatment is still the most expensive step in lignocellulosic biorefinery processes. It must be made cost-effective by minimizing chemical requirements ...

Label-free in situ Imaging of Lignification in Plant Cell Walls

Meeting growing energy demands safely and efficiently is a pressing global challenge. Therefore, research into biofuels production that seeks to find cost-effective and sustainable solutions has become a topical and critical task. Lignocellulosic biomass is poised to become the primary source of biomass for the conversion to liquid biofuels1-6. However, the recalcitrance of these plant cell wall materials to cost-effective and efficient degradation presents a major impediment for their use in the production of biofuels and chemicals4. In particular, lignin, a complex and irregular poly-phenylpropanoid heteropolymer, becomes problematic to the postharvest deconstruction of lignocellulosic biomass. For example in biomass conversion for biofuels, it inhibits saccharification in processes aimed at producing simple sugars for fermentation7. The effective use of plant biomass for industrial purposes is in fact largely dependent on the extent to which the plant cell wall is lignified. The removal of lignin is a costly and limiting factor8 and lignin has therefore become a key plant breeding and genetic engineering target in order to improve cell wall conversion.
Analytical tools that permit the accurate rapid characterization of lignification of plant cell walls become increasingly important for evaluating a large number of breeding populations. Extractive procedures for the isolation of native components such as lignin are inevitably destructive, bringing about significant chemical and structural modifications9-11. Analytical chemical in situ methods are thus invaluable tools for the compositional and structural characterization of lignocellulosic materials. Raman microscopy is a technique that relies on inelastic or Raman scattering of monochromatic light, like that from a laser, where the shift in energy of the laser photons is related to molecular vibrations and presents an intrinsic label-free molecular "fingerprint" of the sample. Raman microscopy can afford non-destructive and comparatively inexpensive measurements with minimal sample preparation, giving insights into chemical composition and molecular structure in a close to native state. Chemical imaging by confocal Raman microscopy has been previously used for the visualization of the spatial distribution of cellulose and lignin in wood cell walls12-14. Based on these earlier results, we have recently adopted this method to compare lignification in wild type and lignin-deficient transgenic Populus trichocarpa (black cottonwood) stem wood15. Analyzing the lignin Raman bands16,17 in the spectral region between 1,600 and 1,700 cm-1, lignin signal intensity and localization were mapped in situ. Our approach visualized differences in lignin content, localization, and chemical composition. Most recently, we demonstrated Raman imaging of cell wall polymers in Arabidopsis thaliana with lateral resolution that is sub-&mu;m18. Here, this method is presented affording visualization of lignin in plant cell walls and comparison of lignification in different tissues, samples or species without staining or labeling of the tissues.

Label-free in situ Imaging of Lignification in Plant Cell Walls

A method based on confocal Raman microscopy is presented that affords label-free visualization of lignin in plant cell walls and comparison of lignification in different tissues, samples or species.

Meeting growing energy demands safely and efficiently is a pressing global challenge. Therefore, research into biofuels production that seeks to find ...

Glycan Profiling of Plant Cell Wall Polymers using Microarrays 

Plant cell walls are complex matrixes of heterogeneous glycans which play an important role in the physiology and development of plants and provide the raw materials for human societies (e.g. wood, paper, textile and biofuel industries)1,2. However, understanding the biosynthesis and function of these components remains challenging.
Cell wall glycans are chemically and conformationally diverse due to the complexity of their building blocks, the glycosyl residues. These form linkages at multiple positions and differ in ring structure, isomeric or anomeric configuration, and in addition, are substituted with an array of non-sugar residues. Glycan composition varies in different cell and/or tissue types or even sub-domains of a single cell wall3. Furthermore, their composition is also modified during development1, or in response to environmental cues4.
In excess of 2,000 genes have Plant cell walls are complex matrixes of heterogeneous glycans been predicted to be involved in cell wall glycan biosynthesis and modification in Arabidopsis5. However, relatively few of the biosynthetic genes have been functionally characterized 4,5. Reverse genetics approaches are difficult because the genes are often differentially expressed, often at low levels, between cell types6. Also, mutant studies are often hindered by gene redundancy or compensatory mechanisms to ensure appropriate cell wall function is maintained7. Thus novel approaches are needed to rapidly characterise the diverse range of glycan structures and to facilitate functional genomics approaches to understanding cell wall biosynthesis and modification.
Monoclonal antibodies (mAbs)8,9 have emerged as an important tool for determining glycan structure and distribution in plants. These recognise distinct epitopes present within major classes of plant cell wall glycans, including pectins, xyloglucans, xylans, mannans, glucans and arabinogalactans. Recently their use has been extended to large-scale screening experiments to determine the relative abundance of glycans in a broad range of plant and tissue types simultaneously9,10,11.
Here we present a microarray-based glycan screening method called Comprehensive Microarray Polymer Profiling (CoMPP) (Figures 1 &amp; 2)10,11 that enables multiple samples (100 sec) to be screened using a miniaturised microarray platform with reduced reagent and sample volumes. The spot signals on the microarray can be formally quantified to give semi-quantitative data about glycan epitope occurrence. This approach is well suited to tracking glycan changes in complex biological systems12 and providing a global overview of cell wall composition particularly when prior knowledge of this is unavailable.

Glycan Profiling of Plant Cell Wall Polymers using Microarrays

A technique called Comprehensive Microarray Polymer Profiling (CoMPP) for the characterisation of plant cell wall glycans is described. This method combines the specificity of monoclonal antibodies directed to defined glycan-epitopes with a miniature microarray analytical platform allowing screening of glycan occurrence in a broad range of biological contexts.

Plant cell walls are complex matrixes of heterogeneous glycans which play an important role in the physiology and development of plants and provide the ...

Histochemical Staining of Arabidopsis thaliana Secondary Cell Wall Elements

Arabidopsis thaliana is a model organism commonly used to understand and manipulate various cellular processes in plants, and it has been used extensively in the study of secondary cell wall formation. Secondary cell wall deposition occurs after the primary cell wall is laid down, a process carried out exclusively by specialized cells such as those forming vessel and fiber tissues. Most secondary cell walls are composed of cellulose (40&#8211;50%), hemicellulose (25&#8211;30%), and lignin (20&#8211;30%). Several mutations affecting secondary cell wall biosynthesis have been isolated, and the corresponding mutants may or may not exhibit obvious biochemical composition changes or visual phenotypes since these mutations could be masked by compensatory responses. Staining procedures have historically been used to show differences on a cellular basis. These methods are exclusively visual means of analysis; nevertheless their role in rapid and critical analysis is of great importance. Congo red and calcofluor white are stains used to detect polysaccharides, whereas M&#228;ule and phloroglucinol are commonly used to determine differences in lignin, and toluidine blue O is used to differentially stain polysaccharides and lignin. The seemingly simple techniques of sectioning, staining, and imaging can be a challenge for beginners. Starting with sample preparation using the A. thaliana model, this study details the protocols of a variety of staining methodologies that can be easily implemented for observation of cell and tissue organization in secondary cell walls of plants.

Histochemical Staining of Arabidopsis thaliana Secondary Cell Wall Elements

Plant cell wall composition varies between tissue types and can include lignin, cellulose, hemicelluloses, and pectin. Various staining techniques have been developed to visualize differences at the cell-type level. This paper is a compilation of commonly used cell wall staining techniques.

Arabidopsis thaliana is a model organism commonly used to understand and manipulate various cellular processes in plants, and it has been used extensively ...

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

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