Name: Combining Raman Imaging and Multivariate Analysis to Visualize Lignin, Cellulose, and Hemicellulose in the Plant Cell Wall (Video) | JoVE Journal
Uploaded: 2017-06-10T14:00:00.000+00:00
Duration: 7 min 51 s
Description: Learn to visualize lignin, cellulose, and hemicellulose in plant cell walls using Raman imaging and multivariate analysis. This non-destructive method enables detailed chemical mapping with minimal sample preparation.

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5 minutes

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/pubmed?term=((Bacterial+Enzymes+Involved+in+Lignin+Degradation%5BTitle%5D)+AND+%22J.+Biotechnol%22%5BJournal%5D)">Bacterial Enzymes Involved in Lignin Degradation.</a> J. Biotechnol. 236, 110-119 (2016).</li>
<li>Rosatella, A. A., Afonso, C. A. M. Chapter 2. <a target="_blank" href="http://scholar.google.com/scholar?hl=en&safe=off&q=author%3aRosatella%2C+AA+%22Ionic+Liquids+in+the+Biorefinery+Concept%3A+Challenges+and+Perspectives%22">Ionic Liquids in the Biorefinery Concept: Challenges and Perspectives</a>. , Wiley. 38-64 (2016).</li>
<li>Sun, L., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Understanding+tissue+specific+compositions+of+bioenergy+feedstocks+Through+hyperspectral+Raman+imaging%5BTitle%5D)+AND+%22Bio%22%5BJournal%5D)">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/pubmed?term=((Classification+and+prediction+of+HCC+tissues+by+Raman+imaging+with+identification+of+fatty+acids+as+potential+lipid+biomarkers%5BTitle%5D)+AND+%22J.+Cancer.+Res.+Clin.+Oncol%22%5BJournal%5D)">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://scholar.google.com/scholar?hl=en&safe=off&q=author%3aSchrader%2C+B+%22Infrared+and+Raman+spectroscopy%3A+methods+and+applications%22">Infrared and Raman spectroscopy: methods and applications</a>. , John Wiley and Sons. (2008).</li>
<li>Gierlinger, N., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Imaging+of+plant+cell+walls+by+confocal+Raman+microscopy%5BTitle%5D)+AND+%22Nat.+Protoc%22%5BJournal%5D)">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/pubmed?term=((Online+fluorescence+suppression+in+modulated+Raman+spectroscopy%5BTitle%5D)+AND+%22Anal.+Chem%22%5BJournal%5D)">Online fluorescence suppression in modulated Raman spectroscopy.</a> Anal. Chem. 82 (2), 738-745 (2009).</li>
<li>Schlücker, S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Raman+microspectroscopy%3A+a+comparison+of+point%2C+line%2C+and+wide-field+imaging+methodologies%5BTitle%5D)+AND+%22Anal.+Chem%22%5BJournal%5D)">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.sciencedirect.com/science/article/pii/S0169743998001749">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.sciencedirect.com/science/article/pii/S003259101000046X">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/pubmed?term=((Method+for+removing+spectral+contaminants+to+improve+analysis+of+Raman+imaging+data%5BTitle%5D)+AND+%22Sci.+Rep%22%5BJournal%5D)">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://onlinelibrary.wiley.com/doi/10.1002/jrs.2525/abstract">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="https://www.jstor.org/stable/1267173">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/pubmed?term=((Method+for+automatically+identifying+spectra+of+different+wood+cell+wall+layers+in+Raman+imaging+data+set%5BTitle%5D)+AND+%22Anal.+Chem%22%5BJournal%5D)">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.sciencedirect.com/science/article/pii/S0039914008001641">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, ...

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

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 ...

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 ...

The overall goal of this experiment is to present a general method for visualizing lignin, cellulose, and hemicellulose in the plant cell wall by Raman imaging and multivariate analysis. This method can help answer key questions in the plant biomass field, such as how do the major chemical components distribute in the plant cell wall. The main advantage of this technique is that labor-free and non-destructive information about the sample can be achieved with minimal sample preparation.To begin, cut a small tissue block from the plant sample. Immerse the tissue in boiling deionized water for 30 minutes. Then, immediately transfer it to deionized water at room temperature for 30 minutes.Repeat this step until the tissue sinks to the bottom of the container, indicating that the air in the tissue has been removed and that the tissue has softened. Prepare 20%50%70%and 90%aliquots of PEG and deionized water, as well as pure PEG. Keep the solutions a 65 degrees Celsius.Incubate the tissue in a series of graded PEG baths to displace the water, and allow the PEG to infiltrate. Dry in 20%PEG for one hour, 50%PEG for one and a half hours, 70%PEG for two hours, 90%PEG for two hours, and 100%PEG for 10 hours. Next, pre-warm a cassette at 65 degrees Celsius in an oven.Pour the tissue block containing PEG into the cassette, and then place the tissue block in a desired position using pre-warmed tweezers or needles. Slowly cool down the cassette, and store the tissue at room temperature until use. Dissect the PEG block containing the target tissue into a small block using a sharp razor blade.Mount the small PEG block onto the microtome, and cut thin sections from the PEG block. Then, rinse the sections with deionized water in a watch class 10 times to remove the PEG from the tissue. Next, immerse the sections with toluene and ethanol for six hours 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 chloride in a beaker. Add one section and 3 mL of reaction liquid to a 5 mL vial. Screw on the top of the vial.Then, heat the vial in a water bath at 75 degrees Celsius for two hours to remove the lignin of the tissue. Rinse the section with deionized water in a watch glass 10 times. Next, transfer the untreated and de-lignified section to a glass microscope slide.Unfold the section carefully using brushes or needles. Remove the extra deionized water with tissue paper. Then, immerse the section in deuterium oxide.Cover the sample with a glass cover slip. Seal the cover slip with nail polish to prevent the evaporation of the deuterium oxide. Open the instrument operating software of the confocal Raman microscope.Turn on the laser, and focus on the service of the crystal and silicon with a 100x microscope objective. Click the calibration button to calibrate the instrument. 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. View the sample with the 20x microscope objective and locate the area of interest. Switch to the immersion microscope objective.Apply immersion oil to the cover slip, then focus on the surface of the sample. Next, switch the instrument to the Raman testing mode and turn off the microscope lamp. Chose a mapping area by using a rectangular tool.Alter the step size to determine the number of obtained spectra. Be aware that the step size should be larger than the spot diameter, calculated by the numerical aperture of the objective. Sizes below this will result in over-sampling.Set the optimum spectral parameters to obtain the best signal-to-noise ratio and spectral quality, and an appropriate acquisition time depending on the sample suitability. Generally, input the imaging parameters in the instrument software using a laser wavelength of 532 nanometers, a filter of 100%a hole of 300, a slit of 100, a spectrometer of 1840 inverse centimeters, grooves of 1200T, a 60x oil objective, and an acquisition time of two seconds. Save the spectral data before data processing, and convert the data to a universal format before proceeding to data analysis as described in the text protocol.The original Raman spectra of the poplar cell wall are shown here. Two major noise signals, including baseline drifts and cosmic spikes, spill over into the channels along with the actual signals. These should be removed prior to data analysis.A noise reduction technique such as the Savitzky-Golay algorithm or the wavelet algorithm can be applied to eliminate these noise signals. For lignin imaging, consider the spectral peak around 1600 inverse centimeters due to the aromatic ring symmetric stretching vibration. For polysaccharide imaging, use the spectral peak around 2889 inverse centimeters because of the CH and CH2 stretches.However, Raman images of cellulose and hemicellulose cannot be generated directly. Delignification is a necessary procedure to expose the spectral characteristics of them. Generally, we are new to this method.We struggle, because it requires training in handling and appearance of sample sectioning and the data analysis. After watching this video, you should have a good understanding of how to acquire chemical information about the plant cell wall using in-situ methods. Following this procedure, other methods like chemical pre-treatment can be performed in order to answer additional questions, like the recalcitrance of the plant cell wall.Though this method can provide insight into the internal structural nature and the topochemistry within the plant cell wall, it can also be applied to other biological samples.

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

Research

JoVE Journal

Biochemistry

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.

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 wall derived carbohydrates.

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

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.

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

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.

High-throughput Screening of Recalcitrance Variations in Lignocellulosic Biomass: Total Lignin, Lignin Monomers, and Enzymatic Sugar Release

Crystalline cellulose is an important constituent of the plant cell wall. However, its quantification at a cellular resolution is technically challenging.  Here, we report the use of polarized light technology and root cross sections to obtain information of cell wall composition at a spatiotemporal resolution.

High Resolution Quantification of Crystalline Cellulose Accumulation in Arabidopsis Roots to Monitor Tissue-specific Cell Wall Modifications

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.

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

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

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

This protocol presents a multimodal approach combining Raman spectroscopy and tomographic phase microscopy for label-free, non-invasive phenotyping of living human breast cancer cells. It includes a user-friendly, human-bias-free analysis pipeline for rapid, quantitative morpho-chemical analysis, providing detailed molecular and structural insights with applications in cancer research and cell biology.

A Multimodal Imaging Framework to Advance Phenotyping of Living Label-free Breast Cancer Cells

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

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Learn to visualize lignin, cellulose, and hemicellulose in plant cell walls using Raman imaging and multivariate analysis. This non-destructive method enables detailed chemical mapping with minimal sample preparation.

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

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