Abstract
Raman imaging of plant cell walls represents a nondestructive technique that can provide insights into chemical composition in context with structure at the micrometer level (<0.5 μm). The major steps of the experimental procedure are described: sample preparation (embedding and microcutting), setting the mapping parameters, and finally the calculation of chemical images on the basis of the acquired Raman spectra. Every Raman image is based on thousands of spectra, each being a spatially resolved molecular 'fingerprint' of the cell wall. Multiple components are analyzed within the native cell walls, and insights into polymer composition as well as the orientation of the cellulose microfibrils can be gained. The most labor-intensive step of this process is often the sample preparation, as the imaging approach requires a flat surface of the plant tissue with intact cell walls. After finishing the map (acquisition time is ∼10 min to 10 h, depending on the size of the region of interest and scanning parameters), many possibilities exist for the analysis of spectral data and image generation.
This is a preview of subscription content, access via your institution
Access options
Subscribe to this journal
Receive 12 print issues and online access
$259.00 per year
only $21.58 per issue
Buy this article
- Purchase on Springer Link
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
Similar content being viewed by others
References
Griffith, P.R. Infrared and Raman instrumentation for mapping and imaging. in Infrared and Raman Spectroscopic Imaging (eds. Salzer, R. & Siesler, H.W.) 3–64 (Wiley-VCH, 2009).
Schrader, B. Infrared and Raman Spectroscopy 786 (VCH, 1995).
Schmitt, M. & Popp, J. Raman spectroscopy at the beginning of the twenty-first century. J. Raman Spectrosc. 2006, 37, 20–28.
Hollricher, O. Raman instrumentation for confocal Raman microscopy. in Confocal Raman Microscopy (eds. Diening, T., Hollricher, O. & Toporski, J.) 43–60 (Springer, 2010).
Smith, E. & Dent, G. Modern Raman Spectroscopy—A Practical Approach 210 (John Wiley & Sons, 2005).
Dieing, T. & Hollricher, O. High-resolution, high-speed confocal Raman imaging. Vib. Spectrosc. 48, 22–27 (2008).
Das, R.S. & Agrawal, Y.K. Raman spectroscopy: recent advancements, techniques and applications. Vib. Spectrosc. 57, 163–176 (2011).
Nelson, M.P. & Treado, P.J. Raman imaging instrumentation. in Raman, Infrared, and Near-Infrared Chemical Imaging (eds. Sasic, S. & Ozaki, Y.) 23–55 (John Wiley & Sons, 2010).
Atalla, R.H. & Agarwal, U.P. Recording Raman-spectra from plant cell walls. J. Raman Spectrosc. 17, 229–231 (1986).
Agarwal, U.P. & Atalla, R.H. In situ Raman microprobe studies of plant cell walls—macromolecular organization and compositional variability in the secondary wall of Picea mariana (Mill) Bsp. Planta 169, 325–332 (1986).
Agarwal, U.P. An overview of Raman spectroscopy as applied to lignocellulosic materials. in Advances in Lignocellulosics Characterization (ed. Argyropoulos, D.S.) 209–225 (TAPPI, 1999).
Cosgrove, D.J. Growth of the plant cell wall. Nat. Rev. Mol. Cell Biol. 6, 850–861 (2005).
Gierlinger, N. & Schwanninger, M. The potential of Raman microscopy and Raman imaging in plant research—review. Spectroscopy 21, 69–89 (2007).
Gierlinger, N. et al. Cellulose microfibril orientation of Picea abies and its variability at the micron-level determined by Raman imaging. J. Exp. Botany 61, 587–595 (2010).
Agarwal, U.P., Reiner, R.S. & Ralph, S.A. Cellulose I crystallinity determination using FT-Raman spectroscopy: univariate and multivariate methods. Cellulose 17, 721–733 (2010).
Himmelsbach, D.S., Khahili, S. & Akin, D.E. Near-infrared–Fourier-transform–Raman microspectroscopic imaging of flax stems. Vib. Spectrosc. 19, 361–367 (1999).
Chu, L.Q. et al. Base-induced delignification of Miscanthus × giganteus studied by three-dimensional confocal Raman imaging. Bioresour. Technol. 101, 4919–4925 (2010).
Agarwal, U.P. & Ralph, S.A. FT-Raman spectroscopy of wood: Identifying contributions of lignin and carbohydrate polymers in the spectrum of black spruce (Picea mariana). Appl. Spectrosc. 51, 1648–1655 (1997).
Gierlinger, N. & Schwanninger, M. Chemical imaging of poplar wood cell walls by confocal Raman microscopy. Plant Physiol. 140, 1246–1254 (2006).
Gierlinger, N., Sapei, L. & Paris, O. Insights into the chemical composition of Equisetum hyemale by high resolution Raman imaging. Planta 227, 969–980 (2008).
Richter, S., Mussig, J. & Gierlinger, N. Functional plant cell wall design revealed by the Raman imaging approach. Planta 233, 763–772 (2011).
Agarwal, U.P. Raman imaging to investigate ultrastructure and composition of plant cell walls: distribution of lignin and cellulose in black spruce wood (Picea mariana). Planta 224, 1141–1153 (2006).
Larsen, K.L. & Barsberg, S. Theoretical and Raman spectroscopic studies of phenolic lignin model monomers. J. Phys. Chem. B 114, 8009–8021 (2010).
Takayama, M. et al. Fourier transform Raman assignment of guaiacyl and syringyl marker bands for lignin determination. Spectochim. Acta Part A 53, 1621–1628 (1997).
Perera, P.N. et al. Raman-spectroscopy-based noninvasive microanalysis of native lignin structure. Anal. Bioanal. Chem. 402, 983–987 (2012).
Hanninen, T., Kontturi, E. & Vuorinen, T. Distribution of lignin and its coniferyl alcohol and coniferyl aldehyde groups in Picea abies and Pinus sylvestris as observed by Raman imaging. Phytochemistry 72, 1889–1895 (2011).
Sun, L. et al. Rapid determination of syringyl: guaiacyl ratios using FT-Raman spectroscopy. Biotechnol. Bioeng. 109, 647–656 (2012).
Larsen, K.L. & Barsberg, S. Environmental effects on the lignin model monomer, vanillyl alcohol, studied by Raman Spectroscopy. J. Phys. Chem. B 115, 11470–11480 (2011).
Mathlouthi, M. & Koenig, J.L. Vibrational spectra of carbohydrates. Adv. Carbohydr. Chem. Biochem. 44, 7–89 (1986).
Synytsya, A. et al. Fourier transform Raman and infrared spectroscopy of pectins. Carbohydr. Polym. 54, 97–106 (2003).
Goswami, L. et al. Stress generation in the tension wood of poplar is based on the lateral swelling power of the G-layer. Plant J. 56, 531–538 (2008).
Lehringer, C., Gierlinger, N. & Koch, G. Topochemical investigation on tension wood fibres of Acer spp., Fagus sylvatica L. and Quercus robur L. Holzforschung 62, 255–263 (2008).
Sun, L., Simmons, B.A. & Singh, S. Understanding tissue specific compositions of bioenergy feedstocks through hyperspectral Raman imaging. Biotechnol. Bioeng. 108, 286–295 (2011).
Schreiber, N. et al. G-fibres in storage roots of Trifolium pratense (Fabaceae): tensile stress generators for contraction. Plant J. 61, 854–861 (2010).
Harrington, M.J. et al. Origami-like unfolding of hydro-actuated ice plant seed capsules. Nat. Commun. 2, 337 (2011).
Busch, S. et al. Analysis of self-repair mechanisms of Phaseolus vulgaris var. saxa using near-infrared surface-enhanced Raman spectroscopy. J. Raman Spectrosc. 41, 490–497 (2010).
Barsberg, S. et al. Lignin radicals in the plant cell wall probed by Kerr-gated resonance Raman spectroscopy. Biophys. J. 90, 2978–2986 (2006).
Petrou, M. et al. Fourier-transform Raman spectroscopic study of a Neolithic waterlogged wood assemblage. Anal. Bioanal. Chem. 395, 2131–2138 (2009).
Chundawat, S.P.S. et al. Multi-scale visualization and characterization of lignocellulosic plant cell wall deconstruction during thermochemical pretreatment. Energy Environ. Sci. 4, 973–984 (2011).
Horvath, L. et al. Distribution of wood polymers within the cell wall of transgenic aspen imaged by Raman microscopy. Holzforschung doi:10.1515/hf-2011-0126 (3 March 2012).
Schmidt, M. et al. Label-free in situ imaging of lignification in the cell wall of low lignin transgenic Populus trichocarpa. Planta 230, 589–597 (2009).
Zeng, Y.N. et al. Imaging lignin-downregulated alfalfa using coherent anti-Stokes Raman scattering microscopy. Bioenergy Res. 3, 272–277 (2010).
Gierlinger, N. et al. Raman microscopy: insights into chemistry and structure of biological materials. in Materials Design Inspired by Nature: Function through Inner Architecture (eds. Fratzl, P., Dunlop, J.W.C. & Weinkamer, R.) (Royal Society of Chemistry, 2012).
Gerlach, D. Botanische Mikrotechnik—Eine Einführung (Georg Thieme, 1984).
Kondo, H. Embedment-free section electron microscopy. J. Electron Microsc. 55, 231–243 (2006).
Wolosewick, J.J. The application of polyethylene-glycol (PEG) to electron microscopy. J. Cell Biol. 86, 675–681 (1980).
Everall, N. et al. Optimizing depth resolution in confocal Raman microscopy: a comparison of metallurgical, dry corrected, and oil immersion objectives. Appl. Spectrosc. 61, 251–259 (2007).
de Juan, A. et al. Chemometric tools for image analysis. in Infrared and Raman Spectroscopic Imaging (eds. Salzer, R. & Siesler, H.W.) 65–108 Wiley-VCH, 2009).
Zhang, D.M., Jallad, K.N. & Ben-Amotz, D. Stripping of cosmic spike spectral artifacts using a new upper-bound spectrum algorithm. Appl. Spectrosc. 55, 1523–1531 (2001).
Katsumoto, Y. & Ozaki, Y. Practical algorithm for reducing convex spike noises on a spectrum. Appl. Spectrosc. 57, 317–322 (2003).
Diening, T. & Ibach, W. Software requirements and data analysis in confocal Raman microscopy. in Confocal Raman Microscopy (eds. Diening, T., Hollricher, O. & Toporski, J.) 61–89 (Springer, 2010).
Savitzky, A. & Golay, M.J.E. Smoothing and differentiation of data by simplified least squares procedures. Anal. Chem. 36, 1627–1639 (1964).
Ramos, P.M. & Ruisanchez, I. Noise and background removal in Raman spectra of ancient pigments using wavelet transform. J. Raman Spectrosc. 36, 848–856 (2005).
Perera, P.N. et al. Blind image analysis for the compositional and structural characterization of plant cell walls. Anal. Chim. Acta 702, 172–177 (2011).
Liland, K.H. et al. Customized baseline correction. Chemometrics and Intelligent Laboratory Systems 109, 51–56 (2011).
Schulze, G. et al. Investigation of selected baseline removal techniques as candidates for automated implementation. Appl. Spectrosc. 59, 545–574 (2005).
Prakash, B.D. & Wei, Y.C. A fully automated iterative moving averaging (AIMA) technique for baseline correction. Analyst 136, 3130–3135 (2011).
Schulze, H.G. et al. A model-free, fully automated baseline-removal method for Raman spectra. Appl. Spectrosc. 65, 75–84 (2011).
Zhang, Z.M., Chen, S. & Liang, Y.Z. Baseline correction using adaptive iteratively reweighted penalized least squares. Analyst 135, 1138–1146 (2010).
Schmidt, U. et al. Raman spectral imaging—a nondestructive, high-resolution analysis technique for local stress measurements in silicon. Vib. Spectrosc. 42, 93–97 (2006).
Geladi, P., Grahn, H. & Manley, M. Data analysis and chemometrics for hyperspectral imaging. in Raman, Infrared, and Near-Infrared Chemical Imaging (eds. Sasic, S. & Ozaki, Y.) 93–109 (John Wiley & Sons, 2010).
Shinzawa, H. et al. Multivariate data analysis for Raman spectroscopic imaging. J. Raman Spectrosc. 40, 1720–1725 (2009).
Næs, T. et al. A User-Friendly Guide to Multivariate Calibration and Classification, 1st edn, 344 (NIR Publications, 2002).
Geladi, P. Chemometrics in spectroscopy. Part 1. Classical chemometrics. Spectrochim. Acta Part B 58, 767–782 (2003).
Hastie, T., Tibshirani, R. & Friedman, J. The Elements of Statistical Learning (Springer, 2009).
Agarwal, U.P. & Kawai, N. 'Self-Absorption' phenomenon in near-infrared Fourier transform Raman spectroscopy of cellulosic and lignocellulosic materials. Appl. Spectrosc. 59, 385–388 (2005).
Baranska, M. et al. Identification of secondary metabolites in medicinal and spice plants by NIR-FT-Raman microspectroscopic mapping. Analyst 129, 926–930 (2004).
Barsberg, S., Matousek, P. & Towrie, M. Structural analysis of lignin by resonance Raman spectroscopy. Macromol. Biosci. 5, 743–752 (2005).
Acknowledgements
We thank P. Conchon and C. Coutand (INRA, Clermont-Ferrand, France) for providing the poplar cross-sections and M. Rüggeberg (ETH Zürich) for sharing the PEG-embedding protocol. The Max Planck Institute of Colloids and Interfaces (Potsdam, Germany) is acknowledged for providing the working environment to develop the Raman imaging approach on plant cell walls. We also acknowledge the Austrian Academy of Sciences (APART programme) for financial support.
Author information
Authors and Affiliations
Contributions
N.G. was the principal investigator, developed the Raman imaging approach for plant cell walls and wrote the manuscript. T.K. performed the polarization experiment on the ramie fiber and M.H. developed and described the protocol for cryosectioning of plant cell walls.
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing financial interests.
Supplementary information
Supplementary Figure 1
Schematics of the most important steps of the Polyethylenglycol (PEG) embedding. (PDF 991 kb)
Supplementary Figure 2
Schematic picture of the Confocal Raman microscope (PDF 301 kb)
Rights and permissions
About this article
Cite this article
Gierlinger, N., Keplinger, T. & Harrington, M. Imaging of plant cell walls by confocal Raman microscopy. Nat Protoc 7, 1694–1708 (2012). https://doi.org/10.1038/nprot.2012.092
Published:
Issue Date:
DOI: https://doi.org/10.1038/nprot.2012.092
This article is cited by
-
Localization and characterisation of brown rot in two types of acetylated wood
Cellulose (2024)
-
Tracking deuterium uptake in hydroponically grown maize roots using correlative helium ion microscopy and Raman micro-spectroscopy
Plant Methods (2023)
-
Direct and rapid screening of calcium carbide in ripened bananas using chemometrics-assisted laser Raman spectroscopy
Applied Physics B (2023)
-
Subcellular level impact of extractives on brown rot decay of Norway spruce elucidated by confocal Raman microscopy and multivariate data analysis
Wood Science and Technology (2023)
-
High-spatial-resolution composition analysis of micro/nano-structures with a nanoscale compositional variation
Nano Research (2023)
Comments
By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.