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Imaging of plant cell walls by confocal Raman microscopy

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.

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Figure 1: Raman spectra acquired from a Ramie fiber.
Figure 2: Typical spectra for lignin, pectin and cellulose and their marker bands derived from Raman images.
Figure 3: Schematic of the experimental procedure.
Figure 4: Variations in Raman spectra related to lignin content.
Figure 5: Raman images of Brachypodium distachyon.
Figure 6: Raman images of a poplar tension wood cross-section.
Figure 7: Raman images and spectra based on the same measurements shown in Figure 6.

References

  1. 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).

  2. Schrader, B. Infrared and Raman Spectroscopy 786 (VCH, 1995).

  3. Schmitt, M. & Popp, J. Raman spectroscopy at the beginning of the twenty-first century. J. Raman Spectrosc. 2006, 37, 20–28.

    CAS  Google Scholar 

  4. Hollricher, O. Raman instrumentation for confocal Raman microscopy. in Confocal Raman Microscopy (eds. Diening, T., Hollricher, O. & Toporski, J.) 43–60 (Springer, 2010).

  5. Smith, E. & Dent, G. Modern Raman Spectroscopy—A Practical Approach 210 (John Wiley & Sons, 2005).

  6. Dieing, T. & Hollricher, O. High-resolution, high-speed confocal Raman imaging. Vib. Spectrosc. 48, 22–27 (2008).

    CAS  Google Scholar 

  7. Das, R.S. & Agrawal, Y.K. Raman spectroscopy: recent advancements, techniques and applications. Vib. Spectrosc. 57, 163–176 (2011).

    CAS  Google Scholar 

  8. 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).

  9. Atalla, R.H. & Agarwal, U.P. Recording Raman-spectra from plant cell walls. J. Raman Spectrosc. 17, 229–231 (1986).

    CAS  Google Scholar 

  10. 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).

    PubMed  CAS  Google Scholar 

  11. 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).

  12. Cosgrove, D.J. Growth of the plant cell wall. Nat. Rev. Mol. Cell Biol. 6, 850–861 (2005).

    PubMed  CAS  Google Scholar 

  13. Gierlinger, N. & Schwanninger, M. The potential of Raman microscopy and Raman imaging in plant research—review. Spectroscopy 21, 69–89 (2007).

    CAS  Google Scholar 

  14. 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).

    CAS  Google Scholar 

  15. 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).

    CAS  Google Scholar 

  16. Himmelsbach, D.S., Khahili, S. & Akin, D.E. Near-infrared–Fourier-transform–Raman microspectroscopic imaging of flax stems. Vib. Spectrosc. 19, 361–367 (1999).

    CAS  Google Scholar 

  17. Chu, L.Q. et al. Base-induced delignification of Miscanthus × giganteus studied by three-dimensional confocal Raman imaging. Bioresour. Technol. 101, 4919–4925 (2010).

    PubMed  CAS  Google Scholar 

  18. 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).

    CAS  Google Scholar 

  19. Gierlinger, N. & Schwanninger, M. Chemical imaging of poplar wood cell walls by confocal Raman microscopy. Plant Physiol. 140, 1246–1254 (2006).

    PubMed  PubMed Central  CAS  Google Scholar 

  20. Gierlinger, N., Sapei, L. & Paris, O. Insights into the chemical composition of Equisetum hyemale by high resolution Raman imaging. Planta 227, 969–980 (2008).

    PubMed  CAS  Google Scholar 

  21. Richter, S., Mussig, J. & Gierlinger, N. Functional plant cell wall design revealed by the Raman imaging approach. Planta 233, 763–772 (2011).

    PubMed  CAS  Google Scholar 

  22. 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).

    PubMed  CAS  Google Scholar 

  23. Larsen, K.L. & Barsberg, S. Theoretical and Raman spectroscopic studies of phenolic lignin model monomers. J. Phys. Chem. B 114, 8009–8021 (2010).

    PubMed  CAS  Google Scholar 

  24. 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).

    Google Scholar 

  25. Perera, P.N. et al. Raman-spectroscopy-based noninvasive microanalysis of native lignin structure. Anal. Bioanal. Chem. 402, 983–987 (2012).

    PubMed  CAS  Google Scholar 

  26. 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).

    PubMed  CAS  Google Scholar 

  27. Sun, L. et al. Rapid determination of syringyl: guaiacyl ratios using FT-Raman spectroscopy. Biotechnol. Bioeng. 109, 647–656 (2012).

    PubMed  CAS  Google Scholar 

  28. 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).

    PubMed  CAS  Google Scholar 

  29. Mathlouthi, M. & Koenig, J.L. Vibrational spectra of carbohydrates. Adv. Carbohydr. Chem. Biochem. 44, 7–89 (1986).

    PubMed  CAS  Google Scholar 

  30. Synytsya, A. et al. Fourier transform Raman and infrared spectroscopy of pectins. Carbohydr. Polym. 54, 97–106 (2003).

    CAS  Google Scholar 

  31. 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).

    PubMed  CAS  Google Scholar 

  32. 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).

    CAS  Google Scholar 

  33. Sun, L., Simmons, B.A. & Singh, S. Understanding tissue specific compositions of bioenergy feedstocks through hyperspectral Raman imaging. Biotechnol. Bioeng. 108, 286–295 (2011).

    PubMed  CAS  Google Scholar 

  34. Schreiber, N. et al. G-fibres in storage roots of Trifolium pratense (Fabaceae): tensile stress generators for contraction. Plant J. 61, 854–861 (2010).

    PubMed  CAS  Google Scholar 

  35. Harrington, M.J. et al. Origami-like unfolding of hydro-actuated ice plant seed capsules. Nat. Commun. 2, 337 (2011).

    PubMed  Google Scholar 

  36. 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).

    CAS  Google Scholar 

  37. Barsberg, S. et al. Lignin radicals in the plant cell wall probed by Kerr-gated resonance Raman spectroscopy. Biophys. J. 90, 2978–2986 (2006).

    PubMed  PubMed Central  CAS  Google Scholar 

  38. Petrou, M. et al. Fourier-transform Raman spectroscopic study of a Neolithic waterlogged wood assemblage. Anal. Bioanal. Chem. 395, 2131–2138 (2009).

    PubMed  CAS  Google Scholar 

  39. 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).

    CAS  Google Scholar 

  40. 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).

  41. 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).

    PubMed  PubMed Central  CAS  Google Scholar 

  42. Zeng, Y.N. et al. Imaging lignin-downregulated alfalfa using coherent anti-Stokes Raman scattering microscopy. Bioenergy Res. 3, 272–277 (2010).

    Google Scholar 

  43. 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).

  44. Gerlach, D. Botanische Mikrotechnik—Eine Einführung (Georg Thieme, 1984).

  45. Kondo, H. Embedment-free section electron microscopy. J. Electron Microsc. 55, 231–243 (2006).

    CAS  Google Scholar 

  46. Wolosewick, J.J. The application of polyethylene-glycol (PEG) to electron microscopy. J. Cell Biol. 86, 675–681 (1980).

    PubMed  CAS  Google Scholar 

  47. 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).

    PubMed  CAS  Google Scholar 

  48. 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).

  49. 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).

    CAS  Google Scholar 

  50. Katsumoto, Y. & Ozaki, Y. Practical algorithm for reducing convex spike noises on a spectrum. Appl. Spectrosc. 57, 317–322 (2003).

    PubMed  CAS  Google Scholar 

  51. 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).

  52. Savitzky, A. & Golay, M.J.E. Smoothing and differentiation of data by simplified least squares procedures. Anal. Chem. 36, 1627–1639 (1964).

    CAS  Google Scholar 

  53. 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).

    CAS  Google Scholar 

  54. 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).

    PubMed  CAS  Google Scholar 

  55. Liland, K.H. et al. Customized baseline correction. Chemometrics and Intelligent Laboratory Systems 109, 51–56 (2011).

    CAS  Google Scholar 

  56. Schulze, G. et al. Investigation of selected baseline removal techniques as candidates for automated implementation. Appl. Spectrosc. 59, 545–574 (2005).

    PubMed  CAS  Google Scholar 

  57. Prakash, B.D. & Wei, Y.C. A fully automated iterative moving averaging (AIMA) technique for baseline correction. Analyst 136, 3130–3135 (2011).

    PubMed  CAS  Google Scholar 

  58. Schulze, H.G. et al. A model-free, fully automated baseline-removal method for Raman spectra. Appl. Spectrosc. 65, 75–84 (2011).

    PubMed  CAS  Google Scholar 

  59. Zhang, Z.M., Chen, S. & Liang, Y.Z. Baseline correction using adaptive iteratively reweighted penalized least squares. Analyst 135, 1138–1146 (2010).

    PubMed  CAS  Google Scholar 

  60. 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).

    CAS  Google Scholar 

  61. 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).

  62. Shinzawa, H. et al. Multivariate data analysis for Raman spectroscopic imaging. J. Raman Spectrosc. 40, 1720–1725 (2009).

    CAS  Google Scholar 

  63. Næs, T. et al. A User-Friendly Guide to Multivariate Calibration and Classification, 1st edn, 344 (NIR Publications, 2002).

  64. Geladi, P. Chemometrics in spectroscopy. Part 1. Classical chemometrics. Spectrochim. Acta Part B 58, 767–782 (2003).

    Google Scholar 

  65. Hastie, T., Tibshirani, R. & Friedman, J. The Elements of Statistical Learning (Springer, 2009).

  66. 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).

    PubMed  CAS  Google Scholar 

  67. Baranska, M. et al. Identification of secondary metabolites in medicinal and spice plants by NIR-FT-Raman microspectroscopic mapping. Analyst 129, 926–930 (2004).

    PubMed  CAS  Google Scholar 

  68. Barsberg, S., Matousek, P. & Towrie, M. Structural analysis of lignin by resonance Raman spectroscopy. Macromol. Biosci. 5, 743–752 (2005).

    PubMed  CAS  Google Scholar 

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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.

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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

Correspondence to Notburga Gierlinger.

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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)

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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

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