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Distinguishing cell types or populations based on the computational analysis of their infrared spectra

Nature Protocols volume 5, pages 1748–1760 (2010)Cite this article

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Abstract

Infrared (IR) spectroscopy of intact cells results in a fingerprint of their biochemistry in the form of an IR spectrum; this has given rise to the new field of biospectroscopy. This protocol describes sample preparation (a tissue section or cytology specimen), the application of IR spectroscopy tools, and computational analysis. Experimental considerations include optimization of specimen preparation, objective acquisition of a sufficient number of spectra, linking of the derived spectra with tissue architecture or cell type, and computational analysis. The preparation of multiple specimens (up to 50) takes 8 h; the interrogation of a tissue section can take up to 6 h (∼100 spectra); and cytology analysis (n = 50, 10 spectra per specimen) takes 14 h. IR spectroscopy generates complex data sets and analyses are best when initially based on a multivariate approach (principal component analysis with or without linear discriminant analysis). This results in the identification of class clustering as well as class-specific chemical entities.

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Figure 2: From sample preparation to acquisition of spectra.
Figure 3: Application of sample slide to stage for spectral acquisition.
Figure 1: Schematic of the spectroscopy computational framework.
Figure 4: The methodological approach for deriving point or image maps using FTIR microspectroscopy from a particular sample.
Figure 5: The anticipated spectral acquisition and PCA results.
Figure 6: The anticipated computational analysis results.

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Acknowledgements

Over several years, this work has been funded by the Engineering and Physical Sciences Research Council, by the Rosemere Cancer Foundation and by the Biotechnology and Biological Sciences Research Council. Additionally, we thank the Faculty of Science & Technology (Lancaster University) for capital equipment funding.

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Authors and Affiliations

  1. Centre for Biophotonics, Lancaster Environment Centre, Lancaster University, Lancaster, UK

    Francis L Martin, Jemma G Kelly, Valon Llabjani, Pierre L Martin-Hirsch, Imran I Patel & Júlio Trevisan

  2. Division of Biomedical and Life Sciences, School of Health and Medicine, Lancaster University, Lancaster, UK

    Nigel J Fullwood

  3. Beckman Institute, University of Illinois, Urbana, Illinois, USA

    Michael J Walsh

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Contributions

F.L.M. is the principal investigator. J.G.K., V.L., P.L.M.-H., I.I.P., J.T., N.J.F. and M.J.W. all played a critical role in the method development and experimental design. J.G.K., V.L., I.I.P., J.T. and M.J.W. performed the experiments. J.G.K. and J.T. analyzed the data. P.L.M.-H. contributed clinical insight and material toward analysis. N.J.F. contributed expertise in conventional microscopy. F.L.M. directed the writing of the paper, to which all coauthors contributed substantially.

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Correspondence to Francis L Martin.

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The authors declare no competing financial interests.

Supplementary information

Supplementary Fig. 1

Distinguishing cell types or populations based on the derivation and computational analyses of their infrared spectra (PDF 260 kb)

Supplementary Fig. 2

Screen shots of settings from Collect; Experiment Set-up in OMNIC for FTIR microspectroscopy (Thermo Fisher Scientific) (PDF 1575 kb)

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Martin, F., Kelly, J., Llabjani, V. et al. Distinguishing cell types or populations based on the computational analysis of their infrared spectra. Nat Protoc 5, 1748–1760 (2010). https://doi.org/10.1038/nprot.2010.133

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