Multimodal Vibrational Imaging… All the photons are welcome
In recent years, the development of optic methods such as FTIR, Raman, XRF and MS in biological analysis has become an expanding field of research, driven by recent improvements in instrumentation and advanced multivariate data analysis strategies. Research groups around the world are studying biological systems using an increasing number of analytical techniques. The information obtained from these techniques in terms of lipids, proteins and the general metabolome is complementary, but normally the analysis of the data is performed individually on each technique. The lack of analytical data tools for integrating information obtained from different platforms makes the holistic comprehension of complex biological systems a challenge. The general aim is to develop new chemometric and multivariate imaging approaches to analyse data obtained from different spectroscopic modalities for the holistic analysis of biological samples.
Fig 1. Process to develop new chemometric and multivariate imaging approaches.
Data fusion methods used:
Data fusion can be defined as the process of integrating data obtained from different sources. In the study of complex biological materials, data from complementary sources can be jointly analysed for obtaining a comprehensive understanding of the system1 and can help in the study or diagnosis of illnesses2. Literature shows recent attempts at integrating data provided by different platforms 3,4, such as :
i) Statistical heterospectroscopy is used for the co-analysis of spectral datasets obtained from different spectroscopic platforms with multiple samples. The methodology performs a covariance map between the spectral dataset measured by the different techniques.5 This approach has already been employed for the correlation of NMR and MIR spectra 6 and NMR and CE spectra 7.
ii) Orthogonal partial least squares (O-PLS and O2-PLS) was used in the field of metabolomics and proteomics to integrate for example data from NMR and MS analytical platforms 8.
iii) Joint and Individual Variation Explained (JIVE).9 This method separates the shared patterns among data sources (i.e. the joint structure) from the individual structure of each data source that is unrelated to the joint structure.
Fig 2. Multimodal vibrational imaging of micrasterias (FTIR + RAMAN).
Both Raman and FTIR spectrometers can be integrated into optical microscopes enabling imaging of single cells. We aim to investigate the benefits and challenges of multimodal vibrational imaging of cells, defining multimodal images as hyperspectral images where each pixel is characterised by generating FTIR and Raman spectra. The combination of the two modes is a powerful and complementary approach for the investigation of cells. The multimodal approach provides a powerful method for future instruments that combine both Raman and FTIR imaging platforms.
Fig 3. UHCA of the multimodal image of a Micrasterias. (a) Custer image (b) Average infrared spectra of each class. (c) Visible image. (d) Raman average spectra of each class.
Publications: D. Perez-Guaita, K. Kochan, M. Martin, D. W. Andrew, P. Heraud, J. S. Richards, B. R. Wood, Multimodal vibrational imaging of cells, Vibrational Spectroscopy, DOI: 10.1016/j.vibspec.
Fig 4. Multimodal imaging of algae (FTIR + XRF).
Preliminary studies for the multimodal imaging of an algae cell using infrared and XRF microscopy is shown in Figure. In (a) the different images are registered, making it possible to create an extended data matrix (b). From this extended data matrix a standard PCA is performed (c) revealing similarities between the centre of the images which are shown in the first scores plot accounting for 34% of the explained variance. On the other hand, statistical heterospectroscopy (d) shows evidence of correlations in the second derivative of the IR spectrum with the concentration of the different elements. e.g. a shift of the band at 1260 cm-1 is related to the concentration of Si (to higher wavenumber values) and K (to low wavenumber values).
Example of data treatment for the data fusion of infrared and XRF imaging measurements of algae.
1. E. Acar, A. J. Lawaetz, M. A. Rasmussen and R. Bro, Conf. Proc. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. IEEE Eng. Med. Biol. Soc. Annu. Conf., 2013, 2013, 6023–6026.
2. R. Bro, H. J. Nielsen, F. Savorani, K. Kjeldahl, I. J. Christensen, N. Brünner and A. J. Lawaetz, Metabolomics, 2012, 9, 3–8.
3. T. Skov, A. H. Honoré, H. M. Jensen, T. Næs and S. B. Engelsen, TrAC Trends Anal. Chem., 2014, 60, 71–79.
4. K. Van Deun, I. Van Mechelen, L. Thorrez, M. Schouteden, B. De Moor, M. J. van der Werf, L. De Lathauwer, A. K. Smilde and H. A. L. Kiers, PLoS ONE, 2012, 7, e37840.
5. D. J. Crockford, E. Holmes, J. C. Lindon, R. S. Plumb, S. Zirah, S. J. Bruce, P. Rainville, C. L. Stumpf and J. K. Nicholson, Anal. Chem., 2006, 78, 363–371.
6. G. Graça, A. S. Moreira, A. J. V. Correia, B. J. Goodfellow, A. S. Barros, I. F. Duarte, I. M. Carreira, E. Galhano, C. Pita, M. do C. Almeida and A. M. Gil, Anal. Chim. Acta, 2013, 764, 24–31.
7. I. Garcia-Perez, A. Couto Alves, S. Angulo, J. V. Li, J. Utzinger, T. M. D. Ebbels, C. Legido-Quigley, J. K. Nicholson, E. Holmes and C. Barbas, Anal. Chem., 2010, 82, 203–210.
8. M. Rantalainen, O. Cloarec, O. Beckonert, I. D. Wilson, D. Jackson, R. Tonge, R. Rowlinson, S. Rayner, J. Nickson, R. W. Wilkinson, J. D. Mills, J. Trygg, J. K. Nicholson and E. Holmes, J. Proteome Res., 2006, 5, 2642–2655.
9. E. F. Lock, K. A. Hoadley, J. S. Marron and A. B. Nobel, Ann. Appl. Stat., 2013, 7, 523–542.