Quantitative multi-image analysis for biomedical Raman spectroscopic imaging.

Autor: Hedegaard MA; Department of Chemical Engineering, Biotechnology and Environmental Technology, University of Southern Denmark, Campusvej 55, 5230, Odense M, Denmark. marhe@kbm.sdu.dk., Bergholt MS; Department of Materials, Department of Bioengineering and Institute of Biomedical Engineering, Imperial College London, London, SW7 2AZ, United Kingdom., Stevens MM; Department of Materials, Department of Bioengineering and Institute of Biomedical Engineering, Imperial College London, London, SW7 2AZ, United Kingdom.
Jazyk: angličtina
Zdroj: Journal of biophotonics [J Biophotonics] 2016 May; Vol. 9 (5), pp. 542-50. Date of Electronic Publication: 2016 Feb 02.
DOI: 10.1002/jbio.201500238
Abstrakt: Imaging by Raman spectroscopy enables unparalleled label-free insights into cell and tissue composition at the molecular level. With established approaches limited to single image analysis, there are currently no general guidelines or consensus on how to quantify biochemical components across multiple Raman images. Here, we describe a broadly applicable methodology for the combination of multiple Raman images into a single image for analysis. This is achieved by removing image specific background interference, unfolding the series of Raman images into a single dataset, and normalisation of each Raman spectrum to render comparable Raman images. Multivariate image analysis is finally applied to derive the contributing 'pure' biochemical spectra for relative quantification. We present our methodology using four independently measured Raman images of control cells and four images of cells treated with strontium ions from substituted bioactive glass. We show that the relative biochemical distribution per area of the cells can be quantified. In addition, using k-means clustering, we are able to discriminate between the two cell types over multiple Raman images. This study shows a streamlined quantitative multi-image analysis tool for improving cell/tissue characterisation and opens new avenues in biomedical Raman spectroscopic imaging.
(© 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)
Databáze: MEDLINE
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