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Understanding the biochemical and biophysical properties of live cells is fundamental for unravelling the secrets of many diseases and developing new therapies. Raman micro-spectroscopy is a powerful non-invasive technique that allows in vitro studies of individual living cells or groups of cells without the use of any labels or contrast enhancing chemicals. We describe the use of various multivariate statistical methods, such as Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and Classical Least Square (CLS) fitting, to extract biochemical information related to various cellular events. Such methods are required because of the high complexity of the Raman spectra obtained from living cells. PCA and LDA are used to discriminate between healthy and tumor cells. A leave-one-out cross-validation method indicated high prediction accuracy (95%) in identification of tumorogenic bone cells. The CLS fitting method using commercially available biopolymers makes it possible to monitor biochemical changes during the differentiation of embryonic stem cells and foetal bone cells. The results suggest that in both cases differentiated cells are characterised by lower concentrations of RNA compared to undifferentiated cells. These studies suggest that Raman micro-spectroscopy could become an invaluable tool for in vitro cellular biochemistry studies. © 2005 Published by Elsevier B.V.

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Conference paper

Publication Date





179 - 185