Online fluorescence suppression in modulated raman spectroscopy

Anna Chiara De Luca, Michael Mazilu, Andrew Riches, C. Simon Herrington, Kishan Dholakia

Research output: Contribution to journalArticle

87 Citations (Scopus)

Abstract

Label-free chemical characterization of single cells is an important aim for biomedical research. Standard Raman spectroscopy provides intrinsic biochemical markers for noninvasive analysis of biological samples but is often hindered by the presence of fluorescence background. In this paper, we present an innovative modulated Raman spectroscopy technique to filter out the Raman spectra from the fluorescence background. The method is based on the principle that the fluorescence background does not change whereas the Raman scattering is shifted by the periodical modulation of the laser wavelength. Exploiting this physical property and importantly the multichannel lock-in detection of the Raman signal, the modulation technique fulfills the requirements of an effective fluorescence subtraction method. Indeed, once the synchronization and calibration procedure is performed, minimal user intervention is required, making the method online and less time-consuming than the other fluorescent suppression methods. We analyze the modulated Raman signal and shifted excitation Raman difference spectroscopy (SERDS) signal of 2 μm-sized polystyrene beads suspended in a solution of fluorescent dye as a function of modulation rate. We show that the signal-to-noise ratio of the modulated Raman spectra at the highest modulation rate is 3 times higher than the SERDS one. To finally evaluate the real benefits of the modulated Raman spec- troscopy, we apply our technique to Chinese hamster ovary cells (CHO). Specifically, by analyzing separate spectra from the membrane, cytoplasm, and nucleus of CHO cells, we demonstrate the ability of this method to obtain localized sensitive chemical information from cells, away from the interfering fluorescence background. In particular, statistical analysis of the Raman data and classification using PCA (principal component analysis) indicate that our method allows us to distinguish between different cell locations with higher sensitivity and specificity, avoiding potential misinterpretation of the data obtained using standard background procedures.

Original languageEnglish
Pages (from-to)738-745
Number of pages8
JournalAnalytical Chemistry
Volume82
Issue number2
DOIs
Publication statusPublished - 2010 Jan 15

All Science Journal Classification (ASJC) codes

  • Analytical Chemistry

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