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Development of methodology for Raman microspectroscopic analysis of oral exfoliated cells

Research output: Contribution to journalArticlepeer-review

Abstract

Oral squamous cell carcinoma ranks as the 15th most common cancer worldwide. The present study was undertaken to standardise a protocol for the analysis of oral exfoliated cells using Raman microspectroscopy. For this purpose, samples were obtained from two different sites, based on prevalence of disease (ventral side of the tongue and buccal mucosa). Different oral rinsing agents were employed and it was concluded that non-alcoholic mouthwash adequately removes food debris. Samples were collected using various collection tools and compared. It was observed that endo-cervical brushes yielded cells from deeper layers of the epithelium. Furthermore, monolayer formation of cells was carried out adopting cytospin and ThinPrep techniques and only the ThinPrep method provided flat and separated cells on the glass slide. Raman spectra were acquired from the nuclear and cytoplasmic regions of the cell using an XploRA confocal Raman instrument (HORIBA Jobin Yvon) with a 532 nm laser as the source. Glass spectral contamination was removed using non negatively constrained least squares (NNLS) algorithms. Corrected spectra were subjected to principal components analysis (PCA) which was able to differentiate the nucleus and cytoplasm regions of the cell; based on nucleic acid and protein features, respectively. However, no classification of the two anatomically different sites was observed according to PCA or PCA-LDA (linear discriminant analysis) using either the nuclear or cytoplasmic spectra. Nevertheless, the study has developed a standardised protocol for sample collection, sample preparation, spectral acquisition and data processing for future studies of oral exfoliated cells based on Raman microspectroscopy.

Original languageEnglish
Pages (from-to)937-948
Number of pages12
JournalAnalytical Methods
Volume9
Issue number6
DOIs
Publication statusPublished - 14 Feb 2017

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

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