Characterizing variability in in vivo Raman spectroscopic properties of different anatomical sites of normal tissue in the oral cavity

Abstract

Raman spectroscopy is an inelastic light scattering technique that is capable of probing biochemical and biomolecular structures and conformations of tissue. This study aims to characterize the in vivo Raman spectroscopic properties of different normal oral tissues in the fingerprint region (800-1800 cm −1 ) and to assess distinctive biochemical variations of different anatomical regions in the oral cavity. A specially designed fiber-optic Raman probe with a ball lens was utilized for real-time, in vivo Raman measurements of various oral tissue sites (i.e. inner lip, attached gingiva, floor, dorsal tongue, ventral tongue, hard palate, soft palate, and buccal). The semiquantitative non-negativity-constrained least squares minimization fitting of reference biochemicals representing oral tissue constituents (i.e. hydroxyapatite, keratin, collagen, DNA, and oleic acid) and partial least squares-discriminant analysis (PLS-DA) were employed to assess the significance of inter-anatomical variability. A total of 402 high-quality in vivo oral Raman spectra were acquired from 20 subjects. The histological characteristics of different oral tissues were found to have influence on the in vivo Raman spectra and could be grossly divided into three major clusterings:(1) buccal, inner lip, and soft palate; (2) dorsal, ventral tongue, and floor; (3) gingiva and hard palate. The PLS-DA multiclass algorithms were able to identify different tissue sites with varying accuracies (inner lip 83.1%, attached gingiva 91.3%, floor 86.1%, dorsal tongue 88.8%, ventral tongue 83.1%, hard palate 87.6%, soft palate 83.3%, and buccal mucosa 85.3%), bringing out the similarities among different oral tissues at the biomolecular level. This study discloses that inter-anatomical variability is significant and should be considered as an important parameter in the interpretation and rendering of Raman diagnostic algorithms for oral tissue diagnosis and characterization.

In vivosubsite classification and diagnosis of oral cancers using Raman spectroscopy

“…Previous studies on¯nger-print and high wavenumber region have demonstrated that buccal and labial mucosa, keratinized or masticatory mucosa and specialized mucosa on the tongue can be classi¯ed as distinct clusters of subsites. [34][35][36] While some studies indicate that these subsite-anatomical di®erences confound the healthy and pathological discrimination, 37 other studies have stated that the inherent anatomical di®erences may not hinder healthy vs pathological classi¯cation. 47 In the present study, Raman spectroscopic di®erences between the three common subsites of oral cancer development in the Indian subcontinent-buccal mucosa, movable mucosa of the lip and tongue were evaluated.…”

“…Three major RS studies in thē nger-print and high wavenumber region have also demonstrated di®erences arising due to epithelial and sub-epithelial structures, submucosa and degree of keratinization in subsites of healthy subjects and suggested clustering of sites based on anatomical and spectral similarities. [34][35][36] While the¯rst in vivo study by Guze et al suggested that spectra for di®erent oral sites within the same ethnic group are signi¯cantly di®erent and clearly separable, the consequent study by Bergholt et al divided major subsites into three di®erent clusters based on their histological and spectroscopic characteristics. The three groups included -(a) buccal mucosa, inner lip and soft palate, (b) dorsal, ventral tongue and°o or of mouth, (c) gingiva and hard palate.…”

Raman spectroscopy: techniques and applications in the life sciences

“…Bergholt et al used a Raman fiber probe to study various locations within the oral cavity [426]. Short et al studied the gastrointestinal tract with Raman spectra in the high wavenumber region [427].…”

“…Bergholt et al have shown that the measurement location can have a large effect on the acquired Raman signal [426]. Because Raman spectroscopy is fundamentally a point-based measurement technique, additional information is required to ensure that data is acquired from the correct location.…”

“…While this simplifies the full spectrum into a collection of contributing components, it can sacrifice some of the physical meaning behind these features. Spectral deconvolution has been used to analyze Raman spectra in a variety of applications, including gastric cancer [426], effects of pesticides [472], red blood cell disease [473], breast cancer [474], and stem cell differentiation [475].…”