The poor prognosis of oral cavity squamous cell carcinoma (OCSCC) patients is associated with residual tumor after surgery. Raman spectroscopy has the potential to provide an objective intra-operative evaluation of the surgical margins. Our aim was to understand the discriminatory basis of Raman spectroscopy at a histological level. In total, 127 pseudocolor Raman images were generated from unstained thin tissue sections of 25 samples (11 OCSCC and 14 healthy) of 10 patients. These images were clearly linked to the histopathological evaluation of the same sections after hematoxylin and eosin-staining. In this way, Raman spectra were annotated as OCSCC or as a surrounding healthy tissue structure (i.e., squamous epithelium, connective tissue (CT), adipose tissue, muscle, gland, or nerve). These annotated spectra were used as input for linear discriminant analysis (LDA) models to discriminate between OCSCC spectra and healthy tissue spectra. A database was acquired with 88 spectra of OCSCC and 632 spectra of healthy tissue. The LDA models could distinguish OCSCC spectra from the spectra of adipose tissue, nerve, muscle, gland, CT, and squamous epithelium in 100%, 100%, 97%, 94%, 93%, and 75% of the cases, respectively. More specifically, the structures that were most often confused with OCSCC were dysplastic epithelium, basal layers of epithelium, inflammation-and capillary-rich CT, and connective and glandular tissue close to OCSCC. Our study shows how well Raman spectroscopy enables discrimination between OCSCC and surrounding healthy tissue structures. This knowledge supports the development of robust and reliable classification algorithms for future implementation of Raman spectroscopy in clinical practice.

Human bone marrow stromal-derived mesenchymal stem cells (hBMSCs) will differentiate into chondrocytes in response to defined chondrogenic medium containing transforming growth factor-β (TGFβ). Results in the literature suggest that the three mammalian subtypes of TGFβ (TGFβ1, TGFβ2 and TGFβ3) provoke certain subtype-specific activities. Therefore, the aim of our study was to investigate whether the TGFβ subtypes affect chondrogenic differentiation of in vitro cultured hBMSCs differently. HBMSC pellets were cultured for 5 weeks in chondrogenic media containing either 2.5, 10 or 25 ng/ml of TGFβ1, TGFβ2 or TGFβ3. All TGFβ subtypes showed a comparable dose-response curve, with significantly less cartilage when 2.5 ng/ml was used and no differences between 10 and 25 ng/ml. Four donors with variable chondrogenic capacity were used to evaluate the effect of 10 ng/ml of either TGFβ subtype on cartilage formation. No significant TGFβ subtype-dependent differences were observed in the total amount of collagen or glycosaminoglycans. Cells from a donor with low chondrogenic capacity performed equally badly with all TGFβ subtypes, while a good donor overall performed well. After addition of β-glycerophosphate during the last 2 weeks of culture, the expression of hypertrophy markers was analysed and mineralization was demonstrated by alkaline phosphatase activity and alizarin red staining. No significant TGFβ subtype-dependent differences were observed in expression collagen type X or VEGF secretion. Nevertheless, pellets cultured with TGFβ1 had significantly less mineralization than pellets cultured with TGFβ3. In conclusion, this study suggests that TGFβ subtypes do affect terminal differentiation of in vitro cultured hBMSCs differently.

A Raman tissue spectrum is a quantitative representation of the overall molecular composition of that tissue. Raman spectra are often used as tissue fingerprints without further interpretation of the specific information that they contain about the tissue's molecular composition. In this study, we analyzed the differences in molecular composition between oral cavity squamous cell carcinoma (OCSCC) and healthy tissue structures in tongue, based on their Raman spectra. A total of 1087 histopathologically annotated spectra (142 OCSCC, 202 surface squamous epithelium, 61 muscle, 65 adipose tissue, 581 connective tissue, 26 gland, and 10 nerve) were obtained from Raman maps of 44 tongue samples from 21 patients. A characteristic, average spectrum of each tissue structure was fitted with a set of 55 pure-compound reference spectra, to define the best library of fit-spectra. Reference spectra represented proteins, lipids, nucleic acids, carbohydrates, amino acids and other miscellaneous molecules. A non-negative least-squares algorithm was used for fitting. Individual spectra per histopathological annotation were then fitted with this selected library in order to determine the molecular composition per tissue structure. The spectral contribution per chemical class was calculated. The results show that all characteristic tissue-type spectra could be fitted with a low residual of <4.82%. The content of carbohydrates, proteins and amino acids was the strongest discriminator between OCSCC and healthy tissue. The combination of carbohydrates, proteins and amino acids was used for a classification model of 'tumor' versus 'healthy tissue'. Validation of this model on an independent dataset showed a specificity of 93% at a sensitivity of 100%.