Raman spectroscopy and advanced mathematical modelling in the discrimination of human thyroid cell lines

Raman spectroscopy could offer non-invasive, rapid and an objective nature to cancer diagnostics. However, much work in this field has focused on resolving differences between cancerous and non-cancerous tissues, and lacks the reproducibility and interpretation to be put into clinical practice. Much work is needed on basic cellular differences between malignancy and normal. This would allow the establishment of a clinically relevant cellular based model to translate to tissue classification. Raman spectroscopy provides a very detailed biochemical analysis of the target material and to 'unlock' this potential requires sophisticated mathematical modelling such as neural networks as an adjunct to data interpretation. Commercially obtained cancerous and non-cancerous cells, cultured in the laboratory were used in Raman spectral measurements. Data trends were visualised through PCA and then subjected to neural network analysis based on self-organising maps; consisting of m maps, where m is the number of classes to be recognised. Each map approximates the statistical distribution of a given class. The neural network analysis provided a 95% accuracy for identification of the cancerous cell line and 92% accuracy for normal cell line. In this preliminay study we have demonstrated th ability to distinguish between "normal" and cancerous commercial cell lines. This encourages future work to establish the reasons underpinning these spectral differences and to move forward to more complex systems involving tissues. We have also shown that the use of sophisticated mathematical modelling allows a high degree of discrimination of 'raw' spectral data

Optical detection and grading of lung neoplasia by Raman microspectroscopy

The aim of this study was to investigate whether Raman spectroscopy could be used to identify and potentially grade lung neoplasia in cell samples. Normal human bronchial epithelial cells (HBEpCs) were analyzed by Raman spectroscopy and compared with (i) HBEpCs expressing human papillomavirus (HPV) type 16 E7 or CDK4; (ii) the immortalized bronchial epithelial cell line BEP2D and (iii) its asbestos-transformed derivative AsbTB2A. Overall, Raman spectroscopy, in combination with a linear discriminant analysis algorithm, was able to identify abnormal cells with a sensitivity of 91% and a specificity of 75%. Subdivision of the cell types into 3 groups, representing normal cells (HBEpCs), cells with extended lifespan (HBEpCs expressing HPV 16 E7 or CDK4) and immortalized/transformed cells (BEP2D and AsbTB2A) showed that Raman spectroscopy identifies cells in these categories correctly with sensitivities of 75, 79 and 87%, and specificities of 91, 85 and 96%, respectively. In conclusion, Raman spectroscopy can, with high sensitivity, detect the presence of neoplastic development in lung cells and identify the stage of this development accurately, suggesting that this minimally invasive optical technology has potential for lung cancer diagnosis. (C) 2008 Wiley-Liss. Inc.</p

Early detection of cervical neoplasia by Raman spectroscopy

Early detection of malignant tumours, or their precursor lesions, improves patient outcome. High risk human papillomavirus (HPV), particularly HPV16, infection can lead to the development of uterine cervical neoplasia, and therefore, the identification in clinical samples of the effects of HPV infection may have clinical value. In this report, we apply Raman microspectroscopy to live and fixed cultured cells to discriminate between defined cell types. Raman spectra were acquired from primary human keratinocytes (PHK), PHK expressing the E7 gene of HPV 16 (PHK E7) and CaSki cells, an HPV16-containing cervical carcinoma-derived cell line. Averaged Raman spectra showed variations, mostly in peaks originating from DNA and proteins, consistent with HPV gene expression and cellular changes associated with neoplasia, in both live and fixed cells. Principal component analysis produced good discrimination between the cell types, with sensitivities of up to 100% for the comparison of fixed PHK and CaSki. These results demonstrate the ability of Raman spectroscopy to discriminate between cell types representing different stages of cervical neoplasia. More specifically, this technique was able to identify cells expressing the HPV 16 E7 gene accurately and objectively, suggesting that this approach may be of value in diagnosis. Moreover, the ability to detect the effects of the virus in fixed samples also demonstrates the compatibility of Raman spectroscopy with current cervical screening methods. (c) 2007 Wiley-Liss, Inc.</p

A biospectroscopic interrogation of fine needle aspirates points towards segregation between graded categories:an initial study towards diagnostic screening

Fine needle aspirates (FNAs) of suspicious breast lesions are often used to aid the diagnosis of female breast cancer. Biospectroscopy tools facilitate the acquisition of a biochemical cell fingerprint representative of chemical bonds present in a biological sample. The mid-infrared (IR; 4,000-400 cm(-1)) is absorbed by the chemical bonds present, allowing one to derive an absorbance spectrum. Complementary to IR spectroscopy, Raman spectroscopy measures the scattering by chemical bonds following excitation by a laser to generate an intensity spectrum. Our objective was to apply these methods to determine whether a biospectroscopy approach could objectively segregate different categories of FNAs. FNAs of breast tissue were collected (n = 48) in a preservative solution and graded into categories by a cytologist as C1 (non-diagnostic), C2 (benign), C3 (suspicious, probably benign) or C5 (malignant) or C4 (suspicious, probably malignant); no samples falling within this category were identified during the collection period of the study. Following washing, the cellular material was transferred onto BaF(2) (IR-transparent) slides for interrogation by Raman or Fourier-transform IR (FTIR) microspectroscopy. In some cases where sufficient material was obtained, this was transferred to low-E (IR-reflective) glass slides for attenuated total reflection-FTIR spectroscopy. The spectral datasets produced from these techniques required multivariate analysis for data handling. Principal component analysis followed by linear discriminant analysis was performed independently on each of the spectral datasets for only C2, C3 and C5. The resulting scores plots revealed a marked overlap of C2 with C3 and C5, although the latter pair were both significantly segregated (