Raman spectroscopy for cytopathology of exfoliated cervical cells.

Cervical cancer is the fourth most common cancer affecting women worldwide but mortality can be decreased by early detection of pre-malignant lesions. The Pap smear test is the most commonly used method in cervical cancer screening programmes. Although specificity is high for this test, it is widely acknowledged that sensitivity can be poor mainly due to the subjective nature of the test. There is a need for new objective tests for the early detection of pre-malignant cervical lesions. Over the past two decades, Raman spectroscopy has emerged as a promising new technology for cancer screening and diagnosis. The aim of this study was to evaluate the potential of Raman spectroscopy for cervical cancer screening using both Cervical Intraepithelial Neoplasia (CIN) and Squamous Intraepithelial Lesion (SIL) classification terminology. ThinPrep® Pap samples were recruited from a cervical screening population. Raman spectra were recorded from single cell nuclei and subjected to multivariate statistical analysis. Normal and abnormal ThinPrep® samples were discriminated based on the biochemical fingerprint of the cells using Principal Component Analysis (PCA). Principal Component Analysis - Linear Discriminant Analysis (PCA-LDA) was employed to build classification models based on either CIN or SIL terminology. This study has shown that Raman spectroscopy can be successfully applied to the study of routine cervical cytology samples from a cervical screening programme and that the use of CIN terminology resulted in improved sensitivity for high grade cases.

Analyses of ionizing radiation effects in vitro in peripheral blood lymphocytes with Raman spectroscopy.

The use of Raman spectroscopy to measure the biochemical profile of healthy and diseased cells and tissues may be a potential solution to many diagnostic problems in the clinic. Although extensively used to identify changes in the biochemical profiles of cancerous cells and tissue, Raman spectroscopy has been used less often for analyzing changes to the cellular environment by external factors such as ionizing radiation. In tandem with this, the biological impact of low doses of ionizing radiation remains poorly understood. Extensive studies have been performed on the radiobiological effects associated with radiation doses above 0.1 Gy, and are well characterized, but recent studies on low-dose radiation exposure have revealed complex and highly variable responses. We report here the novel finding that demonstrate the capability of Raman spectroscopy to detect radiation-induced damage responses in isolated lymphocytes irradiated with doses of 0.05 and 0.5 Gy. Lymphocytes were isolated from peripheral blood in a cohort of volunteers, cultured ex vivo and then irradiated. Within 1 h after irradiation spectral effects were observed with Raman microspectroscopy and principal component analysis and linear discriminant analysis at both doses relative to the sham-irradiated control (0 Gy). Cellular DNA damage was confirmed using parallel γ-H2AX fluorescence measurements on the extracted lymphocytes per donor and per dose. DNA damage measurements exhibited interindividual variability among both donors and dose, which matched that seen in the spectral variability in the lymphocyte cohort. Further evidence of links between spectral features and DNA damage was also observed, which may potentially allow noninvasive insight into the DNA remodeling that occurs after exposure to ionizing radiation.

Competitive evaluation of data mining algorithms for use in classification of leukocyte subtypes with Raman microspectroscopy.

Raman microspectroscopy has been investigated for some time for use in label-free cell sorting devices. These approaches require coupling of the Raman spectrometer to complex data mining algorithms for identification of cellular subtypes such as the leukocyte subpopulations of lymphocytes and monocytes. In this study, three distinct multivariate classification approaches, (PCA-LDA, SVMs and Random Forests) are developed and tested on their ability to classify the cellular subtype in extracted peripheral blood mononuclear cells (T-cell lymphocytes from myeloid cells), and are evaluated in terms of their respective classification performance. A strategy for optimisation of each of the classification algorithm is presented with emphasis on reduction of model complexity in each of the algorithms. The relative classification performance and performance characteristics are highlighted, overall suggesting the radial basis function SVM as a robust option for classification of leukocytes with Raman microspectroscopy.

Imaging live cells grown on a three dimensional collagen matrix using Raman microspectroscopy.

Three dimensional collagen gels have been used as matrices for the imaging of live cells by Raman spectroscopy. The study is conducted on a human lung adenocarcinoma (A549) and a spontaneously immortalized human epithelial keratinocyte (HaCaT) cell line. The lateral resolution of the system has been estimated to be <1.5 µm making it possible to access the subcellular organization. Using K-means clustering analysis, it is shown that the different subcellular compartments of individual cells can be identified and differentiated. The biochemical specificity of the information contained in the Raman spectra allows the visualization of differences in the molecular signature of the different sub-cellular structures. Furthermore, to enhance the chemical information obtained from the spectra, principal component analysis has been employed, allowing the identification of spectral windows with a high variability. The comparison between the loadings calculated and spectra from pure biochemical compounds enables the correlation of the variations observed with the molecular content of the different cellular compartments.

Three dimensional collagen gels as a cell culture matrix for the study of live cells by Raman spectroscopy.

Three dimensional collagen gels are evaluated as matrices for the study of live cells by Raman spectroscopy. The study is conducted on a human lung adenocarcinoma (A549) and a spontaneously immortalized human epithelial keratinocyte (HaCaT) cell line. It is demonstrated, using the Alamar Blue assay, that both cell models exhibit enhanced viability in collagen matrices compared to quartz substrates, commonly used for Raman spectroscopy. Using principal component analysis, it is shown that the Raman spectral analysis of cells in collagen matrices is minimally contaminated by substrate contributions and the cell to cell spectral variations are greatly reduced compared to those measured on quartz substrates. Furthermore, the spectral measurements are seen to have little contribution from the cell culture medium, implying that cultures can be kept viable over prolonged measurement or mapping procedures.

Spectroscopic and chemometric approaches to radiobiological analyses.

Vibrational spectroscopy is an attractive modality for the analysis of biological samples, providing a complete non-invasive acquisition of the biochemical fingerprint of the sample. It has been demonstrated that this data provides the means to assay multiple functional responses of a biological system at a spatial resolution as low as a micron within the sample. As the interaction of ionizing radiation with biological systems involves chemical reactions between the products of radiation-induced damage and various structural and functional units within the cell, the vibrational spectroscopic modalities have received attention as potential measurement platforms for the in situ examination of the chemistry of biological species in radiobiology. This presents challenges in relation to sample preparation and the construction of suitable analytical methodologies. In this work protocols for sample preparation and approaches to multivariate analysis of vibrational spectra in radiobiological analysis are detailed and the utility of the methodology in analyzing the evolution of biochemical responses to radiobiological damage are highlighted.

Fourier transform infrared microspectroscopy and multivariate methods for radiobiological dosimetry.

The scientific literature contains an ever-growing number of reports of applications of vibrational spectroscopy as a multivariate non-invasive tool for analysis of biological effects at the molecular level. Recently, Fourier transform infrared microspectroscopy (FTIRM) has been demonstrated to be sensitive to molecular events occurring in cells and tissue after exposure to ionizing radiation. In this work the application of FTIRM in the examination of dose-dependent molecular effects occurring in skin cells after exposure to ionizing radiation with the use of partial least-squares regression (PLSR) and generalized regression neural networks (GRNN) was studied. The methodology is shown to be sensitive to molecular events occurring with radiation dose and time after exposure. The variation in molecular species with dose and time after irradiation is shown to be non-linear by virtue of the higher modeling efficiency yielded from the non-linear algorithms. Dose prediction efficiencies of approximately +/-10 mGy were achieved at 96 h after irradiation, highlighting the potential applications of the methodology in radiobiological dosimetry.

Vibrational spectroscopy for cervical cancer pathology, from biochemical analysis to diagnostic tool.

Cervical cancer is the second most common cancer in women worldwide with 80% of cases arising in the developing world. The mortality associated with cervical cancer can be reduced if this disease is detected at the early stages of development or at the pre-malignant state (cervical intraepithelial neoplasia, CIN). The aim of this study was to investigate the potential of Raman spectroscopy as a diagnostic tool to detect biochemical changes accompanying cervical cancer progression. Raman spectra were acquired from proteins, nucleic acids, lipids and carbohydrates in order to gain an insight into the biochemical composition of cells and tissues. Spectra were also obtained from histological samples of normal, CIN and invasive carcinoma tissue from 40 patients. Multivariate analysis of the spectra was carried out to develop a classification model to discriminate normal from abnormal tissue. The results show that Raman spectroscopy displays a high sensitivity to biochemical changes in tissue during disease progression resulting in an exceptional prediction accuracy when discriminating between normal cervical tissue, invasive carcinoma and cervical intraepithelial neoplasia (CIN). Raman spectroscopy shows enormous clinical potential as a rapid non-invasive diagnostic tool for cervical and other cancers.