Study of cells and tissue: Live cells microspectroscopy and multivariate data analysis

The first part of this thesis focuses on in vivo infrared (IR) spectroscopic methods for detection of spectral changes in live cells during the division cycle. Detecting these changes is of prime importance for the development of spectroscopic methods of diagnosis.; The second part deals with coupling infrared spectral imaging, multivariate data analysis, such as hierarchical cluster analysis (HCA), and classification by artificial neural networks (ANN). These methods promise to be a powerful tool for the detection and identification of cancer cells within lymph nodes.; HeLa cells were grown either in a specially designed liquid cell or deposited on IR microscope slides and sealed in a custom made IR cell. Perkin-Elmer Spotlight 300 and Smiths Detection IlluminatIR FT-IR microspectrometers were used for data acquisition.; Sections of lymph nodes, approximately 5 mum thick, were placed onto IR microscope slides for infrared analysis. Data acquisition was performed on an FT-IR imaging system, Perkin-Elmer Spotlight 300. IR spectra were imported to CytoSpecRTM software to execute data preprocessing for subsequent ANN data analysis, which was performed using NeuroDeveloper RTM software.; The author demonstrates that it is feasible to collect IR spectral data from live cells in an aqueous environment. This opens a wide variety of experiments on subjects such as drug uptake, drug mechanism and apoptosis.; The second part of the thesis indicates that IR spectral imaging, in conjunction with hierarchical cluster analysis and ANN data classification, offers potential for a quick, automated screening and diagnosis of cancer in lymph nodes.