Fourier transform infrared (FTIR) spectroscopy has been established as a fast spectroscopic method for biochemical analysis of cells and tissues. In this research we aimed to investigate FTIR's utility for identifying and characterizing different modes of cell death, using leukemic cell lines as a model system. CCRF-CEM and U937 leukemia cells were treated with arabinoside and doxorubicin apoptosis inducers, as well as with potassium cyanide, saponin, freezing-thawing, and H(2)O(2) necrosis inducers. Cell death mode was determined by various gold standard biochemical methods in parallel with FTIR-microscope measurements. Both cell death modes exhibit large spectral changes in lipid absorbance during apoptosis and necrosis; however, these changes are similar and thus cannot be used to distinguish apoptosis from necrosis. In contrast to the above confounding factor, our results reveal that apoptosis and necrosis can still be distinguished by the degree of DNA opaqueness to infrared light. Moreover, these two cell death modes also can be differentiated by their infrared absorbance, which relates to the secondary structure of total cellular protein. In light of these findings, we conclude that, because of its capacity to monitor multiple biomolecular parameters, FTIR spectroscopy enables unambiguous and easy analysis of cell death modes and may be useful for biochemical and medical applications.

BackgroundMost of the blood tests aiming for breast cancer screening rely on quantification of a single or few biomarkers. The aim of this study was to evaluate the feasibility of detecting breast cancer by analyzing the total biochemical composition of plasma as well as peripheral blood mononuclear cells (PBMCs) using infrared spectroscopy.MethodsBlood was collected from 29 patients with confirmed breast cancer and 30 controls with benign or no breast tumors, undergoing screening for breast cancer. PBMCs and plasma were isolated and dried on a zinc selenide slide and measured under a Fourier transform infrared (FTIR) microscope to obtain their infrared absorption spectra. Differences in the spectra of PBMCs and plasma between the groups were analyzed as well as the specific influence of the relevant pathological characteristics of the cancer patients.ResultsSeveral bands in the FTIR spectra of both blood components significantly distinguished patients with and without cancer. Employing feature extraction with quadratic discriminant analysis, a sensitivity of ~90 % and a specificity of ~80 % for breast cancer detection was achieved. These results were confirmed by Monte Carlo cross-validation. Further analysis of the cancer group revealed an influence of several clinical parameters, such as the involvement of lymph nodes, on the infrared spectra, with each blood component affected by different parameters.ConclusionThe present preliminary study suggests that FTIR spectroscopy of PBMCs and plasma is a potentially feasible and efficient tool for the early detection of breast neoplasms. An important application of our study is the distinction between benign lesions (considered as part of the non-cancer group) and malignant tumors thus reducing false positive results at screening. Furthermore, the correlation of specific spectral changes with clinical parameters of cancer patients indicates for possible contribution to diagnosis and prognosis.

Elucidation of the evolution of inflammatory bowel disease (IBD)

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We have developed a novel approach for detection of cancer based on biochemical analysis of peripheral blood plasma using Fourier transform infrared spectroscopy. This approach has proven to be quick, safe, minimal invasive, and effective. Our approach recognizes any signs of solid tumor presence, regardless of location in the body or cancer type by measuring a spectrum that gives information regarding the total molecular composition and structure of the peripheral blood samples. The analysis includes clinically relevant preprocessing and feature extraction with principal component analysis, and uses Fisher's linear discriminant analysis to classify between cancer patients and healthy controls. We evaluated our method with leave-one-out cross validation and were able to establish sensitivity of 93.33%, specificity of 87.8%, and overall accuracy of 90.7%. Using our method for cancer detection should result in fewer unnecessary invasive procedures and yield fast detection of solid tumors.