Spectral Cytopathology, SCP, is a novel approach for diagnostic differentiation of disease in individual exfoliated cells. SCP is carried out by collecting information on each cell’s biochemical composition via an infrared micro spectral measurement, followed by multivariate data analysis. Deviations from a cell’s natural composition produce specific spectral patterns that are exclusive to the cause of the deviation or disease. These unique spectral patterns are reproducible and can be identified and employed via multivariate statistical methods to detect cells compromised at the molecular level by dysplasia, neoplasia, or viral infection. In this proof of concept study, a benchmark for the sensitivity of SCP is established by classifying healthy oral squamous cells according to their anatomical origin in the oral cavity. Classification is achieved by spectrally detecting cells with unique protein expressions: for example, the squamous cells of the tongue are the only cell type in the oral cavity that have intracytoplasmic significant amounts of keratin, which allows them to be spectrally differentiated from other oral mucosa cells.Furthermore, thousands of cells from a number of clinical specimens were examined, among them squamous cell carcinoma, malignancy associate changes including reactive atypia, and infection by the herpes simplex virus. Due to its sensitivity to molecular changes, SCP often can detect the onset of disease earlier than is currently possible by cytopathology visualization. Since SCP is based on automated instrumentation and unsupervised software, it constitutes a diagnostic workup of medical samples devoid of bias and inconsistency. Therefore, SCP shows potential as complementary tool in medical cytopathology.
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