Identification of cells, tissues or evaluation of their state and pathology relies nowadays mainly on light microscopy, genetic analysis or molecular characterization using specific cell or tissue markers. However, changes in cell phenotype that do not show alterations in cell morphology, karyotype composition, genome re-arrangements or simply are not covered by particular molecular markers may stay unrecognized. Advanced bioanalytical methods, such matrix-assisted laser desorption/ionization – time of flight mass spectrometry (MALDI-TOF MS) provide a powerful tool for discrimination and characterization of various chemical compounds, dependent on their mass to charge ratio (m/z). MALDI-TOF mass spectra generated from ionized molecules desorbed from the whole cells and tissues are very complex and depend strongly on the experimental conditions, matrix choice, machine setup or even the operator skill. However, they may serve as input data for sophisticated mathematical analysis, e.g. artificial intelligence or artificial neural networks (AI/ANN) resulting in complementary image of cell or tissue compositions. Direct whole cell- or tissue-MALDI-TOF MS followed by AI/ANN bypass the need for particular markers and/or direct observation that is sometimes limited. By using anonymous mass spectra aligned with already known characteristics may provide fast, robust and independent method for recognition of the whole cells and tissues and to be suitable even for automated diagnostics. This projects aims for adaptation of current MALDI-TOF technology for cytological and histological evaluations and together with AI/ANN develop a framework for diagnosing of tissue samples, archive slides or clinical-grade cell cultures.

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