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Project information
Bioanalytical Cell and Tissue Authentication using Physical Chemistry Methods and Artificial Intelligence

Information

This project doesn't include Institute of Computer Science. It includes Faculty of Medicine. Official project website can be found on muni.cz.
Project Identification
MUNI/M/0041/2013
Project Period
5/2013 - 12/2015
Investor / Pogramme / Project type
Masaryk University
  • Grant Agency of Masaryk University
  • INTERDISCIPLINARY - Interdisciplinary research projects
MU Faculty or unit
Faculty of Medicine
Other MU Faculty/Unit
Faculty of Science

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.

Publications

Total number of publications: 13

2016

2015

2014

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