RAPID AUTHENTICATION OF ANIMAL-CELL LINES USING PYROLYSIS MASS-SPECTROMETRY AND AUTOASSOCIATIVE ARTIFICIAL NEURAL NETWORKS

Pyrolysis mass spectrometry (PyMS) was used to produce biochemical fin gerprints from replicate frozen cell cultures of mouse macrophage hybr idoma 2C11-12, human leukaemia K562, baby hamster kidney BHK 21/C13, a nd mouse tumour BW-O, and a fresh culture of Chinese hamster ovary CHO cells. The dimensionality of these data was reduced by the unsupervis ed feature extraction pattern recognition technique of auto-associativ e neural networks. The clusters observed were compared with the groups obtained from the more conventional statistical approaches of hierarc hical cluster analysis. It was observed that frozen and fresh cell lin e cultures gave very different pyrolysis mass spectra. When only the f rozen animal cells were analysed by PyMS, auto-associative artificial neural networks (ANNs) were employed to discriminate between them succ essfully. Furthermore, very similar classifications were observed when the same spectral data were analysed using hierarchical cluster analy sis. We demonstrate that this approach can detect the contamination of cell lines with low numbers of bacteria and fungi; this approach coul d plausibly be extended for the rapid detection of mycoplasma infectio n in animal cell lines. The major advantages that PyMS offers over mor e conventional methods used to type cell lines and to screen for micro bial infection, such as DNA fingerprinting, are its speed, sensitivity and the ability to analyse hundreds of samples per day. We conclude t hat the combination of PyMS and ANNs can provide a rapid and accurate discriminatory technique for the authentication of animal cell line cu ltures.