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Rapid authentication of animal cell lines using pyrolysis mass spectrometry and auto-associative artificial neural networks (1996)

Goodacre, Royston ; Rischert, Deborah J. ; Evans, Peter M. ; [et al.]

Springer

Cytotechnology 21 (1996), S. 231-241

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ISSN: 1573-0778

Keywords: authentication ; auto-associative neural networks ; chemometrics ; feature extraction ; pyrolysis mass spectrometry ; cell line

Source: Springer Online Journal Archives 1860-2000

Topics: Biology , Medicine , Process Engineering, Biotechnology, Nutrition Technology

Notes: Summary Pyrolysis mass spectrometry (PyMS) was used to produce biochemical fingerprints from replicate frozen cell cultures of mouse macrophage hybridoma 2C11-12, human leukaemia K562, baby hamster kidney BHK 21/C13, and mouse tumour BW-O, and a fresh culture of Chinese hamster ovary CHO cells. The dimensionality of these data was reduced by the unsupervised feature extraction pattern recognition technique of auto-associative neural networks. The clusters observed were compared with the groups obtained from the more conventional statistical approaches of hierarchical cluster analysis. It was observed that frozen and fresh cell line cultures gave very different pyrolysis mass spectra. When only the frozen animal cells were analysed by PyMS, auto-associative artificial neural networks (ANNs) were employed to discriminate between them successfully. Furthermore, very similar classifications were observed when the same spectral data were analysed using hierarchical cluster analysis. We demonstrate that this approach can detect the contamination of cell lines with low numbers of bacteria and fungi; this approach could plausibly be extended for the rapid detection of mycoplasma infection in animal cell lines. The major advantages that PyMS offers over more conventional methods used to type cell lines and to screen for microbial infection, such as DNA fingerprinting, are its speed, sensitivity and the ability to analyse hundreds of samples per day. We conclude that the combination of PyMS and ANNs can provide a rapid and accurate discriminatory technique for the authentication of animal cell line cultures.

Type of Medium: Electronic Resource

URL: http://dx.doi.org/10.1007/BF00365346

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Rapid screening for metabolite overproduction in fermentor broths, using pyrolysis mass spectrometry with multivariate calibration and artificial neural networks (1994)

Goodacre, Royston ; Trew, Sally ; Wrigley-Jones, Carys ; [et al.]

New York, NY [u.a.] : Wiley-Blackwell

Biotechnology and Bioengineering 44 (1994), S. 1205-1216

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ISSN: 0006-3592

Keywords: pyrolysis mass spectrometry ; artificial neural networks ; fermentor broths ; regression analysis ; chemometrics ; Chemistry ; Biochemistry and Biotechnology

Source: Wiley InterScience Backfile Collection 1832-2000

Topics: Biology , Process Engineering, Biotechnology, Nutrition Technology

Notes: Binary mixtures of model systems consisting of the antibiotic ampicillin with either Escherichia coli or Staphylococcus auresu were subjected to pyrolysis mass spectrometry (PyMS). To deconvolute the pyrolysis mass spectra, so as to obtain quantitative information on the concentration of ampicilin in the mixtures, partial least squares regression (PLS), principal components regression (PCR), and fully interconnected feedforward artificial neural networks (ANNs) were studied. In the latter case, the weights were modified using the standard backpropagation algorithm, and the nodes used a sigmoidal squsahing funciton. It was found that each of the methods could be used to provide calibration models which gave excellent predictions for the concentrations of ampicillin in samples on which they had not been trained. Furthermore, ANNs trained to predict the amount of ampicilin in E. coli were able to generalise so as to predict the concentration of ampicillin in a S. aureus background, illustrating the robustness of ANNs to rather substantial variations in the biological background. The PyMS of the complex mixture of ampicilin in bacteria could not be expressed simply in terms of additive combinations of the spectra describing the pure components of the mixtures and their relative concentrations. Intermolecular reactions took place in the pyrolysate, leading to a lack of superposition of the spectral components and to a dependence of the normalized mass spectrum on sample size. Samples from fermentations of a single organism in a complex production medium were also analyzed quantitatively for a drug of commercial interest. The drug could also be quantified in a variety of mutant-producing strains cultivated in the same medium. The combination of PyMS and ANNs constitutes a novel, rapid, and convenient method for exploitation in strain improvement screening programs. © 1994 John Wiley & Sons, Inc.

Additional Material: 8 Ill.