Abstract
Cross-contamination of eukaryotic cell lines used in biomedical research represents a highly relevant problem. Analysis of repetitive DNA sequences, such as Short Tandem Repeats (STR), or Simple Sequence Repeats (SSR), is a widely accepted, simple, and commercially available technique to authenticate cell lines. However, it provides only qualitative information that depends on the extent of reference databases for interpretation. In this work, we developed and validated a rapid and routinely applicable method for evaluation of cell culture cross-contamination levels based on mass spectrometric fingerprints of intact mammalian cells coupled with artificial neural networks (ANNs). We used human embryonic stem cells (hESCs) contaminated by either mouse embryonic stem cells (mESCs) or mouse embryonic fibroblasts (MEFs) as a model. We determined the contamination level using a mass spectra database of known calibration mixtures that served as training input for an ANN. The ANN was then capable of correct quantification of the level of contamination of hESCs by mESCs or MEFs. We demonstrate that MS analysis, when linked to proper mathematical instruments, is a tangible tool for unraveling and quantifying heterogeneity in cell cultures. The analysis is applicable in routine scenarios for cell authentication and/or cell phenotyping in general.
Highlights
In current biomedical research, cells cultured in vitro are irreplaceable experimental models and biotechnological tools
We demonstrate that mass spectra contain sufficient information to identify the presence of individual cell types in mixtures, and we report for the first time that artificial neural networks (ANNs) analysis of mass spectra from two-component mixtures can correctly predict the level of cell cross-contamination in very complex microenvironment
We prepared calibration datasets consisting of twenty-eight defined two-component mixtures of human embryonic stem cells (hESCs) + mouse embryonic fibroblasts (MEFs), thirty-four mixtures of hESCs + mouse embryonic stem cells (mESCs) (Fig 1A and 1D), and pure cell populations, with total cell numbers of 1×106 (Fig 1B and 1E)
Summary
Cells cultured in vitro are irreplaceable experimental models and biotechnological tools. We demonstrate that mass spectra contain sufficient information to identify the presence of individual cell types in mixtures, and we report for the first time that ANN analysis of mass spectra from two-component mixtures can correctly predict the level of cell cross-contamination in very complex microenvironment. The intensities of processed mass spectra served as the input, while the number of contaminating cells in the two-component mixtures was the output.
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