Multivariate Calibration Approach for Quantitative Determination of Cell-Line Cross Contamination by Intact Cell Mass Spectrometry and Artificial Neural Networks - PubMed (original) (raw)

Multivariate Calibration Approach for Quantitative Determination of Cell-Line Cross Contamination by Intact Cell Mass Spectrometry and Artificial Neural Networks

Elisa Valletta et al. PLoS One. 2016.

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.

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Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Fig 1

Fig 1

(A) Colony of human embryonic stem cells (hESCs) cultured on a feeder layer of mouse embryonic fibroblasts. (B) Experimental ratios of two-component mixtures of hESCs + MEFs. (C) Representative mass spectra of selected two-component mixtures of hESCs + MEFs. (D) Colony of mouse embryonic stem cells (mESCs) in a feeder-free culture. (E) Experimental ratios of two-component mixtures of hESCs + mESCs. (F) Representative mass spectra of selected two-component mixtures of hESCs + mESCs.

Fig 2

Fig 2

(A) Principal component analysis of mass spectra dataset containing intensities of 84 m/z for pure MEF and hESC populations and their 1:1 mixture. (B) Scree plot documenting the presence of three factors contributing predominantly to the overall variability in the analyzed dataset. (C) Pre-processed MALDI-TOF mass spectra for pure hESCs and MEFs and a hESCs + MEFs two-component mixture containing 99% hESCs and 1% MEFs. The spectra were normalized to vector of unit length (a.u.). Asterisks indicate peaks at m/z 3992 and 9908. (D) Surface plot of intensities of peaks at m/z 3992 and 9908 versus the number of MEFs in hESCs + MEFs two-component mixtures.

Fig 3

Fig 3

(A) Optimal ANN architecture (one Input layer, one Hidden layer with four neurons, and one Output layer). (B) Training and leave-one-out verification plot of the RMS versus the number of training cycles (epochs). First 50 000 iterations are shown. The inset shows a detailed plot for the first 10 000 training cycles.

Fig 4

Fig 4

(A) Correlation between ANN-predicted number of cells and the experimental number of MEFs in two-component mixtures of hESCs + MEFs. The inset shows the correlation between experimental and predicted values in low concentration ranges of MEFs up to the 50×103 cells in the two-component mixtures. (B) Overview of Residuals (difference between ANN-predicted number of cells and the experimental values) versus the experimental number of MEF cells in two-component mixtures of hESCs + MEFs. (C) Correlation between ANN-predicted number of cells and the experimental number of mESCs in two-component mixtures of hESCs + mESCs. (D) Overview of Residuals (difference between ANN-predicted number of cells and the experimental values) versus the experimental number of mESC cells in the two-component mixtures of hESCs + mESCs.

Fig 5

Fig 5. Overview for quantitative ANN-coupled MS-based analysis of cross-contamination.

Fig 6

Fig 6. Experimental schematic of the multivariate calibration-based ANN spectral analysis.

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Grants and funding

This study was supported by Grant Agency of Masaryk University (MUNI/M/0041/2013 and MUNI/A/1558/2014), from the European Regional Development Fund (CZ.1.05/1.1.00/02.0123 and CZ.1.07/2.3.00/20.0185) and by funds from the Faculty of Medicine MU to junior researcher (Petr Vaňhara). Elisa Valletta gratefully acknowledges the Masaryk University and its Department of Chemistry, for the kind hospitality and for funding her stay in Brno. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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