Multivariate Calibration Approach for Quantitative Determination of Cell-Line Cross Contamination by Intact Cell Mass Spectrometry and Artificial Neural Networks
Jazyk angličtina Země Spojené státy americké Médium electronic-ecollection
Typ dokumentu časopisecké články, práce podpořená grantem
PubMed
26821236
PubMed Central
PMC4731057
DOI
10.1371/journal.pone.0147414
PII: PONE-D-15-42075
Knihovny.cz E-zdroje
- MeSH
- analýza hlavních komponent MeSH
- buněčné linie MeSH
- hmotnostní spektrometrie metody MeSH
- kalibrace MeSH
- kokultivační techniky MeSH
- lidé MeSH
- lidské embryonální kmenové buňky fyziologie MeSH
- multivariační analýza MeSH
- myši MeSH
- neuronové sítě (počítačové) * MeSH
- odběr biologického vzorku MeSH
- zvířata MeSH
- Check Tag
- lidé MeSH
- myši MeSH
- zvířata MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
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.
Department of Chemical and Geological Sciences University of Cagliari Monserrato Italy
Department of Chemistry Faculty of Science Masaryk University Brno Czech Republic
Department of Histology and Embryology Faculty of Medicine Masaryk University Brno Czech Republic
Zobrazit více v PubMed
Capes-Davis A, Theodosopoulos G, Atkin I, Drexler HG, Kohara A, MacLeod RAF, et al. (2010) Check your cultures! A list of cross-contaminated or misidentified cell lines. International Journal of Cancer 127: 1–8. PubMed
Marx V (2014) Cell-line authentication demystified. Nature Methods 11: 483–+. PubMed
Masters JRW (2010) Cell line misidentification: the beginning of the end. Nature Reviews Cancer 10: 441–448. 10.1038/nrc2852 PubMed DOI
Nardone RM (2007) Eradication of cross-contaminated cell lines: A call for action. Cell Biology and Toxicology 23: 367–372. PubMed
Hynds RE, Giangreco A (2013) Concise Review: The Relevance of Human Stem Cell-Derived Organoid Models for Epithelial Translational Medicine. Stem Cells 31: 417–422. 10.1002/stem.1290 PubMed DOI PMC
Mehling M, Tay S (2014) Microfluidic cell culture. Current Opinion in Biotechnology 25: 95–102. 10.1016/j.copbio.2013.10.005 PubMed DOI
Nienow AW (2006) Reactor engineering in large scale animal cell culture. Cytotechnology 50: 9–33. 10.1007/s10616-006-9005-8 PubMed DOI PMC
Baradez MO, Lekishvili T, Marshall D (2015) Rapid phenotypic fingerprinting of cell products by robust measurement of ubiquitous surface markers. Cytometry Part A 87A: 624–635. PubMed
Didion JP, Buus RJ, Naghashfar Z, Threadgill DW, Morse HC 3rd, de Villena FP (2014) SNP array profiling of mouse cell lines identifies their strains of origin and reveals cross-contamination and widespread aneuploidy. BMC Genomics 15: 847 10.1186/1471-2164-15-847 PubMed DOI PMC
Brougham DF, Ivanova G, Gottschalk M, Collins DM, Eustace AJ, O'Connor R, et al. (2011) Artificial neural networks for classification in metabolomic studies of whole cells using 1H nuclear magnetic resonance. J Biomed Biotechnol 2011: 158094 10.1155/2011/158094 PubMed DOI PMC
Houska J, Pena-Mendez EM, Hernandez-Fernaud JR, Salido E, Hampl A, Havel J, et al. (2014) Tissue profiling by nanogold-mediated mass spectrometry and artificial neural networks in the mouse model of human primary hyperoxaluria 1. Journal of Applied Biomedicine 12: 119–125.
Lui H, Zhao JH, McLean D, Zeng HS (2012) Real-time Raman Spectroscopy for In Vivo Skin Cancer Diagnosis. Cancer Research 72: 2491–2500. 10.1158/0008-5472.CAN-11-4061 PubMed DOI
Munteanu B, von Reitzenstein C, Hansch GM, Meyer B, Hopf C (2012) Sensitive, robust and automated protein analysis of cell differentiation and of primary human blood cells by intact cell MALDI mass spectrometry biotyping. Analytical and Bioanalytical Chemistry 404: 2277–2286. 10.1007/s00216-012-6357-0 PubMed DOI
Buchanan CM, Malik AS, Cooper GJS (2007) Direct visualisation of peptide hormones in cultured pancreatic islet alpha- and beta-cells by intact-cell mass spectrometry. Rapid Communications in Mass Spectrometry 21: 3452–3458. PubMed
Lokhov P, Balashova E, Dashtiev M (2009) Cell proteomic footprint. Rapid Communications in Mass Spectrometry 23: 680–682. 10.1002/rcm.3928 PubMed DOI
Maurer K, Eschrich K, Schellenberger W, Bertolini J, Rupf S, Remmerbach TW (2013) Oral brush biopsy analysis by MALDI-ToF Mass Spectrometry for early cancer diagnosis. Oral Oncology 49: 152–156. 10.1016/j.oraloncology.2012.08.012 PubMed DOI
Munteanu B, Hopf C (2013) Emergence of whole-cell MALDI-MS biotyping for high-throughput bioanalysis of mammalian cells? Bioanalysis 5: 885–893. 10.4155/bio.13.47 PubMed DOI
Zhang X, Scalf M, Berggren TW, Westphall MS, Smith LM (2006) Identification of mammalian cell lines using MALDI-TOF and LC-ESI-MS/MS mass spectrometry. Journal of the American Society for Mass Spectrometry 17: 490–499. PubMed
Dong HJ, Shen W, Cheung MTW, Liang YM, Cheung HY, Allmaier G, et al. (2011) Rapid detection of apoptosis in mammalian cells by using intact cell MALDI mass spectrometry. Analyst 136: 5181–5189. 10.1039/c1an15750g PubMed DOI
Hanrieder J, Wicher G, Bergquist J, Andersson M, Fex-Svenningsen A (2011) MALDI mass spectrometry based molecular phenotyping of CNS glial cells for prediction in mammalian brain tissue. Analytical and Bioanalytical Chemistry 401: 135–147. 10.1007/s00216-011-5043-y PubMed DOI
Munteanu B, Meyer B, von Reitzenstein C, Burgermeister E, Bog S, Pahl A, et al. (2014) Label-Free in Situ Monitoring of Histone Deacetylase Drug Target Engagement by Matrix-Assisted Laser Desorption Ionization-Mass Spectrometry Biotyping and Imaging. Analytical Chemistry 86: 4642–4647. 10.1021/ac500038j PubMed DOI
Povey JF, O'Malley CJ, Root T, Martin EB, Montague GA, Feary M, et al. (2014) Rapid high-throughput characterisation, classification and selection of recombinant mammalian cell line phenotypes using intact cell MALDI-ToF mass spectrometry fingerprinting and PLS-DA modelling. Journal of Biotechnology 184: 84–93. 10.1016/j.jbiotec.2014.04.028 PubMed DOI
Volta P, Riccardi N, Lauceri R, Tonolla M (2012) Discrimination of freshwater fish species by Matrix-Assisted Laser Desorption/Ionization-Time Of Flight Mass Spectrometry (MALDI-TOF MS): a pilot study. Journal of Limnology 71: 164–169.
Chiu NH, Jia Z, Diaz R, Wright P (2015) Rapid differentiation of in vitro cellular responses to toxic chemicals by using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry. Environ Toxicol Chem 34: 161–166. 10.1002/etc.2774 PubMed DOI
Kober SL, Meyer-Alert H, Grienitz D, Hollert H, Frohme M (2015) Intact cell mass spectrometry as a rapid and specific tool for the differentiation of toxic effects in cell-based ecotoxicological test systems. Anal Bioanal Chem. PubMed PMC
Asakawa D, Sakakura M, Takayama M (2012) Matrix effect on in-source decay products of peptides in matrix-assisted laser desorption/ionization. Mass Spectrom (Tokyo) 1: A0002. PubMed PMC
Bas D, Boyaci IH (2007) Modeling and optimization I: Usability of response surface methodology. Journal of Food Engineering 78: 836–845.
Goodacre R, Neal MJ, Kell DB (1996) Quantitative analysis of multivariate data using artificial neural networks: A tutorial review and applications to the deconvolution of pyrolysis mass spectra. Zentralblatt Fur Bakteriologie-International Journal of Medical Microbiology Virology Parasitology and Infectious Diseases 284: 516–539. PubMed
Li H, Zhang YX, Polaskova P, Havel J (2002) Enhancement of precision in the analysis of medicines by capillary electrophoresis using artificial neural networks. Acta Chimica Sinica 60: 1264–1268.
Amato F, Lopez A, Pena-Mendez EM, Vanhara P, Hampl A, Havel J (2013) Artificial neural networks in medical diagnosis. Journal of Applied Biomedicine 11: 47–58.
Basheer IA, Hajmeer M (2000) Artificial neural networks: fundamentals, computing, design, and application. Journal of Microbiological Methods 43: 3–31. PubMed
International Stem Cell Initiative, Adewumi O, Aflatoonian B, Ahrlund-Richter L, Amit M, Andrews PW, et al. (2007) Characterization of human embryonic stem cell lines by the International Stem Cell Initiative. Nat Biotechnol 25: 803–816. PubMed
Kotasova H, Vesela I, Kucera J, Houdek Z, Prochazkova J, Kralickova M, et al. (2012) Phosphoinositide 3-kinase inhibition enables retinoic acid-induced neurogenesis in monolayer culture of embryonic stem cells. Journal of Cellular Biochemistry 113: 563–570. 10.1002/jcb.23380 PubMed DOI
Barta T, Vinarsky V, Holubcova Z, Dolezalova D, Verner J, Pospisilova S, et al. (2010) Human embryonic stem cells are capable of executing G1/S checkpoint activation. Stem Cells 28: 1143–1152. 10.1002/stem.451 PubMed DOI
Holubcova Z, Matula P, Sedlackova M, Vinarsky V, Dolezalova D, Barta T, et al. (2011) Human embryonic stem cells suffer from centrosomal amplification. Stem Cells 29: 46–56. 10.1002/stem.549 PubMed DOI
Adewumi O, Aflatoonian B, Ahrlund-Richter L, Amit M, Andrews PW, Beighton G, et al. (2007) Characterization of human embryonic stem cell lines by the International Stem Cell Initiative. Nature Biotechnology 25: 803–816. PubMed
Amps K, Andrews PW, Anyfantis G, Armstrong L, Avery S, Baharvand H, et al. (2011) Screening ethnically diverse human embryonic stem cells identifies a chromosome 20 minimal amplicon conferring growth advantage. Nature Biotechnology 29: 1132–U1113. 10.1038/nbt.2051 PubMed DOI PMC
Hilario M, Kalousis A, Pellegrini C, Muller M (2006) Processing and classification of protein mass spectra. Mass Spectrometry Reviews 25: 409–449. PubMed
Mevik B-H, Wehrens R (2007) The pls Package: Principal Component and Partial Least Squares Regression in R. Journal of Statistical Software, 18: 1–24.
Amato F, Gonzalez-Hernandez JL, Havel J (2012) Artificial neural networks combined with experimental design: a "soft" approach for chemical kinetics. Talanta 93: 72–78. 10.1016/j.talanta.2012.01.044 PubMed DOI
Pivetta T, Isaia F, Trudu F, Pani A, Manca M, Perra D, et al. (2013) Development and validation of a general approach to predict and quantify the synergism of anti-cancer drugs using experimental design and artificial neural networks. Talanta 115: 84–93. 10.1016/j.talanta.2013.04.031 PubMed DOI
Alfassi ZB (2004) On the normalization of a mass spectrum for comparison of two spectra. Journal of the American Society for Mass Spectrometry 15: 385–387. PubMed
Filzmoser P, Gschwandtner M, Todorov V (2012) Review of sparse methods in regression and classification with application to chemometrics. Journal of Chemometrics 26: 42–51.
Friedman J, Hastie T, Tibshirani R (2010) Regularization Paths for Generalized Linear Models via Coordinate Descent. Journal of Statistical Software 33: 1–22. PubMed PMC
Chun H, Keles S (2010) Sparse partial least squares regression for simultaneous dimension reduction and variable selection. Journal of the Royal Statistical Society Series B-Statistical Methodology 72: 3–25. PubMed PMC
Rasmussen MA, Bro R (2012) A tutorial on the Lasso approach to sparse modeling. Chemometrics and Intelligent Laboratory Systems 119: 21–31.
Wold S, Sjöström M, Eriksson L (2001) PLS-regression: a basic tool of chemometrics. Chemometrics and Intelligent Laboratory Systems 58: 109–130.
Dittrich P, Ibanez AJ (2015) Analysis of metabolites in single cells-what is the best micro-platform? Electrophoresis. PubMed
Xie W, Gao D, Jin F, Jiang Y, Liu H (2015) Study of Phospholipids in Single Cells Using an Integrated Microfluidic Device Combined with Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry. Anal Chem 87: 7052–7059. 10.1021/acs.analchem.5b00010 PubMed DOI