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Influence of media selection on NMR based metabolic profiling of human cell lines



Comparative metabolic profiling of different human cancer cell lines can reveal metabolic pathways up-regulated or down-regulated in each cell line, potentially providing insight into distinct metabolism taking place in different types of cancer cells. It is noteworthy, however, that human cell lines available from public repositories are deposited with recommended media for optimal growth, and if cell lines to be compared are cultured on different growth media, this introduces a potentially serious confounding variable in metabolic profiling studies designed to identify intrinsic metabolic pathways active in each cell line.


The goal of this study was to determine if the culture media used to grow human cell lines had a significant impact on the measured metabolic profiles.


NMR-based metabolic profiles of hydrophilic extracts of three human pancreatic cancer cell lines, AsPC-1, MiaPaCa-2 and Panc-1, were compared after culture on Dulbecco’s Modified Eagle Medium (DMEM) or Roswell Park Memorial Institute (RPMI-1640) medium.


Comparisons of the same cell lines cultured on different media revealed that the concentrations of many metabolites depended strongly on the choice of culture media. Analyses of different cell lines grown on the same media revealed insight into their metabolic differences.


The choice of culture media can significantly impact metabolic profiles of human cell lines and should be considered an important variable when designing metabolic profiling studies. Also, the metabolic differences of cells cultured on media recommended for optimal growth in comparison to a second growth medium can reveal critical insight into metabolic pathways active in each cell line.

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  1. Amores-Sánchez, M. I., & Medina, M. (1999). Glutamine, as a precursor of glutathione, and oxidative stress. Molecular Genetics and Metabolism, 67(2), 100–105.

  2. Balsa-Martinez, E., & Puigserver, P. (2015). Cancer cells hijack gluconeogenic enzymes to fuel cell growth. Molecular Cell, 60(4), 509–511.

  3. Bao, B., Azmi, A. S., Aboukameel, A., Ahmad, A., Bolling-Fischer, A., Sethi, S., et al. (2017). Pancreatic cancer stem-like cells display aggressive behavior mediated via activation of FoxQ1. The Journal of Biological Chemistry.

  4. Bayet-Robert, M., Loiseau, D., Rio, P., Demidem, A., Barthomeuf, C., Stepien, G., & Morvan, D. (2010). Quantitative two-dimensional HRMAS 1H-NMR spectroscopy-based metabolite profiling of human cancer cell lines and response to chemotherapy. Magnetic Resonance in Medicine, 63(5), 1172–1183.

  5. Brougham, D. F., Ivanova, G., Gottschalk, M., Collins, D. M., Eustace, A. J., O’Connor, R., et al. (2011). Artificial neural networks for classification in metabolomic studies of whole cells using 1H nuclear magnetic resonance. Journal of Biomedicine & Biotechnology, 2011, 158094.

  6. Cano, K. E., Li, Y.-J., & Chen, Y. (2010). NMR metabolomic profiling reveals new roles of SUMOylation in DNA damage response. Journal of Proteome Research, 9(10), 5382–5388.

  7. Cantó, C., Menzies, K. J., & Auwerx, J. (2015). NAD+ metabolism and the control of energy homeostasis: A balancing act between mitochondria and the nucleus. Cell Metabolism, 22, 31–53.

  8. Cao, M. D., Lamichhane, S., Lundgren, S., Bofin, A., Fjøsne, H., Giskeødegård, G. F., & Bathen, T. F. (2014). Metabolic characterization of triple negative breast cancer. BMC Cancer, 14(1), 941.

  9. Chatterjee, N., Yang, J., Yoon, D., Kim, S., Joo, S.-W., & Choi, J. (2017). Differential crosstalk between global DNA methylation and metabolomics associated with cell type specific stress response by pristine and functionalized MWCNT. Biomaterials, 115, 167–180.

  10. Chihanga, T., Ma, Q., Nicholson, J. D., Ruby, H. N., Edelmann, R. E., Devarajan, P., & Kennedy, M. A. (2017). NMR spectroscopy and electron microscopy identification of metabolic and ultrastructural changes to the kidney following ischemia reperfusion injury. American Journal of Physiology - Renal Physiology.

  11. Cuperlovic-Culf, M., Touaibia, M., St-Coeur, P.-D., Poitras, J., Morin, P., & Culf, A. S. (2014). Metabolic effects of known and novel HDAC and SIRT inhibitors in glioblastomas independently or combined with temozolomide. Metabolites, 4(3), 807–830.

  12. Daye, D., & Wellen, K. E. (2012a). Metabolic reprogramming in cancer: Unraveling the role of glutamine in tumorigenesis. Seminars in Cell and Developmental Biology, 23(4), 362–369.

  13. de Santana-Filho, A. P., Jacomasso, T., Riter, D. S., Barison, A., Iacomini, M., Winnischofer, S. M. B., & Sassaki, G. L. (2017). NMR metabolic fingerprints of murine melanocyte and melanoma cell lines: Application to biomarker discovery. Scientific Reports, 7, 42324.

  14. Deer, E. L., Gonzalez-Hernandez, J., Coursen, J. D., Shea, J. E., Ngatia, J., Scaife, C. L., et al. (2010). Phenotype and genotype of pancreatic cancer cell lines. Pancreas, 39(4), 425.

  15. Gierok, P., Harms, M., Richter, E., Hildebrandt, J.-P., Lalk, M., Mostertz, J., & Hochgräfe, F. (2014). Staphylococcus aureus alpha-toxin mediates general and cell type-specific changes in metabolite concentrations of immortalized human airway epithelial cells. PLoS ONE, 9(4), e94818.

  16. Goodpaster, A. M., & Kennedy, M. A. (2011). Quantification and statistical significance analysis of group separation in NMR-based metabonomics studies. Chemometrics and Intelligent Laboratory Systems, 109(2), 162–170.

  17. Goodpaster, A. M., Ramadas, E. H., & Kennedy, M. A. (2011). Potential effect of diaper and cotton ball contamination on NMR-and LC/MS-based metabonomics studies of urine from newborn babies. Analytical Chemistry, 83(3), 896–902.

  18. Goodpaster, A. M., Romick-Rosendale, L. E., & Kennedy, M. A. (2010). Statistical significance analysis of nuclear magnetic resonance-based metabonomics data. Analytical Biochemistry, 401(1), 134–143.

  19. Gradiz, R., Silva, H. C., Carvalho, L., Botelho, M. F., & Mota-Pinto, A. (2016). MIA PaCa-2 and PANC-1—Pancreas ductal adenocarcinoma cell lines with neuroendocrine differentiation and somatostatin receptors. Scientific Reports, 6(1), 21648.

  20. Jain, M., Kami, K., Ueno, Y., Naraoka, H., Tomita, M., & Nishioka, T. (2013). Oncometabolites: Linking altered metabolism with cancer. Science, 336(6084), 1040–1044.

  21. Lane, A. N., Tan, J., Wang, Y., Yan, J., Higashi, R. M., & Fan, T. W.-M. (2017). Probing the metabolic phenotype of breast cancer cells by multiple tracer stable isotope resolved metabolomics. Metabolic Engineering.

  22. Lefort, N., Brown, A., Lloyd, V., Ouellette, R., Touaibia, M., Culf, A. S., & Cuperlovic-Culf, M. (2014). 1H NMR metabolomics analysis of the effect of dichloroacetate and allopurinol on breast cancers. Journal of Pharmaceutical and Biomedical Analysis, 93, 77–85.

  23. Li, C., Zhang, G., Zhao, L., Ma, Z., & Chen, H. (2016). Metabolic reprogramming in cancer cells: Glycolysis, glutaminolysis, and Bcl-2 proteins as novel therapeutic targets for cancer. World Journal of Surgical Oncology, 14(1), 15.

  24. Lin, S.-H., Liu, T., Ming, X., Tang, Z., Fu, L., Schmitt-Kopplin, P., et al. (2016). Regulatory role of hexosamine biosynthetic pathway on hepatic cancer stem cell marker CD133 under low glucose conditions. Scientific Reports, 6(1), 21184.

  25. MacIntyre, D. A., Melguizo Sanchís, D., Jiménez, B., Moreno, R., Stojkovic, M., & Pineda-Lucena, A. (2011). Characterisation of human embryonic stem cells conditioning media by 1H-nuclear magnetic resonance spectroscopy. PLoS ONE, 6(2), e16732.

  26. Maria, R. M., Altei, W. F., Andricopulo, A. D., Becceneri, A. B., Cominetti, M. R., Venâncio, T., & Colnago, L. A. (2015). Characterization of metabolic profile of intact non-tumor and tumor breast cells by high-resolution magic angle spinning nuclear magnetic resonance spectroscopy. Analytical Biochemistry, 488, 14–18.

  27. Morin, P. J., Ferguson, D., LeBlanc, L. M., Hébert, M. J. G., Paré, A. F., Jean-François, J., et al. (2013). NMR metabolomics analysis of the effects of 5-lipoxygenase inhibitors on metabolism in glioblastomas. Journal of Proteome Research, 12(5), 2165–2176.

  28. Pan, X., Wilson, M., McConville, C., Arvanitis, T. N., Griffin, J. L., Kauppinen, R. A., & Peet, A. C. (2013). Increased unsaturation of lipids in cytoplasmic lipid droplets in DAOY cancer cells in response to cisplatin treatment. Metabolomics, 9(3), 722–729.

  29. Pan, X., Wilson, M., Mirbahai, L., McConville, C., Arvanitis, T. N., Griffin, J. L., et al. (2011). In vitro metabonomic study detects increases in UDP-GlcNAc and UDP-GalNAc, as early phase markers of cisplatin treatment response in brain tumor cells. Journal of Proteome Research, 10(8), 3493–3500.

  30. Pignatelli, M., Durbin, H., Bodmer, W. F., Hu, S., Klug, T., Zurawski, V., et al. (1990). Carcinoembryonic antigen functions as an accessory adhesion molecule mediating colon epithelial cell-collagen interactions. Proceedings of the National Academy of Sciences USA, 87(4), 1541–1545.

  31. Righi, V., Roda, J. M., Paz, J., Mucci, A., Tugnoli, V., Rodriguez-Tarduchy, G., et al. (2009). 1H HR-MAS and genomic analysis of human tumor biopsies discriminate between high and low grade astrocytomas. NMR in Biomedicine, 22(6), 629–637.

  32. Rogatzki, M. J., Ferguson, B. S., Goodwin, M. L., & Gladden, L. B. (2015). Lactate is always the end product of glycolysis. Frontiers in Neuroscience, 9(FEB), 1–7.

  33. Röhrig, F., & Schulze, A. (2016). The multifaceted roles of fatty acid synthesis in cancer. Nature Reviews Cancer, 16(11), 732–749.

  34. Santoyo-Ramos, P., Cristina, M., & Robles-Flores, M. (2012). The role of O-Linked β-N-acetylglucosamine (GlcNAc) modification in cell signaling. In Glycosylation. InTech.

  35. Schaffer, S. W., Allo, S., Harada, H., Stroo, W., Azuma, J., & Hamaguchi, T. (1989). Mechanism underlying the membrane-stabilizing activity of taurine. In H. Iwata, J. B. Lombardini & T. Segawa (Eds.), Taurine and the heart. Developments in cardiovascular medicine (Vol. 93). Boston: Springer.

  36. Schwarzfischer, P., Reinders, J., Dettmer, K., Kleo, K., Dimitrova, L., Hummel, M., et al. (2017). Comprehensive metaboproteomics of Burkitt’s and diffuse large B-cell lymphoma cell lines and primary tumor tissues reveals distinct differences in pyruvate content and metabolism. Journal of Proteome Research, 16(3), 1105–1120.

  37. Sciacovelli, M., & Frezza, C. (2016). Oncometabolites: Unconventional triggers of oncogenic signalling cascades. Free Radical Biology & Medicine, 100, 175–181.

  38. Sethi, J. K., & Vidal-Puig, A. (2010). Wnt signalling and the control of cellular metabolism. Biochemical Journal, 427(1). Retrieved May 20, 2017 from

  39. Shao, W., Gu, J., Huang, C., Liu, D., Huang, H., Huang, Z., et al. (2014). Malignancy-associated metabolic profiling of human glioma cell lines using 1H NMR spectroscopy. Molecular Cancer, 13, 197.

  40. Sorice, A., Siano, F., Capone, F., Guerriero, E., Picariello, G., Budillon, A., et al. (2016). Potential anticancer effects of polyphenols from chestnut shell extracts: Modulation of cell growth, and cytokinomic and metabolomic profiles. Molecules, 21(10), 1411.

  41. Spratlin, J. L., Pitts, T. M., Kulikowski, G. N., Morelli, M. P., Tentler, J. J., Serkova, N. J., & Eckhardt, S. G. (2011). Synergistic activity of histone deacetylase and proteasome inhibition against pancreatic and hepatocellular cancer cell lines. Anticancer Research, 31(4), 1093–103. Retrieved June 8, 2017 from

  42. Stipanuk, M. H., Dominy, J. E., Lee, J., & Coloso, R. M. (2006). Mammalian cysteine metabolism: New insights into regulation. The Journal of Nutrition, 136, 1652–1659.

  43. Teahan, O., Bevan, C. L., Waxman, J., & Keun, H. C. (2011). Metabolic signatures of malignant progression in prostate epithelial cells. The International Journal of Biochemistry & Cell Biology, 43(7), 1002–1009.

  44. Tiziani, S., Lodi, A., Khanim, F. L., Viant, M. R., Bunce, C. M., & Günther, U. L. (2009). Metabolomic profiling of drug responses in acute myeloid leukaemia cell lines. PLoS ONE, 4(1), e4251.

  45. Tripathi, P., Kamarajan, P., Somashekar, B. S., Mackinnon, N., Chinnaiyan, A. M., Kapila, Y. L., et al. (2012). Delineating metabolic signatures of head and neck squamous cell carcinoma: Phospholipase A2, a potential therapeutic target. International Journal of Biochemistry and Cell Biology, 44, 1852–1861.

  46. Vitvitsky, V., Garg, S. K., & Banerjee, R. (2011). Taurine biosynthesis by neurons and astrocytes. The Journal of biological chemistry, 286(37), 32002–32010.

  47. Wallace, M., Whelan, H., & Brennan, L. (2013). Metabolomic analysis of pancreatic beta cells following exposure to high glucose. Biochimica et Biophysica Acta (BBA): General Subjects, 1830(3), 2583–2590.

  48. Watanabe, M., Sheriff, S., Lewis, K. B., Cho, J., Tinch, S. L., Balasubramaniam, A., & Kennedy, M. A. (2012). Metabolic profiling comparison of human pancreatic ductal epithelial cells and three pancreatic cancer cell lines using NMR based metabonomics HHS public access. Journal of Molecular Biomarkers & Diagnosis.

  49. Wen, H., Xu, W. J., Jin, X., Oh, S., Phan, C. H. D., Song, J., et al. (2015). The roles of IP3 receptor in energy metabolic pathways and reactive oxygen species homeostasis revealed by metabolomic and biochemical studies. Biochimica et Biophysica Acta (BBA): Molecular Cell Research, 1853(11), 2937–2944.

  50. Wheaton, W. W., & Chandel, N. S. (2011). Hypoxia. 2. Hypoxia regulates cellular metabolism. American Journal of Physiology: Cell Physiology, 300(3). Retrieved July 20, 2017 from

  51. Wu, G., Fang, Y.-Z., Yang, S., Lupton, J. R., & Turner, N. D. (2004). Glutathione metabolism and its implications for health. The Journal of Nutrition, 134(3), 489–492. Retrived July 25, 2017 from

  52. Xia, J., Mandal, R., Sinelnikov, I., Broadhurst, D., & Wishart, D. S. (2012). MetaboAnalyst 2.0—A comprehensive server for metabolomic data analysis. Nucleic Acids Research, 40, W127–W133.

  53. Xia, J., Psychogios, N., Young, N., & Wishart, D. S. (2009). MetaboAnalyst: A web server for metabolomic data analysis and interpretation. Nucleic Acids Research, 37, W652–W660.

  54. Xia, J., Sinelnikov, I., Han, B., & Wishart, D. S. (2015). MetaboAnalyst 3.0—Making metabolomics more meaningful. Nucleic Acids Research, 43, W251–W257.

  55. Xia, J., & Wishart, D. S. (2011a). Web-based inference of biological patterns, functions and pathways from metabolomic data using MetaboAnalyst. Nature Protocols, 6(6), 743–760.

  56. Xia, J., & Wishart, D. S. (2011b). Metabolomic data processing, analysis, and interpretation using MetaboAnalyst. Current Protocols in Bioinformatics.

  57. Xia, J., & Wishart, D. S. (2016) Using metaboanalyst 3.0 for comprehensive metabolomics data analysis. Current Protocols in Bioinformatics, 55, 14.10.1–14.10.91.

  58. Yang, H., Zhou, L., Shi, Q., Zhao, Y., Lin, H., Zhang, M., & Zhao, S. (2015). SIRT 3-dependent GOT 2 acetylation status affects the malate–aspartate NADH shuttle activity and pancreatic tumor growth. The EMBO Journal, 34(8), 1110–1125.

  59. Yin, T., Zhang, Z., Cao, B., Duan, Q., Shi, P., Zhao, H., et al. (2016). Bmi1 inhibition enhances the sensitivity of pancreatic cancer cells to gemcitabine. Oncotarget, 7(24), 37192–37204.

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The instrumentation used in this work was obtained with the support of Miami University and the Ohio Board of Regents with funds used to establish the Ohio Eminent Scholar Laboratory where the work was performed.

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Correspondence to Michael A. Kennedy.

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Chihanga, T., Hausmann, S.M., Ni, S. et al. Influence of media selection on NMR based metabolic profiling of human cell lines. Metabolomics 14, 28 (2018).

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  • NMR
  • Metabonomics
  • Panc-1
  • MiaPaCa-2
  • AsPC-1
  • Culture media