Skip to main content

Experimental design and reporting standards for metabolomics studies of mammalian cell lines

Cellular and Molecular Life Sciences volume 74, pages 4421–4441 (2017)Cite this article

Abstract

Metabolomics is an analytical technique that investigates the small biochemical molecules present within a biological sample isolated from a plant, animal, or cultured cells. It can be an extremely powerful tool in elucidating the specific metabolic changes within a biological system in response to an environmental challenge such as disease, infection, drugs, or toxins. A historically difficult step in the metabolomics pipeline is in data interpretation to a meaningful biological context, for such high-variability biological samples and in untargeted metabolomics studies that are hypothesis-generating by design. One way to achieve stronger biological context of metabolomic data is via the use of cultured cell models, particularly for mammalian biological systems. The benefits of in vitro metabolomics include a much greater control of external variables and no ethical concerns. The current concerns are with inconsistencies in experimental procedures and level of reporting standards between different studies. This review discusses some of these discrepancies between recent studies, such as metabolite extraction and data normalisation. The aim of this review is to highlight the importance of a standardised experimental approach to any cultured cell metabolomics study and suggests an example procedure fully inclusive of information that should be disclosed in regard to the cell type/s used and their culture conditions. Metabolomics of cultured cells has the potential to uncover previously unknown information about cell biology, functions and response mechanisms, and so the accurate biological interpretation of the data produced and its ability to be compared to other studies should be considered vitally important.

This is a preview of subscription content, access via your institution.

Access options

Buy single article

Instant access to the full article PDF.

USD 39.95

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Fig. 1

Adapted from Dettmer et al. [5]

Fig. 2
Fig. 3

References

  1. León Z, García-Cañaveras JC, Donato MT, Lahoz A (2013) Mammalian cell metabolomics: experimental design and sample preparation. Electrophoresis 34(19):2762–2775

    PubMed  Google Scholar 

  2. Idle JR, Gonzalez FJ (2007) Metabolomics. Cell Metab 6:348–351

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  3. Blow N (2008) Biochemistry’s new look. Nature 455:697–700

    CAS  Article  PubMed  Google Scholar 

  4. Fiehn O (2002) Metabolomics—the link between genotypes and phenotypes. Plant Mol Biol 48(1–2):155–171

    CAS  Article  PubMed  Google Scholar 

  5. Dettmer K, Aronov PA, Hammock BD (2007) Mass spectrometry-based metabolomics. Mass Spectrom Rev 26(1):51–78

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  6. Dunn WB, Mamas M, Heazell A (2013) Metabolomics and its role in the study of mammalian systems. Metabolomics in practice: successful strategies to generate and analyze metabolic data. Wiley-VCH, Weinheim, pp 345–377

    Chapter  Google Scholar 

  7. Zhang A, Sun H, Wang P, Han Y, Wang X (2012) Modern analytical techniques in metabolomics analysis. Analyst 137:293–300

    CAS  Article  PubMed  Google Scholar 

  8. Wishart DS (2008) Quantitative metabolomics using NMR. Trends Anal Chem 27:228–237

    CAS  Article  Google Scholar 

  9. Alonso A, Marsal S, Julia A (2015) Analytical methods in untargeted metabolomics: state of the art in 2015. Front Bioeng Biotechnol 3:23

    Article  PubMed  PubMed Central  Google Scholar 

  10. Dunn WB, Broadhurst D, Begley P, Zelena E, Francis-McIntyre S, Anderson N, Brown M, Knowles JD, Halsall A, Haselden JN, Nicholls AW, Wilson ID, Kell DB, Goodacre R, Consortium THSMH (2011) Procedures for large-scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry. Nat Protocols 6(7):1060–1083

    CAS  Article  PubMed  Google Scholar 

  11. Fuhrer T, Zamboni N (2015) High-throughput discovery metabolomics. Curr Opin Biotechnol 31:73–78

    CAS  Article  PubMed  Google Scholar 

  12. Sévin DC, Kuehne A, Zamboni N, Sauer U (2015) Biological insights through nontargeted metabolomics. Curr Opin Biotechnol 34:1–8

    Article  PubMed  Google Scholar 

  13. Xia J, Wishart DS (2016) Using MetaboAnalyst 3.0 for comprehensive metabolomics data analysis. Curr Protoc Bioinform 55:14.10.11–14.10.91

    Google Scholar 

  14. Zhang A, Sun H, Xu H, Qiu S, Wang X (2013) Cell metabolomics. OMICS 17(10):495–501

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  15. Sims JK, Manteiger S, Lee K (2013) Towards high resolution analysis of metabolic flux in cells and tissues. Curr Opin Biotechnol 24:933–939

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  16. Balcke GU, Kolle SN, Kamp H, Bethan B, Looser R, Wagner S, Landsiedel R, van Ravenzwaay B (2011) Linking energy metabolism in mitochondrial respiration—a metabolomics in vitro approach. Toxicol Lett 203(3):200–209

    CAS  Article  PubMed  Google Scholar 

  17. Veyrat-Durebex C, Corcia P, Piver E, Devos D, Dangoumau A, Gouel F, Vourc’h P, Emond P, Laumonnier F, Nadal-Desbarats L, Gordon PH, Andres CR, Blasco H (2016) Disruption of TCA cycle and glutamate metabolism identified by metabolomics in an in vitro model of amyotrophic lateral sclerosis. Mol Neurobiol 53(10):6910–6924

    CAS  Article  PubMed  Google Scholar 

  18. Wegner A, Meiser J, Weindl D, Hiller K (2015) How metabolites modulate metabolic flux. Curr Opin Biotechnol 34:16–22

    CAS  Article  PubMed  Google Scholar 

  19. Kalluri U, Naiker M, Myers MA (2014) Cell culture metabolomics in the diagnosis of lung cancer—the influence of cell culture conditions. J Breath Res 8(2):027109

    CAS  Article  PubMed  Google Scholar 

  20. Halama A (2014) Metabolomics in cell culture—a strategy to study crucial metabolic pathways in cancer development and the response to treatment. Arch Biochem Biophys 564:100–109

    CAS  Article  PubMed  Google Scholar 

  21. Halama A, Riesen N, Moller G, Hrabě de Angelis M, Adamski J (2013) Identification of biomarkers for apoptosis in cancer cell lines using metabolomics: tools for individualized medicine. J Intern Med 274(5):425–439

    CAS  Article  PubMed  Google Scholar 

  22. Bouhifd M, Hartung T, Hogberg HT, Kleensang A, Zhao L (2013) Toxicometabolomics. J Appl Toxicol 33(12):1365–1383

    CAS  Article  PubMed  Google Scholar 

  23. Johnson CH, Patterson AD, Idle JR, Gonzalez FJ (2012) Xenobiotic metabolomics: major impact on the metabolome. Annu Rev Pharmacol Toxicol 52:37–56

    CAS  Article  PubMed  Google Scholar 

  24. van Vliet E, Morath S, Eskes C, Linge J, Rappsilber J, Honegger P, Hartung T, Coecke S (2008) A novel in vitro metabolomics approach for neurotoxicity testing, proof of principle for methyl mercury chloride and caffeine. Neurotoxicology 29(1):1–12

    Article  PubMed  Google Scholar 

  25. Van den Hof WFPM, Ruiz-Aracama A, Van Summeren A, Jennen DGJ, Gaj S, Coonen MLJ, Brauers K, Wodzig WKWH, van Delft JHM, Kleinjans JCS (2015) Integrating multiple omics to unravel mechanisms of cyclosporin A induced hepatotoxicity in vitro. Toxicol In Vitro 29(3):489–501

    Article  PubMed  Google Scholar 

  26. Ellis JK, Athersuch TJ, Cavill R, Radford R, Slattery C, Jennings P, McMorrow T, Ryan MP, Ebbels TMD, Keun HC (2011) Metabolic response to low-level toxicant exposure in a novel renal tubule epithelial cell system. Mol Biosyst 7:247–257

    CAS  Article  PubMed  Google Scholar 

  27. Rubakhin SS, Lanni EJ, Sweedler JV (2013) Progress towards single cell metabolomics. Curr Opin Biotechnol 24:95–104

    CAS  Article  PubMed  Google Scholar 

  28. Zenobi R (2013) Single-cell metabolomics: analytical and biological perspectives. Science 342:1243259

    CAS  Article  PubMed  Google Scholar 

  29. Aurich MK, Paglia G, Rolfsson Ó, Hrafnsdóttir S, Magnúsdóttir M, Stefaniak MM, Palsson BØ, Fleming RMT, Thiele I (2015) Prediction of intracellular metabolic states from extracellular metabolomic data. Metabolomics 11:603–619

    CAS  Article  PubMed  Google Scholar 

  30. Broadhurst DI, Kell DB (2006) Statistical strategies for avoiding false discoveries in metabolomics and related experiments. Metabolomics 2(4):171–196

    CAS  Article  Google Scholar 

  31. Hrydziuszko O, Viant MR (2012) Missing values in mass spectrometry based metabolomics: an undervalued step in the data processing pipeline. Metabolomics 8(1):S161–S174

    Article  Google Scholar 

  32. Grissa D, Petera M, Brandolini M, Napoli A, Comte B, Pujos-Guillot E (2016) Feature selection methods for early predictive biomarker discovery using untargeted metabolomic data. Front Mol Biosci 3:30

    Article  PubMed  PubMed Central  Google Scholar 

  33. Gromski PS, Xu Y, Kotze HL, Correa E, Ellis DI, Armitage EG, Turner ML, Goodacre R (2014) Influence of missing values substitutes on multivariate analysis of metabolomics data. Metabolites 4(2):433–452

    Article  PubMed  PubMed Central  Google Scholar 

  34. Villas-Boas SG, Koulmann A, Lane GA (2007) Analytical methods from the perspective of method standardization. In: Nielsen J, Jewett MC (eds) Metabolomics: a powerful tool in systems biology, Topics in Current Genetics, vol 18. Springer, Berlin, pp 11–52

    Chapter  Google Scholar 

  35. van der Werf MJ, Takors R, Smedsgaard J, Nielsen J, Ferenci T, Portais JC, Wittmann C, Hooks M, Tomassini A, Oldiges M, Fostel J, Sauer U (2007) Standard reporting requirements for biological samples in metabolomics experiments: microbial and in vitro biology experiments. Metabolomics 3:189–194

    Article  PubMed  PubMed Central  Google Scholar 

  36. Lindon J, Nicholson J, Holmes E, Keun H, Craig A, Pearce J, Bruce S, Hardy N, Sansone S, Antti H, Jonsson P, Daykin C, Navarange M, Beger R, Verheij E, Amberg A, Baunsgaard D, Cantor G, Lehman-McKeeman L, Earll M, Wold S, Johansson E, Haselden J, Kramer K, Thomas C, Lindberg J, Schuppe-Koistinen I, Wilson I, Reily M, Robertson D, Senn H, Krotzky A, Kockhar S, Powell J, van der Ouderaa F, Plumb R, Schaefer H, Spraul M (2005) Summary recommendations for standardization and reporting of metabolic analysis. Nat Biotechnol 23:833–838

    CAS  Article  PubMed  Google Scholar 

  37. National Research Council (NRC) (2007) Toxicity Testing in the 21st Century: A Vision and a Strategy. National Academies Press, Washington DC, USA

    Google Scholar 

  38. Davis M, Boekelheide K, Boverhof DR, Eichenbaum G, Hartung T, Holsapple MP, Jones TW, Richard AM, Watkins PB (2013) The new revolution in toxicology: the good, the bad, and the ugly. Ann N Y Acad Sci 1278:11–24

    Article  PubMed  Google Scholar 

  39. Ramirez T, Daneshian M, Kamp H, Bois FY, Clench MR, Coen M, Donley B, Fischer SM, Ekman DR, Fabian E, Guillou C, Heuer J, Hogberg HT, Jungnickel H, Keun HC, Krennrich G, Krupp E, Luch A, Noor F, Peter E, Riefke B, Seymour M, Skinner N, Smirnova L, Verheij E, Wagner S, Hartung T, van-Ravenzwaay B, Leist M (2013) Metabolomics in toxicology and preclinical research. Altex 30(2):209–225

    Article  PubMed  PubMed Central  Google Scholar 

  40. Prot JM, Leclerc E (2012) The current status of alternatives to animal testing and predictive toxicology methods using liver microfluidic biochips. Ann Biomed Eng 40(6):1228–1243

    Article  PubMed  Google Scholar 

  41. Valeria R, Luisa S, Adele M, Stefania B, Fabio T, Nicoletta B, Carmine PM, Silvia A (2016) Changes in the NMR metabolic profile of live human neuron-like SH-SY5Y cells exposed to interferon-α2. J Neuroimmune Pharmacol 11(1):142–152

    Article  PubMed  Google Scholar 

  42. Zhaoa C, Dub H, Xuc L, Wange J, Tange L, Caog Y, Lig C, Wangg Q, Liue Y, Shanh F, Fenga J, Xub F, Gaoe P (2015) Metabolomic analysis revealed glycylglycine accumulation in astrocytes after methionine enkephalin administration exhibiting neuron protective effects. J Pharm Biomed Anal 115:48–54

    Article  Google Scholar 

  43. Liu S, Wang W, Zhou X, Gu R, Ding Z (2014) Dose responsive effects of cisplatin in L02 cells using NMR-based metabolomics. Environ Toxicol Pharmacol 37:150–157

    Article  PubMed  Google Scholar 

  44. Snouber LC, Bunescu A, Naudot M, Legallais C, Brochot C, Dumas ME, Elena-Herrmann B, Leclerc E (2013) Metabolomics-on-a-chip of hepatotoxicity induced by anticancer drug flutamide and its active metabolite hydroxyflutamide using HepG2/C3a microfluidic biochips. Toxicol Sci 132(1):8–20

    Article  Google Scholar 

  45. Massimi M, Tomassini A, Sciubba F, Sobolev AP, Devirgiliis LC, Miccheli A (2012) Effects of resveratrol on HepG2 cells as revealed by 1H-NMR based metabolic profiling. Biochim Biophys Acta 1820:1–8

    CAS  Article  PubMed  Google Scholar 

  46. Ruiz-Aracama A, Peijnenburg A, Kleinjans J, Jennen D, vanDelft J, Hellfrisch C, Lommen A (2011) An untargeted mulit-technique metabolomics approach to studying intracellular metabolites of HepG2 cells exposed to 2,3,7,8-tetrachlorodibenzo-p-dioxin. BMC Genom 12:251–270

    CAS  Article  Google Scholar 

  47. Garcia-Canaveras JC, Castell JV, Donato MT, Lahoz A (2016) A metabolomics cell-based approach for anticipating and investigating drug-induced liver injury. Sci Rep 6:27239

    Article  PubMed  PubMed Central  Google Scholar 

  48. Purwaha P, Lorenzi PL, Silva LP, Hawke DH, Weinstein JN (2014) Targeted metabolomic analysis of amino acid response to l-asparaginase in adherent cells. Metabolomics 10:909–919

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  49. Hu Y, Qi Y, Liu H, Fan G, Chai Y (2013) Effects of celastrol on human cervical cancer cells as revealed by ion-trap gas chromatography-mass spectrometry based metabolic profiling. Biochim Biophys Acta 1830(3):2779–2789

    CAS  Article  PubMed  Google Scholar 

  50. Zhang R, Zhuang X, Zong L, Liu S, Liu Z, Song F (2016) Investigations on the cell metabolomics basis of multidrug resistance from tumor cells by ultra-performance liquid chromatography-mass spectrometry. Anal Bioanal Chem 408:5843–5854

    CAS  Article  PubMed  Google Scholar 

  51. Wilmes A, Limonciel A, Aschauer L, Moenks K, Bielow C, Leonard MO, Hamon J, Carpi D, Ruzek S, Handler A, Schmal O, Herrgen K, Bellwon P, Burek C, Truisi GL, Hewitt P, Consiglio ED, Testai E, Blaauboer BJ, Guillou C, Huber CG, Lukas A, Pfaller W, Mueller SO, Bois FY, Dekant W, Jennings P (2013) Application of integrated transcriptomic, proteomic and metabolomic profiling for the delineation of mechanisms of drug induced cell stress. J Proteomics 79:180–194

    CAS  Article  PubMed  Google Scholar 

  52. Huang S-M, Zuo X, Li JJE, Li SFY, Bay BH, Ong CN (2012) Metabolomics studies show dose-dependent toxicity induced by SiO2 nanoparticles in MRC-5 human fetal lung fibroblasts. Adv Healthc Mater 1(6):779–784

    CAS  Article  PubMed  Google Scholar 

  53. Strigun A, Wahrheit J, Beckers S, Heinzle E, Noor F (2011) Metabolic profiling using HPLC allows classification of drugs according to their mechanisms of action in HL-1 cardiomyocytes. Toxicol Appl Pharmacol 252:183–191

    CAS  Article  PubMed  Google Scholar 

  54. Nicolae A, Wahrheit J, Bahnemann J, Zeng A-P, Heinzle E (2014) Non-stationary 13C metabolic flux analysis of Chinese hamster ovary cells in batch culture using extracellular labeling highlights metabolic reversibility and compartmentation. BMC Syst Biol 8:50–64

    Article  PubMed  PubMed Central  Google Scholar 

  55. Krömer JO, Dietmair S, Jacob SS, Nielsen LK (2011) Quantification of l-alanyl-l-glutamine in mammalian cell culture broth: evaluation of different detectors. Anal Biochem 416:129–131

    Article  PubMed  Google Scholar 

  56. Aranibar N, Borys M, Mackin NA, Ly V, Abu-Absi N, Abu-Absi S, Niemitz M, Schilling B, Li ZJ, Brock B, Russell-II RJ, Tymiak A, Reily MD (2011) NMR-based metabolomics of mammalian cell and tissue cultures. J Biomol NMR 49:195–206

    CAS  Article  PubMed  Google Scholar 

  57. Selvarasu S, Ho YS, Chong WPK, Wong NSC, Yusufi FNK, Lee YY, Yap MGS, Lee D-Y (2012) Comgined in silico modeling and metabolomics analysis to characterize fed-batch CHO cell culture. Biotechnol Bioeng 109(6):1415–1429

    CAS  Article  PubMed  Google Scholar 

  58. Dietmair S, Hodson MP, Quek L-E, Timmins NE, Chrysanthopoulos P, Jacob SS, Gray P, Nielsen LK (2012) Metabolite profiling of CHO cells with different growth characteristics. Biotechnol Bioeng 109(6):1404–1414

    CAS  Article  PubMed  Google Scholar 

  59. Jin C, Liu Y, Sun L, Chen T, Zhang Y, Zhao A, Wang X, Cristau M, Wang K, Jia W (2012) Metabolic profiling reveals disorder of carbohydrate metabolism in mouse fibroblast cells induced by titanium dioxide nanoparticles. J Appl Toxicol 33:1442–1450

    Article  PubMed  Google Scholar 

  60. Wallace M, Whelan H, Brennan L (2013) Metabolomic analysis of pancreatic beta cells following exposure to high glucose. Biochim Biophys Acta 1830:2583–2590

    CAS  Article  PubMed  Google Scholar 

  61. Čuperlović-Culf M, Barnett DA, Culf AS, Chute I (2010) Cell culture metabolomics: applications and future directions. Drug Discov Today 15(15/16):610–621

    Article  PubMed  Google Scholar 

  62. Álvarez-Sánchez B, Priego-Capote F, Luque de Castro MD (2010) Metabolomics analysis II: preparation of biological samples prior to detection. Trends Anal Chem 29(2):120–127

    Article  Google Scholar 

  63. Dettmer K, Nürnberger N, Kaspar H, Gruber MA, Almstetter MF, Oefner PJ (2011) Metabolite extraction from adherently growing mammalian cells for metabolomics studies: optimization of harvesting and extraction protocols. Anal Bioanal Chem 399(3):1127–1139

    CAS  Article  PubMed  Google Scholar 

  64. Dietmair S, Timmins NE, Gray PP, Nielsen LK, Kromer JO (2010) Towards quantitative metabolomics of mammalian cells: development of a metabolite extraction protocol. Anal Biochem 404(2):155–164

    CAS  Article  PubMed  Google Scholar 

  65. Kapoore RV, Coyle R, Staton CA, Brown NJ, Vaidyanathan S (2017) Influence of washing and quenching in profiling the metabolome of adherent mammalian cells: a case study with the metastatic breast cancer cell line MDA-MB-231. Analyst 142:2038–2049

    CAS  Article  PubMed  Google Scholar 

  66. Kapoore RV, Coyle R, Staton CA, Brown NJ, Vaidyanathan S (2015) Cell line dependence of metabolite leakage in metabolome analyses of adherent normal and cancer cell lines. Metabolomics 11:1743–1755

    CAS  Article  Google Scholar 

  67. Martineau E, Tea I, Loaec G, Giraudeau P, Akoka S (2011) Strategy for choosing extraction procedures for NMR-based metabolomic analysis of mammalian cells. Anal Bioanal Chem 401:2133–2142

    CAS  Article  PubMed  Google Scholar 

  68. Danielsson APH, Moritz T, Mulder H, Spégel P (2010) Development and optimization of a metabolomic method for analysis of adherent cell cultures. Anal Biochem 404(1):30–39

    CAS  Article  PubMed  Google Scholar 

  69. Teng Q, Huang W, Collette TW, Ekman DR, Tan C (2009) A direct cell quenching method for cell-culture based metabolomics. Metabolomics 5:199–208

    CAS  Article  Google Scholar 

  70. Matheus M, Hansen S, Rozet E, Peixoto P, Maquoi E, Lambert V, Noel A, Frederich M, Mottet D, deTullio P (2014) An easy, convenient cell and tissue extraction protocol for nuclear magnetic resonance metabolomics. Phytochem Anal 25:342–349

    CAS  Article  PubMed  Google Scholar 

  71. Muschet C, Möller G, Prehn C, Hrabē de Angelis M, Adamski J, Tokarz J (2016) Removing the bottlenecks of cell culture metabolomics: fast normalization procedure, correlation of metabolites to cell number, and impact of the cell harvesting method. Metabolomics 12(10):151

    Article  PubMed  PubMed Central  Google Scholar 

  72. Garcia-Canaveras JC, Lopez S, Castell JV, Donato MT, Laboz A (2016) Extending metabolome coverage for untargeted metabolite profiling of ahderent cultured hepatic cells. Anal Bioanal Chem 408:1217–1230

    CAS  Article  PubMed  Google Scholar 

  73. Peterson AL, Walker AK, Sloan EK, Creek DJ (2016) Optimized method for untargeted metabolomics analysis of MDA-MB-231 breast cancer cells. Metabolites 6(4):30–46

    Article  PubMed Central  Google Scholar 

  74. Madji Hounoum B, Blasco H, Nadal-Desbarats L, Diémé B, Montigny F, Andres CR, Emond P, Mavel S (2015) Analytical methodology for metabolomics study of adherent mammalian cells using NMR, C-MS and LC–HRMS. Anal Bioanal Chem 407:8861–8872

    CAS  Article  PubMed  Google Scholar 

  75. Ser Z, Liu X, Tang NN, Locasale JW (2015) Extraction parameters for metabolomics from cultured cells. Anal Biochem 475:22–28

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  76. Rahman AMA, Pawling J, Ryczko M, Caudy AA (2014) Targeted metabolomics in cultured cells and tissues by mass spectrometry: method development and validation. Anal Chim Acta 845:53–61

    Article  PubMed  Google Scholar 

  77. Bi H, Krausz KW, Manna SK, Li F, Johnson CH, Gonzalez FJ (2013) Optimization of harvesting, extraction, and analytical protocols for UPLC–ESI–MS-based metabolomics analysis of adherent mammalian cancer cells. Anal Bioanal Chem 405:5279–5289

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  78. Hutschenreuther A, Kiontke A, Birkenmeier G, Birkemeyer C (2012) Comparison of extraction conditions and normalization approaches for cellular metabolomics of adherent growing cells with GC–MS. Anal Methods 4:1953–1963

    CAS  Article  Google Scholar 

  79. Lorenz MA, Burant CF, Kennedy RT (2011) Reducing time and increasing sensitivity in sample preparation for adherent mammalian cell metabolomics. Anal Chem 83:3406–3414

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  80. Sheikh KD, Khanna S, Byers SW, Fornace A, Cheema AK (2011) Small molecule metabolite extraction strategy for improving LC/MS detection of cancer cell metabolome. J Biomol Tech 22(1):1–4

    PubMed  PubMed Central  Google Scholar 

  81. Guida RD, Engel J, Allwood JW, Weber RJM, Jones MR, Sommer U, Viant MR, Dunn WB (2016) Non-targeted UHPLC–MS metabolomic data processing methods: a comparative investigation of normalisation, missing value imputation, transformation and scaling. Metabolomics 12:93

    Article  PubMed  PubMed Central  Google Scholar 

  82. Xi B, Gu H, Baniasadi H, Raftery D (2014) Statistical analysis and modeling of mass spectrometry-based metabolomics data. Methods Mol Biol 1198:333–353

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  83. Boccard J, Veuthey JL, Rudaz S (2010) Knowledge discovery in metabolomics: an overview of MS data handling. J Sep Sci 33(3):290–304

    CAS  Article  PubMed  Google Scholar 

  84. Cao B, Aa J, Wang G, Wu X, Liu L, Li M, Shi J, Wang X, Zhao C, Zheng T, Guo S, Duan J (2011) GC–TOFMS analysis of metabolites in adherent MDCK cells and a novel strategy for identifying intracellular metabolic markers for use as cell amount indicators in data normalization. Anal Bioanal Chem 400(9):2983–2993

    CAS  Article  PubMed  Google Scholar 

  85. Silva LP, Lorenzi PL, Purwaha P, Yong V, Hawke DH, Weinstein JN (2013) Measurement of DNA concentration as a normalization strategy for metabolomic data from adherent cell lines. Anal Chem 85(20):9536–9542

    CAS  Article  PubMed  Google Scholar 

Download references

Acknowledgements

This review was supported in part by the Australian Government Research Training Program Scholarship.

Author information

Authors and Affiliations

  1. Separation Sciences and Metabolomics Laboratories, Murdoch University, Perth, WA, Australia

    Sarah Hayton, Garth L. Maker & Robert D. Trengove

  2. School of Veterinary and Life Sciences, Murdoch University, 90 South St, Murdoch, WA, 6150, Australia

    Sarah Hayton, Garth L. Maker & Ian Mullaney

Authors
  1. Sarah Hayton
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Garth L. Maker
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ian Mullaney
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Robert D. Trengove
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Garth L. Maker.

Ethics declarations

Conflict of interest

The author declares that they have no conflict of interest.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Hayton, S., Maker, G.L., Mullaney, I. et al. Experimental design and reporting standards for metabolomics studies of mammalian cell lines. Cell. Mol. Life Sci. 74, 4421–4441 (2017). https://doi.org/10.1007/s00018-017-2582-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00018-017-2582-1

Keywords

  • Metabolomics
  • Cell culture
  • In vitro
  • Methods
  • Standardisation
  • Experimental design