Skip to main content
SpringerLink
Log in

Analytical strategies for studying stem cell metabolism

  • Review
  • Published:
Frontiers in Biology

Abstract

Owing to their capacity for self-renewal and pluripotency, stem cells possess untold potential for revolutionizing the field of regenerative medicine through the development of novel therapeutic strategies for treating cancer, diabetes, cardiovascular and neurodegenerative diseases. Central to developing these strategies is improving our understanding of biological mechanisms responsible for governing stem cell fate and self-renewal. Increasing attention is being given to the significance of metabolism, through the production of energy and generation of small molecules, as a critical regulator of stem cell functioning. Rapid advances in the field of metabolomics now allow for in-depth profiling of stem cells both in vitro and in vivo, providing a systems perspective on key metabolic and molecular pathways which influence stem cell biology. Understanding the analytical platforms and techniques that are currently used to study stem cell metabolomics, as well as how new insights can be derived from this knowledge, will accelerate new research in the field and improve future efforts to expand our understanding of the interplay between metabolism and stem cell biology.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Allen G I, Maletić-Savatić M (2011). Sparse non-negative generalized PCA with applications to metabolomics. Bioinformatics, 27(21): 3029–3035

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Allen JE, Saroya BS, Kunkel M, et al (2014) Apoptotic circulating tumor cells (CTCs) in the peripheral blood of metastatic colorectal cancer patients are associated with liver metastasis but not CTCs. Oncotarget 5: 1753–1760

    PubMed Central  PubMed  Google Scholar 

  • Amantonico A, Oh J Y, Sobek J, Heinemann M, Zenobi R (2008). Mass spectrometric method for analyzing metabolites in yeast with single cell sensitivity. Angew Chem Int Ed Engl, 47(29): 5382–5385

    Article  CAS  PubMed  Google Scholar 

  • Antoniewicz M R, Kelleher J K, Stephanopoulos G (2007). Elementary metabolite units (EMU): a novel framework for modeling isotopic distributions. Metab Eng, 9(1): 68–86

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Blaise B J, Navratil V, Domange C, Shintu L, Dumas M E, Elena-Herrmann B, Emsley L, Toulhoat P (2010). Two-dimensional statistical recoupling for the identification of perturbed metabolic networks from NMR spectroscopy. J Proteome Res, 9(9): 4513–4520

    Article  CAS  PubMed  Google Scholar 

  • Blaise B J, Shintu L, Elena B, Emsley L, Dumas ME, Toulhoat P (2009). Statistical recoupling prior to significance testing in nuclear magnetic resonance based metabonomics. Anal Chem, 81(15): 6242–6251

    Article  CAS  PubMed  Google Scholar 

  • Bochner B R, Siri M, Huang R H, Noble S, Lei X H, Clemons P A, Wagner B K (2011). Assay of the multiple energy-producing pathways of mammalian cells. PLoS ONE, 6(3): e18147

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Buchsbaum M S, Hazlett E A (1998). Positron emission tomography studies of abnormal glucose metabolism in schizophrenia. Schizophr Bull, 24(3): 343–364

    Article  CAS  PubMed  Google Scholar 

  • Castaldi P J, Dahabreh I J, Ioannidis J P (2011). An empirical assessment of validation practices for molecular classifiers. Brief Bioinform, 12(3): 189–202

    Article  PubMed Central  PubMed  Google Scholar 

  • Castro-Perez J, Roddy T P, Nibbering N M, Shah V, McLaren D G, Previs S, Attygalle A B, Herath K, Chen Z, Wang S P, Mitnaul L, Hubbard B K, Vreeken R J, Johns D G, Hankemeier T (2011). Localization of fatty acyl and double bond positions in phosphatidylcholines using a dual stage CID fragmentation coupled with ion mobility mass spectrometry. J Am Soc Mass Spectrom, 22(9): 1552–1567

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Coen M, Holmes E, Lindon J C, Nicholson J K (2008). NMR-based metabolic profiling and metabonomic approaches to problems in molecular toxicology. Chem Res Toxicol, 21(1): 9–27

    Article  CAS  PubMed  Google Scholar 

  • Craig A, Cloarec O, Holmes E, Nicholson J K, Lindon J C (2006). Scaling and normalization effects in NMR spectroscopic metabonomic data sets. Anal Chem, 78(7): 2262–2267

    Article  CAS  PubMed  Google Scholar 

  • Dass C (2007) Fundamentals of contemporary mass spectrometry, Hoboken, New Jersey: John Wiley. Sons, Inc.

    Book  Google Scholar 

  • de Graaf A A, Maathuis A, de Waard P, Deutz N E, Dijkema C, de Vos W M, Venema K (2010). Profiling human gut bacterial metabolism and its kinetics using [U-13C]glucose and NMR. NMR Biomed, 23(1): 2–12

    Article  PubMed  Google Scholar 

  • de Graaf R A (2008). In vivo NMR Spectroscopy: Principles and Techniques. New Jersey: John Wiley. Sons, Inc.

    Google Scholar 

  • DeFeo E M, Cheng L L (2010). Characterizing human cancer metabolomics with ex vivo 1H HRMAS MRS. Technol Cancer Res Treat, 9(4): 381–391

    Article  CAS  PubMed  Google Scholar 

  • Duarte I F, Lamego I, Rocha C, Gil A M (2009). NMR metabonomics for mammalian cell metabolism studies. Bioanalysis, 1(9): 1597–1614

    Article  CAS  PubMed  Google Scholar 

  • Dunn WB, Bailey N J, Johnson H E (2005). Measuring the metabolome: current analytical technologies. Analyst (Lond), 130(5): 606–625

    Article  CAS  Google Scholar 

  • Dunn W B, Broadhurst D I, Atherton H J, Goodacre R, Griffin J L (2011). Systems level studies of mammalian metabolomes: the roles of mass spectrometry and nuclear magnetic resonance spectroscopy. Chem Soc Rev, 40(1): 387–426

    Article  CAS  PubMed  Google Scholar 

  • Fancy S A, Beckonert O, Darbon G, Yabsley W, Walley R, Baker D, Perkins G L, Pullen F S, Rumpel K (2006). Gas chromatography/flame ionisation detection mass spectrometry for the detection of endogenous urine metabolites for metabonomic studies and its use as a complementary tool to nuclear magnetic resonance spectroscopy. Rapid Commun Mass Spectrom, 20(15): 2271–2280

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  • Folick A, Min W, Wang M C (2011). Label-free imaging of lipid dynamics using Coherent Anti-stokes Raman Scattering (CARS) and Stimulated Raman Scattering (SRS) microscopy. Curr Opin Genet Dev, 21(5): 585–590

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Folmes C D, Nelson T J, Martinez-Fernandez A, Arrell D K, Lindor J Z, Dzeja P P, Ikeda Y, Perez-Terzic C, Terzic A (2011). Somatic oxidative bioenergetics transitions into pluripotency-dependent glycolysis to facilitate nuclear reprogramming. Cell Metab, 14(2): 264–271

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Gika H G, Theodoridis G A, Plumb R S, Wilson I D (2014). Current practice of liquid chromatography-mass spectrometry in metabolomics and metabonomics. J Pharm Biomed Anal, 87: 12–25

    Article  CAS  PubMed  Google Scholar 

  • Glazko G V, Emmert-Streib F (2009). Unite and conquer: univariate and multivariate approaches for finding differentially expressed gene sets. Bioinformatics, 25(18): 2348–2354

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Goodacre R, Vaidyanathan S, Dunn W B, Harrigan G G, Kell D B (2004). Metabolomics by numbers: acquiring and understanding global metabolite data. Trends Biotechnol, 22(5): 245–252

    Article  CAS  PubMed  Google Scholar 

  • Griffin J L, Bollard M, Nicholson J K, Bhakoo K (2002). Spectral profiles of cultured neuronal and glial cells derived from HRMAS (1) H NMR spectroscopy. NMR Biomed, 15(6): 375–384

    Article  CAS  PubMed  Google Scholar 

  • Guidoni L, Ricci-Vitiani L, Rosi A, Palma A, Grande S, Luciani A M, Pelacchi F, di Martino S, Colosimo C, Biffoni M, De Maria R, Pallini R, Viti V (2014). 1H NMR detects different metabolic profiles in glioblastoma stem-like cells. NMR Biomed, 27(2): 129–145

    Article  CAS  PubMed  Google Scholar 

  • Heinemann M, Zenobi R (2011). Single cell metabolomics. Curr Opin Biotechnol, 22(1): 26–31

    Article  CAS  PubMed  Google Scholar 

  • Ioannidis J P, Khoury M J (2011). Improving validation practices in “omics” research. Science, 334(6060): 1230–1232

    Article  CAS  PubMed  Google Scholar 

  • Ito K, Suda T (2014). Metabolic requirements for the maintenance of self-renewing stem cells. Nat Rev Mol Cell Biol, 15(4): 243–256

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Kanehisa M, Goto S (2000). KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res, 28(1): 27–30

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Kind T, Fiehn O (2009). What are the obstacles for an integrated system for comprehensive interpretation of cross-platform metabolic profile data? Bioanalysis, 1(9): 1511–1514

    Article  CAS  PubMed  Google Scholar 

  • Kind T, Wohlgemuth G, Lee Y, Lu Y, Palazoglu M, Shahbaz S, Fiehn O (2009). FiehnLib: mass spectral and retention index libraries for metabolomics based on quadrupole and time-of-flight gas chromatography/mass spectrometry. Anal Chem, 81(24): 10038–10048

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Klerk L A, Dankers P Y, Popa E R, Bosman A W, Sanders M E, Reedquist K A, Heeren R M (2010). TOF-secondary ion mass spectrometry imaging of polymeric scaffolds with surrounding tissue after in vivo implantation. Anal Chem, 82(11): 4337–4343

    Article  CAS  PubMed  Google Scholar 

  • Knobloch M, Braun S M, Zurkirchen L, von Schoultz C, Zamboni N, Araúzo-Bravo M J, Kovacs W J, Karalay O, Suter U, Machado R A, Roccio M, Lutolf M P, Semenkovich C F, Jessberger S (2013). Metabolic control of adult neural stem cell activity by Fasn-dependent lipogenesis. Nature, 493(7431): 226–230

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Kulak A, Duarte J M, Do K Q, Gruetter R (2010). Neurochemical profile of the developing mouse cortex determined by in vivo 1H NMR spectroscopy at 14.1 T and the effect of recurrent anaesthesia. J Neurochem, 115(6): 1466–1477

    Article  CAS  PubMed  Google Scholar 

  • Liimatainen T J, Erkkilä A T, Valonen P, Vidgren H, Lakso M, Wong G, Gröhn O H, Ylä-Herttuala S, Hakumäki J M (2008). 1H MR spectroscopic imaging of phospholipase-mediated membrane lipid release in apoptotic rat glioma in vivo. Magn Reson Med, 59(6): 1232–1238

    Article  CAS  PubMed  Google Scholar 

  • Loewenbrück K F, Fuchs B, Hermann A, Brandt M, Werner A, Kirsch M, Schwarz S, Schwarz J, Schiller J, Storch A (2011). Proton MR spectroscopy of neural stem cells: does the proton-NMR peak at 1.28 ppm function as a biomarker for cell type or state? Rejuvenation Res, 14(4): 371–381

    Article  PubMed  Google Scholar 

  • Luo J, Vijayasankaran N, Autsen J, Santuray R, Hudson T, Amanullah A, Li F (2012). Comparative metabolite analysis to understand lactate metabolism shift in Chinese hamster ovary cell culture process. Biotechnol Bioeng, 109(1): 146–156

    Article  CAS  PubMed  Google Scholar 

  • Maher A D, Fonville J M, Coen M, Lindon J C, Rae C D, Nicholson J K (2012). Statistical total correlation spectroscopy scaling for enhancement of metabolic information recovery in biological NMR spectra. Anal Chem, 84(2): 1083–1091

    Article  CAS  PubMed  Google Scholar 

  • Manganas L N, Zhang X, Li Y, Hazel R D, Smith S D, Wagshul M E, Henn F, Benveniste H, Djuric P M, Enikolopov G, Maletic-Savatic M (2007). Magnetic resonance spectroscopy identifies neural progenitor cells in the live human brain. Science, 318(5852): 980–985

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Meissen J K, Yuen B T, Kind T, Riggs JW, Barupal D K, Knoepfler P S, Fiehn O (2012). Induced pluripotent stem cells show metabolomic differences to embryonic stem cells in polyunsaturated phosphatidylcholines and primary metabolism. PLoS ONE, 7(10): e46770

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Milacic M, Haw R, Rothfels K, Wu G, Croft D, Hermjakob H, D’Eustachio P, Stein L (2012). Annotating cancer variants and anticancer therapeutics in reactome. Cancers (Basel), 4(4): 1180–1211

    Article  CAS  Google Scholar 

  • Mountford C E, Stanwell P, Lin A, Ramadan S, Ross B (2010). Neurospectroscopy: the past, present and future. Chem Rev, 110(5): 3060–3086

    Article  CAS  PubMed  Google Scholar 

  • Mushtaq M Y, Choi Y H, Verpoorte R, Wilson E G (2014). Extraction for metabolomics: access to the metabolome. Phytochem Anal, 25(4): 291–306

    Article  CAS  PubMed  Google Scholar 

  • Nevedomskaya E, Ramautar R, Derks R, Westbroek I, Zondag G, van der Pluijm I, Deelder A M, Mayboroda O A (2010). CE-MS for metabolic profiling of volume-limited urine samples: application to accelerated aging TTD mice. J Proteome Res, 9(9): 4869–4874

    Article  CAS  PubMed  Google Scholar 

  • Nicholson J K, Holmes E, Kinross JM, Darzi AW, Takats Z, Lindon J C (2012). Metabolic phenotyping in clinical and surgical environments. Nature, 491(7424): 384–392

    Article  CAS  PubMed  Google Scholar 

  • Nishida K, Ono K, Kanaya S, Takahashi K (2014). KEGGscape: a Cytoscape app for pathway data integration. F1000Res, 3: 144

    PubMed Central  PubMed  Google Scholar 

  • Nishimura D (2000) Biotech software & Internet report. Larchmont, NY: Mary Ann Liebert, Inc.

    Google Scholar 

  • Panopoulos A D, Yanes O, Ruiz S, Kida Y S, Diep D, Tautenhahn R, Herrerías A, Batchelder E M, Plongthongkum N, Lutz M, Berggren WT, Zhang K, Evans R M, Siuzdak G, Izpisua Belmonte J C (2012). The metabolome of induced pluripotent stem cells reveals metabolic changes occurring in somatic cell reprogramming. Cell Res, 22(1): 168–177

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Peterson C, Vannucci M, Karakas C, Choi W, Ma L, Maletić-Savatić M (2013). Inferring metabolic networks using the Bayesian adaptive graphical lasso with informative priors. Stat Interface, 6(4): 547–558

    Article  PubMed Central  PubMed  Google Scholar 

  • Putluri N, Shojaie A, Vasu V T, Vareed S K, Nalluri S, Putluri V, Thangjam G S, Panzitt K, Tallman C T, Butler C, Sana T R, Fischer S M, Sica G, Brat D J, Shi H, Palapattu G S, Lotan Y, Weizer A Z, Terris M K, Shariat S F, Michailidis G, Sreekumar A (2011). Metabolomic profiling reveals potential markers and bioprocesses altered in bladder cancer progression. Cancer Res, 71(24): 7376–7386

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Quinn K P, Sridharan G V, Hayden R S, Kaplan D L, Lee K, Georgakoudi I (2013). Quantitative metabolic imaging using endogenous fluorescence to detect stem cell differentiation. Sci Rep, 3: 3432

    Article  PubMed Central  PubMed  Google Scholar 

  • Ramm P, Bettscheider M, Beier D, Kalbitzer H R, Kremer W, Bogdahn U, Hau P, Aigner L, Beier C P (2011). 1H-nuclear magnetic resonance spectroscopy of glioblastoma cancer stem cells. Stem Cells Dev, 20(12): 2189–2195

    Article  CAS  PubMed  Google Scholar 

  • Ramm Sander P, Hau P, Koch S, Schütze K, Bogdahn U, Kalbitzer H R, Aigner L (2013). Stem cell metabolic and spectroscopic profiling. Trends Biotechnol, 31(3): 204–213

    Article  CAS  PubMed  Google Scholar 

  • Rando T A (2006). Stem cells, ageing and the quest for immortality. Nature, 441(7097): 1080–1086

    Article  CAS  PubMed  Google Scholar 

  • Robinette S L, Veselkov K A, Bohus E, Coen M, Keun H C, Ebbels TM, Beckonert O, Holmes E C, Lindon J C, Nicholson J K (2009). Cluster analysis statistical spectroscopy using nuclear magnetic resonance generated metabolic data sets from perturbed biological systems. Anal Chem, 81(16): 6581–6589

    Article  CAS  PubMed  Google Scholar 

  • Sana T R, Waddell K, Fischer S M (2008). A sample extraction and chromatographic strategy for increasing LC/MS detection coverage of the erythrocyte metabolome. J Chromatogr B Analyt Technol Biomed Life Sci, 871(2): 314–321

    Article  CAS  PubMed  Google Scholar 

  • Sands C J, Coen M, Ebbels T M, Holmes E, Lindon J C, Nicholson J K (2011). Data-driven approach for metabolite relationship recovery in biological 1H NMR data sets using iterative statistical total correlation spectroscopy. Anal Chem, 83(6): 2075–2082

    Article  CAS  PubMed  Google Scholar 

  • Sepúlveda D E, Andrews B A, Papoutsakis E T, Asenjo J A (2010). Metabolic flux analysis of embryonic stem cells using three distinct differentiation protocols and comparison to gene expression patterns. Biotechnol Prog, 26(5): 1222–1229

    Article  PubMed  Google Scholar 

  • Ser Z, Liu X, Tang N N, Locasale J W (2015). Extraction parameters for metabolomics from cultured cells. Anal Biochem, 475: 22–28

    Article  CAS  PubMed  Google Scholar 

  • Shah S H, Kraus W E, Newgard C B (2012). Metabolomic profiling for the identification of novel biomarkers and mechanisms related to common cardiovascular diseases: form and function. Circulation, 126(9): 1110–1120

    Article  PubMed Central  PubMed  Google Scholar 

  • Sierra A, Encinas J M, Deudero J J, Chancey J H, Enikolopov G, Overstreet-Wadiche L S, Tsirka S E, Maletic-Savatic M (2010). Microglia shape adult hippocampal neurogenesis through apoptosis-coupled phagocytosis. Cell Stem Cell, 7(4): 483–495

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Smith L M, Maher A D, Cloarec O, Rantalainen M, Tang H, Elliott P, Stamler J, Lindon J C, Holmes E, Nicholson J K (2007). Statistical correlation and projection methods for improved information recovery from diffusion-edited NMR spectra of biological samples. Anal Chem, 79(15): 5682–5689

    Article  CAS  PubMed  Google Scholar 

  • Soares D P, Law M (2009). Magnetic resonance spectroscopy of the brain: review of metabolites and clinical applications. Clin Radiol, 64(1): 12–21

    Article  CAS  PubMed  Google Scholar 

  • Sowell R A, Koeniger S L, Valentine S J, Moon M H, Clemmer D E (2004). Nanoflow LC/IMS-MS and LC/IMS-CID/MS of protein mixtures. J Am Soc Mass Spectrom, 15(9): 1341–1353

    Article  CAS  PubMed  Google Scholar 

  • Sreekumar A, Poisson L M, Rajendiran T M, Khan A P, Cao Q, Yu J, Laxman B, Mehra R, Lonigro R J, Li Y, Nyati M K, Ahsan A, Kalyana-Sundaram S, Han B, Cao X, Byun J, Omenn G S, Ghosh D, Pennathur S, Alexander D C, Berger A, Shuster J R, Wei J T, Varambally S, Beecher C, Chinnaiyan A M (2009). Metabolomic profiles delineate potential role for sarcosine in prostate cancer progression. Nature, 457(7231): 910–914

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Stringari C, Wang H, Geyfman M, Crosignani V, Kumar V, Takahashi J S, Andersen B, Gratton E (2015). In vivo single-cell detection of metabolic oscillations in stem cells. Cell Rep, 10: 1–7

    Article  CAS  PubMed  Google Scholar 

  • Takeuchi K, Ohishi M, Ota S, Suzumura K, Naraoka H, Ohata T, Seki J, Miyamae Y, Honma M, Soga T (2013). Metabolic profiling to identify potential serum biomarkers for gastric ulceration induced by nonsteroid anti-inflammatory drugs. J Proteome Res, 12(3): 1399–1407

    Article  CAS  PubMed  Google Scholar 

  • Turner W S, Seagle C, Galanko J A, Favorov O, Prestwich G D, Macdonald J M, Reid L M (2008). Nuclear magnetic resonance metabolomic footprinting of human hepatic stem cells and hepatoblasts cultured in hyaluronan-matrix hydrogels. Stem Cells, 26(6): 1547–1555

    Article  CAS  PubMed  Google Scholar 

  • Ulrich E L, Akutsu H, Doreleijers J F, Harano Y, Ioannidis Y E, Lin J, Livny M, Mading S, Maziuk D, Miller Z, Nakatani E, Schulte C F, Tolmie D E, Kent Wenger R, Yao H, Markley J L (2008). BioMagResBank. Nucleic Acids Res, 36(Database issue): D402–D408

    PubMed Central  CAS  PubMed  Google Scholar 

  • Urban M, Enot D P, Dallmann G, Körner L, Forcher V, Enoh P, Koal T, Keller M, Deigner H P (2010). Complexity and pitfalls of mass spectrometry-based targeted metabolomics in brain research. Anal Biochem, 406(2): 124–131

    Article  CAS  PubMed  Google Scholar 

  • Urenjak J, Williams S R, Gadian D G, Noble M (1993). Proton nuclear magnetic resonance spectroscopy unambiguously identifies different neural cell types. J Neurosci, 13(3): 981–989

    CAS  PubMed  Google Scholar 

  • Vacanti N M, Metallo C M (2013). Exploring metabolic pathways that contribute to the stem cell phenotype. Biochim Biophys Acta, 1830(2): 2361–2369

    Article  CAS  PubMed  Google Scholar 

  • Vandersypen L M, Steffen M, Breyta G, Yannoni C S, Sherwood M H, Chuang I L (2001). Experimental realization of Shor’s quantum factoring algorithm using nuclear magnetic resonance. Nature, 414(6866): 883–887

    Article  CAS  PubMed  Google Scholar 

  • Vingara L K, Yu H J, Wagshul M E, Serafin D, Christodoulou C, Pelczer I, Krupp L B, Maletić-Savatić M (2013). Metabolomic approach to human brain spectroscopy identifies associations between clinical features and the frontal lobe metabolome in multiple sclerosis. Neuroimage, 82: 586–594

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Wang J, Alexander P, Wu L, Hammer R, Cleaver O, McKnight S L (2009). Dependence of mouse embryonic stem cells on threonine catabolism. Science, 325(5939): 435–439

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Warburg O (1956). On the origin of cancer cells. Science, 123(3191): 309–314

    Article  CAS  PubMed  Google Scholar 

  • Weckwerth W, Morgenthal K (2005). Metabolomics: from pattern recognition to biological interpretation. Drug Discov Today, 10(22): 1551–1558

    Article  CAS  PubMed  Google Scholar 

  • Wishart D S, Tzur D, Knox C, Eisner R, Guo A C, Young N, Cheng D, Jewell K, Arndt D, Sawhney S, Fung C, Nikolai L, Lewis M, Coutouly M A, Forsythe I, Tang P, Shrivastava S, Jeroncic K, Stothard P, Amegbey G, Block D, Hau D D, Wagner J, Miniaci J, Clements M, Gebremedhin M, Guo N, Zhang Y, Duggan G E, Macinnis G D, Weljie A M, Dowlatabadi R, Bamforth F, Clive D, Greiner R, Li L, Marrie T, Sykes B D, Vogel H J, Querengesser L (2007). HMDB: the Human Metabolome Database. Nucleic Acids Res, 35(Database issue): D521–D526

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Wu H, Southam A D, Hines A, Viant M R (2008). High-throughput tissue extraction protocol for NMR- and MS-based metabolomics. Anal Biochem, 372(2): 204–212

    Article  CAS  PubMed  Google Scholar 

  • Yanes O, Clark J, Wong D M, Patti G J, Sánchez-Ruiz A, Benton H P, Trauger S A, Desponts C, Ding S, Siuzdak G (2010). Metabolic oxidation regulates embryonic stem cell differentiation. Nat Chem Biol, 6(6): 411–417

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Yu Y, Ramachandran P V, Wang M C (2014). Shedding new light on lipid functions with CARS and SRS microscopy. Biochim Biophys Acta, 1841(8): 1120–1129

    Article  CAS  PubMed  Google Scholar 

  • Zamboni N, Fendt SM, Rühl M, Sauer U (2009). (13)C-based metabolic flux analysis. Nat Protoc, 4(6): 878–892

    Article  CAS  PubMed  Google Scholar 

  • Zenobi R (2013). Single-cell metabolomics: analytical and biological perspectives. Science, 342(6163): 1243259

    Article  CAS  PubMed  Google Scholar 

  • Zhang X, Li M, Agrawal A, San K Y (2011). Efficient free fatty acid production in Escherichia coli using plant acyl-ACP thioesterases. Metab Eng, 13(6): 713–722

    Article  CAS  PubMed  Google Scholar 

  • Zinnel N F, Pai P J and Russell D H. (2012) Ion mobility-mass spectrometry (IM-MS) for top-down proteomics: increased dynamic range affords increased sequence coverage. Anal Chem, 84: 3390–3397

    Article  CAS  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Molecular and Cell Biology, Baylor College of Medicine, Houston, TX, 77030, USA

    James M. Arnold & Arun Sreekumar

  2. Program in Developmental Biology and Medical Scientist Training Program, Baylor College of Medicine, Jan and Dan Duncan Neurological Research Institute at Texas Children’s Hospital, Houston, TX, 77030, USA

    William T. Choi & Mirjana Maletić-Savatić

  3. Departments of Pediatrics-Neurology and Neuroscience, and Program in Structural and Computational Biology and Molecular Biophysics, Baylor College of Medicine, Houston, TX, 77030, USA

    Mirjana Maletić-Savatić

Authors
  1. James M. Arnold
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. William T. Choi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Arun Sreekumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mirjana Maletić-Savatić
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mirjana Maletić-Savatić.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Arnold, J.M., Choi, W.T., Sreekumar, A. et al. Analytical strategies for studying stem cell metabolism. Front. Biol. 10, 141–153 (2015). https://doi.org/10.1007/s11515-015-1357-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11515-015-1357-z

Keywords

Search

Navigation