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 develo** 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.
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References
Allen G I, Maletić-Savatić M (2011). Sparse non-negative generalized PCA with applications to metabolomics. Bioinformatics, 27(21): 3029–3035
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
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
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
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
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
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
Buchsbaum M S, Hazlett E A (1998). Positron emission tomography studies of abnormal glucose metabolism in schizophrenia. Schizophr Bull, 24(3): 343–364
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
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
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
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
Dass C (2007) Fundamentals of contemporary mass spectrometry, Hoboken, New Jersey: John Wiley. Sons, Inc.
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
de Graaf R A (2008). In vivo NMR Spectroscopy: Principles and Techniques. New Jersey: John Wiley. Sons, Inc.
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
Duarte I F, Lamego I, Rocha C, Gil A M (2009). NMR metabonomics for mammalian cell metabolism studies. Bioanalysis, 1(9): 1597–1614
Dunn WB, Bailey N J, Johnson H E (2005). Measuring the metabolome: current analytical technologies. Analyst (Lond), 130(5): 606–625
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
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
Fiehn O (2002). Metabolomics—the link between genotypes and phenotypes. Plant Mol Biol, 48(1–2): 155–171
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
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
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
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
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
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
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
Heinemann M, Zenobi R (2011). Single cell metabolomics. Curr Opin Biotechnol, 22(1): 26–31
Ioannidis J P, Khoury M J (2011). Improving validation practices in “omics” research. Science, 334(6060): 1230–1232
Ito K, Suda T (2014). Metabolic requirements for the maintenance of self-renewing stem cells. Nat Rev Mol Cell Biol, 15(4): 243–256
Kanehisa M, Goto S (2000). KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res, 28(1): 27–30
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
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
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
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
Kulak A, Duarte J M, Do K Q, Gruetter R (2010). Neurochemical profile of the develo** 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
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
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
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
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
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
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
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
Mountford C E, Stanwell P, Lin A, Ramadan S, Ross B (2010). Neurospectroscopy: the past, present and future. Chem Rev, 110(5): 3060–3086
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
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
Nicholson J K, Holmes E, Kinross JM, Darzi AW, Takats Z, Lindon J C (2012). Metabolic phenoty** in clinical and surgical environments. Nature, 491(7424): 384–392
Nishida K, Ono K, Kanaya S, Takahashi K (2014). KEGGscape: a Cytoscape app for pathway data integration. F1000Res, 3: 144
Nishimura D (2000) Biotech software & Internet report. Larchmont, NY: Mary Ann Liebert, Inc.
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
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
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
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
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
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
Rando T A (2006). Stem cells, ageing and the quest for immortality. Nature, 441(7097): 1080–1086
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
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
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
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
Ser Z, Liu X, Tang N N, Locasale J W (2015). Extraction parameters for metabolomics from cultured cells. Anal Biochem, 475: 22–28
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
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
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
Soares D P, Law M (2009). Magnetic resonance spectroscopy of the brain: review of metabolites and clinical applications. Clin Radiol, 64(1): 12–21
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
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
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
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
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
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
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
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
Vacanti N M, Metallo C M (2013). Exploring metabolic pathways that contribute to the stem cell phenotype. Biochim Biophys Acta, 1830(2): 2361–2369
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
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
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
Warburg O (1956). On the origin of cancer cells. Science, 123(3191): 309–314
Weckwerth W, Morgenthal K (2005). Metabolomics: from pattern recognition to biological interpretation. Drug Discov Today, 10(22): 1551–1558
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
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
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
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
Zamboni N, Fendt SM, Rühl M, Sauer U (2009). (13)C-based metabolic flux analysis. Nat Protoc, 4(6): 878–892
Zenobi R (2013). Single-cell metabolomics: analytical and biological perspectives. Science, 342(6163): 1243259
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
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
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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
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DOI: https://doi.org/10.1007/s11515-015-1357-z