Skip to main content
SpringerLink
Log in

Metabolomics in Stem Cell Biology Research

  • Protocol
  • First Online:
Computational Stem Cell Biology

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1975))

Abstract

Stem cell research has been greatly facilitated by comprehensive and integrative multi-omics studies. As a unique approach of functional analysis, metabolomics measures many metabolites and activities of metabolic pathways which can directly indicate cellular energetic status, cell proliferation and fitness, and stem cell fate choices such as self-renewal versus differentiation. Here we describe the methods of applying metabolomics, 13C-labeled glucose and glutamine tracing with mouse embryonic stem cells (ES cells), metabolite analysis using mass spectrometry tools, and the following statistical and computational modeling analysis. Integration of these methods into the more common gene expression and epigenetics analysis toolbox will help to generate a more complete picture and in-depth understanding of one’s stem cells of interest.

Zhen Sun and Jing Zhao are co-first authors.

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

Access this chapter

Log in via an institution
Protocol
USD 49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 109.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 139.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Bian Q, Cahan P (2016) Computational tools for stem cell biology. Trends Biotechnol 34(12):993–1009

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Zhang J et al (2011) UCP2 regulates energy metabolism and differentiation potential of human pluripotent stem cells. EMBO J 30:4860–4873

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Zhang J et al (2016) LIN28 regulates stem cell metabolism and conversion to primed pluripotency cell stem cell article LIN28 regulates stem cell metabolism and conversion to primed pluripotency. Cell Stem Cell 19:66–80

    Article  CAS  PubMed  Google Scholar 

  4. Folmes CD et al (2011) Somatic oxidative bioenergetics transitions into pluripotency-dependent glycolysis to facilitate nuclear reprogramming. Cell Metab 14:264–271

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Nagaraj R et al (2017) Nuclear localization of mitochondrial TCA cycle enzymes as a critical step in mammalian zygotic genome activation. Cell 168:210–223.e11

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Carey BW et al (2015) Intracellular α-ketoglutarate maintains the pluripotency of embryonic stem cells. Nature 518:413–416

    Article  CAS  PubMed  Google Scholar 

  7. Panopoulos AD et al (2012) The metabolome of induced pluripotent stem cells reveals metabolic changes occurring in somatic cell reprogramming. Cell Res 22(1):168–177

    Article  CAS  PubMed  Google Scholar 

  8. Sperber H et al (2015) The metabolome regulates the epigenetic landscape during naive-to-primed human embryonic stem cell transition. Nat Cell Biol 17:1523–1535

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Chandrasekaran S et al (2017) Comprehensive mapping of pluripotent stem cell metabolism using dynamic genome-scale network modeling. Cell Rep 21(10):2965–2977

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Weinberger L et al (2016) Dynamic stem cell states: naive to primed pluripotency in rodents and humans. Nat Rev Mol Cell Biol 17:155–169

    Article  CAS  PubMed  Google Scholar 

  11. Yuan M et al (2012) A positive/negative ion-switching, targeted mass spectrometry-based metabolomics platform for bodily fluids, cells, and fresh and fixed tissue. Nat Protoc 7:872–881

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Theodoridis GA et al (2012) Liquid chromatography-mass spectrometry based global metabolite profiling: a review. Anal Chim Acta 711:7–16

    Article  CAS  PubMed  Google Scholar 

  13. Zhang T, Watson DG (2015) A short review of applications of liquid chromatography mass spectrometry based metabolomics techniques to the analysis of human urine. Analyst 140(9):2907–2915

    Article  CAS  PubMed  Google Scholar 

  14. Roux A et al (2011) Applications of liquid chromatography coupled to mass spectrometry-based metabolomics in clinical chemistry and toxicology: a review. Clin Biochem 44(1):119–135

    Article  CAS  PubMed  Google Scholar 

  15. Siskos AP et al (2017) Interlaboratory Reproducibility of a Targeted Metabolomics Platform for Analysis of Human Serum and Plasma. Anal Chem 89(1):656–665

    Article  CAS  PubMed  Google Scholar 

  16. Myers OD et al (2017) Detailed investigation and comparison of the XCMS and MZmine 2 chromatogram construction and chromatographic peak detection methods for preprocessing mass spectrometry metabolomics data. Anal Chem 89(17):8689–8695

    Article  CAS  PubMed  Google Scholar 

  17. Tian T-F et al (2016) Web server for peak detection, baseline correction, and alignment in two-dimensional gas chromatography mass spectrometry-based metabolomics data. Anal Chem 88(21):10395–10403

    Article  CAS  PubMed  Google Scholar 

  18. Zhang JQ et al (2009) Review of peak detection algorithms in liquid-chromatography-mass spectrometry. Curr Genomics 10(6):388–401

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Smith CA et al (2006) XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification. Anal Chem 78(3):779–787

    Article  CAS  PubMed  Google Scholar 

  20. Mahieu NG, Genenbacher JL, Patti GJ (2016) A roadmap for the XCMS family of software solutions in metabolomics. Curr Opin Chem Biol 30:87–93

    Article  CAS  PubMed  Google Scholar 

  21. Hu M et al (2016) Optimization of LC Orbitrap HRMS acquisition and MZmine 2 data processing for nontarget screening of environmental samples using design of experiments. Anal Bioanal Chem 408(28):7905–7915

    Article  CAS  PubMed  Google Scholar 

  22. Katajamaa M, Miettinen J, Oresic M (2006) MZmine: toolbox for processing and visualization of mass spectrometry based molecular profile data. Bioinformatics 22(5):634–636

    Article  CAS  PubMed  Google Scholar 

  23. van den Berg RA et al (2006) Centering, scaling, and transformations: improving the biological information content of metabolomics data. BMC Genomics 7:142

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  24. Rui Alves M (2012) Evaluation of the predictive power of biplot axes to automate the construction and layout of biplots based on the accuracy of direct readings from common outputs of multivariate analyses: 1. Application to principal component analysis. J Chemom 26(5):180–190

    Article  CAS  Google Scholar 

  25. Jiang Q, Yan X (2013) Weighted kernel principal component analysis based on probability density estimation and moving window and its application in nonlinear chemical process monitoring. Chemom Intell Lab Syst 127:121–131

    Article  CAS  Google Scholar 

  26. Gika HG et al (2012) A QC approach to the determination of day-to-day reproducibility and robustness of LC-MS methods for global metabolite profiling in metabonomics/metabolomics. Bioanalysis 4(18):2239–2247

    Article  CAS  PubMed  Google Scholar 

  27. Godzien J et al (2014) Controlling the quality of metabolomics data: new strategies to get the best out of the QC sample. Metabolomics 11(3):518–528

    Article  CAS  Google Scholar 

  28. Wang S-Y, Kuo C-H, Tseng YJ (2013) Batch normalizer: a fast total abundance regression calibration method to simultaneously adjust batch and injection order effects in liquid chromatography/time-of-flight mass spectrometry-based metabolomics data and comparison with current calibration methods. Anal Chem 85(2):1037–1046

    Article  CAS  PubMed  Google Scholar 

  29. Zhao Y et al (2016) A novel strategy for large-scale metabolomics study by calibrating gross and systematic errors in gas chromatography-mass spectrometry. Anal Chem 88(4):2234–2242

    Article  CAS  PubMed  Google Scholar 

  30. Vinaixa M et al (2012) A guideline to univariate statistical analysis for LC/MS-based untargeted metabolomics-derived data. Meta 2(4):775–795

    CAS  Google Scholar 

  31. Patti GJ et al (2013) A view from above: cloud plots to visualize global metabolomic data. Anal Chem 85(2):798–804

    Article  CAS  PubMed  Google Scholar 

  32. Wiklund S et al (2008) Visualization of GC/TOF-MS-based metabolomics data for identification of biochemically interesting compounds using OPLS class models. Anal Chem 80(1):115–122

    Article  CAS  PubMed  Google Scholar 

  33. Shen X et al (2016) Normalization and integration of large-scale metabolomics data using support vector regression. Metabolomics 12(5):89

    Article  CAS  Google Scholar 

  34. Li H, Liang Y, Xu Q (2009) Support vector machines and its applications in chemistry. Chemom Intell Lab Syst 95(2):188–198

    Article  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Basic Medical Sciences, Center for Stem Cell and Regenerative Medicine, The First Affiliated Hospital, Institute of Hematology, School of Medicine, Zhejiang University, Zhejiang, Hangzhou, China

    Zhen Sun, Jing Zhao, Hua Yu, Chenyang Zhang & Jin Zhang

  2. Department of Molecular Pharmacology and Experimental Therapeutics, Center for Individualized Medicine, Mayo Clinic, Rochester, MN, USA

    Hu Li

  3. Dalian ChemDataSolution Information Technology Co. Ltd., Dalian, China

    Zhongda Zeng

Authors
  1. Zhen Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jing Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hua Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Chenyang Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Hu Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Zhongda Zeng
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Jin Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jin Zhang .

Editor information

Editors and Affiliations

  1. Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA

    Patrick Cahan

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Science+Business Media, LLC, part of Springer Nature

About this protocol

Check for updates. Verify currency and authenticity via CrossMark

Cite this protocol

Sun, Z. et al. (2019). Metabolomics in Stem Cell Biology Research. In: Cahan, P. (eds) Computational Stem Cell Biology. Methods in Molecular Biology, vol 1975. Humana, New York, NY. https://doi.org/10.1007/978-1-4939-9224-9_15

Download citation

  • DOI: https://doi.org/10.1007/978-1-4939-9224-9_15

  • Published:

  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-4939-9223-2

  • Online ISBN: 978-1-4939-9224-9

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics

Search

Navigation