Skip to main content
SpringerLink
Log in

Analysis of Proteome Dynamics in Mice by Isotopic Labeling

  • Protocol
  • First Online:
Shotgun Proteomics

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1156))

Abstract

Recent advances in mass spectrometry and in vivo isotopic labeling have enabled proteome-wide analyses of protein turnover in complex organisms. Here, we describe a protocol for analyzing protein turnover rates in mouse tissues by comprehensive 15N labeling. The procedure involves the complete isotopic labeling of blue green algae (Spirulina platensis) with 15N and utilizing it as a source of dietary nitrogen for mice. We outline a detailed protocol for in-house production of 15N-labeled algae, labeling of mice, and analysis of isotope incorporation kinetics by mass spectrometry. The methodology can be adapted to analyze proteome dynamics in most murine tissues and may be particularly useful in the analysis of proteostatic disruptions in mouse models of disease.

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 89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 119.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
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. Borsook H, Keighley GL (1935) The “continuing” metabolism of nitrogen in animals. Proc R Soc Lond B 118:488

    Article  CAS  Google Scholar 

  2. Schoenheimer R (1942) The dynamic state of body constituents. Harvard University Press, Boston

    Google Scholar 

  3. Claydon AJ, Beynon R (2012) Proteome dynamics: revisiting turnover with a global perspective. Mol Cell Proteomics 11(12):1551–1565. doi:10.1074/mcp.O112.022186

    Article  PubMed Central  PubMed  Google Scholar 

  4. Hinkson IV, Elias JE (2011) The dynamic state of protein turnover: it’s about time. Trends Cell Biol 21(5):293–303. doi:10.1016/j.tcb.2011.02.002

    Article  CAS  PubMed  Google Scholar 

  5. Hochstrasser M (1996) Ubiquitin-dependent protein degradation. Annu Rev Genet 30:405–439. doi:10.1146/annurev.genet.30.1.405

    Article  CAS  PubMed  Google Scholar 

  6. Klionsky DJ (2007) Autophagy: from phenomenology to molecular understanding in less than a decade. Nat Rev Mol Cell Biol 8(11):931–937. doi:10.1038/nrm2245

    Article  CAS  PubMed  Google Scholar 

  7. Lopez-Otin C, Overall CM (2002) Protease degradomics: a new challenge for proteomics. Nat Rev Mol Cell Biol 3(7):509–519. doi:10.1038/nrm858

    Article  CAS  PubMed  Google Scholar 

  8. Schoenheimer R, Rittenberg D, Foster GL et al (1938) The application of the nitrogen isotope N15 for the study of protein metabolism. Science 88(2295):599–600. doi:10.1126/science.88.2295.599

    Article  CAS  PubMed  Google Scholar 

  9. Hellerstein MK, Neese RA (1992) Mass isotopomer distribution analysis: a technique for measuring biosynthesis and turnover of polymers. Am J Physiol 263(5 Pt 1):E988–E1001

    CAS  PubMed  Google Scholar 

  10. Doherty MK, Whitehead C, McCormack H et al (2005) Proteome dynamics in complex organisms: using stable isotopes to monitor individual protein turnover rates. Proteomics 5(2):522–533. doi:10.1002/pmic.200400959

    Article  CAS  PubMed  Google Scholar 

  11. Ong SE, Mann M (2007) Stable isotope labeling by amino acids in cell culture for quantitative proteomics. Methods Mol Biol 359:37–52. doi:10.1007/978-1-59745-255-7_3

    Article  CAS  PubMed  Google Scholar 

  12. Schwanhausser B, Busse D, Li N et al (2011) Global quantification of mammalian gene expression control. Nature 473(7347):337–342. doi:10.1038/nature10098

    Article  PubMed  Google Scholar 

  13. Cambridge SB, Gnad F, Nguyen C et al (2011) Systems-wide proteomic analysis in mammalian cells reveals conserved, functional protein turnover. J Proteome Res 10(12):5275–5284. doi:10.1021/pr101183k

    Article  CAS  PubMed  Google Scholar 

  14. Price JC, Guan S, Burlingame A et al (2010) Analysis of proteome dynamics in the mouse brain. Proc Natl Acad Sci U S A 107(32):14508–14513. doi:10.1073/pnas.1006551107

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  15. Wu CC, MacCoss MJ, Howell KE et al (2004) Metabolic labeling of mammalian organisms with stable isotopes for quantitative proteomic analysis. Anal Chem 76(17):4951–4959. doi:10.1021/ac049208j

    Article  CAS  PubMed  Google Scholar 

  16. Bodzon-Kulakowska A, Bierczynska-Krzysik A, Dylag T et al (2007) Methods for samples preparation in proteomic research. J Chromatogr B Analyt Technol Biomed Life Sci 849(1–2):1–31. doi:10.1016/j.jchromb.2006.10.040

    Article  CAS  PubMed  Google Scholar 

  17. Hoopmann MR, Chavez JD, Bruce JE (2011) SILACtor: software to enable dynamic SILAC studies. Anal Chem 83(22):8403–8410. doi:10.1021/ac2017053

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  18. Hsieh EJ, Shulman NJ, Dai DF et al (2012) Topograph, a software platform for precursor enrichment corrected global protein turnover measurements. Mol Cell Proteomics 11(11):1468–1474. doi:10.1074/mcp.O112.017699

    Article  PubMed Central  PubMed  Google Scholar 

  19. Guan S, Price JC, Prusiner SB et al (2011) A data processing pipeline for mammalian proteome dynamics studies using stable isotope metabolic labeling. Mol Cell Proteomics 10(12):M111.010728. doi:10.1074/mcp.M111.010728

    Article  PubMed Central  PubMed  Google Scholar 

  20. Guan S, Price JC, Ghaemmaghami S et al (2012) Compartment modeling for mammalian protein turnover studies by stable isotope metabolic labeling. Anal Chem 84(9):4014–4021. doi:10.1021/ac203330z

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  21. Eng JK, Searle BC, Clauser KR et al (2011) A face in the crowd: recognizing peptides through database search. Mol Cell Proteomics 10(11):R111.009522. doi:10.1074/mcp.R111.009522

    Article  PubMed Central  PubMed  Google Scholar 

  22. Chalkley RJ, Baker PR, Huang L et al (2005) Comprehensive analysis of a multidimensional liquid chromatography mass spectrometry dataset acquired on a quadrupole selecting, quadrupole collision cell, time-of-flight mass spectrometer: II. New developments in Protein Prospector allow for reliable and comprehensive automatic analysis of large datasets. Mol Cell Proteomics 4(8):1194–1204. doi:10.1074/mcp.D500002-MCP200

    Article  CAS  PubMed  Google Scholar 

  23. Kessner D, Chambers M, Burke R et al (2008) ProteoWizard: open source software for rapid proteomics tools development. Bioinformatics 24(21):2534–2536. doi:10.1093/bioinformatics/btn323

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  24. Sykes MT, Williamson JR (2008) Envelope: interactive software for modeling and fitting complex isotope distributions. BMC Bioinformatics 9:446. doi:10.1186/1471-2105-9-446

    Article  PubMed Central  PubMed  Google Scholar 

  25. Zhang Y, Reckow S, Webhofer C et al (2011) Proteome scale turnover analysis in live animals using stable isotope metabolic labeling. Anal Chem 83(5):1665–1672. doi:10.1021/ac102755n

    Article  CAS  PubMed  Google Scholar 

  26. Molina E, Fernandez J, Acien FG et al (2001) Tubular photobioreactor design for algal cultures. J Biotechnol 92(2):113–131

    Article  CAS  PubMed  Google Scholar 

  27. Price JC, Khambatta CF, Li KW et al (2012) The effect of long term calorie restriction on in vivo hepatic proteostasis: a novel combination of dynamic and quantitative proteomics. Mol Cell Proteomics 11(12):1801–1814. doi:10.1074/mcp.M112.021204

    Article  PubMed Central  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Chemistry and Biochemistry, Brigham Young University, Provo, UT, 84604, USA

    John C. Price

  2. Department of Biology, University of Rochester, River Campus Box 270211, 500 Joseph C. Wilson Boulevard, Rochester, NY, 14627, USA

    Sina Ghaemmaghami

Authors
  1. John C. Price
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sina Ghaemmaghami
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sina Ghaemmaghami .

Editor information

Editors and Affiliations

  1. Ludwig-Maximilians-University, Munich, Germany

    Daniel Martins-de-Souza

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer Science+Business Media New York

About this protocol

Cite this protocol

Price, J.C., Ghaemmaghami, S. (2014). Analysis of Proteome Dynamics in Mice by Isotopic Labeling. In: Martins-de-Souza, D. (eds) Shotgun Proteomics. Methods in Molecular Biology, vol 1156. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-0685-7_7

Download citation

  • DOI: https://doi.org/10.1007/978-1-4939-0685-7_7

  • Published:

  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-0684-0

  • Online ISBN: 978-1-4939-0685-7

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics

Search

Navigation