Skip to main content

Stable Isotope Labeling in Mammals (SILAM)

  • Protocol
  • First Online:
Shotgun Proteomics

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

Abstract

Analysis of animal models of disease is essential to the understanding of human disease and the identification of potential targets for clinical drugs. Global analysis of proteins by mass spectrometry is an important tool for these studies. Stable isotope labeling in mammals (SILAM) was developed to quantitate the proteomes of rodents using mass spectrometry. The crux of SILAM analysis is the complete labeling of all proteins in a rodent with heavy nitrogen (15N). These 15N tissues are then employed as an internal standard for quantitative proteomics analysis using a high-resolution and mass-accuracy mass spectrometer.

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

Access this chapter

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. Liao L, McClatchy DB, Yates JR (2009) Shotgun proteomics in neuroscience. Neuron 63(1):12–26

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  2. McClatchy DB et al (2007) 15 N metabolic labeling of mammalian tissue with slow protein turnover. J Proteome Res 6(5): 2005–2010

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  3. McClatchy DB et al (2012) Dynamics of subcellular proteomes during brain development. J Proteome Res 11(4):2467–2479

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  4. Butko MT et al (2013) In vivo quantitative proteomics of somatosensory cortical synapses shows which protein levels are modulated by sensory deprivation. Proc Natl Acad Sci U S A 110(8):E726–E735

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  5. McClatchy DB et al (2011) Differential proteomic analysis of mammalian tissues using SILAM. PLoS One 6(1):e16039

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  6. Liao L et al (2008) Quantitative analysis of brain nuclear phosphoproteins identifies developmentally regulated phosphorylation events. J Proteome Res 7(11):4743–4755

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  7. Price JC et al (2010) Analysis of proteome dynamics in the mouse brain. Proc Natl Acad Sci U S A 107(32):14508–14513

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  8. Liao L et al (2012) 15 N-labeled brain enables quantification of proteome and phosphoproteome in cultured primary neurons. J Proteome Res 11(2):1341–1353

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  9. Filiou MD et al (2012) The 15 N isotope effect in Escherichia coli: a neutron can make the difference. Proteomics 12(21):3121–3128

    Article  CAS  PubMed  Google Scholar 

  10. Wu CC et al (2004) Metabolic labeling of mammalian organisms with stable isotopes for quantitative proteomic analysis. Anal Chem 76(17):4951–4959

    Article  CAS  PubMed  Google Scholar 

  11. McClatchy DB, Yates JR III (2008) Stable isotope labeling of mammals (SILAM). CSH Protoc 2008

    Google Scholar 

  12. Washburn MP, Wolters D, Yates JR III (2001) Large-scale analysis of the yeast proteome by multidimensional protein identification technology. Nat Biotechnol 19(3):242–247

    Article  CAS  PubMed  Google Scholar 

  13. Xu T et al (2006) ProLuCID, a fast and sensitive tandem mass spectra-based protein identification program. Mol Cell Proteomics 5(10):1(S174)

    Google Scholar 

  14. Cociorva D, L Tabb D, Yates JR (2007) Validation of tandem mass spectrometry database search results using DTASelect. Curr Protoc Bioinformatics Chapter 13:Unit 13 4

    Google Scholar 

  15. Park SK, Yates JR III (2010) Census for proteome quantification. Curr Protoc Bioinformatics Chapter 13:Unit 13 12 1-11

    Google Scholar 

  16. MacCoss MJ et al (2003) A correlation algorithm for the automated quantitative analysis of shotgun proteomics data. Anal Chem 75(24): 6912–6921

    Article  CAS  PubMed  Google Scholar 

  17. Ting L et al (2009) Normalization and statistical analysis of quantitative proteomics data generated by metabolic labeling. Mol Cell Proteomics 8(10):2227–2242

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  18. Liu H, Sadygov RG, Yates JR III (2004) A model for random sampling and estimation of relative protein abundance in shotgun proteomics. Anal Chem 76(14):4193–4201

    Article  CAS  PubMed  Google Scholar 

  19. Zhang Y et al (2010) Refinements to label free proteome quantitation: how to deal with peptides shared by multiple proteins. Anal Chem 82(6):2272–2281

    Article  CAS  PubMed  Google Scholar 

  20. Li Z et al (2012) Systematic comparison of label-free, metabolic labeling, and isobaric chemical labeling for quantitative proteomics on LTQ Orbitrap Velos. J Proteome Res 11(3):1582–1590

    Article  CAS  PubMed  Google Scholar 

  21. Venable JD et al (2004) Automated approach for quantitative analysis of complex peptide mixtures from tandem mass spectra. Nat Methods 1(1):39–45

    Article  CAS  PubMed  Google Scholar 

  22. Chen EI et al (2008) Comparisons of mass spectrometry compatible surfactants for global analysis of the mammalian brain proteome. Anal Chem 80(22):8694–8701

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  23. Haegler K et al (2009) QuantiSpec–Quantitative mass spectrometry data analysis of (15)N-metabolically labeled proteins. J Proteomics 71(6):601–608

    Article  CAS  PubMed  Google Scholar 

  24. Frank E et al (2009) Stable isotope metabolic labeling with a novel N-enriched bacteria diet for improved proteomic analyses of mouse models for psychopathologies. PLoS One 4(11):e7821

    Article  PubMed Central  PubMed  Google Scholar 

  25. Delahunty C, Yates JR III (2005) Protein identification using 2D-LC-MS/MS. Methods 35(3):248–255

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

We acknowledge funding from NIH grants R01 MH067880 and P41 RR011823. We also like to thank Dr. Claire Delahunty for her comments on the manuscript.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to John R. Yates III .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer Science+Business Media New York

About this protocol

Cite this protocol

McClatchy, D.B., Yates, J.R. (2014). Stable Isotope Labeling in Mammals (SILAM). 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_8

Download citation

  • DOI: https://doi.org/10.1007/978-1-4939-0685-7_8

  • 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