Design and Application of Super-SILAC for Proteome Quantification

Part of the Methods in Molecular Biology book series (MIMB, volume 1188)

Abstract

Stable isotope labeling with amino acids in cell culture (SILAC) is considered the most accurate method for proteome quantification by mass spectrometry. As it relies on active protein translation, it was traditionally limited to cells in culture and was not applicable to tissues. We have previously developed the super-SILAC mix, which is a mixture of several cell lines that serves as an internal spike-in standard for the study of human tumor tissue. The super-SILAC mix greatly improves the quantification accuracy while lowering error rates, and it is a simple, economic, and robust technique. Here we describe the design and application of super-SILAC to a broad range of biological systems, for basic biological research as well as clinical one.

Key words

Mass spectrometry Proteomics Isotope labeling Super-SILAC FASP 

References

  1. 1.
    Gstaiger M, Aebersold R (2009) Applying mass spectrometry-based proteomics to genetics, genomics and network biology. Nat Rev Genet 10:617–627PubMedCrossRefGoogle Scholar
  2. 2.
    Cravatt BF, Simon GM, Yates JR III (2007) The biological impact of mass-spectrometry-based proteomics. Nature 450:991–1000PubMedCrossRefGoogle Scholar
  3. 3.
    Mann M, Kelleher NL (2008) Precision proteomics: the case for high resolution and high mass accuracy. Proc Natl Acad Sci U S A 105:18132–18138PubMedCentralPubMedCrossRefGoogle Scholar
  4. 4.
    Cox J, Mann M (2011) Quantitative, high-resolution proteomics for data-driven systems biology. Annu Rev Biochem 80:273–299PubMedCrossRefGoogle Scholar
  5. 5.
    Choudhary C, Mann M (2010) Decoding signalling networks by mass spectrometry-based proteomics. Nat Rev Mol Cell Biol 11:427–439PubMedCrossRefGoogle Scholar
  6. 6.
    Mallick P, Kuster B (2010) Proteomics: a pragmatic perspective. Nat Biotechnol 28:695–709PubMedCrossRefGoogle Scholar
  7. 7.
    Ong SE, Mann M (2005) Mass spectrometry-based proteomics turns quantitative. Nat Chem Biol 1:252–262PubMedCrossRefGoogle Scholar
  8. 8.
    Ishihama Y, Sato T, Tabata T et al (2005) Quantitative mouse brain proteomics using culture-derived isotope tags as internal standards. Nat Biotechnol 23:617–621PubMedCrossRefGoogle Scholar
  9. 9.
    Geiger T, Cox J, Ostasiewicz P et al (2010) Super-SILAC mix for quantitative proteomics of human tumor tissue. Nat Methods 7:383–385PubMedCrossRefGoogle Scholar
  10. 10.
    Deeb SJ, D’souza RC, Cox J et al (2012) Super-SILAC allows classification of diffuse large B-cell lymphoma subtypes by their protein expression profiles. Mol Cell Proteomics 11:77–89PubMedCentralPubMedCrossRefGoogle Scholar
  11. 11.
    Geiger T, Wisniewski JR, Cox J et al (2011) Use of stable isotope labeling by amino acids in cell culture as a spike-in standard in quantitative proteomics. Nat Protoc 6:147–157PubMedCrossRefGoogle Scholar
  12. 12.
    Wisniewski JR, Zougman A, Nagaraj N et al (2009) Universal sample preparation method for proteome analysis. Nat Methods 6:359–362PubMedCrossRefGoogle Scholar
  13. 13.
    Wisniewski JR, Zougman A, Mann M (2009) Combination of FASP and StageTip-based fractionation allows in-depth analysis of the hippocampal membrane proteome. J Proteome Res 8:5674–5678PubMedCrossRefGoogle Scholar
  14. 14.
    Cox J, Matic I, Hilger M et al (2009) A practical guide to the MaxQuant computational platform for SILAC-based quantitative proteomics. Nat Protoc 4:698–705PubMedCrossRefGoogle Scholar
  15. 15.
    Cox J, Mann M (2008) MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat Biotechnol 26:1367–1372PubMedCrossRefGoogle Scholar
  16. 16.
    Britton HTS, Robinson RA (1931) CXCVIII.-Universal buffer solutions and the dissociation constant of veronal. J Chem Soc (Resumed): 1456–1462Google Scholar
  17. 17.
    Rappsilber J, Mann M, Ishihama Y (2007) Protocol for micro-purification, enrichment, pre-fractionation and storage of peptides for proteomics using StageTips. Nat Protoc 2:1896–1906PubMedCrossRefGoogle Scholar
  18. 18.
    Bendall SC, Hughes C, Stewart MH et al (2008) Prevention of amino acid conversion in SILAC experiments with embryonic stem cells. Mol Cell Proteomics 7:1587–1597PubMedCentralPubMedCrossRefGoogle Scholar
  19. 19.
    Van Hoof D, Pinkse MW, Oostwaard DW et al (2007) An experimental correction for arginine-to-proline conversion artifacts in SILAC-based quantitative proteomics. Nat Methods 4:677–678PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.The Department of Human Molecular Genetics and Biochemistry, Sackler Faculty of MedicineTel Aviv UniversityTel AvivIsrael

Personalised recommendations