Skip to main content
SpringerLink
Log in

Label-Free Protein Quantitation Using Weighted Spectral Counting

  • Protocol
  • First Online:
Quantitative Methods in Proteomics

Part of the book series: Methods in Molecular Biology ((MIMB,volume 893))

Abstract

Mass spectrometry (MS)-based shotgun proteomics allows protein identifications even in complex biological samples. Protein abundances can then be estimated from the counts of MS/MS spectra attributable to each protein, provided that one corrects for differential MS-detectability of the contributing peptides. We describe the use of a method, APEX, which calculates Absolute Protein EXpression levels based on learned correction factors, MS/MS spectral counts, and each protein’s probability of correct identification.

The APEX-based calculations consist of three parts: (1) Using training data, peptide sequences and their sequence properties, a model is built that can be used to estimate MS-detectability (O i) for any given protein. (2) Absolute abundances of proteins measured in an MS/MS experiment are calculated with information from spectral counts, identification probabilities and the learned O i-values. (3) Simple statistics allow for significance analysis of differential expression in two distinct biological samples, i.e., measuring relative protein abundances. APEX-based protein abundances span more than four orders of magnitude and are applicable to mixtures of hundreds to thousands of proteins from any type of organism.

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 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 159.00
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
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

Abbreviations

APEX:

Absolute Protein EXpression

MS:

Mass spectrometry

MS/MS:

Tandem mass spectrometry

References

  1. Nesvizhskii AI, Keller A, Kolker E, Aebersold R (2003) A statistical model for identifying proteins by tandem mass spectrometry. Anal Chem 75:4646–4658

    Article  PubMed  CAS  Google Scholar 

  2. Oda Y, Huang K, Cross FR et al (1999) Accurate quantitation of protein expression and site-specific phosphorylation. Proc Natl Acad Sci USA 96:6591–6596

    Article  PubMed  CAS  Google Scholar 

  3. Ong SE, Mann M (2005) Mass spectrometry-based proteomics turns quantitative. Nat Chem Biol 1:252–262

    Article  PubMed  CAS  Google Scholar 

  4. Ong SE, Blagoev B, Kratchmarova I et al (2002) Stable isotope labeling by amino acids in cell culture, SILAC, as a simple and accurate approach to expression proteomics. Mol Cell Proteomics 1:376–386

    Article  PubMed  CAS  Google Scholar 

  5. Gygi SP, Rochon Y, Franza BR, Aebersold R (1999) Correlation between protein and mRNA abundance in yeast. Mol Cell Biol 19:1720–1730

    PubMed  CAS  Google Scholar 

  6. Gerber SA, Rush J, Stemman O et al (2003) Absolute quantification of proteins and phosphoproteins from cell lysates by tandem MS. Proc Natl Acad Sci USA 100:6940–6945

    Article  PubMed  CAS  Google Scholar 

  7. Ishihama Y, Oda Y, Tabata T et al (2005) Exponentially modified protein abundance index (emPAI) for estimation of absolute protein amount in proteomics by the number of sequenced peptides per protein. Mol Cell Proteomics 4:1265–1272

    Article  PubMed  CAS  Google Scholar 

  8. Silva JC, Gorenstein MV, Li GZ et al (2006) Absolute quantification of proteins by LCMSE: a virtue of parallel MS acquisition. Mol Cell Proteomics 5:144–156

    PubMed  CAS  Google Scholar 

  9. Malmstrom J, Beck M, Schmidt A et al (2009) Proteome-wide cellular protein concentrations of the human pathogen Leptospira interrogans. Nature 460:762–765

    Article  PubMed  Google Scholar 

  10. Kislinger T, Gramolini AO, Pan Y et al (2005) Proteome dynamics during C2C12 myoblast differentiation. Mol Cell Proteomics 4:887–901

    Article  PubMed  CAS  Google Scholar 

  11. Kislinger T, Cox B, Kannan A et al (2006) Global survey of organ and organelle protein expression in mouse: combined proteomic and transcriptomic profiling. Cell 125:173–186

    Article  PubMed  CAS  Google Scholar 

  12. Blondeau F, Ritter B, Allaire PD et al (2004) Tandem MS analysis of brain clathrin-coated vesicles reveals their critical involvement in synaptic vesicle recycling. Proc Natl Acad Sci USA 101:3833–3838

    Article  PubMed  CAS  Google Scholar 

  13. States DJ, Omenn GS, Blackwell TW et al (2006) Challenges in deriving high-confidence protein identifications from data gathered by a HUPO plasma proteome collaborative study. Nat Biotechnol 24:333–338

    Article  PubMed  CAS  Google Scholar 

  14. Florens L, Washburn MP, Raine JD et al (2002) A proteomic view of the Plasmodium falciparum life cycle. Nature 419:520–526

    Article  PubMed  CAS  Google Scholar 

  15. Gao J, Friedrichs MS, Dongre AR, Opiteck GJ (2005) Guidelines for the routine application of the peptide hits technique. J Am Soc Mass Spectrom 16:1231–1238

    Google Scholar 

  16. Gao J, Opiteck GJ, Friedrichs MS et al (2003) Guidelines for the routine application of the peptide hits technique. J Proteome Res 2:643–649

    Article  PubMed  CAS  Google Scholar 

  17. Liu H, Sadygov RG, Yates JR 3rd (2004) A model for random sampling and estimation of relative protein abundance in shotgun proteomics. Anal Chem 76:4193–4201

    Article  PubMed  CAS  Google Scholar 

  18. Steen H, Pandey A (2002) Proteomics goes quantitative: measuring protein abundance. Trends Biotechnol 20:361–364

    Article  PubMed  CAS  Google Scholar 

  19. Elias JE, Gibbons FD, King OD et al (2004) Intensity-based protein identification by machine learning from a library of tandem mass spectra. Nat Biotechnol 22:214–219

    Article  PubMed  CAS  Google Scholar 

  20. Gay S, Binz PA, Hochstrasser DF, Appel RD (2002) Peptide mass fingerprinting peak intensity prediction: extracting knowledge from spectra. Proteomics 2:1374–1391

    Article  PubMed  CAS  Google Scholar 

  21. Craig R, Cortens JP, Beavis RC (2005) The use of proteotypic peptide libraries for protein identification. Rapid Commun Mass Spectrom 19:1844–1850

    Article  PubMed  CAS  Google Scholar 

  22. Kuster B, Schirle M, Mallick P, Aebersold R (2005) Scoring proteomes with proteotypic peptide probes. Nat Rev Mol Cell Biol 6:577–583

    Article  PubMed  CAS  Google Scholar 

  23. Le Bihan T, Robinson MD, Stewart II, Figeys D (2004) Definition and characterization of a “trypsinosome” from specific peptide characteristics by nano-HPLC-MS/MS and in silico analysis of complex protein mixtures. J Proteome Res 3:1138–1148

    Article  PubMed  Google Scholar 

  24. Mallick P, Schirle M, Chen SS et al (2007) Computational prediction of proteotypic peptides for quantitative proteomics. Nat Biotechnol 25:125–131

    Article  PubMed  CAS  Google Scholar 

  25. Tang H, Arnold RJ, Alves P et al (2006) A computational approach toward label-free protein quantification using predicted peptide detectability. Bioinformatics 22:e481–e488

    Article  PubMed  CAS  Google Scholar 

  26. Lu P, Vogel C, Wang R, Yao et al (2007) Absolute protein expression profiling estimates the relative contributions of transcriptional and translational regulation. Nat Biotechnol 25:117–124

    Article  PubMed  CAS  Google Scholar 

  27. Ghaemmaghami S, Huh WK, Bower K et al (2003) Global analysis of protein expression in yeast. Nature 425:737–741

    Article  PubMed  CAS  Google Scholar 

  28. Newman JR, Ghaemmaghami S, Ihmels J et al (2006) Single-cell proteomic analysis of S. cerevisiae reveals the architecture of biological noise. Nature 441:840–846

    Article  PubMed  CAS  Google Scholar 

  29. Futcher B, Latter GI, Monardo P et al (1999) A sampling of the yeast proteome. Mol Cell Biol 19:7357–7368

    PubMed  CAS  Google Scholar 

  30. Lopez-Campistrous A, Semchuk P, Burke L et al (2005) Localization, annotation, and comparison of the Escherichia coli K-12 proteome under two states of growth. Mol Cell Proteomics 4:1205–1209

    Article  PubMed  CAS  Google Scholar 

  31. Laurent J, Vogel C, Kwon T et al (2010) Protein abundances are more conserved than mRNA abundances across diverse taxa. Proteomics 23(10):4209–4212

    Article  Google Scholar 

  32. Wang R, Marcotte EM (2008) The proteomic response of Mycobacterium smegmatis to anti-tuberculosis drugs suggests targeted pathways. J Proteome Res 7:855–865

    Article  PubMed  CAS  Google Scholar 

  33. Baerenfaller K, Grossmann J, Grobei MA et al (2008) Genome-scale proteomics reveals Arabidopsis thaliana gene models and proteome dynamics. Science 320:938–941

    Article  PubMed  CAS  Google Scholar 

  34. Vogel C, de Sousa AR, Ko D et al (2010) Sequence signatures and mRNA concentration can explain two-thirds of protein abundance variation in a human cell line. Mol Syst Biol 6:400

    Article  PubMed  Google Scholar 

  35. Schmidt MW, Houseman A, Ivanov AR, Wolf DA (2007) Comparative proteomic and transcriptomic profiling of the fission yeast Schizosaccharomyces pombe. Mol Syst Biol 3:79

    Article  PubMed  Google Scholar 

  36. Schrimpf SP, Weiss M, Reiter L et al (2009) Comparative functional analysis of the Caenorhabditis elegans and Drosophila melanogaster proteomes. PLoS Biol 7:e48

    Article  PubMed  Google Scholar 

  37. Keller A, Nesvizhskii AI, Kolker E, Aebersold R (2002) Empirical statistical model to estimate the accuracy of peptide identifications made by MS/MS and database search. Anal Chem 74:5383–5392

    Article  PubMed  CAS  Google Scholar 

  38. Braisted JC, Kuntumalla S, Vogel C et al (2008) Quantitative proteomics tool: generating protein quantitation estimates from LC-MS/MS proteomics results. BMC Bioinformatics 9:529

    Article  PubMed  Google Scholar 

  39. Vogel C, Marcotte EM (2008) Calculating absolute and relative protein abundance from mass spectrometry-based protein expression data. Nat Protoc 3:1444–1451

    Article  PubMed  CAS  Google Scholar 

  40. Cagney G, Amiri S, Premawaradena T et al (2003) In silico proteome analysis to facilitate proteomics experiments using mass spectrometry. Proteome Sci 1:5

    Article  PubMed  Google Scholar 

  41. Neidhardt FC, Umbarger HE (eds) (1996) Escherichia coli and Salmonella typhimurium: cellular and molecular biology, part 4. ASM Press, Washington, DC

    Google Scholar 

  42. Sundararaj S, Guo A, Habibi-Nazhad B et al (2004) The CyberCell Database (CCDB): a comprehensive, self-updating, relational database to coordinate and facilitate in silico modeling of Escherichia coli. Nucleic Acids Res 32:D293–D295

    Article  PubMed  CAS  Google Scholar 

  43. Fasman GD ed. (1976) “Handbook of Biochemistry and Molecular Biology”, 3rd ed., Proteins – Volume 1, CRC Press, Cleveland

    Article  PubMed  CAS  Google Scholar 

  44. Chou PY, Fasman GD (1978) Prediction of the secondary structure of proteins from their amino acid sequence. Adv. Enzymol. 47: 45–148

    Article  PubMed  CAS  Google Scholar 

  45. Wertz DH, Scheraga HA (1978) Influence of water on protein structure. An analysis of the preferences of amino acid residues for the inside or outside and for specific conformations in a protein molecule. Macro­molecules 11:9–15

    Article  PubMed  CAS  Google Scholar 

  46. Zimmerman JM, Eliezer N, Simha R (1968) The characterization of amino acid sequences in proteins by statistical methods. J Theor Biol 21:170–201

    Article  PubMed  CAS  Google Scholar 

  47. Klein P, Kanehisa M, DeLisi C (1984) Prediction of protein function from sequence properties: Discriminant analysis of a data base. Biochim Biophys Acta 787:221–226

    Article  PubMed  CAS  Google Scholar 

  48. Eisenberg D, McLachlan AD (1986) Solva­tion energy in protein folding and binding. Nature 319:199–203

    Article  PubMed  CAS  Google Scholar 

  49. Fauchere JL, Charton M, Kier LB, Verloop A, Pliska V (1988) Amino acid side chain parameters for correlation studies in biology and pharmacology. Int J Peptide Protein Res 32:269–278

    Article  PubMed  CAS  Google Scholar 

  50. Vihinen M, Torkkila E, Riikonen P (1994) Accuracy of protein flexibility predictions. Proteins 19:141–149

    Article  PubMed  CAS  Google Scholar 

  51. Guy HR (1985) Amino acid side-chain partition energies and distribution of residues in soluble proteins. Biophys J 47:61–70

    Article  PubMed  CAS  Google Scholar 

  52. Nozaki Y, Tanford C (1971) The solubility of amino acids and two glycine peptides in aqueous ethanol and dioxane solutions. J Biol Chem 246:2211–2217

    Article  PubMed  CAS  Google Scholar 

Download references

Acknowledgments

C.V. acknowledges support by the International Human Frontier Science Program. We thank John Braisted and Srilatha Kuntumalla from JCVI for many useful discussions regarding the APEX calculations. This work was supported by grants from the Welch (F-1515) and Packard Foundations, the National Science Foundation, and National Institutes of Health (to E.M.M.).

Author information

Authors and Affiliations

  1. Department of Biology, Center for Genomics and Systems Biology, New York University, New York, NY, USA

    Christine Vogel

  2. Center for Systems and Synthetic Biology, Institute for Cellular and Molecular Biology, University of Texas at Austin, Austin, TX, USA

    Edward M. Marcotte

Authors
  1. Christine Vogel
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Edward M. Marcotte
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Edward M. Marcotte .

Editor information

Editors and Affiliations

  1. Fak. Medizin, Medizinische Proteom Center, Universität Bochum, Universitätsstraße 150, Bochum, 44801, Germany

    Katrin Marcus

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer Science+Business Media, LLC

About this protocol

Cite this protocol

Vogel, C., Marcotte, E.M. (2012). Label-Free Protein Quantitation Using Weighted Spectral Counting. In: Marcus, K. (eds) Quantitative Methods in Proteomics. Methods in Molecular Biology, vol 893. Humana Press, Totowa, NJ. https://doi.org/10.1007/978-1-61779-885-6_20

Download citation

  • DOI: https://doi.org/10.1007/978-1-61779-885-6_20

  • Published:

  • Publisher Name: Humana Press, Totowa, NJ

  • Print ISBN: 978-1-61779-884-9

  • Online ISBN: 978-1-61779-885-6

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics

Search

Navigation