Skip to main content

Peptide-to-Protein Summarization: An Important Step for Accurate Quantification in Label-Based Proteomics

  • Protocol
  • First Online:
Mass Spectrometry of Proteins

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1977))

Abstract

Quantitative MS/MS-based measurements are assessed at the peptide spectrum level and substantial variance is frequently observed for any given protein. Protein quantification requires a peptide-to-protein summarization step. This important step has been little investigated and most strategies only rely on quantitative spectrum values, ignoring a wealth of additional feature information is available for peptide spectra.

In this chapter, we discuss summarization methods that can be applied for label-based protein quantification. In particular, we focus on strategies using peptide spectrum characteristics in addition to quantitative values for protein abundance inference. We highlight significant relations of spectrum features and quantification accuracy to assess the reliability of spectra and the development of a correction. As a result, spectra of lower quality are identified, their impact is minimized and overall protein quantification is improved. Here, we investigate different peptide features in detail, emphasize the benefits of integrating spectrum feature information, and provide recommendations on the usage of the methods.

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 99.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 129.99
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. Neilson KA, Ali NA, Muralidharan S et al (2011) Less label, more free: approaches in label-free quantitative mass spectrometry. Proteomics 11:535–553

    Article  CAS  Google Scholar 

  2. 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  CAS  Google Scholar 

  3. Ross PL, Huang YN, Marchese JN et al (2004) Multiplexed protein quantitation in Saccharomyces cerevisiae using amine-reactive isobaric tagging reagents. Mol Cell Proteomics 3:1154–1169

    Article  CAS  Google Scholar 

  4. Thompson A, Schafer J, Kuhn K et al (2003) Tandem mass tags: a novel quantification strategy for comparative analysis of complex protein mixtures by MS/MS. Anal Chem 75:1895–1904

    Article  CAS  Google Scholar 

  5. Gan CS, Chong PK, Pham TK et al (2007) Technical, experimental, and biological variations in isobaric tags for relative and absolute quantitation (iTRAQ). J Proteome Res 6:821–827

    Article  CAS  Google Scholar 

  6. Karp NA, Huber W, Sadowski PG et al (2010) Addressing accuracy and precision issues in iTRAQ quantitation. Mol Cell Proteomics 9:1885–1897

    Article  CAS  Google Scholar 

  7. Kirchner M, Renard BY, Kothe U et al (2010) Computational protein profile similarity screening for quantitative mass spectrometry experiments. Bioinformatics 26:77–83

    Article  CAS  Google Scholar 

  8. Hultin-Rosenberg L, Forshed J, RMM B et al (2013) Defining, comparing, and improving iTRAQ quantification in mass spectrometry proteomics data. Mol Cell Proteomics 12:2021–2031

    Article  CAS  Google Scholar 

  9. Vaudel M, Sickmann A, Martens L (2010) Peptide and protein quantification: a map of the minefield. Proteomics 10:650–670

    Article  CAS  Google Scholar 

  10. Burkhart JM, Vaudel M, Zahedi RP et al (2011) iTRAQ protein quantification: a quality-controlled workflow. Proteomics 11:1125–1134

    Article  CAS  Google Scholar 

  11. Rauniyar N, Yates JR (2014) Isobaric labeling-based relative quantification in shotgun proteomics. J Proteome Res 13:5293–5309

    Article  CAS  Google Scholar 

  12. Ow SY, Salim M, Noirel J et al (2009) iTRAQ underestimation in simple and complex mixtures: “the good, the bad and the ugly”. J Proteome Res 8:5347–5355

    Article  CAS  Google Scholar 

  13. Sandberg A, RMM B, Lehtiö J et al (2014) Quantitative accuracy in mass spectrometry based proteomics of complex samples: the impact of labeling and precursor interference. J Proteome 96:133–144

    Article  CAS  Google Scholar 

  14. Shadforth IP, Dunkley TP, Lilley KS et al (2005) i-Tracker: for quantitative proteomics using iTRAQ. BMC Genomics 6:145

    Article  Google Scholar 

  15. Boehm AM, Pütz S, Altenhöfer D et al (2007) Precise protein quantification based on peptide quantification using iTRAQ. BMC Bioinformatics 8:214

    Article  Google Scholar 

  16. Vaudel M, Burkhart JM, Radau S et al (2012) Integral quantification accuracy estimation for reporter ion-based quantitative proteomics (iQuARI). J Proteome Res 11:5072–5080

    Article  CAS  Google Scholar 

  17. Muth T, Keller D, Puetz SM et al (2010) jTraqX: a free, platform independent tool for isobaric tag quantitation at the protein level. Proteomics 10:1223–1225

    Article  CAS  Google Scholar 

  18. Arntzen MO, Koehler CJ, Barsnes H et al (2011) IsobariQ: software for isobaric quantitative proteomics using IPTL, iTRAQ, and TMT. J Proteome Res 10:913–920

    Article  CAS  Google Scholar 

  19. Wen B, Zhou R, Feng Q et al (2014) IQuant: an automated pipeline for quantitative proteomics based upon isobaric tags. Proteomics 14:2280–2285

    Article  CAS  Google Scholar 

  20. Hu J, Qian J, Borisov O et al (2006) Optimized proteomic analysis of a mouse model of cerebellar dysfunction using amine-specific isobaric tags. Proteomics 6:4321–4334

    Article  CAS  Google Scholar 

  21. Lin WT, Hung WN, Yian YH et al (2006) Multi-Q: a fully automated tool for multiplexed protein quantitation. J Proteome Res 5:2328–2338

    Article  CAS  Google Scholar 

  22. Onsongo G, Stone MD, Van Riper SK et al (2010) LTQ-iQuant: a freely available software pipeline for automated and accurate protein quantification of isobaric tagged peptide data from LTQ instruments. Proteomics 10:3533–3538

    Article  CAS  Google Scholar 

  23. Breitwieser FP, Muller A, Dayon L et al (2011) General statistical modeling of data from protein relative expression isobaric tags. J Proteome Res 10:2758–2766

    Article  CAS  Google Scholar 

  24. Zhou C, Walker MJ, Williamson AJ et al (2014) A hierarchical statistical modeling approach to analyze proteomic isobaric tag for relative and absolute quantitation data. Bioinformatics 30:549–558

    Article  Google Scholar 

  25. Fusaro VA, Mani DR, Mesirov JP et al (2009) Prediction of high-responding peptides for targeted protein assays by mass spectrometry. Nat Biotechnol 27:190–198

    Article  CAS  Google Scholar 

  26. Hill EG, Schwacke JH, Comte-Walters S et al (2008) A statistical model for iTRAQ data analysis. J Proteome Res 7:3091–3101

    Article  CAS  Google Scholar 

  27. Fischer M, Renard BY (2016) iPQF: a new peptide-to-protein summarization method using peptide spectra characteristics to improve protein quantification. Bioinformatics 32:1040–1047

    Article  CAS  Google Scholar 

  28. Ting L, Rad R, Gygi SP et al (2011) MS3 eliminates ratio distortion in isobaric multiplexed quantitative proteomics. Nat Methods 8:937–940

    Article  CAS  Google Scholar 

  29. Carrillo B, Yanofsky C, Laboissiere S et al (2010) Methods for combining peptide intensities to estimate relative protein abundance. Bioinformatics 26:98–103

    Article  CAS  Google Scholar 

  30. Mahoney DW, Therneau TM, Heppelmann CJ et al (2011) Relative quantification: characterization of bias, variability and fold changes in mass spectrometry data from iTRAQ-labeled peptides. J Proteome Res 10:4325–4333

    Article  CAS  Google Scholar 

  31. Enke CG (2001) The science of chemical analysis and the technique of mass spectrometry. Int J Mass Spectrom 212:1–11

    Article  CAS  Google Scholar 

  32. Anderle M, Roy S, Lin H et al (2004) Quantifying reproducibility for differential proteomics: noise analysis for protein liquid chromatography-mass spectrometry of human serum. Bioinformatics 20:3575–3582

    Article  CAS  Google Scholar 

  33. Gatto L, Lilley KS (2012) MSnbase—an R/Bioconductor package for isobaric tagged mass spectrometry data visualization, processing and quantitation. Bioinformatics 28:288–289

    Article  CAS  Google Scholar 

  34. Pedrioli PGA, Eng JK, Hubley R et al (2004) A common open representation of mass spectrometry data and its application to proteomics research. Nat Biotechnol 22:1459–1466

    Article  CAS  Google Scholar 

  35. Martens L, Chambers M, Sturm M et al (2011) mzML—a community standard for mass spectrometry data. Mol Cell Proteomics 10:R110.000133

    Article  Google Scholar 

  36. Lai X, Wang L, Tang H et al (2011) A novel alignment method and multiple filters for exclusion of unqualified peptides to enhance label-free quantification using peptide intensity in LC-MS/MS. J Proteome Res 10:4799–4812

    Article  CAS  Google Scholar 

  37. Goeminne LJE, Gevaert K, Clement L (2016) Peptide-level robust ridge regression improves estimation, sensitivity, and specificity in data-dependent quantitative label-free shotgun proteomics. Mol Cell Proteomics 15:657–668

    Article  CAS  Google Scholar 

  38. Anders S, Huber W (2010) Differential expression analysis for sequence count data. Genome Biol 11:R106

    Article  CAS  Google Scholar 

  39. Robinson MD, McCarthy DJ, Smyth GK (2010) edgeR: a bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26:139–140

    Article  CAS  Google Scholar 

  40. Kammers K, Cole RN, Tiengwe C et al (2015) Detecting significant changes in protein abundance. EuPA Open Proteom 7:11–19

    Article  CAS  Google Scholar 

  41. Fischer M, Strauch B, Renard BY (2017) Abundance estimation and differential testing on strain level in metagenomics data. Bioinformatics 33:i124–i132

    Article  CAS  Google Scholar 

  42. 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  CAS  Google Scholar 

  43. Tsou C-C, Tsai C-F, Tsui Y-H et al (2010) IDEAL-Q, an automated tool for label-free quantitation analysis using an efficient peptide alignment approach and spectral data validation. Mol Cell Proteomics 9:131–144

    Article  CAS  Google Scholar 

  44. Zhang B, Pirmoradian M, Zubarev R et al (2017) Covariation of peptide abundances accurately reflects protein concentration differences. Mol Cell Proteomics 16:936–948

    Article  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Bernhard Y. Renard .

Editor information

Editors and Affiliations

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

Fischer, M., Muth, T., Renard, B.Y. (2019). Peptide-to-Protein Summarization: An Important Step for Accurate Quantification in Label-Based Proteomics. In: Evans, C., Wright, P., Noirel, J. (eds) Mass Spectrometry of Proteins. Methods in Molecular Biology, vol 1977. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-9232-4_11

Download citation

  • DOI: https://doi.org/10.1007/978-1-4939-9232-4_11

  • Published:

  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-9231-7

  • Online ISBN: 978-1-4939-9232-4

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics