Skip to main content
SpringerLink
Log in

TMT One-Stop Shop: From Reliable Sample Preparation to Computational Analysis Platform

  • Protocol
  • First Online:
Proteome Bioinformatics

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1549))

Abstract

In this chapter we describe the workflow we use for labeled quantitative proteomics analysis using tandem mass tags (TMT) starting with the sample preparation and ending with the multivariate analysis of the resulting data. We detail the step-by-step process from sample processing, labeling, fractionation, and data processing using Proteome Discoverer through to data analysis and interpretation in the context of a multi-run experiment. The final analysis and data interpretation rely on an R package we call TMTPrepPro, which are deployed on a local GenePattern server, and used for generating various outputs which are also outlined herein.

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. Reich M, Liefeld T, Gould J, Lerner J, Tamayo P, Mesirov JP (2006) GenePattern 2.0. Nat Genet 38(5):500–501

    Article  CAS  PubMed  Google Scholar 

  2. Yates JR, Ruse CI, Nakorchevsky A (2009) Proteomics by mass spectrometry: approaches, advances, and applications. Annu Rev Biomed Eng 11:49–79

    Article  CAS  PubMed  Google Scholar 

  3. Neilson KA, Ali NA, Muralidharan S, Mirzaei M, Mariani M, Assadourian G, Lee A, Van Sluyter SC, Haynes PA (2011) Less label, more free: approaches in label‐free quantitative mass spectrometry. Proteomics 11(4):535–553

    Article  CAS  PubMed  Google Scholar 

  4. Patel VJ, Thalassinos K, Slade SE, Connolly JB, Crombie A, Murrell JC, Scrivens JH (2009) A comparison of labeling and label-free mass spectrometry-based proteomics approaches. J Proteome Res 8(7):3752–3759

    Article  CAS  PubMed  Google Scholar 

  5. Ross PL, Huang YN, Marchese JN, Williamson B, Parker K, Hattan S, Khainovski N, Pillai S, Dey S, Daniels S (2004) Multiplexed protein quantitation in Saccharomyces cerevisiae using amine-reactive isobaric tagging reagents. Mol Cell Proteomics 3(12):1154–1169

    Article  CAS  PubMed  Google Scholar 

  6. Gygi SP, Rist B, Gerber SA, Turecek F, Gelb MH, Aebersold R (1999) Quantitative analysis of complex protein mixtures using isotope-coded affinity tags. Nat Biotechnol 17(10):994–999

    Article  CAS  PubMed  Google Scholar 

  7. Boersema PJ, Raijmakers R, Lemeer S, Mohammed S, Heck AJR (2009) Multiplex peptide stable isotope dimethyl labeling for quantitative proteomics. Nat Protoc 4(4):484–494

    Article  CAS  PubMed  Google Scholar 

  8. Thompson A, Schäfer J, Kuhn K, Kienle S, Schwarz J, Schmidt G, Neumann T, Hamon C (2003) Tandem mass tags: a novel quantification strategy for comparative analysis of complex protein mixtures by MS/MS. Anal Chem 75(8):1895–1904

    Article  CAS  PubMed  Google Scholar 

  9. Oda Y, Huang K, Cross FR, Cowburn D, Chait BT (1999) Accurate quantitation of protein expression and site-specific phosphorylation. Proc Natl Acad Sci 96(12):6591–6596

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Ong S-E, Blagoev B, Kratchmarova I, Kristensen DB, Steen H, Pandey A, Mann M (2002) Stable isotope labeling by amino acids in cell culture, SILAC, as a simple and accurate approach to expression proteomics. Mol Cell Proteomics 1(5):376–386

    Article  CAS  PubMed  Google Scholar 

  11. Braun CR, Bird GH, Wühr M, Erickson BK, Rad R, Walensky LD, Gygi SP, Haas W (2015) Generation of multiple reporter ions from a single isobaric reagent increases multiplexing capacity for quantitative proteomics. Anal Chem 87(19):9855–9863. doi:10.1021/acs.analchem.5b02307

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Savitski MM, Sweetman G, Askenazi M, Marto JA, Lang M, Zinn N, Bantscheff M (2011) Delayed fragmentation and optimized isolation width settings for improvement of protein identification and accuracy of isobaric mass tag quantification on Orbitrap-type mass spectrometers. Anal Chem 83(23):8959–8967

    Article  CAS  PubMed  Google Scholar 

  13. Ting L, Rad R, Gygi SP, Haas W (2011) MS3 eliminates ratio distortion in isobaric multiplexed quantitative proteomics. Nat Methods 8(11):937–940

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. McAlister GC, Nusinow DP, Jedrychowski MP, Wühr M, Huttlin EL, Erickson BK, Rad R, Haas W, Gygi SP (2014) MultiNotch MS3 enables accurate, sensitive, and multiplexed detection of differential expression across cancer cell line proteomes. Anal Chem 86(14):7150–7158. doi:10.1021/ac502040v

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Wenger CD, Lee MV, Hebert AS, McAlister GC, Phanstiel DH, Westphall MS, Coon JJ (2011) Gas-phase purification enables accurate, multiplexed proteome quantification with isobaric tagging. Nat Methods 8(11):933–935

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Neilson KA, Keighley T, Pascovici D, Cooke B, Haynes PA (2013) Label-free quantitative shotgun proteomics using normalized spectral abundance factors. In: Ming Z, Timothy V (eds) Proteomics for biomarker discovery. Springer, New York, NY, pp 205–222

    Chapter  Google Scholar 

  17. Pascovici D, Song X, Solomon PS, Winterberg B, Mirzaei M, Goodchild A, Stanley WC, Liu J, Molloy MP (2015) Combining protein ratio p-values as a pragmatic approach to the analysis of Multirun iTRAQ experiments. J Proteome Res 14(2):738–746. doi:10.1021/pr501091e

    Article  CAS  PubMed  Google Scholar 

  18. 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(12):1367–1372

    Article  CAS  PubMed  Google Scholar 

  19. Oberg AL, Mahoney DW, Eckel-Passow JE, Malone CJ, Wolfinger RD, Hill EG, Cooper LT, Onuma OK, Spiro C, Therneau TM, Bergen HR (2008) Statistical analysis of relative labeled mass spectrometry data from complex samples using ANOVA. J Proteome Res 7(1):225–233. doi:10.1021/pr700734f

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Herbrich SM, Cole RN, West KP Jr, Schulze K, Yager JD, Groopman JD, Christian P, Wu L, O’Meally RN, May DH, McIntosh MW, Ruczinski I (2013) Statistical inference from multiple iTRAQ experiments without using common reference standards. J Proteome Res 12(2):594–604

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Meitei NS, Apte A, Biswas A, Pitman M, Samir P, Link AJ (2015) Statistical analysis of multiple iTRAQ/TMT experiments labeled with variable reporter ion tags using ProteoIQ. Poster.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Medicine and Health Sciences, Department of Chemistry and Biomolecular Sciences, Macquarie University, Sydney, NSW, 2109, Australia

    Mehdi Mirzaei, Yunqi Wu, Paul Haynes & Mark P. Molloy

  2. Department of Chemistry and Biomolecular Sciences, Australian Proteome Analysis Facility, Macquarie University, E8C310, Research Park Drive, Sydney, NSW, 2109, Australia

    Dana Pascovici, Jemma X. Wu, Brett Cooke & Mark P. Molloy

  3. Department of Cell Biology, Harvard Medical School, Boston, MA, USA

    Joel Chick

Authors
  1. Mehdi Mirzaei
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dana Pascovici
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jemma X. Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Joel Chick
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yunqi Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Brett Cooke
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Paul Haynes
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Mark P. Molloy
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mark P. Molloy .

Editor information

Editors and Affiliations

  1. Department of Biochemistry and Genetics, La Trobe Institute for Molecular Science, La Trobe University, Melbourne, Victoria, Australia

    Shivakumar Keerthikumar

  2. Department of Biochemistry and Genetics, La Trobe Institute for Molecular Science, La Trobe University, Melbourne, Victoria, Australia

    Suresh Mathivanan

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer Science+Business Media LLC

About this protocol

Cite this protocol

Mirzaei, M. et al. (2017). TMT One-Stop Shop: From Reliable Sample Preparation to Computational Analysis Platform. In: Keerthikumar, S., Mathivanan, S. (eds) Proteome Bioinformatics. Methods in Molecular Biology, vol 1549. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-6740-7_5

Download citation

  • DOI: https://doi.org/10.1007/978-1-4939-6740-7_5

  • Published:

  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-6738-4

  • Online ISBN: 978-1-4939-6740-7

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics

Search

Navigation