Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

TMTpro reagents: a set of isobaric labeling mass tags enables simultaneous proteome-wide measurements across 16 samples

Nature Methods volume 17, pages 399–404 (2020)Cite this article

Subjects

Abstract

Isobaric labeling empowers proteome-wide expression measurements simultaneously across multiple samples. Here an expanded set of 16 isobaric reagents based on an isobutyl-proline immonium ion reporter structure (TMTpro) is presented. These reagents have similar characteristics to existing tandem mass tag reagents but with increased fragmentation efficiency and signal. In a proteome-scale example dataset, we compared eight common cell lines with and without Torin1 treatment with three replicates, quantifying more than 8,800 proteins (mean of 7.5 peptides per protein) per replicate with an analysis time of only 1.1 h per proteome. Finally, we modified the thermal stability assay to examine proteome-wide melting shifts after treatment with DMSO, 1 or 20 µM staurosporine with five replicates. This assay identified and dose-stratified staurosporine binding to 228 cellular kinases in just one, 18-h experiment. TMTpro reagents allow complex experimental designs—all with essentially no missing values across the 16 samples and no loss in quantitative integrity.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Overview of TMTpro reagents for sample multiplexing.
Fig. 2: Comparison of TMT0- and TMTpro0-labeled samples.
Fig. 3: Application of TMTpro reagents to eight human cell lines treated with Torin1.
Fig. 4: Application of TMTpro to a two-dimensional PISA assay.

Similar content being viewed by others

Data availability

The MS data have been deposited in the ProteomeXchange Consortium with the dataset identifier PXD014369, PXD016491 and PXD016940. The list of human kinases was downloaded from UniProt on 9 June 2019 (https://www.uniprot.org/docs/pkinfam). Affinity data of 442 human kinases to staurosporine were downloaded from the database ‘The IUPHAR/BPS Guide to Pharmacology’ on 14 May 2019 (https://www.guidetopharmacology.org/).

References

  1. Li, H. et al. Current trends in quantitative proteomics—an update. J. Mass Spectrom. 52, 319–341 (2017).

    Article  CAS  Google Scholar 

  2. Pappireddi, N., Martin, L. & Wuhr, M. A review on quantitative multiplexed proteomics. Chem. Bio. Chem. 20, 1210–1224 (2019).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  4. Rauniyar, N. & Yates, J. R. 3rd Isobaric labeling-based relative quantification in shotgun proteomics. J. Proteome Res. 13, 5293–5309 (2014).

    Article  CAS  Google Scholar 

  5. Vasaikar, S. et al. Proteogenomic analysis of human colon cancer reveals new therapeutic opportunities. Cell 177, 1035–1049 e1019 (2019).

    Article  CAS  Google Scholar 

  6. Chick, J. M. et al. Defining the consequences of genetic variation on a proteome-wide scale. Nature 534, 500–505 (2016).

    Article  CAS  Google Scholar 

  7. Dayon, L. et al. Relative quantification of proteins in human cerebrospinal fluids by MS/MS using 6-plex isobaric tags. Anal. Chem. 80, 2921–2931 (2008).

    Article  CAS  Google Scholar 

  8. Paulo, J. A. et al. Effects of MEK inhibitors GSK1120212 and PD0325901 in vivo using 10-plex quantitative proteomics and phosphoproteomics. Proteomics 15, 462–473 (2015).

    Article  CAS  Google Scholar 

  9. Stepanova, E., Gygi, S. P. & Paulo, J. A. Filter-based protein digestion (FPD): a detergent-free and scaffold-based strategy for TMT workflows. J. Proteome Res. 17, 1227–1234 (2018).

    Article  CAS  Google Scholar 

  10. McAlister, G. C. et al. Increasing the multiplexing capacity of TMTs using reporter ion isotopologues with isobaric masses. Anal. Chem. 84, 7469–7478 (2012).

    Article  CAS  Google Scholar 

  11. Thompson, A. et al. TMTpro: design, synthesis, and initial evaluation of a proline-based isobaric 16-plex tandem mass tag reagent set. Anal. Chem. 91, 15941–15950 (2019).

    Article  CAS  Google Scholar 

  12. McAlister, G. C. et al. MultiNotch MS3 enables accurate, sensitive, and multiplexed detection of differential expression across cancer cell line proteomes. Anal. Chem. 86, 7150–7158 (2014).

    Article  CAS  Google Scholar 

  13. Erickson, B. K. et al. Active instrument engagement combined with a real-time database search for improved performance of sample multiplexing workflows. J. Proteome Res. 18, 1299–1306 (2019).

    Article  CAS  Google Scholar 

  14. Schweppe, D. K. et al. Full-featured, real-time database searching platform enables fast and accurate multiplexed quantitative proteomics. Preprint at bioRxiv https://doi.org/10.1101/668533 (2019).

  15. Kim, J. & Guan, K. L. mTOR as a central hub of nutrient signalling and cell growth. Nat. Cell Biol. 21, 63–71 (2019).

    Article  CAS  Google Scholar 

  16. Saxton, R. A. & Sabatini, D. M. mTOR Signaling in growth, metabolism, and disease. Cell 169, 361–371 (2017).

    Article  CAS  Google Scholar 

  17. Thoreen, C. C. et al. An ATP-competitive mammalian target of rapamycin inhibitor reveals rapamycin-resistant functions of mTORC1. J. Biol. Chem. 284, 8023–8032 (2009).

    Article  CAS  Google Scholar 

  18. Navarrete-Perea, J., Yu, Q., Gygi, S. P. & Paulo, J. A. Streamlined tandem mass tag (SL-TMT) protocol: an efficient strategy for quantitative (phospho)proteome profiling using tandem mass tag-synchronous precursor selection-MS3. J. Proteome Res. 17, 2226–2236 (2018).

    Article  CAS  Google Scholar 

  19. Mizushima, N. A brief history of autophagy from cell biology to physiology and disease. Nat. Cell Biol. 20, 521–527 (2018).

    Article  CAS  Google Scholar 

  20. Dikic, I. & Elazar, Z. Mechanism and medical implications of mammalian autophagy. Nat. Rev. Mol. Cell Biol. 19, 349–364 (2018).

    Article  CAS  Google Scholar 

  21. An, H. et al. TEX264 Is an endoplasmic reticulum-resident ATG8-interacting protein critical for ER remodeling during nutrient stress. Mol. Cell. 74, 891–908 e810 (2019).

    Article  CAS  Google Scholar 

  22. Wu, R. et al. Correct interpretation of comprehensive phosphorylation dynamics requires normalization by protein expression changes. Mol. Cell Proteom. 10, M111 009654 (2011).

    Article  Google Scholar 

  23. Ben-Sahra, I., Howell, J. J., Asara, J. M. & Manning, B. D. Stimulation of de novo pyrimidine synthesis by growth signaling through mTOR and S6K1. Science 339, 1323–1328 (2013).

    Article  CAS  Google Scholar 

  24. Barilari, M. et al. ZRF1 is a novel S6 kinase substrate that drives the senescence programme. EMBO J. 36, 736–750 (2017).

    Article  CAS  Google Scholar 

  25. Gaetani, M. et al. Proteome integral solubility alteration: a high-throughput proteomics assay for target deconvolution. J. Proteome Res. 18, 4027–4037 (2019).

    Article  CAS  Google Scholar 

  26. Savitski, M. M. et al. Tracking cancer drugs in living cells by thermal profiling of the proteome. Science 346, 1255784 (2014).

    Article  Google Scholar 

  27. Harding, S. D. et al. The IUPHAR/BPS Guide to PHARMACOLOGY in 2018: updates and expansion to encompass the new guide to IMMUNOPHARMACOLOGY. Nucleic Acids Res. 46, D1091–D1106 (2018).

    Article  CAS  Google Scholar 

  28. Becher, I. et al. Thermal profiling reveals phenylalanine hydroxylase as an off-target of panobinostat. Nat. Chem. Biol. 12, 908–910 (2016).

    Article  CAS  Google Scholar 

  29. Dai, L. et al. Horizontal cell biology: monitoring global changes of protein interaction states with the proteome-wide cellular thermal shift assay (CETSA). Annu. Rev. Biochem. 88, 383–408 (2019).

    Article  CAS  Google Scholar 

  30. Savitski, M. M. et al. Multiplexed proteome dynamics profiling reveals mechanisms controlling protein homeostasis. Cell 173, 260–274 e225 (2018).

    Article  CAS  Google Scholar 

  31. Zecha, J. et al. Peptide level turnover measurements enable the study of proteoform dynamics. Mol. Cell Proteom. 17, 974–992 (2018).

    Article  CAS  Google Scholar 

  32. Dephoure, N. & Gygi, S. P. Hyperplexing: a method for higher-order multiplexed quantitative proteomics provides a map of the dynamic response to rapamycin in yeast. Sci. Signal. 5, rs2 (2012).

    Article  Google Scholar 

  33. Hebert, A. S. et al. Neutron-encoded mass signatures for multiplexed proteome quantification. Nat. Methods 10, 332–334 (2013).

    Article  CAS  Google Scholar 

  34. Mulvey, C. M. et al. Using hyperLOPIT to perform high-resolution mapping of the spatial proteome. Nat. Protoc. 12, 1110–1135 (2017).

    Article  CAS  Google Scholar 

  35. Hughes, C. S. et al. Single-pot, solid-phase-enhanced sample preparation for proteomics experiments. Nat. Protoc. 14, 68–85 (2019).

    Article  CAS  Google Scholar 

  36. Elias, J. E. & Gygi, S. P. Target-decoy search strategy for increased confidence in large-scale protein identifications by mass spectrometry. Nat. Methods 4, 207–214 (2007).

    Article  CAS  Google Scholar 

  37. Huttlin, E. L. et al. A tissue-specific atlas of mouse protein phosphorylation and expression. Cell 143, 1174–1189 (2010).

    Article  CAS  Google Scholar 

  38. Savitski, M. M., Wilhelm, M., Hahne, H., Kuster, B. & Bantscheff, M. A scalable approach for protein false discovery rate estimation in large proteomic data sets. Mol. Cell Proteom. 14, 2394–2404 (2015).

    Article  CAS  Google Scholar 

  39. Beausoleil, S. A., Villen, J., Gerber, S. A., Rush, J. & Gygi, S. P. A probability-based approach for high-throughput protein phosphorylation analysis and site localization. Nat. Biotechnol. 24, 1285–1292 (2006).

    Article  CAS  Google Scholar 

  40. Huang da, W., Sherman, B. T. & Lempicki, R. A. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat. Protoc. 4, 44–57 (2009).

    Article  Google Scholar 

Download references

Acknowledgements

We thank members of the Gygi Laboratory, particularly R. Rad, at Harvard Medical School. This work was funded in part by NIH grant nos. 1R01GM132129 (to J.A.P.) and 5R01GM067945 (to S.P.G), and the Mark Foundation for Cancer Research Fellow of the Damon Runyon Cancer Research Foundation DRG 2359-19 (to J.G.V.V.).

Author information

Authors and Affiliations

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

    Jiaming Li, Jonathan G. Van Vranken, Laura Pontano Vaites, Devin K. Schweppe, Edward L. Huttlin, Steven P. Gygi & Joao A. Paulo

  2. Thermo Fisher Scientific, Rockford, IL, USA

    Chris Etienne, Premchendar Nandhikonda, Ryan D. Bomgarden & John C. Rogers

  3. Thermo Fisher Scientific, San Jose, CA, USA

    Rosa Viner & Aaron M. Robitaille

  4. Proteome Sciences, London, UK

    Andrew H. Thompson, Karsten Kuhn & Ian Pike

Authors
  1. Jiaming Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jonathan G. Van Vranken
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Laura Pontano Vaites
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Devin K. Schweppe
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Edward L. Huttlin
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Chris Etienne
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Premchendar Nandhikonda
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Rosa Viner
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Aaron M. Robitaille
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Andrew H. Thompson
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Karsten Kuhn
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Ian Pike
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Ryan D. Bomgarden
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. John C. Rogers
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Steven P. Gygi
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Joao A. Paulo
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

J.L. prepared the cell line samples treated with Torin1 for MS analysis, performed the data analyses, prepared the figures and wrote the manuscript. J.G.V.V. prepared and conducted the PISA experiments and prepared associated figures. L.P.V. proposed the eight cell lines, then grew and treated the lines for Torin1 experiments and performed western blotting experiments. D.K.S. developed and implemented the real-time online searching tool. E.L.H. advised on data analyses. R.V. and A.M.R. provided additional reagent characterization and advice. C.E., P.N., R.D.B. and J.C.R. further characterized the TMTpro reagents, initiated the collaboration and provided input and oversight for the project. Reagents were conceived, developed, synthesized and characterized by K.K., A.H.T. and I.P. S.P.G. oversaw the project and edited the manuscript. J.A.P. oversaw the project, performed the experiment for comparison of TMT0 and TMTpro0, ran the MS analysis, performed the data analyses and wrote the manuscript. All authors approved the manuscript.

Corresponding authors

Correspondence to Steven P. Gygi or Joao A. Paulo.

Ethics declarations

Competing interests

The TMTpro reagents were commercialized by ThermoFisher Scientific in September 2019. C.E., P.N., R.V., A.M.R., R.D.B. and J.C.R. are employees of ThermoFisher Scientific. A.H.T. and I.P. were employees of Proteome Sciences. K.K. is an employee of Proteome Sciences. S.P.G. is a member of the scientific advisory board for ThermoFisher Scientific.

Additional information

Peer review information Allison Doerr was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary Figs. 1–12.

Reporting Summary

Supplementary Table 1

Protein quantifications in 48 cell line samples treated with DMSO and Torin1 (RTS–SPS–MS3).

Supplementary Table 2

Protein quantifications in replicate1 of the 48 cell line samples treated with DMSO and Torin1 (hrMS2 and SPS–MS3).

Supplementary Table 3

Phosphorylation quantifications in 48 cell line samples treated with DMSO and Torin1.

Supplementary Table 4

Protein quantifications in the PISA assay (RTS–SPS–MS3).

Supplementary Table 5

Protein quantifications in the PISA assay (hrMS2).

Supplementary Table 6

Correction factors of the TMTpro reagents used in this work.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, J., Van Vranken, J.G., Pontano Vaites, L. et al. TMTpro reagents: a set of isobaric labeling mass tags enables simultaneous proteome-wide measurements across 16 samples. Nat Methods 17, 399–404 (2020). https://doi.org/10.1038/s41592-020-0781-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41592-020-0781-4

This article is cited by

Search

Advanced search

Quick links

Nature Briefing: Translational Research

Sign up for the Nature Briefing: Translational Research newsletter — top stories in biotechnology, drug discovery and pharma.

Get what matters in translational research, free to your inbox weekly. Sign up for Nature Briefing: Translational Research