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Reproducible workflow for multiplexed deep-scale proteome and phosphoproteome analysis of tumor tissues by liquid chromatography–mass spectrometry

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Mertins, Philipp, Tang, Lauren C., Krug, Karsten, Clark, David J., Gritsenko, Marina A., Chen, Lijun, Clauser, Karl R., Clauss, Therese R., Shah, Punit, Gillette, Michael A., Petyuk, Vladislav A., Thomas, Stefani N., Mani, D. R., Mundt, Filip, Moore, Ronald J., Hu, Yingwei, Zhao, Rui, Schnaubelt, Michael, Keshishian, Hasmik, Monroe, Matthew E., Zhang, Zhen, Udeshi, Namrata D., Mani, Deepak, Davies, Sherri R., Townsend, R. Reid, Chan, Daniel W., Smith, Richard D., Zhang, Hui, Liu, Tao, Carr, Steven A.·2018·Nature Protocols

Abstract

Here we present an optimized workflow for global proteome and phosphoproteome analysis of tissues or cell lines that uses isobaric tags (TMT (tandem mass tags)-10) for multiplexed analysis and relative quantification, and provides 3× higher throughput than iTRAQ (isobaric tags for absolute and relative quantification)-4-based methods with high intra- and inter-laboratory reproducibility. The workflow was systematically characterized and benchmarked across three independent laboratories using two distinct breast cancer subtypes from patient-derived xenograft models to enable assessment of proteome and phosphoproteome depth and quantitative reproducibility. Each plex consisted of ten samples, each being 300 μg of peptide derived from <50 mg of wet-weight tissue. Of the 10,000 proteins quantified per sample, we could distinguish 7,700 human proteins derived from tumor cells and 3100 mouse proteins derived from the surrounding stroma and blood. The maximum deviation across replicates and laboratories was <7%, and the inter-laboratory correlation for TMT ratio–based comparison of the two breast cancer subtypes was r > 0.88. The maximum deviation for the phosphoproteome coverage was <24% across laboratories, with an average of >37,000 quantified phosphosites per sample and differential quantification correlations of r > 0.72. The full procedure, including sample processing and data generation, can be completed within 10 d for ten tissue samples, and 100 samples can be analyzed in ~4 months using a single LC-MS/MS instrument. The high quality, depth, and reproducibility of the data obtained both within and across laboratories should enable new biological insights to be obtained from mass spectrometry-based proteomics analyses of cells and tissues together with proteogenomic data integration. This protocol describes a workflow for multiplexed deep-scale, quantitative proteome and phosphoproteome analysis of tumor tissue samples. The procedure includes step-by-step instructions for all stages, from sample preparation to data analysis.

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  1. Zhang, H. et al. Integrated proteogenomic characterization of human high-grade serous ovarian cancer. Cell 166, 755–765 (2016). · DOI · Paper page
  2. Ntai, I. et al. Integrated bottom-up and top-down proteomics of patient-derived breast tumor xenografts. Mol. Cell. Proteomics 15, 45–56 (2016). · DOI
  3. Coscia, F. et al. Integrative proteomic profiling of ovarian cancer cell lines reveals precursor cell associated proteins and functional status. Nat. Commun. 7, 1–14 (2016). · DOI
  4. Geiser, L., Dayon, L., Vaezzadeh, A. R. & Hochstrasser, D. F. Shotgun proteomics: a relative quantitative approach using off-gel electrophoresis and LC-MS/MS. Methods Mol. Biol. 681, 459–472 (2011). · DOI
  5. Dwivedi, R. C. et al. Practical implementation of 2D HPLC scheme with accurate peptide retention prediction in both dimensions for high-throughput bottom-up proteomics. Anal. Chem. 80, 7036–7042 (2008). · DOI
  6. Gilar, M., Olivova, P., Daly, A. E. & Gebler, J. C. Orthogonality of separation in two-dimensional liquid chromatography. Anal. Chem. 77, 6426–6434 (2005). · DOI · Paper page
  7. Delmotte, N., Lasaosa, M., Tholey, A., Heinzle, E. & Huber, C. G. Two-dimensional reversed-phase × ion-pair reversed-phase HPLC: an alternative approach to high-resolution peptide separation for shotgun proteome analysis. J. Proteome Res. 6, 4363–4373 (2007). · DOI
  8. Song, C. et al. Reversed-phase-reversed-phase liquid chromatography approach with high orthogonality for multidimensional separation of phosphopeptides. Anal. Chem. 82, 53–56 (2010). · DOI
  9. Wang, Y. et al. Reversed-phase chromatography with multiple fraction concatenation strategy for proteome profiling of human MCF10A cells. Proteomics 11, 2019–2026 (2011). · DOI · Paper page
  10. Mertins, P. et al. Integrated proteomic analysis of post-translational modifications by serial enrichment. Nat. Methods 10, 634–637 (2013). · DOI · Paper page
  11. Batth, T. S., Francavilla, C. & Olsen, J. V. Off-line high-pH reversed-phase fractionation for in-depth phosphoproteomics. J. Proteome Res. 13, 6176–6186 (2014). · DOI · Paper page
  12. Rauniyar, N. & Yates, J. R. Isobaric labeling-based relative quantification in shotgun proteomics. J. Proteome Res. 13, 5293-5309 (2014). · DOI · Paper page
  13. Geiger, T., Cox, J., Ostasiewicz, P., Wisniewski, J. R. & Mann, M. Super-SILAC mix for quantitative proteomics of human tumor tissue. Nat. Methods 7, 383–385 (2010). · DOI · Paper page
  14. Molina, H. et al. Temporal profiling of the adipocyte proteome during differentiation using a five-plex SILAC based strategy. J. Proteome Res. 8, 48–58 (2009). · DOI
  15. Bantscheff, M., Schirle, M., Sweetman, G., Rick, J. & Kuster, B. Quantitative mass spectrometry in proteomics: a critical review. Anal. Bioanal. Chem. 389, 1017–1031 (2007). · DOI · Paper page
  16. Rauniyar, N. & Yates, J. R. III Isobaric labeling-based relative quantification in shotgun proteomics. J. Proteome Res. 13, 5293–5309 (2014). · DOI · Paper page
  17. 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). · DOI · Paper page
  18. Pichler, P. et al. Peptide labeling with isobaric tags yields higher identification rates using iTRAQ 4-plex compared to TMT 6-plex and iTRAQ 8-plex on LTQ Orbitrap. Anal. Chem. 82, 6549–6558 (2010). · DOI
  19. Raso, C. et al. Characterization of breast cancer interstitial fluids by TmT labeling, LTQ-Orbitrap Velos mass spectrometry, and pathway analysis. J. Proteome Res. 11, 3199–3210 (2012). · DOI
  20. Werner, T. et al. Ion coalescence of neutron encoded TMT 10-plex reporter ions. Anal. Chem. 86, 3594–3601 (2014). · DOI · Paper page
  21. Mertins, P. et al. Ischemia in tumors induces early and sustained phosphorylation changes in stress kinase pathways but does not affect global protein levels. Mol. Cell. Proteomics. 13, 1690–1704 (2014). · DOI · Paper page
  22. Svinkina, T. et al. Deep, quantitative coverage of the lysine acetylome using novel anti-acetyl-lysine antibodies and an optimized proteomic workflow. Mol. Cell. Proteomics 14, 2429–2440 (2015). · DOI
  23. Böhm, G. et al. Low-pH solid-phase amino labeling of complex peptide digests with TMTs improves peptide identification rates for multiplexed global phosphopeptide analysis. J. Proteome Res. 14, 2500–2510 (2015). · DOI
  24. Wiktorowicz, J. E., English, R. D., Wu, Z. & Kurosky, A. Model studies on iTRAQ modification of peptides: sequence-dependent reaction specificity. J. Proteome Res. 11, 1512–1520 (2012). · doi:10.1021/pr2003165
  25. Thingholm, T. E., Palmisano, G., Kjeldsen, F. & Larsen, M. R. Undesirable charge-enhancement of isobaric tagged phosphopeptides leads to reduced identification efficiency. J. Proteome Res. 9, 4045–4552 (2010). · DOI
  26. Karp, N. A. et al. Addressing accuracy and precision issues in iTRAQ quantitation. Mol. Cell. Proteomics 9, 1885–1897 (2010). · DOI · Paper page
  27. Savitski, M. M. et al. Measuring and managing ratio compression for accurate iTRAQ/TMT quantification. J. Proteome Res. 12, 3586–3598 (2013). · DOI · Paper page
  28. May, J. C. & McLean, J. A. Ion mobility-mass spectrometry: time-dispersive instrumentation. Anal. Chem. 87, 1422–1436 (2014). · DOI · Paper page
  29. Pfammatter, S., Bonneil, W. & Thibault, P. Improvement of quantitative measurements in multiplex proteomics using high-field asymmetric waveform ion mobility spectrometry. J. Proteome Res. 15, 4653 (2016). · DOI
  30. Ibrahim, Y. M. et al. New frontiers for mass spectrometry based upon structures for lossless ion manipulations. Analyst 142, 1010–1021 (2017). · DOI
  31. Shadforth, I. P., Dunkley, T. P., Lilley, K. S. & Bessant, C. i-Tracker: for quantitative proteomics using iTRAQ. BMC Genomics 6, 145–150 (2005). · DOI · Paper page
  32. Rappsilber, J., Mann, M. & Ishihama, Y. Protocol for micro-purification, enrichment, pre-fractionation and storage of peptides for proteomics using StageTips. Nat. Protocols 2, 1896–1906 (2007). · DOI · Paper page
  33. Rodriguez, J., Gupta, N., Smith, R. D. & Pevzner, P. A. Does trypsin cut before proline? J. Proteome Res. 7, 300–305 (2008). · DOI · Paper page
  34. Mertins, P. et al. iTRAQ labeling is superior to mTRAQ for quantitative global proteomics and phosphoproteomics. Mol. Cell. Proteomics 11, M111.014423 (2012). · DOI
  35. Wenger, C. D. et al. Gas-phase purification enables accurate, multiplexed proteome quantification with isobaric tagging. Nat. Methods 8, 933–935 (2011). · DOI
  36. Tabb, D. et al. Reproducibility of differential proteomic technologies in CPTAC fractionated xenografts. J. Proteome Res. 15, 691–706 (2016). · DOI
  37. Hogrebe, A. et al. Benchmarking common quantification strategies for large-scale phosphoproteomics. Nat. Commun. 9, 1045 (2018). · DOI · Paper page

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Metadata

Authors
Mertins, P., Tang, L. C., Krug, K., Clark, D. J., Gritsenko, M. A., Chen, L., Clauser, K. R., Clauss, T. R., Shah, P., Gillette, M. A., Petyuk, V. A., Thomas, S. N., Mani, D. R., Mundt, F., Moore, R. J., Hu, Y., Zhao, R., Schnaubelt, M., Keshishian, H., Monroe, M. E., Zhang, Z., Udeshi, N. D., Mani, D., Davies, S. R., Townsend, R. R., Chan, D. W., Smith, R. D., Zhang, H., Liu, T., & Carr, S. A.
Venue
Nature Protocols
Published
2018-07
Volume
13
Issue
7
Pages
1632-1661
ISSN
  • 1750-2799 (ISSN-L)
  • 1754-2189
OpenAlex
W2857722519
Cited by
479
References
7
FWCI
15.93
Type
journal article
Language
en
Open access
green
Licence
www.springer.com/tdm (tdm)

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Mertins, P., Tang, L. C., Krug, K., Clark, D. J., Gritsenko, M. A., Chen, L., Clauser, K. R., Clauss, T. R., Shah, P., Gillette, M. A., Petyuk, V. A., Thomas, S. N., Mani, D. R., Mundt, F., Moore, R. J., Hu, Y., Zhao, R., Schnaubelt, M., Keshishian, H., . . . Carr, S. A. (2018). Reproducible workflow for multiplexed deep-scale proteome and phosphoproteome analysis of tumor tissues by liquid chromatography–mass spectrometry. Nature Protocols. https://doi.org/10.1038/s41596-018-0006-9

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