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Drift time-specific collision energies enable deep-coverage data-independent acquisition proteomics

Abstract

We present a data-independent acquisition mass spectrometry method, ultradefinition (UD) MSE. This approach utilizes ion mobility drift time-specific collision-energy profiles to enhance precursor fragmentation efficiency over current MSE and high-definition (HD) MSE data-independent acquisition techniques. UDMSE provided high reproducibility and substantially improved proteome coverage of the HeLa cell proteome compared to previous implementations of MSE, and it also outperformed a state-of-the-art data-dependent acquisition workflow. Additionally, we report a software tool, ISOQuant, for processing label-free quantitative UDMSE data.

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Figure 1: UDMSE concept.
Figure 2: ISOQuant improves data quality and reproducibility.
Figure 3: Comparison of MSE, HDMSE and UDMSE.

References

  1. Aebersold, R. & Mann, M. Nature 422, 198–207 (2003).

    CAS  Article  Google Scholar 

  2. Geromanos, S.J. et al. Proteomics 9, 1683–1695 (2009).

    CAS  Article  Google Scholar 

  3. Michalski, A., Cox, J. & Mann, M. J. Proteome Res. 10, 1785–1793 (2011).

    CAS  Article  Google Scholar 

  4. Kelstrup, C.D., Young, C., Lavallee, R., Nielsen, M.L. & Olsen, J.V. J. Proteome Res. 11, 3487–3497 (2012).

    CAS  Article  Google Scholar 

  5. Venable, J.D., Dong, M., Wohlschlegel, J., Dillin, A. & Yates, J.R. Nat. Methods 1, 39–45 (2004).

    CAS  Article  Google Scholar 

  6. Geiger, T., Cox, J. & Mann, M. Mol. Cell. Proteomics 9, 2252–2261 (2010).

    CAS  Article  Google Scholar 

  7. Panchaud, A., Jung, S., Shaffer, S.A . Aitchison, J.D. & Goodlett, D.R. Anal. Chem. 83, 2250–2257 (2011).

    CAS  Article  Google Scholar 

  8. Gillet, L.C. et al. Mol. Cell. Proteomics 11, O111.016717 (2012).

    Article  Google Scholar 

  9. Silva, J.C. et al. Anal. Chem. 77, 2187–2200 (2005).

    CAS  Article  Google Scholar 

  10. Lee, S. et al. Int. J. Mass Spectrom. 309, 154–160 (2012).

    CAS  Article  Google Scholar 

  11. Valentine, S.J. et al. J. Proteome Res. 10, 2318–2329 (2011).

    CAS  Article  Google Scholar 

  12. Baker, E.S. et al. J. Proteome Res. 9, 997–1006 (2010).

    CAS  Article  Google Scholar 

  13. Geromanos, S.J., Hughes, C., Ciavarini, S., Vissers, J.P.C. & Langridge, J.I. Anal. Bioanal. Chem. 404, 1127–1139 (2012).

    CAS  Article  Google Scholar 

  14. Maclean, B. et al. Anal. Chem. 82, 10116–10124 (2010).

    CAS  Article  Google Scholar 

  15. Cox, J. & Mann, M. Nat. Biotechnol. 26, 1367–1372 (2008).

    CAS  Article  Google Scholar 

  16. Serang, O. & Noble, W. Stat. Interface 5, 3–20 (2012).

    Article  Google Scholar 

  17. Silva, J.C., Gorenstein, M.V., Li, G.-Z., Vissers, J.P.C. & Geromanos, S.J. Mol. Cell. Proteomics 5, 144–156 (2006).

    CAS  Article  Google Scholar 

  18. Vizcaíno, J.A. et al. Nucleic Acids Res. 41, D1063–D1069 (2013).

    Article  Google Scholar 

  19. Wiśniewski, J.R., Zougman, A., Nagaraj, N. & Mann, M. Nat. Methods 6, 359–362 (2009).

    Article  Google Scholar 

  20. Tenzer, S. et al. ACS Nano 5, 7155–7167 (2011).

    CAS  Article  Google Scholar 

  21. Giles, K. et al. Rapid Commun. Mass Spectrom. 18, 2401–2414 (2004).

    CAS  Article  Google Scholar 

  22. MacLean, B. et al. Bioinformatics 26, 966–968 (2010).

    CAS  Article  Google Scholar 

  23. Podwojski, K. et al. Bioinformatics 25, 758–764 (2009).

    CAS  Article  Google Scholar 

  24. Sakoe, H. & Chiba, S. Inst. Electr. Comm. Eng. Japan 136 (1970).

  25. Ester, M., Kriegel, H.P., Sander, J. & Xu, X. in 2nd Int. Conf. Knowl. Discov. Data Min. (eds. Simoudis, E., Han, J. & Fayyad, U.) 226–231 (AAAI Press, 1996).

  26. Li, G.-Z. et al. Proteomics 9, 1696–1719 (2009).

    CAS  Article  Google Scholar 

  27. Stanley, J.R. et al. Anal. Chem. 83, 6135–6140 (2011).

    CAS  Article  Google Scholar 

  28. Cleveland, W.S. J. Am. Stat. Assoc. 74, 829 (1979).

    Article  Google Scholar 

  29. Nesvizhskii, A.I., Keller, A., Kolker, E. & Aebersold, R. Anal. Chem. 75, 4646–4658 (2003).

    CAS  Article  Google Scholar 

Download references

Acknowledgements

We thank R. Spohrer for excellent sample preparation; W. Thompson and K. Giles for discussions on ion-mobility separations and data-independent acquisition methods; A. Savidor and A. Gabashvili for assistance in the DDA analysis; all ISOQuant beta testers for their continuing critical evaluation of the software; and H. Vissers and K. Richardson for discussions on data evaluation. This work was supported by grants from the Deutsche Forschungsgemeinschaft (INST 371/23-1 FUGG) to S.T., H.S. and the BMBF (e:Bio Express2Present, 0316179C) to S.T., as well by as the Forschungszentrum Immunologie (FZI), the Naturwissenschaftlich-Medizinische Forschungszentrum (NMFZ) and the Focus Program Translational Neurosciences (FTN) of the Johannes Gutenberg University Mainz.

Author information

Authors and Affiliations

Authors

Contributions

U.D. and S.T. designed and performed the DIA experiments. Y.L. performed the DDA experiments and DDA data evaluation. J.K. designed and wrote the software. J.K. and S.T. developed algorithms. U.D., J.K., P.N. and S.T. analyzed the DIA data. U.D., J.K., H.S. and S.T. contributed to overall design of the project. All authors prepared and reviewed the manuscript.

Corresponding author

Correspondence to Stefan Tenzer.

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Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–13, Supplementary Tables 1–4 and 9, and Supplementary Notes 1 and 2 (PDF 5277 kb)

Supplementary Table 5

List of identified proteins in tryptic HeLa cell lysate after analysis with MSE, HDMSE and UDMSE. (XLSX 4155 kb)

Supplementary Table 6

MaxQuant report of the DDA-analysis of tryptic HeLa cell lysate. (XLSX 4175 kb)

Supplementary Table 7

ISOQuant quantification Excel report for hybrid proteome sample using high confidence settings. (XLSX 1820 kb)

Supplementary Table 8

High confidence and MaxID settings for ISOQuant processing. (XLSX 11 kb)

Supplementary Software

Supplementary Software (ZIP 30644 kb)

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Distler, U., Kuharev, J., Navarro, P. et al. Drift time-specific collision energies enable deep-coverage data-independent acquisition proteomics. Nat Methods 11, 167–170 (2014). https://doi.org/10.1038/nmeth.2767

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