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.

  • Protocol
  • Published:

Label-free quantification in ion mobility–enhanced data-independent acquisition proteomics

Nature Protocols volume 11, pages 795–812 (2016)Cite this article

Subjects

Abstract

Unbiased data-independent acquisition (DIA) strategies have gained increased popularity in the field of quantitative proteomics. The integration of ion mobility separation (IMS) into DIA workflows provides an additional dimension of separation to liquid chromatography-mass spectrometry (LC-MS), and it increases the achievable analytical depth of DIA approaches. Here we provide a detailed protocol for a label-free quantitative proteomics workflow based on ion mobility–enhanced DIA, which synchronizes precursor ion drift times with collision energies to improve precursor fragmentation efficiency. The protocol comprises a detailed description of all major steps including instrument setup, filter-aided sample preparation, LC-IMS-MS analysis and data processing. Our protocol can handle proteome samples of any complexity, and it enables a highly reproducible and accurate precursor intensity–based label-free quantification of up to 5,600 proteins across multiple runs in complete cellular lysates. Depending on the number of samples to be analyzed, the protocol takes a minimum of 3 d to complete from proteolytic digestion to data evaluation.

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

Figure 1: Schematic overview of the protocol.
Figure 2: LC and nanospray performance.
Figure 3: IMS tuning.
Figure 4: Results of HeLa analysis.
Figure 5: Label-free quantification using ISOQuant.

Similar content being viewed by others

References

  1. Mallick, P. & Kuster, B. Proteomics: a pragmatic perspective. Nat. Biotechnol. 28, 695–709 (2010).

    Article  CAS  Google Scholar 

  2. Gstaiger, M. & Aebersold, R. Applying mass spectrometry-based proteomics to genetics, genomics and network biology. Nat. Rev. Genet. 10, 617–627 (2009).

    Article  CAS  Google Scholar 

  3. Bensimon, A., Heck, A.J.R. & Aebersold, R. Mass spectrometry-based proteomics and network biology. Annu. Rev. Biochem. 81, 379–405 (2012).

    Article  CAS  Google Scholar 

  4. Neilson, K.A. et al. Less label, more free: approaches in label-free quantitative mass spectrometry. Proteomics 11, 535–553 (2011).

    Article  CAS  Google Scholar 

  5. Filiou, M.D., Martins-de-Souza, D., Guest, P.C., Bahn, S. & Turck, C.W. To label or not to label: applications of quantitative proteomics in neuroscience research. Proteomics 12, 736–747 (2012).

    Article  CAS  Google Scholar 

  6. Nahnsen, S., Bielow, C., Reinert, K. & Kohlbacher, O. Tools for label-free peptide quantification. Mol. Cell. Proteomics 12, 549–556 (2013).

    Article  CAS  Google Scholar 

  7. Tate, S., Larsen, B., Bonner, R. & Gingras, A.-C. Label-free quantitative proteomics trends for protein-protein interactions. J. Proteomics 1–11 (2012).

  8. Evans, C. et al. An insight into iTRAQ: where do we stand now? Anal. Bioanal. Chem. 404, 1011–1027 (2012).

    Article  CAS  Google Scholar 

  9. Bantscheff, M., Lemeer, S., Savitski, M.M. & Kuster, B. Quantitative mass spectrometry in proteomics: critical review update from 2007 to the present. Anal. Bioanal. Chem. 404, 939–965 (2012).

    Article  CAS  Google Scholar 

  10. Distler, U., Kuharev, J. & Tenzer, S. Biomedical applications of ion mobility-enhanced data-independent acquisition-based label-free quantitative proteomics. Expert Rev. Proteomics 11, 675–684 (2014).

    Article  CAS  Google Scholar 

  11. Daly, C.E., Ng, L.L., Hakimi, A., Willingale, R. & Jones, D.J.L. Qualitative and quantitative characterization of plasma proteins when incorporating traveling wave ion mobility into a liquid chromatography-mass spectrometry workflow for biomarker discovery: use of product ion quantitation as an alternative data analysis. Anal. Chem. 86, 1972–1979 (2014).

    Article  CAS  Google Scholar 

  12. McQueen, P. et al. Whole cell, label free protein quantitation with data independent acquisition: quantitation at the MS2 level. Proteomics 15, 16–24 (2015).

    Article  CAS  Google Scholar 

  13. Chapman, J.D., Goodlett, D.R. & Masselon, C.D. Multiplexed and data-independent tandem mass spectrometry for global proteome profiling. Mass Spectrom. Rev. 33, 452–470 (2014).

    Article  CAS  Google Scholar 

  14. Silva, J.C. et al. Quantitative proteomic analysis by accurate mass retention time pairs. Anal. Chem. 77, 2187–200 (2005).

    Article  CAS  Google Scholar 

  15. Bateman, R.H. et al. A novel precursor ion discovery method on a hybrid quadrupole orthogonal acceleration time-of-flight (Q-TOF) mass spectrometer for studying protein phosphorylation. J. Am. Soc. Mass Spectrom. 13, 792–803 (2002).

    Article  CAS  Google Scholar 

  16. Li, G.-Z. et al. Database searching and accounting of multiplexed precursor and product ion spectra from the data independent analysis of simple and complex peptide mixtures. Proteomics 9, 1696–1719 (2009).

    Article  CAS  Google Scholar 

  17. Valentine, S.J. et al. Developing liquid chromatography ion mobility mass spectometry techniques. Expert Rev. Proteomics 2, 553–565 (2005).

    Article  CAS  Google Scholar 

  18. Baker, E.S. et al. An LC-IMS-MS platform providing increased dynamic range for high-throughput proteomic studies. J. Proteome Res. 9, 997–1006 (2010).

    Article  CAS  Google Scholar 

  19. Fenn, L.S., Kliman, M., Mahsut, A., Zhao, S.R. & McLean, J.A. Characterizing ion mobility-mass spectrometry conformation space for the analysis of complex biological samples. Anal. Bioanal. Chem. 394, 235–244 (2009).

    Article  CAS  Google Scholar 

  20. Geromanos, S.J., Hughes, C., Ciavarini, S., Vissers, J.P.C. & Langridge, J.I. Using ion purity scores for enhancing quantitative accuracy and precision in complex proteomics samples. Anal. Bioanal. Chem. 404, 1127–1139 (2012).

    Article  CAS  Google Scholar 

  21. Distler, U. et al. Drift time-specific collision energies enable deep-coverage data-independent acquisition proteomics. Nat. Methods 11, 167–170 (2014).

    Article  CAS  Google Scholar 

  22. Shliaha, P.V., Bond, N.J., Gatto, L. & Lilley, K.S. Effects of traveling wave ion mobility separation on data independent acquisition in proteomics studies. J. Proteome Res. 12, 2323–2339 (2013).

    Article  CAS  Google Scholar 

  23. Rodriguez-Suarez, E. et al. An ion mobility assisted data independent LC-MS strategy for the analysis of complex biological samples. Curr. Anal. Chem. 9, 199–211 (2013).

    CAS  Google Scholar 

  24. Schwanhäusser, B. et al. Global quantification of mammalian gene expression control. Nature 473, 337–342 (2011).

    Article  Google Scholar 

  25. Qi, D. et al. A software toolkit and interface for performing stable isotope labeling and top3 quantification using Progenesis LC-MS. OMICS 16, 489–495 (2012).

    Article  CAS  Google Scholar 

  26. Grossmann, J. et al. Implementation and evaluation of relative and absolute quantification in shotgun proteomics with label-free methods. J. Proteomics 73, 1740–1746 (2010).

    Article  CAS  Google Scholar 

  27. Ahrné, E., Molzahn, L., Glatter, T. & Schmidt, A. Critical assessment of proteome-wide label-free absolute abundance estimation strategies. Proteomics 13, 2567–2578 (2013).

    Article  Google Scholar 

  28. Silva, J.C., Gorenstein, M.V., Li, G.-Z., Vissers, J.P.C. & Geromanos, S.J. Absolute quantification of proteins by LCMSE: a virtue of parallel MS acquisition. Mol. Cell. Proteomics 5, 144–156 (2006).

    Article  CAS  Google Scholar 

  29. Kuharev, J., Navarro, P., Distler, U., Jahn, O. & Tenzer, S. In-depth evaluation of software tools for data-independent acquisition based label-free quantification. Proteomics 15, 3140–3151 (2015).

    Article  CAS  Google Scholar 

  30. Docter, D. et al. Quantitative profiling of the protein coronas that form around nanoparticles. Nat. Protoc. 9, 2030–2044 (2014).

    Article  CAS  Google Scholar 

  31. Distler, U. et al. In-depth protein profiling of the postsynaptic density from mouse hippocampus using data-independent acquisition proteomics. Proteomics 14, 2607–2613 (2014).

    Article  CAS  Google Scholar 

  32. Craxton, A. et al. XLS (c9orf142) is a new component of mammalian DNA double-stranded break repair. Cell Death Differ. 22, 890–897 (2015).

    Article  CAS  Google Scholar 

  33. Beasley-Green, A., Bunk, D., Rudnick, P., Kilpatrick, L. & Phinney, K. A proteomics performance standard to support measurement quality in proteomics. Proteomics 12, 923–931 (2012).

    Article  CAS  Google Scholar 

  34. Köcher, T., Pichler, P., Swart, R. & Mechtler, K. Analysis of protein mixtures from whole-cell extracts by single-run nanoLC-MS/MS using ultralong gradients. Nat. Protoc. 7, 882–890 (2012).

    Article  Google Scholar 

  35. Kelstrup, C.D., Young, C., Lavallee, R., Nielsen, M.L. & Olsen, J.V. Optimized fast and sensitive acquisition methods for shotgun proteomics on a quadrupole orbitrap mass spectrometer. J. Proteome Res. 11, 3487–3497 (2012).

    Article  CAS  Google Scholar 

  36. Michalski, A., Cox, J. & Mann, M. More than 100,000 detectable peptide species elute in single shotgun proteomics runs but the majority is inaccessible to data-dependent LC-MS/MS. J. Proteome Res. 10, 1785–1793 (2011).

    Article  CAS  Google Scholar 

  37. Tabb, D.L. Quality assessment for clinical proteomics. Clin. Biochem. 46, 411–420 (2013).

    Article  CAS  Google Scholar 

  38. Walzer, M. et al. qcML: an exchange format for quality control metrics from mass spectrometry experiments. Mol. Cell. Proteomics 13, 1905–1913 (2014).

    Article  CAS  Google Scholar 

  39. Brownridge, P. & Beynon, R.J. The importance of the digest: proteolysis and absolute quantification in proteomics. Methods 54, 351–360 (2011).

    Article  CAS  Google Scholar 

  40. Piehowski, P.D. et al. Sources of technical variability in quantitative LC-MS proteomics: human brain tissue sample analysis. J. Proteome Res. 12, 2128–2137 (2013).

    Article  CAS  Google Scholar 

  41. Little, D. et al. Proteomic differences between male and female anterior cruciate ligament and patellar tendon. PLoS ONE 9, e96526 (2014).

    Article  Google Scholar 

  42. Hoos, M.D. et al. Longitudinal study of differential protein expression in an Alzheimer's mouse model lacking inducible nitric oxide synthase. J. Proteome Res. 12, 4462–4477 (2013).

    Article  CAS  Google Scholar 

  43. Wiśniewski, J.R., Zougman, A., Nagaraj, N. & Mann, M. Universal sample preparation method for proteome analysis. Nat. Methods 6, 359–362 (2009).

    Article  Google Scholar 

  44. Manza, L.L., Stamer, S.L., Ham, A.-J.L., Codreanu, S.G. & Liebler, D.C. Sample preparation and digestion for proteomic analyses using spin filters. Proteomics 5, 1742–1745 (2005).

    Article  CAS  Google Scholar 

  45. Smith, R.D. et al. An accurate mass tag strategy for quantitative and high-throughput proteome measurements. Proteomics 2, 513–523 (2002).

    Article  CAS  Google Scholar 

  46. Wang, X., Stoll, D.R., Schellinger, A.P. & Carr, P.W. Peak capacity optimization of peptide separations in reversed-phase gradient elution chromatography: fixed column format. Anal. Chem. 78, 3406–3416 (2006).

    Article  CAS  Google Scholar 

  47. Hahne, H. et al. DMSO enhances electrospray response, boosting sensitivity of proteomic experiments. Nat. Methods 10, 989–991 (2013).

    Article  CAS  Google Scholar 

  48. Richards, A.L. et al. One-hour proteome analysis in yeast. Nat. Protoc. 10, 701–714 (2015).

    Article  CAS  Google Scholar 

  49. Meyer, J.G. & A Komives, E. Charge state coalescence during electrospray ionization improves peptide identification by tandem mass spectrometry. J. Am. Soc. Mass Spectrom. 23, 1390–1399 (2012).

    Article  CAS  Google Scholar 

  50. Bond, N.J., Shliaha, P.V., Lilley, K.S. & Gatto, L. Improving qualitative and quantitative performance for MSE-based label-free proteomics. J. Proteome Res. 12, 2340–2353 (2013).

    Article  CAS  Google Scholar 

  51. Podwojski, K. et al. Retention time alignment algorithms for LC/MS data must consider non-linear shifts. Bioinformatics 25, 758–764 (2009).

    Article  CAS  Google Scholar 

  52. Crowell, K.L. et al. Increasing confidence of LC-MS identifications by utilizing ion mobility spectrometry. Int. J. Mass Spectrom. 354–355, 312–317 (2013).

    Article  Google Scholar 

  53. Ester, M., Kriegel, H.P., Sander, J. & Xu, X. A density-based algorithm for discovering clusters in large spatial databases with noise. Proc. 2nd Int. Conf. Knowl. Discov. Data Min. 226–231 (1996).

  54. Nesvizhskii, A.I. & Aebersold, R. Interpretation of shotgun proteomic data: the protein inference problem. Mol. Cell. Proteomics 4, 1419–1440 (2005).

    Article  CAS  Google Scholar 

  55. MacLean, B. et al. Skyline: an open source document editor for creating and analyzing targeted proteomics experiments. Bioinformatics 26, 966–968 (2010).

    Article  CAS  Google Scholar 

  56. Sturm, M. et al. OpenMS—an open-source software framework for mass spectrometry. BMC Bioinformatics 9, 163 (2008).

    Article  Google Scholar 

  57. Kelly, R.T. et al. Chemically etched open tubular and monolithic emitters for nanoelectrospray ionization mass spectrometry. Anal. Chem. 78, 7796–7801 (2006).

    Article  CAS  Google Scholar 

  58. Schwarz, A. et al. A systems level analysis reveals transcriptomic and proteomic complexity in Ixodes ricinus midgut and salivary glands during early attachment and feeding. Mol. Cell. Proteomics 13, 2725–2735 (2014).

    Article  CAS  Google Scholar 

  59. Fonslow, B.R. et al. Digestion and depletion of abundant proteins improves proteomic coverage. Nat. Methods 10, 54–56 (2012).

    Article  Google Scholar 

  60. Vizcaíno, J.A. et al. The PRoteomics IDEntifications (PRIDE) database and associated tools: status in 2013. Nucleic Acids Res. 41, D1063–D1069 (2013).

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by grants from the German Federal Ministry for Education and Research (BMBF; Express2Present, 0316179C), the Deutsche Forschungsgemeinschaft (DFG; TE599/1-1), Mainz University (Research Center for Immunotherapy (FZI), and Focus Program Translational Neurosciences (FTN).

Author information

Author notes

  1. Ute Distler and Jörg Kuharev: These authors contributed equally to this work.

Authors and Affiliations

  1. Institute for Immunology, University Medical Center of the Johannes-Gutenberg University Mainz, Mainz, Germany

    Ute Distler, Jörg Kuharev, Pedro Navarro & Stefan Tenzer

  2. Focus Program Translational Neuroscience (FTN), University Medical Center of the Johannes-Gutenberg University Mainz, Mainz, Germany

    Ute Distler

Authors
  1. Ute Distler
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jörg Kuharev
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Pedro Navarro
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Stefan Tenzer
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

U.D., J.K. and S.T. developed the protocol and conducted the experiments. J.K. developed software. U.D., J.K., P.N. and S.T. interpreted the data and drafted the manuscript.

Corresponding author

Correspondence to Stefan Tenzer.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Comparison of sample preparation protocols and LC setup.

In a recent study, Geromanos et al.20 applied an IMS-enhanced MSE-workflow for proteomics using the same instrumentation as in the present protocol. We compared the in-solution digestion as performed by Geromanos et al. (red bars) to the FASP protocol applied in the present study (blue bars) using HeLa cells. LC-MS analysis using MSE and HDMSE acquisition modes were performed as recently described21. Compared to the in-solution digest the number of identified proteins shows an increase of about 16.2% in MSE and 28.4% in HDMSE using FASP digest. Further on, we performed a comparison between the chromatographic setup used in the present protocol and the one used by Geromanos et al. Following parameters were different between the two setups: i) column material (HSS-T3 vs. BEH-C18), ii) column length (25 cm vs. 20 cm), iii) operating temperature (55°C vs. 35°C). Optimizing the chromatographic setup by using HSS-T3 columns (25 cm) and higher separation temperature led to an increase in protein identifications of 33.8% (MSE) and 15.4% (HDMSE), respectively.

Supplementary Figure 2 Chromatographic resolution.

Screenshots of extracted ion chromatograms (XICs) of m/z 534.28 (15 mDa extraction window) are shown over a time frame of 50 min (upper panel) and 5 min (lower panel), respectively. Runs are visualized with the vendor instrument control software MassLynx (version 4.1; Waters Corporation). With the chromatographic setup described in the present protocol peak widths at half height of about 0.1 min at the start of the gradient and about 0.25 min at the end of the gradient can be achieved. The data are also depicted in Figure 2a in the main manuscript.

Supplementary Figure 3 Spray stability.

Screenshots of LC-MS runs performed with stable and instable nanospray conditions. Runs are displayed in vendor instrument control software MassLynx (version 4.1; Waters Corporation). The data from the upper two runs are also depicted in Figure 2b in the main manuscript. Only the uppermost run shows stable nanospray. The other runs were all performed with unstable, sputtering nanospray hampering proper sample analysis. When setting up your system make sure to operate with stable nanospray conditions.

Supplementary Figure 4 Increase in fragmentation efficiency using drift time specific collision energies.

Screenshots of total ion currents (TIC) of LC-MS runs performed with (a) UDMSE and (b) HDMSE. Runs are displayed in vendor instrument control software MassLynx (version 4.1; Waters Corporation). While displaying similar TICs in the MS1 trace, MS2 traces display about 1.5 times higher TICs for UDMSE methods. This observation can be used as a quick initial check if fragmentation efficiencies are increased in your UDMSE method as compared to your HDMSE method.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–4, Supplementary Methods 2–4 and Supplementary Manual (PDF 5694 kb)

Supplementary Method 1

MS tune page file. (TXT 85 kb)

Supplementary Table 1

Settings for ISOQuant post-processing for label-free quantification. (XLSX 11 kb)

Supplementary Data 1

ISOQuant protein quantification report for Pierce HeLa sample. (XLSX 1904 kb)

Supplementary Data 2

ISOQuant protein quantification report for FASP HeLa sample. (XLSX 1949 kb)

Supplementary Data 3

Numbers of identified peptides and proteins in HeLa samples. (XLSX 35 kb)

Supplementary Data 4

ISOQuant protein quantification report for hybrid proteome samples acquired with MSE. (XLSX 484 kb)

Supplementary Data 5

ISOQuant protein quantification report for hybrid proteome samples acquired with UDMSE. (XLSX 1087 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Distler, U., Kuharev, J., Navarro, P. et al. Label-free quantification in ion mobility–enhanced data-independent acquisition proteomics. Nat Protoc 11, 795–812 (2016). https://doi.org/10.1038/nprot.2016.042

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nprot.2016.042

This article is cited by

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

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