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diaPASEF: parallel accumulation–serial fragmentation combined with data-independent acquisition


Data-independent acquisition modes isolate and concurrently fragment populations of different precursors by cycling through segments of a predefined precursor m/z range. Although these selection windows collectively cover the entire m/z range, overall, only a few per cent of all incoming ions are isolated for mass analysis. Here, we make use of the correlation of molecular weight and ion mobility in a trapped ion mobility device (timsTOF Pro) to devise a scan mode that samples up to 100% of the peptide precursor ion current in m/z and mobility windows. We extend an established targeted data extraction workflow by inclusion of the ion mobility dimension for both signal extraction and scoring and thereby increase the specificity for precursor identification. Data acquired from whole proteome digests and mixed organism samples demonstrate deep proteome coverage and a high degree of reproducibility as well as quantitative accuracy, even from 10 ng sample amounts.

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Fig. 1: The diaPASEF acquisition method.
Fig. 2: Efficiency of different data acquisition methods.
Fig. 3: Ion mobility–aware targeted data extraction.
Fig. 4: Single-run HeLa proteome analysis with diaPASEF.
Fig. 5: Label-free protein quantification benchmark.
Fig. 6: High-throughput and high duty cycle diaPASEF analysis.

Data availability

The mass spectrometry raw data and spectral libraries generated and analyzed during the current study have been deposited with the ProteomeXchange Consortium via the PRIDE45 partner repository with the dataset identifier PXD017703. Homo sapiens (taxon identifier: 9606) and S.cerevisiae (taxon identifier: 559292) proteome databases were downloaded from Source data are provided with this paper.

Code availability

Code is available under the three-clause BSD license on and


  1. 1.

    Altelaar, A. F. M., Munoz, J. & Heck, A. J. R. Next-generation proteomics: towards an integrative view of proteome dynamics. Nat. Rev. Genet. 14, 35–48 (2012).

    PubMed  Google Scholar 

  2. 2.

    Larance, M. & Lamond, A. I. Multidimensional proteomics for cell biology. Nat. Rev. Mol. Cell Biol. 16, 269–280 (2015).

    CAS  PubMed  Google Scholar 

  3. 3.

    Aebersold, R. & Mann, M. Mass-spectrometric exploration of proteome structure and function. Nature 537, 347–355 (2016).

    CAS  PubMed  Google Scholar 

  4. 4.

    Bekker-Jensen, D. B. et al. An optimized shotgun strategy for the rapid generation of comprehensive human proteomes. Cell Syst. 4, 587–599.e4 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  5. 5.

    Wang, D. et al. A deep proteome and transcriptome abundance atlas of 29 healthy human tissues. Mol. Syst. Biol. 15, e8503 (2019).

    PubMed  PubMed Central  Google Scholar 

  6. 6.

    Röst, H. L., Malmström, L. & Aebersold, R. Reproducible quantitative proteotype data matrices for systems biology. Mol. Biol. Cell 26, 3926–3931 (2015).

    PubMed  PubMed Central  Google Scholar 

  7. 7.

    Doerr, A. DIA mass spectrometry. Nat. Methods 12, 35 (2015).

    CAS  Google Scholar 

  8. 8.

    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).

    CAS  PubMed  Google Scholar 

  9. 9.

    Ludwig, C. et al. Data‐independent acquisition‐based SWATH‐MS for quantitative proteomics: a tutorial. Mol. Syst. Biol. 14, e8126 (2018).

    PubMed  PubMed Central  Google Scholar 

  10. 10.

    Gillet, L. C., Leitner, A. & Aebersold, R. Mass spectrometry applied to bottom-up proteomics: entering the high-throughput era for hypothesis testing. Annu. Rev. Anal. Chem. 9, 449–472 (2016).

    Google Scholar 

  11. 11.

    Bilbao, A. et al. Processing strategies and software solutions for data-independent acquisition in mass spectrometry. Proteomics 15, 964–980 (2015).

    CAS  PubMed  Google Scholar 

  12. 12.

    Bruderer, R. et al. Optimization of experimental parameters in data-independent mass spectrometry significantly increases depth and reproducibility of results. Mol. Cell. Proteom. 16, 2296–2309 (2017).

    CAS  Google Scholar 

  13. 13.

    Pino, L. K., Just, S. C., MacCoss, M. J. & Searle, B. C. Acquiring and analyzing data independent acquisition proteomics experiments without spectrum libraries. Mol. Cell. Proteom. 19, 1088–1103 (2020).

    Google Scholar 

  14. 14.

    McLean, J. A., Ruotolo, B. T., Gillig, K. J. & Russell, D. H. Ion mobility–mass spectrometry: a new paradigm for proteomics. Int. J. Mass Spectrom. 240, 301–315 (2005).

    CAS  Google Scholar 

  15. 15.

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

    CAS  PubMed  Google Scholar 

  16. 16.

    Helm, D. et al. Ion mobility tandem mass spectrometry enhances performance of bottom-up proteomics. Mol. Cell. Proteom. 13, 3709–3715 (2014).

    CAS  Google Scholar 

  17. 17.

    Ewing, M. A., Glover, M. S. & Clemmer, D. E. Hybrid ion mobility and mass spectrometry as a separation tool. J. Chromatogr. A 1439, 3–25 (2016).

    CAS  PubMed  Google Scholar 

  18. 18.

    Fernandez-Lima, F. A., Kaplan, D. A. & Park, M. A. Note: Integration of trapped ion mobility spectrometry with mass spectrometry. Rev. Sci. Instrum. 82, 126106 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. 19.

    Fernandez-Lima, F., Kaplan, D. A., Suetering, J. & Park, M. A. Gas-phase separation using a trapped ion mobility spectrometer. Int. J. Ion Mobil. Spectrom. 14, 93–98 (2011).

    Google Scholar 

  20. 20.

    Ridgeway, M. E., Lubeck, M., Jordens, J., Mann, M. & Park, M. A. Trapped ion mobility spectrometry: a short review. Int. J. Mass Spectrom. 425, 22–35 (2018).

    CAS  Google Scholar 

  21. 21.

    Meier, F. et al. Parallel accumulation–serial fragmentation (PASEF): multiplying sequencing speed and sensitivity by synchronized scans in a trapped ion mobility device. J. Proteome Res. 14, 5378–5387 (2015).

    CAS  PubMed  Google Scholar 

  22. 22.

    Meier, F. et al. Online parallel accumulation–serial fragmentation (PASEF) with a novel trapped ion mobility mass spectrometer. Mol. Cell. Proteom. 17, 2534–2545 (2018).

    CAS  Google Scholar 

  23. 23.

    Vasilopoulou, C. G. et al. Trapped ion mobility spectrometry and PASEF enable in-depth lipidomics from minimal sample amounts. Nat. Commun. 11, 331 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. 24.

    Silveira, J. A., Ridgeway, M. E., Laukien, F. H., Mann, M. & Park, M. A. Parallel accumulation for 100% duty cycle trapped ion mobility-mass spectrometry. Int. J. Mass Spectrom. 413, 168–175 (2017).

    CAS  Google Scholar 

  25. 25.

    Röst, H. L. et al. OpenSWATH enables automated, targeted analysis of data-independent acquisition MS data. Nat. Biotechnol. 32, 219–223 (2014).

    PubMed  Google Scholar 

  26. 26.

    Gillet, L. C. et al. Targeted data extraction of the MS/MS spectra generated by data-independent acquisition: a new concept for consistent and accurate proteome analysis. Mol. Cell. Proteom. 11, O111.016717 (2012).

    Google Scholar 

  27. 27.

    Cox, J. & Mann, M. MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat. Biotechnol. 26, 1367–1372 (2008).

    CAS  Google Scholar 

  28. 28.

    Prianichnikov, N. et al. MaxQuant software for ion mobility enhanced shotgun proteomics. Mol. Cell. Proteom. 19, 1058–1069 (2020).

    Google Scholar 

  29. 29.

    Rosenberger, G. et al. Statistical control of peptide and protein error rates in large-scale targeted data-independent acquisition analyses. Nat. Methods 14, 921–927 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. 30.

    Röst, H. L. et al. OpenMS: a flexible open-source software platform for mass spectrometry data analysis. Nat. Methods 13, 741–748 (2016).

    PubMed  Google Scholar 

  31. 31.

    Bache, N. et al. A novel LC system embeds analytes in pre-formed gradients for rapid, ultra-robust proteomics.Mol. Cell. Proteom. 17, 2284–2296 (2018).

    CAS  Google Scholar 

  32. 32.

    Beck, S. et al. The impact II, a very high-resolution quadrupole time-of-flight instrument (QTOF) for deep shotgun proteomics. Mol. Cell. Proteom. 14, 2014–2029 (2015).

    CAS  Google Scholar 

  33. 33.

    Searle, B. C., Lawrence, R. T., MacCoss, M. J. & Villén, J. Thesaurus: quantifying phosphopeptide positional isomers. Nat. Methods 16, 703–706 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  34. 34.

    Bekker-Jensen, D. B. et al. Rapid and site-specific deep phosphoproteome profiling by data-independent acquisition without the need for spectral libraries. Nat. Commun. 11, 787 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  35. 35.

    Glover, M. S. et al. Examining the influence of phosphorylation on peptide ion structure by ion mobility spectrometry-mass spectrometry. J. Am. Soc. Mass Spectrom. 27, 786–794 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. 36.

    Kulak, N. A., Pichler, G., Paron, I., Nagaraj, N. & Mann, M. Minimal, encapsulated proteomic-sample processing applied to copy-number estimation in eukaryotic cells. Nat. Methods 11, 319–324 (2014).

    CAS  PubMed  Google Scholar 

  37. 37.

    Wang, H. et al. Development and evaluation of a micro- and nanoscale proteomic sample preparation method. J. Proteome Res. 4, 2397–2403 (2005).

    CAS  PubMed  PubMed Central  Google Scholar 

  38. 38.

    Kulak, N. A., Geyer, P. E. & Mann, M. Loss-less nano-fractionator for high sensitivity, high coverage proteomics. Mol. Cell. Proteom. 16, 694–705 (2017).

    CAS  Google Scholar 

  39. 39.

    Meier, F. et al. Deep learning the collisional cross sections of the peptide universe from a million training samples. Preprint at bioRxiv (2020).

  40. 40.

    Röst, H. L. et al. TRIC: an automated alignment strategy for reproducible protein quantification in targeted proteomics. Nat. Methods 13, 777–783 (2016).

    PubMed  PubMed Central  Google Scholar 

  41. 41.

    Cox, J. et al. Accurate proteome-wide label-free quantification by delayed normalization and maximal peptide ratio extraction, termed MaxLFQ. Mol. Cell. Proteom. 13, 2513–2526 (2014).

    CAS  Google Scholar 

  42. 42.

    Pham, T. V., Henneman, A. A. & Jimenez, C. R. iq: an R package to estimate relative protein abundances from ion quantification in DIA-MS-based proteomics. Bioinformatics 36, 2611–2613 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  43. 43.

    Wiśniewski, J. R., Hein, M. Y., Cox, J. & Mann, M. A ‘proteomic ruler’ for protein copy number and concentration estimation without spike-in standards. Mol. Cell. Proteom. 13, 3497–3506 (2014).

    Google Scholar 

  44. 44.

    Tyanova, S. et al. The Perseus computational platform for comprehensive analysis of (prote)omics data. Nat. Methods 13, 731–740 (2016).

    CAS  Google Scholar 

  45. 45.

    Vizcaíno, J. A. et al. 2016 update of the PRIDE database and its related tools. Nucleic Acids Res. 44, D447–D456 (2016).

    PubMed  Google Scholar 

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This work was partially supported by the German Research Foundation (DFG-Gottfried Wilhelm Leibniz Prize granted to M.M., grant no. MA 1764/2-1) and by the Max Planck Society for the Advancement of Science (M.M.). This work was partially supported by the Government of Canada through Genome Canada (grant no. 15411) and by the Canadian Institutes for Health Research (H.L.R.). B.C.C. was supported by a Swiss National Science Foundation Ambizione grant (no. PZ00P3_161435). R.A. was supported by the Swiss National Science Foundation (grant no. 3100A0-688 107679) and the European Research Council (ERC-20140AdG 670821). We thank our colleagues in the Department of Proteomics and Signal Transduction (Max Planck Institute of Biochemistry) and at Bruker Daltonik for discussions and help; in particular J. Müller, A. Strasser, C. Deiml and I. Paron for technical support.

Author information




F.M., R.A., B.C.C., H.L.R. and M.M. conceptualized and designed the study; F.M. and M.M. conceived the acquisition mode; H.L.R. conceived the data analysis software; F.M., A.-D.B., S.K.-S., M.L., O.R., N.B. and B.C.C. performed experiments; A.H. and M.F. contributed to the software development; F.M., A.-D.B., M.F., A.H., I.B., E.V., S.K.-S., B.C.C., H.L.R. and M.M. analyzed the data; F.M., R.A., B.C.C., H.L.R. and M.M. wrote the manuscript.

Corresponding authors

Correspondence to Ben C. Collins, Hannes L. Röst or Matthias Mann.

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Competing interests

S.K.-S., M.L. and O.R. are employees of Bruker Daltonik. N.B. is an employee of and M.M. a shareholder in Evosep Biosystems. All other authors have no competing interests.

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

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Supplementary information

Supplementary Information

Supplementary Figs. 1–12.

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Source Data Fig. 5

Protein quantification results.

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Meier, F., Brunner, AD., Frank, M. et al. diaPASEF: parallel accumulation–serial fragmentation combined with data-independent acquisition. Nat Methods 17, 1229–1236 (2020).

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