Assessing the Relationship Between Mass Window Width and Retention Time Scheduling on Protein Coverage for Data-Independent Acquisition

  • Wenxue Li
  • Hao Chi
  • Barbora Salovska
  • Chongde Wu
  • Liangliang Sun
  • George Rosenberger
  • Yansheng LiuEmail author
Focus: Emerging Investigators: Research Article


Due to the technical advances of mass spectrometers, particularly increased scanning speed and higher MS/MS resolution, the use of data-independent acquisition mass spectrometry (DIA-MS) became more popular, which enables high reproducibility in both proteomic identification and quantification. The current DIA-MS methods normally cover a wide mass range, with the aim to target and identify as many peptides and proteins as possible and therefore frequently generate MS/MS spectra of high complexity. In this report, we assessed the performance and benefits of using small windows with, e.g., 5-m/z width across the peptide elution time. We further devised a new DIA method named RTwinDIA that schedules the small isolation windows in different retention time blocks, taking advantage of the fact that larger peptides are normally eluting later in reversed phase chromatography. We assessed the direct proteomic identification by using shotgun database searching tools such as MaxQuant and pFind, and also Spectronaut with an external comprehensive spectral library of human proteins. We conclude that algorithms like pFind have potential in directly analyzing DIA data acquired with small windows, and that the instrumental time and DIA cycle time, if prioritized to be spent on small windows rather than on covering a broad mass range by large windows, will improve the direct proteome coverage for new biological samples and increase the quantitative precision. These results further provide perspectives for the future convergence between DDA and DIA on faster MS analyzers.


Data-independent acquisition Isolation windows Maxquant pFind Spectronaut 



We thank Lukas Reiter and Oliver Bernhardt from Biognosys AG and Daoyang Chen from Michigan State University for the helpful discussions. We thank Semin He from Institute of Computing Technology CAS Beijing for the resource support in pFind analysis. This research was supported in part by Pilot Grants from Yale Cancer Systems Biology Symposium and Yale Cancer Center.

Supplementary material

13361_2019_2243_MOESM1_ESM.pdf (2.8 mb)
ESM 1 (PDF 2847 kb)
13361_2019_2243_MOESM2_ESM.xlsx (22 kb)
ESM 2 (XLSX 21.8 kb)
13361_2019_2243_MOESM3_ESM.xlsx (12 kb)
ESM 3 (XLSX 11.8 kb)
13361_2019_2243_MOESM4_ESM.xlsx (18 kb)
ESM 4 (XLSX 17.7 kb)


  1. 1.
    Aebersold, R., Mann, M.: Mass-spectrometric exploration of proteome structure and function. Nature. 537, 347–355 (2016)CrossRefPubMedGoogle Scholar
  2. 2.
    Venable, J.D., Dong, M.Q., Wohlschlegel, J., Dillin, A., Yates, J.R.: Automated approach for quantitative analysis of complex peptide mixtures from tandem mass spectra. Nat. Methods. 1, 39–45 (2004)CrossRefPubMedGoogle Scholar
  3. 3.
    Panchaud, A., Scherl, A., Shaffer, S.A., von Haller, P.D., Kulasekara, H.D., Miller, S.I., et al.: Precursor acquisition independent from ion count: how to dive deeper into the proteomics ocean. Anal. Chem. 81, 6481–6488 (2009)CrossRefPubMedPubMedCentralGoogle Scholar
  4. 4.
    Ting, Y.S., Egertson, J.D., Payne, S.H., Kim, S., MacLean, B., Kall, L., et al.: Peptide-centric proteome analysis: an alternative strategy for the analysis of tandem mass spectrometry data. Mol. Cell. Proteomics : MCP. 14, 2301–2307 (2015)CrossRefPubMedGoogle Scholar
  5. 5.
    Gillet, L.C., Navarro, P., Tate, S., Rost, H., Selevsek, N., Reiter, L., 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. Proteomics : MCP. 11, O111 016717 (2012)CrossRefPubMedGoogle Scholar
  6. 6.
    Sajic, T., Liu, Y., Aebersold, R.: Using data-independent, high-resolution mass spectrometry in protein biomarker research: perspectives and clinical applications. Proteomics Clin. Appl. 9, 307–321 (2015)CrossRefPubMedGoogle Scholar
  7. 7.
    Bruderer, R., Bernhardt, O.M., Gandhi, T., Miladinovic, S.M., Cheng, L.Y., Messner, S., et al.: Extending the limits of quantitative proteome profiling with data-independent acquisition and application to acetaminophen-treated three-dimensional liver microtissues. Mol Cell Proteomics : MCP. 14, 1400–1410 (2015)CrossRefPubMedGoogle Scholar
  8. 8.
    Bruderer, R., Bernhardt, O.M., Gandhi, T., Xuan, Y., Sondermann, J., Schmidt, M., et al.: Optimization of experimental parameters in data-independent mass spectrometry significantly increases depth and reproducibility of results. Mol. Cell. Proteomics : MCP. 16, 2296–2309 (2017)CrossRefPubMedGoogle Scholar
  9. 9.
    Kelstrup, C.D., Bekker-Jensen, D.B., Arrey, T.N., Hogrebe, A., Harder, A., Olsen, J.V.: Performance evaluation of the Q Exactive HF-X for shotgun proteomics. J. Proteome Res. 17, 727–738 (2018)CrossRefPubMedGoogle Scholar
  10. 10.
    Egertson, J.D., Kuehn, A., Merrihew, G.E., Bateman, N.W., MacLean, B.X., Ting, Y.S., et al.: Multiplexed MS/MS for improved data-independent acquisition. Nat. Methods. 10, 744–746 (2013)CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Moseley, M.A., Hughes, C.J., Juvvadi, P.R., Soderblom, E.J., Lennon, S., Perkins, S.R., et al.: Scanning quadrupole data-independent acquisition, part a: qualitative and quantitative characterization. J. Proteome Res. 17, 770–779 (2018)CrossRefPubMedGoogle Scholar
  12. 12.
    Kaufmann, A., Walker, S.: Comparison of linear intrascan and interscan dynamic ranges of Orbitrap and ion-mobility time-of-flight mass spectrometers. Rapid Commun. Mass Spectrom. 31, 1915–1926 (2017)CrossRefPubMedGoogle Scholar
  13. 13.
    Ludwig, C., Gillet, L., Rosenberger, G., Amon, S., Collins, B.C., Aebersold, R.: Data-independent acquisition-based SWATH-MS for quantitative proteomics: a tutorial. Mol. Syst. Biol. 14, e8126 (2018)CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    Liu, Y., Borel, C., Li, L., Muller, T., Williams, E.G., Germain, P.L., et al.: Systematic proteome and proteostasis profiling in human trisomy 21 fibroblast cells. Nat. Commun. 8, 1212 (2017)CrossRefPubMedPubMedCentralGoogle Scholar
  15. 15.
    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)CrossRefGoogle Scholar
  16. 16.
    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)CrossRefPubMedGoogle Scholar
  17. 17.
    Chi, H., Liu, C., Yang, H., Zeng, W.-F., Wu, L., Zhou, W.-J., et al.: Comprehensive identification of peptides in tandem mass spectra using an efficient open search engine. Nat. Biotechnol. 36, 1059 (2018)CrossRefGoogle Scholar
  18. 18.
    Leinonen, R., Diez, F.G., Binns, D., Fleischmann, W., Lopez, R., Apweiler, R.: UniProt archive. Bioinformatics (Oxford, England). 20, 3236–3237 (2004)CrossRefGoogle Scholar
  19. 19.
    Rosenberger, G., Koh, C.C., Guo, T., Rost, H.L., Kouvonen, P., Collins, B.C., et al.: A repository of assays to quantify 10,000 human proteins by SWATH-MS. Sci. Data. 1, 140031 (2014)CrossRefPubMedPubMedCentralGoogle Scholar
  20. 20.
    Bruderer, R., Bernhardt, O.M., Gandhi, T., Reiter, L.: High-precision iRT prediction in the targeted analysis of data-independent acquisition and its impact on identification and quantitation. Proteomics. 16, 2246–2256 (2016)CrossRefPubMedPubMedCentralGoogle Scholar
  21. 21.
    Rosenberger, G., Bludau, I., Schmitt, U., Heusel, M., Hunter, C.L., Liu, Y., et al.: Statistical control of peptide and protein error rates in large-scale targeted data-independent acquisition analyses. Nat. Methods. 14, 921–927 (2017)CrossRefPubMedPubMedCentralGoogle Scholar
  22. 22.
    Perez-Riverol, Y., Csordas, A., Bai, J., Bernal-Llinares, M., Hewapathirana, S., Kundu, D.J., et al.: The PRIDE database and related tools and resources in 2019: improving support for quantification data. Nucleic Acids Res. 47, D442–D450 (2019)CrossRefPubMedGoogle Scholar
  23. 23.
    Espadas, G., Borras, E., Chiva, C., Sabido, E.: Evaluation of different peptide fragmentation types and mass analyzers in data-dependent methods using an Orbitrap fusion Lumos Tribrid mass spectrometer. Proteomics. 17, 1600416 (2017)Google Scholar
  24. 24.
    Mehnert, M., Li, W., Wu, C., Salovska, B., Liu, Y.: Combining rapid data independent acquisition and CRISPR gene deletion for studying potential protein functions: a case of HMGN1. Proteomics. e1800438 (2019).
  25. 25.
    Zhang, B., Pirmoradian, M., Chernobrovkin, A., Zubarev, R.A.: DeMix workflow for efficient identification of cofragmented peptides in high resolution data-dependent tandem mass spectrometry. Mol. Cell. Proteomics : MCP. 13, 3211–3223 (2014)CrossRefPubMedGoogle Scholar
  26. 26.
    Liu, Y., Mi, Y., Mueller, T., Kreibich, S., Williams, E.G., Van Drogen, A., et al.: Multi-omic measurements of heterogeneity in HeLa cells across laboratories. Nat. Biotechnol. 37, 314–322 (2019)Google Scholar
  27. 27.
    Panchaud, A., Jung, S., Shaffer, S.A., Aitchison, J.D., Goodlett, D.R.: Faster, quantitative, and accurate precursor acquisition independent from ion count. Anal. Chem. 83, 2250–2257 (2011)CrossRefPubMedPubMedCentralGoogle Scholar
  28. 28.
    Searle, B.C., Pino, L.K., Egertson, J.D., Ting, Y.S., Lawrence, R.T., MacLean, B.X., et al.: Chromatogram libraries improve peptide detection and quantification by data independent acquisition mass spectrometry. Nat. Commun. 9, 5128 (2018)CrossRefPubMedPubMedCentralGoogle Scholar
  29. 29.
    Chen, D., Ludwig, K.R., Krokhin, O.V., Spicer, V., Yang, Z., Shen, X., et al.: Capillary zone electrophoresis-tandem mass spectrometry for large-scale Phosphoproteomics with the production of over 11,000 Phosphopeptides from the Colon carcinoma HCT116 cell line. Anal. Chem. 91, 2201–2208 (2019)CrossRefPubMedGoogle Scholar
  30. 30.
    Picotti, P., Aebersold, R.: Selected reaction monitoring-based proteomics: workflows, potential, pitfalls and future directions. Nat. Methods. 9, 555–566 (2012)CrossRefPubMedGoogle Scholar
  31. 31.
    Liu, Y., Buil, A., Collins, B.C., Gillet, L.C., Blum, L.C., Cheng, L.Y., et al.: Quantitative variability of 342 plasma proteins in a human twin population. Mol. Syst. Biol. 11, 786 (2015)CrossRefPubMedPubMedCentralGoogle Scholar
  32. 32.
    Bruderer, R., Muntel, J., Muller, S., Bernhardt, O.M., Gandhi, T., Cominetti, O., et al.: Analysis of 1508 plasma samples by capillary flow data-independent acquisition profiles proteomics of weight loss and maintenance. Mol. Cell. Proteomics : MCP. (2019).
  33. 33.
    Rost, H.L., Rosenberger, G., Navarro, P., Gillet, L., Miladinovic, S.M., Schubert, O.T., et al.: OpenSWATH enables automated, targeted analysis of data-independent acquisition MS data. Nat. Biotech. 32, 219–223 (2014)CrossRefGoogle Scholar
  34. 34.
    Collins, B.C., Hunter, C.L., Liu, Y., Schilling, B., Rosenberger, G., Bader, S.L., et al.: Multi-laboratory assessment of reproducibility, qualitative and quantitative performance of SWATH-mass spectrometry. Nat. Commun. 8, 291 (2017)CrossRefPubMedPubMedCentralGoogle Scholar
  35. 35.
    MacLean, B., Tomazela, D.M., Shulman, N., Chambers, M., Finney, G.L., Frewen, B., et al.: Skyline: an open source document editor for creating and analyzing targeted proteomics experiments. Bioinformatics. 26, 966–968 (2010)CrossRefPubMedPubMedCentralGoogle Scholar
  36. 36.
    Navarro, P., Kuharev, J., Gillet, L.C., Bernhardt, O.M., MacLean, B., Rost, H.L., et al.: A multicenter study benchmarks software tools for label-free proteome quantification. Nat. Biotechnol. 34, 1130–1136 (2016)CrossRefPubMedPubMedCentralGoogle Scholar
  37. 37.
    de Godoy, L.M., Olsen, J.V., Cox, J., Nielsen, M.L., Hubner, N.C., Frohlich, F., et al.: Comprehensive mass-spectrometry-based proteome quantification of haploid versus diploid yeast. Nature. 455, 1251–1254 (2008)CrossRefPubMedGoogle Scholar

Copyright information

© American Society for Mass Spectrometry 2019

Authors and Affiliations

  1. 1.Yale Cancer Biology InstituteYale UniversityWest HavenUSA
  2. 2.Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS)Institute of Computing Technology, CASBeijingChina
  3. 3.Department of Genome IntegrityInstitute of Molecular Genetics of the Czech Academy of SciencesPragueCzech Republic
  4. 4.Department of ChemistryMichigan State UniversityEast LansingUSA
  5. 5.Department of Systems BiologyColumbia UniversityNew YorkUSA
  6. 6.Department of PharmacologyYale University School of MedicineNew HavenUSA

Personalised recommendations