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

Nature Methods volume 17, pages 1229–1236 (2020)Cite this article

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Abstract

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 https://www.uniprot.org. Source data are provided with this paper.

Code availability

Code is available under the three-clause BSD license on https://github.com/OpenMS/OpenMS and https://github.com/Roestlab/dia-pasef.

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Acknowledgements

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.

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Authors and Affiliations

  1. Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany

    Florian Meier, Andreas-David Brunner, Isabell Bludau, Eugenia Voytik & Matthias Mann

  2. Functional Proteomics, Jena University Hospital, Jena, Germany

    Florian Meier

  3. Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Canada

    Max Frank, Annie Ha & Hannes L. Röst

  4. Bruker Daltonik GmbH, Bremen, Germany

    Stephanie Kaspar-Schoenefeld, Markus Lubeck & Oliver Raether

  5. Evosep Biosystems, Odense, Denmark

    Nicolai Bache

  6. Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland

    Ruedi Aebersold & Ben C. Collins

  7. Faculty of Science, University of Zurich, Zurich, Switzerland

    Ruedi Aebersold

  8. School of Biological Sciences, Queen’s University of Belfast, Belfast, UK

    Ben C. Collins

  9. NNF Center for Protein Research, University of Copenhagen, Copenhagen, Denmark

    Matthias Mann

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Contributions

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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Source data

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). https://doi.org/10.1038/s41592-020-00998-0

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