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ma=86400 An accessible workflow for high-sensitivity proteomics using parallel accumulation–serial fragmentation (PASEF) | Nature Protocols
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An accessible workflow for high-sensitivity proteomics using parallel accumulation–serial fragmentation (PASEF)

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Abstract

Deep and accurate proteome analysis is crucial for understanding cellular processes and disease mechanisms; however, it is challenging to implement in routine settings. In this protocol, we combine a robust chromatographic platform with a high-performance mass spectrometric setup to enable routine yet in-depth proteome coverage for a broad community. This entails tip-based sample preparation and pre-formed gradients (Evosep One) combined with a trapped ion mobility time-of-flight mass spectrometer (timsTOF, Bruker). The timsTOF enables parallel accumulation–serial fragmentation (PASEF), in which ions are accumulated and separated by their ion mobility, maximizing ion usage and simplifying spectra. Combined with data-independent acquisition (DIA), it offers high peak sampling rates and near-complete ion coverage. Here, we explain how to balance quantitative accuracy, specificity, proteome coverage and sensitivity by choosing the best PASEF and DIA method parameters. The protocol describes how to set up the liquid chromatography–mass spectrometry system and enables PASEF method generation and evaluation for varied samples by using the py_diAID tool to optimally position isolation windows in the mass-to-charge and ion mobility space. Biological projects (e.g., triplicate proteome analysis in two conditions) can be performed in 3 d with ~3 h of hands-on time and minimal marginal cost. This results in reproducible quantification of 7,000 proteins in a human cancer cell line in quadruplicate 21-min injections and 29,000 phosphosites for phospho-enriched quadruplicates. Synchro-PASEF, a highly efficient, specific and novel scan mode, can be analyzed by Spectronaut or AlphaDIA, resulting in superior quantitative reproducibility because of its high sampling efficiency.

Key points

  • There are three main challenges for mass spectrometry–based proteomics: sensitivity of mass spectrometry detection, selectivity in protein identification and speed of analysis. This protocol focuses on improving all three by using parallel accumulation–serial fragmentation (PASEF), which builds on incorporating trapped ion mobility upstream of quadrupole time-of-flight mass spectrometry.

  • Optimal PASEF and data-independent acquisition (DIA) methods are generated by py_diAID.

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Fig. 1: A robust, standardized PASEF workflow.
Fig. 2: Generation of dia-PASEF and synchro-PASEF on the basis of a representative, pre-acquired single run.
Fig. 3: Optimization of method parameters for DIA combined with PASEF.
Fig. 4: dia-PASEF acquisition schemes plotted on top of various sample types.
Fig. 5: Expected proteomics depth and quantitative reproducibility for proteomics and phosphoproteomics studies.

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Data availability

Parameter files of acquisition methods and mass spectrometry raw files have been deposited with MassIVE with the identifier MSV000094389. Supplementary Table 1 is a roadmap linking the raw files and output files. The Homo sapiens (taxon identifier: 9606) proteome database was downloaded from https://www.uniprot.org. Figures 4 and 5 show the following publicly available MS datasets of the proteomics repositories PRIDE (PRoteomics IDEntification Database) or MassIVE (Mass Spectrometry Interactive Virtual Environment): PXD03412817, PXD0240433, PXD0386995, PXD03194072, PXD03863235, MSV00009255751 and MSV00008802073.

Code availability

Code for the tool py_diAID and the main analyses is available under the Apache License 2.0 at https://github.com/MannLabs/pydiaid.

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Acknowledgements

We are grateful for fruitful discussions and thoughtful feedback from our colleagues in the Department of Proteomics and Signal Transduction at the Max Planck Institute of Biochemistry as well as at Bruker Daltonics, Evosep Biosystems and Biognosys AG. In particular, we thank S. Strasser, S. Steigerwald, M. Thielert, F. Rosenberger, E. C. M. Itang, P. Koval, I. Paron, O. Raether, F. Krohs, M. Lubeck, C. Krisp, O. Bjeld Hørning, N. Bache and M. Puchalska. Furthermore, we are grateful to V. Demichev for his instructions on using DIA-NN. While preparing this manuscript, the authors used ChatGPT-4 by OpenAI and Claude 3.5 Sonnet by Anthropic to improve readability and conciseness. After using this service, the authors reviewed and edited the content as necessary and take full responsibility for the final content of the publication. This study was supported by the Max-Planck Society for Advancement of Science, European Union’s Horizon 2020 research and innovation program under grant agreement No. 874839 ISLET and by the Bavarian State Ministry of Health and Care through the research project DigiMed Bayern (www.digimed-bayern.de).

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Authors and Affiliations

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

    Patricia Skowronek, Georg Wallmann, Maria Wahle, Sander Willems & Matthias Mann

  2. Research and Development, Bruker Belgium nv., Kontich, Belgium

    Sander Willems

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Contributions

P.S. and M.M. conceptualized this study. M.M. supervised the project. P.S. and S.W. extended the tool py_diAID to enable the generation of the synchro-PASEF method. P.S. and M.W. performed the MS experiments. P.S. and G.W. analyzed the data. P.S. and M.W. conceived the steps of the protocol. P.S. and M.M. wrote the manuscript with input from all authors.

Corresponding author

Correspondence to Matthias Mann.

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Competing interests

M.M. is an indirect investor in Evosep Biosystems. S.W. is an employee of Bruker Daltonics. All other authors declare no competing interests.

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Nature Protocols thanks David Gomez-Zepeda, Wenqing Shui and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Key references

Meier, F. et al. Nat. Methods 17, 1229–1236 (2020): https://doi.org/10.1038/s41592-020-00998-0

Meier, F. et al. Mol. Cell. Proteom. 20, 100138 (2021): https://doi.org/10.1016/j.mcpro.2021.100138

Skowronek, P. et al. Mol. Cell. Proteom. 21, 100279 (2022): https://doi.org/10.1016/j.mcpro.2022.100279

Skowronek, P. et al. Mol. Cell. Proteom. 22, 100489 (2023): https://doi.org/10.1016/j.mcpro.2022.100489

Extended data

Extended Data Fig. 1 TimsControl settings.

The settings mentioned in Tables 1 and 2 are labeled with the corresponding numbers. Reprinted with permission from Bruker Daltonics.

Extended Data Fig. 2 Navigating in HyStar.

The numbers support the description in the corresponding procedure steps. Reprinted with permission from Bruker Daltonics.

Extended Data Fig. 3 Py_diAID parameters.

Top panel: Parameters to define a method. Bottom panel: Parameters to define the Bayesian optimization process, along with ranges of scan area coordinates (A1, A2, B1, B2) for the algorithm to evaluate.

Extended Data Fig. 4 Py_diAID parameters.

Top panel: Parameters to define the scan area of a synchro-PASEF method. Bottom panel: Parameters to define the acquisition scheme.

Supplementary information

Supplementary Information

Supplementary Figures 1–12 and Supplementary Methods

Reporting Summary

Supplementary Data 1

dia-PASEF method

Supplementary Data 2

synchro-PASEF method

Supplementary Table 1

Roadmap linking the raw files and output files

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Skowronek, P., Wallmann, G., Wahle, M. et al. An accessible workflow for high-sensitivity proteomics using parallel accumulation–serial fragmentation (PASEF). Nat Protoc (2025). https://doi.org/10.1038/s41596-024-01104-w

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