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An accessible workflow for high-sensitivity proteomics using parallel accumulation-serial fragmentation (PASEF)

Skowronek, Patricia, Wallmann, Georg, Wahle, Maria, Willems, Sander, Mann, Matthias·2025·Nature Protocols
DOI 10.1038/s41596-024-01104-w
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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 qua…

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Metadata

Authors
Skowronek, P., Wallmann, G., Wahle, M., Willems, S., & Mann, M.
ORCID
Venue
Nature Protocols
Publisher
Springer Science and Business Media LLC
Published
2025-01-17
Volume
20
Issue
6
Pages
1700-1729
ISSN
  • 1754-2189
  • 1750-2799
DOI
10.1038/s41596-024-01104-w
Cited by
43
References
9
Language
en
Licence
Funders
  • Max-Planck-Gesellschaft
  • EC | Horizon 2020 Framework Programme (874839 ISLET)
  • Bavarian State Ministry of Health and Care (Bayerisches Staatsministerium für Gesundheit, Pflege) through the research project DigiMed Bayern

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