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Rapid and site-specific deep phosphoproteome profiling by data-independent acquisition without the need for spectral libraries

Author

Listed:
  • Dorte B. Bekker-Jensen

    (University of Copenhagen)

  • Oliver M. Bernhardt

    (Biognosys AG)

  • Alexander Hogrebe

    (University of Copenhagen)

  • Ana Martinez-Val

    (University of Copenhagen)

  • Lynn Verbeke

    (Biognosys AG)

  • Tejas Gandhi

    (Biognosys AG)

  • Christian D. Kelstrup

    (University of Copenhagen)

  • Lukas Reiter

    (Biognosys AG)

  • Jesper V. Olsen

    (University of Copenhagen)

Abstract

Quantitative phosphoproteomics has transformed investigations of cell signaling, but it remains challenging to scale the technology for high-throughput analyses. Here we report a rapid and reproducible approach to analyze hundreds of phosphoproteomes using data-independent acquisition (DIA) with an accurate site localization score incorporated into Spectronaut. DIA-based phosphoproteomics achieves an order of magnitude broader dynamic range, higher reproducibility of identification, and improved sensitivity and accuracy of quantification compared to state-of-the-art data-dependent acquisition (DDA)-based phosphoproteomics. Notably, direct DIA without the need of spectral libraries performs close to analyses using project-specific libraries, quantifying > 20,000 phosphopeptides in 15 min single-shot LC-MS analysis per condition. Adaptation of a 3D multiple regression model-based algorithm enables global determination of phosphorylation site stoichiometry in DIA. Scalability of the DIA approach is demonstrated by systematically analyzing the effects of thirty kinase inhibitors in context of epidermal growth factor (EGF) signaling showing that specific protein kinases mediate EGF-dependent phospho-regulation.

Suggested Citation

  • Dorte B. Bekker-Jensen & Oliver M. Bernhardt & Alexander Hogrebe & Ana Martinez-Val & Lynn Verbeke & Tejas Gandhi & Christian D. Kelstrup & Lukas Reiter & Jesper V. Olsen, 2020. "Rapid and site-specific deep phosphoproteome profiling by data-independent acquisition without the need for spectral libraries," Nature Communications, Nature, vol. 11(1), pages 1-12, December.
  • Handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-14609-1
    DOI: 10.1038/s41467-020-14609-1
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    Cited by:

    1. Yi Yang & Qun Fang, 2024. "Prediction of glycopeptide fragment mass spectra by deep learning," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    2. Trendelina Rrustemi & Katrina Meyer & Yvette Roske & Bora Uyar & Altuna Akalin & Koshi Imami & Yasushi Ishihama & Oliver Daumke & Matthias Selbach, 2024. "Pathogenic mutations of human phosphorylation sites affect protein–protein interactions," Nature Communications, Nature, vol. 15(1), pages 1-19, December.

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