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Micropillar arrays, wide window acquisition and AI-based data analysis improve comprehensiveness in multiple proteomic applications

Author

Listed:
  • Manuel Matzinger

    (Research Institute of Molecular Pathology (IMP), Vienna BioCenter)

  • Anna Schmücker

    (Austrian Academy of Sciences, Vienna BioCenter (VBC)
    MRC (Medical Research Council) London Institute of Medical Sciences, Du Cane Road
    Imperial College London, Hammersmith Hospital Campus, Du Cane Road)

  • Ramesh Yelagandula

    (Austrian Academy of Sciences, Vienna BioCenter (VBC)
    Austrian Academy of Sciences, Vienna BioCenter (VBC)
    Cell Fate & Disease, Centre for DNA Fingerprinting and Diagnostics (CDFD), Uppal)

  • Karel Stejskal

    (Research Institute of Molecular Pathology (IMP), Vienna BioCenter
    Austrian Academy of Sciences, Vienna BioCenter (VBC)
    Austrian Academy of Sciences, Vienna BioCenter (VBC))

  • Gabriela Krššáková

    (Research Institute of Molecular Pathology (IMP), Vienna BioCenter
    Austrian Academy of Sciences, Vienna BioCenter (VBC)
    Austrian Academy of Sciences, Vienna BioCenter (VBC))

  • Frédéric Berger

    (Austrian Academy of Sciences, Vienna BioCenter (VBC))

  • Karl Mechtler

    (Research Institute of Molecular Pathology (IMP), Vienna BioCenter
    Austrian Academy of Sciences, Vienna BioCenter (VBC)
    Austrian Academy of Sciences, Vienna BioCenter (VBC))

  • Rupert L. Mayer

    (Research Institute of Molecular Pathology (IMP), Vienna BioCenter)

Abstract

Comprehensive proteomic analysis is essential to elucidate molecular pathways and protein functions. Despite tremendous progress in proteomics, current studies still suffer from limited proteomic coverage and dynamic range. Here, we utilize micropillar array columns (µPACs) together with wide-window acquisition and the AI-based CHIMERYS search engine to achieve excellent proteomic comprehensiveness for bulk proteomics, affinity purification mass spectrometry and single cell proteomics. Our data show that µPACs identify ≤50% more peptides and ≤24% more proteins, while offering improved throughput, which is critical for large (clinical) proteomics studies. Combining wide precursor isolation widths of m/z 4–12 with the CHIMERYS search engine identified +51–74% and +59–150% more proteins and peptides, respectively, for single cell, co-immunoprecipitation, and multi-species samples over a conventional workflow at well-controlled false discovery rates. The workflow further offers excellent precision, with CVs

Suggested Citation

  • Manuel Matzinger & Anna Schmücker & Ramesh Yelagandula & Karel Stejskal & Gabriela Krššáková & Frédéric Berger & Karl Mechtler & Rupert L. Mayer, 2024. "Micropillar arrays, wide window acquisition and AI-based data analysis improve comprehensiveness in multiple proteomic applications," Nature Communications, Nature, vol. 15(1), pages 1-17, December.
    • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-45391-z
    DOI: 10.1038/s41467-024-45391-z
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