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Analytic framework for peptidomics applied to large-scale neuropeptide identification

Author

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  • Anna Secher

    (Faculty of Health and Medical Sciences, Novo Nordisk Foundation Center for Protein Research, University of Copenhagen
    Histology and Imaging, Novo Nordisk A/S)

  • Christian D. Kelstrup

    (Faculty of Health and Medical Sciences, Novo Nordisk Foundation Center for Protein Research, University of Copenhagen)

  • Kilian W. Conde-Frieboes

    (Protein & Peptide Chemistry, Novo Nordisk A/S)

  • Charles Pyke

    (Histology and Imaging, Novo Nordisk A/S)

  • Kirsten Raun

    (Incretin & Obesity Pharmacology, Novo Nordisk A/S)

  • Birgitte S. Wulff

    (Incretin & Obesity Research, Novo Nordisk A/S)

  • Jesper V. Olsen

    (Faculty of Health and Medical Sciences, Novo Nordisk Foundation Center for Protein Research, University of Copenhagen)

Abstract

Large-scale mass spectrometry-based peptidomics for drug discovery is relatively unexplored because of challenges in peptide degradation and identification following tissue extraction. Here we present a streamlined analytical pipeline for large-scale peptidomics. We developed an optimized sample preparation protocol to achieve fast, reproducible and effective extraction of endogenous peptides from sub-dissected organs such as the brain, while diminishing unspecific protease activity. Each peptidome sample was analysed by high-resolution tandem mass spectrometry and the resulting data set was integrated with publically available databases. We developed and applied an algorithm that reduces the peptide complexity for identification of biologically relevant peptides. The developed pipeline was applied to rat hypothalamus and identifies thousands of neuropeptides and their post-translational modifications, which is combined in a resource format for visualization, qualitative and quantitative analyses.

Suggested Citation

  • Anna Secher & Christian D. Kelstrup & Kilian W. Conde-Frieboes & Charles Pyke & Kirsten Raun & Birgitte S. Wulff & Jesper V. Olsen, 2016. "Analytic framework for peptidomics applied to large-scale neuropeptide identification," Nature Communications, Nature, vol. 7(1), pages 1-10, September.
    • Handle: RePEc:nat:natcom:v:7:y:2016:i:1:d:10.1038_ncomms11436
    DOI: 10.1038/ncomms11436
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    Cited by:

    1. Christian T. Madsen & Jan C. Refsgaard & Felix G. Teufel & Sonny K. Kjærulff & Zhe Wang & Guangjun Meng & Carsten Jessen & Petteri Heljo & Qunfeng Jiang & Xin Zhao & Bo Wu & Xueping Zhou & Yang Tang &, 2022. "Combining mass spectrometry and machine learning to discover bioactive peptides," Nature Communications, Nature, vol. 13(1), pages 1-17, December.
    2. Amanda L. Wiggenhorn & Hind Z. Abuzaid & Laetitia Coassolo & Veronica L. Li & Julia T. Tanzo & Wei Wei & Xuchao Lyu & Katrin J. Svensson & Jonathan Z. Long, 2023. "A class of secreted mammalian peptides with potential to expand cell-cell communication," Nature Communications, Nature, vol. 14(1), pages 1-13, December.

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