Mass spectrometry-based plasma proteomics: considerations from sample collection to achieving translational data

Vera Ignjatovic*, Philipp E. Geyer, Krishnan K. Palaniappan, Jessica E. Chaaban, Gilbert S. Omenn, Mark S. Baker, Eric W. Deutsch, Jochen M. Schwenk

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    103 Citations (Scopus)


    The proteomic analysis of human blood and blood-derived products (e.g., plasma) offers an attractive avenue to translate research progress from the laboratory into the clinic. However, due to its unique protein composition, performing proteomics assays with plasma is challenging. Plasma proteomics has regained interest due to recent technological advances, but challenges imposed by both complications inherent to studying human biology (e.g., interindividual variability) and analysis of biospecimens (e.g., sample variability), as well as technological limitations remain. As part of the Human Proteome Project (HPP), the Human Plasma Proteome Project (HPPP) brings together key aspects of the plasma proteomics pipeline. Here, we provide considerations and recommendations concerning study design, plasma collection, quality metrics, plasma processing workflows, mass spectrometry (MS) data acquisition, data processing, and bioinformatic analysis. With exciting opportunities in studying human health and disease though this plasma proteomics pipeline, a more informed analysis of human plasma will accelerate interest while enhancing possibilities for the incorporation of proteomics-scaled assays into clinical practice.

    Original languageEnglish
    Pages (from-to)4085-4097
    Number of pages13
    JournalJournal of Proteome Research
    Issue number12
    Early online date1 Oct 2019
    Publication statusPublished - 6 Dec 2019


    • bioinformatic analysis
    • blood
    • data acquisition
    • data processing
    • Human Plasma Proteome Project (HPPP)
    • Human Proteome Project (HPP)
    • mass spectrometry (MS)
    • plasma
    • plasma processing workflows
    • quality metrics
    • sample collection
    • serum
    • study design


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