Benchmarking Plasma Proteomics Workflows and Their Correlation to Clinical Routine Protein Assays

Plasma proteomics based on mass spectrometry has great potential for biomarker discovery. Plasma is challenging for mass spectrometry due to the high dynamic range in protein abundance. Several workflows have been developed to overcome this, and in this study, we compare prominent enrichment and depletion workflows using platelet-poor plasma (PPP), platelet-rich plasma (PRP), and serum (SER). Our results show that depletion workflows including Top14 depletion and acid precipitation allow quantification of very different proteomes than methods based on enrichments of extracellular vesicles such as bead-based enrichment or ultracentrifugation. Enrichment methods are superior in terms of proteome depth and quantitative performance but may be less robust in large cohorts. There is a very high correlation between PPP and PRP samples for all methods and less to SER samples, especially with enrichment workflows. The correlation of 10 protein measurements, performed by clinical routine processes on a Cobas system, showed heterogeneous results. Low-abundant proteins with biological dynamics within a healthy cohort, including C-reactive protein and lipoprotein(a), correlated very well to proteomics-based workflows, while others, including albumin and transferrin, correlated poorly. In conclusion, the workflow for plasma proteomics should be aligned with the aim of the analysis and setup of the sample collection.

Authors

Institutions

Publication Details

Journal
Journal of Proteome Research
Published
2026-05-02
DOI
https://doi.org/10.1021/acs.jproteome.6c00078
Primary Topic
Advanced Proteomics Techniques and Applications
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Benchmarking Plasma Proteomics Workflows and Their Correlation to Clinical Routine Protein Assays

Ole Østergaard, Anders Kverneland, Steffen Ullitz Thorsen, Jesper Velgaard Olsen et al.
Journal of Proteome Research
Advanced Proteomics Techniques and Applications
article

Benchmarking Plasma Proteomics Workflows and Their Correlation to Clinical Routine Protein Assays

Ole Østergaard, Anders Kverneland, Steffen Ullitz Thorsen, Jesper Velgaard Olsen, Mia Ø Johansen, Luisa Schmidt, Christina Christoffersen, Ruth Frikke Schmidt
article en

Abstract

Plasma proteomics based on mass spectrometry has great potential for biomarker discovery. Plasma is challenging for mass spectrometry due to the high dynamic range in protein abundance. Several workflows have been developed to overcome this, and in this study, we compare prominent enrichment and depletion workflows using platelet-poor plasma (PPP), platelet-rich plasma (PRP), and serum (SER). Our results show that depletion workflows including Top14 depletion and acid precipitation allow quantification of very different proteomes than methods based on enrichments of extracellular vesicles such as bead-based enrichment or ultracentrifugation. Enrichment methods are superior in terms of proteome depth and quantitative performance but may be less robust in large cohorts. There is a very high correlation between PPP and PRP samples for all methods and less to SER samples, especially with enrichment workflows. The correlation of 10 protein measurements, performed by clinical routine processes on a Cobas system, showed heterogeneous results. Low-abundant proteins with biological dynamics within a healthy cohort, including C-reactive protein and lipoprotein(a), correlated very well to proteomics-based workflows, while others, including albumin and transferrin, correlated poorly. In conclusion, the workflow for plasma proteomics should be aligned with the aim of the analysis and setup of the sample collection.

Journal of Proteome Research
University of Copenhagen (DK), Novo Nordisk Foundation (DK), Copenhagen University Hospital (DK), Gentofte Hospital (DK), Rigshospitalet (DK), National Cancer Center (US), Cellular Research (United States) (US)
Danmarks Grundforskningsfond, Novo Nordisk Fonden, Danish Agency for Science and Higher Education
No poverty
Openalex Percentile: Top 14%
Advanced Proteomics Techniques and Applications
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.