Back

Benchmarking enrichment and depletion methods for quantitative plasma proteomics in different plasma types and the correlation to clinical routine assays.

Kverneland, A.; Ostergaard, O.; Schmidt, L. M.; Johansen, M. O.; Thorsen, S. U.; Schmidt, R. F.; Christoffersen, C.; Olsen, J. V.

2025-11-03 biochemistry
10.1101/2025.11.03.686186 bioRxiv
Show abstract

Plasma proteomics based on mass spectrometry has great potential for biomarker discovery. Plasma is challenging for mass spectrometry due to high dynamic range in protein abundance. Several workflows have been developed to overcome this, and in this study, we compare prominent workflows using platelet-poor-(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 with 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 proteomic 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 the setup of the sample collection. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=85 SRC="FIGDIR/small/686186v1_ufig1.gif" ALT="Figure 1"> View larger version (20K): org.highwire.dtl.DTLVardef@10b62a7org.highwire.dtl.DTLVardef@79d4f1org.highwire.dtl.DTLVardef@8b21a3org.highwire.dtl.DTLVardef@4d85d9_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical AbstractC_FLOATNO C_FIG

Matching journals

The top 2 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.