Deep plasma proteomics: depletion or nanoparticle enrichment?
Plasma is the most informative and the most difficult sample in proteomics. How the two main strategies for its dynamic range differ, and how to choose for a cohort.
The problem
Plasma protein concentrations span more than ten orders of magnitude, and a few dozen proteins account for most of the mass (Anderson & Anderson, Mol Cell Proteomics 2002). Inject neat plasma and the mass spectrometer spends its sampling capacity on albumin, immunoglobulins and transferrin. The proteins that carry disease information, cytokines, tissue-leakage proteins, signalling molecules, sit several orders of magnitude below them.
Immunodepletion
Antibody columns remove a defined set of the most abundant proteins (commonly 6, 14 or 20). What remains is enriched for everything else. The approach is well established and predictable, but it has two costs: a fraction of non-target proteins is removed with the targets through binding and carrier effects, and the columns are expensive, finite-use and slow at cohort scale.
Nanoparticle enrichment
Surface-functionalised nanoparticles incubated with plasma bind proteins according to their affinity for the particle surface rather than their abundance, so the protein population recovered after a wash has a compressed dynamic range. The principle was demonstrated at scale with multi-nanoparticle protein-corona workflows (Blume et al., Nat Commun 2020). The practical consequences are small sample input (tens of microlitres), compatibility with magnetic-bead automation, and depth that scales with the number of particle chemistries and the instrument behind them.
Choosing for a study
| Situation | Leaning |
|---|---|
| Discovery cohort, biomarker hunting, limited plasma volume | Nanoparticle enrichment |
| Targeted follow-up of known mid-abundance proteins | Often neither; PRM on neat or lightly depleted plasma |
| Legacy comparability with an existing depletion dataset | Depletion |
| Very large cohort with automation available | Nanoparticle enrichment on a liquid handler |
| Budget-constrained pilot | Run both on a handful of samples and compare depth and CV |
The strategic argument for depth at cohort scale was made by Geyer and colleagues (Mol Syst Biol 2017): rather than hunting single candidates in small groups and validating them later by immunoassay, profile the plasma proteome of large, well-phenotyped cohorts and let the pattern across hundreds of proteins carry the signal. Nanoparticle enrichment is what has made that design affordable, and the nanoparticle-protein interaction can be tuned so that different particle surfaces sample different parts of the proteome (Ferdosi et al., PNAS 2022).
Whichever route is taken, two controls are not optional in plasma work: a pooled quality-control sample injected throughout the batch, and randomisation of sample order against the biological groups. Batch and collection effects are routinely larger than biology in plasma studies.
We supply Proteonano™ nanoparticle enrichment kits and run plasma enrichment as a service; an 8-test evaluation pack is the usual way to start.
References
- Anderson NL, Anderson NG. The human plasma proteome: history, character, and diagnostic prospects. Mol Cell Proteomics 2002;1:845-867. Open access PubMed · DOI
- Geyer et al. Revisiting biomarker discovery by plasma proteomics. Mol Syst Biol 2017. Open access PubMed · DOI · Full text (PMC)
- Blume JE et al. Rapid, deep and precise profiling of the plasma proteome with multi-nanoparticle protein corona. Nat Commun 2020;11:3662. Open access PubMed · DOI · Full text (PMC)
- Ferdosi et al. Engineered nanoparticles enable deep proteomics studies at scale by leveraging tunable nano-bio interactions. Proc Natl Acad Sci U S A 2022. Open access PubMed · DOI · Full text (PMC)
Citations verified against PubMed records; “Open access” marks papers whose full text is freely available via PubMed Central or the journal.
Written by the omicslink proteomics team. Reviewed November 2026.
Discuss a plasma pilot