MassIVE MSV000097796

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Automated MagNet Enrichment Unlocks Deep and Cost-Effective LC-MS Plasma Proteomics

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Description

Plasma is an ideal material for proteomics due to its diverse protein content reflecting physiological and pathological states, and its compatibility with minimally invasive sampling. Deep proteomic profiling of plasma is limited by high-abundant proteins that mask the detection of low-abundant proteins. To overcome this, we compared five plasma protein enrichment methods, MagNet, ENRICHplus, ENRICHiST, EasySep, and EXONET, against neat plasma using LC-MS proteomics. All five methods substantially increased protein identifications, with MagNet, ENRICHplus, EasySep, and EXONET yielding up to 4200 proteins per sample, over 7-fold more than neat plasma, using a 44-minute gradient on the Evosep One and data-independent acquisition on the timsTOF Pro 2. These methods enriched extracellular vesicle-associated proteins while effectively depleting high-abundant proteins. To further enhance performance and scalability, we optimized the MagNet protocol by increasing the plasma-to-bead ratio and automated the workflow, including Evotip loading, on the Biomek i5 liquid handler. The automated MagNet, combined with the Orbitrap Astral mass spectrometer, yielded up to 4500 proteins per sample with a throughput of 100 samples per day. The workflow demonstrated high reproducibility and a remarkably low total cost of just a few dollars per sample. Newer enrichment methods (Proteonano, P2-iST Plasma and P2) showed improved plasma proteome coverage compared with MagNet but are likely to incur higher costs. The streamlined MagNet enrichment strategy enables affordable, scalable, high-throughput LC-MS plasma proteomics, supporting biomarker discovery across large cohorts. [doi:10.25345/C5V40KB62] [dataset license: CC0 1.0 Universal (CC0 1.0)]

Keywords: plasma ; blood ; plasma enrichment ; extracellular vesicles ; plasma proteomics ; DatasetType:Proteomics

Contact

Principal Investigators:
(in alphabetical order) 		Salla Keskitalo, Institute of Biotechnology, Helsinki Institute of Life Science HiLIFE, University of Helsinki, Finland
Submitting User: sallak

Publications

Järvinen E, Liu X, Varjosalo M, Keskitalo S.
Automated Mag-Net Enrichment Unlocks Deep and Cost-Effective LC-MS Plasma Proteomics.
J Proteome Res. Epub 2025 Dec 21.

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