Automated Mag-Net Enrichment Unlocks Deep and Cost-Effective LC–MS Plasma Proteomics

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, Mag-Net, ENRICHplus, ENRICHiST, EasySep, and EXONET, against neat plasma using LC-MS proteomics. All five methods substantially increased protein identifications, with Mag-Net, ENRICHplus, EasySep, and EXONET yielding up to 4200 proteins per sample, over 7-fold more than neat plasma, using a 44 min 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 Mag-Net protocol by increasing the plasma-to-bead ratio and automated the workflow, including Evotip loading, on the Biomek i5 liquid handler. The automated Mag-Net, 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 Mag-Net but are likely to incur higher costs. The streamlined Mag-Net enrichment strategy enables affordable, scalable, high-throughput LC-MS plasma proteomics, supporting biomarker discovery across large cohorts.

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Publication Details

Journal
Journal of Proteome Research
Published
2025-12-21
DOI
https://doi.org/10.1021/acs.jproteome.5c00420
Citations
1
Primary Topic
Advanced Proteomics Techniques and Applications
Type
article
Field-Weighted Citation Impact
0.76
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article

Automated Mag-Net Enrichment Unlocks Deep and Cost-Effective LC–MS Plasma Proteomics

1 citations
Journal of Proteome Research
Advanced Proteomics Techniques and Applications
0.76
article

Automated Mag-Net Enrichment Unlocks Deep and Cost-Effective LC–MS Plasma Proteomics

article en
1 citations

Abstract

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, Mag-Net, ENRICHplus, ENRICHiST, EasySep, and EXONET, against neat plasma using LC-MS proteomics. All five methods substantially increased protein identifications, with Mag-Net, ENRICHplus, EasySep, and EXONET yielding up to 4200 proteins per sample, over 7-fold more than neat plasma, using a 44 min 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 Mag-Net protocol by increasing the plasma-to-bead ratio and automated the workflow, including Evotip loading, on the Biomek i5 liquid handler. The automated Mag-Net, 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 Mag-Net but are likely to incur higher costs. The streamlined Mag-Net enrichment strategy enables affordable, scalable, high-throughput LC-MS plasma proteomics, supporting biomarker discovery across large cohorts.

Journal of Proteome ResearchVol. 25(2)
University of Helsinki (FI), Czech Academy of Sciences, Institute of Biotechnology (CZ), HiLIFE – Elämäntieteiden Instituutti
Openalex Percentile: Top 23%
Advanced Proteomics Techniques and Applications
0.76
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Automated Mag-Net Enrichment Unlocks Deep and Cost-Effective LC–MS Plasma Proteomics · Journal of Proteome Research (2025) | TGRS Research Map | TGRS