Dataset Information

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

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, 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.

INSTRUMENT(S): Orbitrap Astral, timsTOF Pro 2

ORGANISM(S): Homo Sapiens (ncbitaxon:9606)

SUBMITTER: Salla Keskitalo

PROVIDER: MSV000097796 | MassIVE | Mon May 05 06:10:00 BST 2025

REPOSITORIES: MassIVE

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Publications

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

Järvinen Erkka E Liu Xiaonan X Varjosalo Markku M Keskitalo Salla S

Journal of proteome research 20251221 2

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 substantial ...[more]

PMID: 41422412

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Project description:Plasma proteomics holds immense potential for clinical research and biomarker discovery, serving as a non-invasive "liquid biopsy" for tissue sampling. Mass spectrometry (MS)-based proteomics, thanks to improvement in speed and robustness, emerges as an ideal technology for exploring the plasma proteome for its unbiased and highly specific protein identification and quantification. Despite its potential, plasma proteomics is still a challenge due to the vast dynamic range of protein abundance, hindering the detection of less abundant proteins. Different approaches can help overcome this challenge. Conventional depletion methods face limitations in cost, throughput, accuracy, and off-target depletion. Nanoparticle-based enrichment shows promise in compressing dynamic range, but cost remains a constraint. Enrichment strategies for extracellular vesicles (EVs) can enhance plasma proteome coverage dramatically, but current methods are still too laborious for large series. Neat plasma remains popular for its cost-effectiveness, time efficiency, and low volume requirement. We used a test set of 33 plasma samples for all evaluations. Samples were digested using Strap and analysed on Evosep and nanoElute coupled to a timsTOF Pro using different elution gradients and ion mobility ranges. Data were mainly analyzed using library-free searched using DIANN. This study explores ways to improve proteome coverage in neat plasma both in MS data acquisition and MS data analysis. We demonstrate the value of sampling smaller hydrophilic peptides, increasing chromatographic separation, and using library-free searches. Additionally, we introduce the EV boost approach, that leverages on the extracellular vesicle fraction to enhances protein identification in neat plasma samples. Globally, out optimized analysis workflow allows the quantification of over 1000 proteins in neat plasma with a 24SPD throughput. We believe that these considerations can be of help independently of the LC-MS platform used.