Advancing plasma proteomics: Quantitative and high-throughput EV analysis with automated Mag-Net workflow, Evosep One, and Orbitrap Astral mass spectrometer

Significance of the topic

Plasma proteomics is central to translational research because blood plasma is an accessible matrix that reflects systemic physiology and pathology. However, the plasma proteome spans a very wide dynamic range and contains abundant interfering species, which limit detection of low-abundance biomarkers. Enriching extracellular vesicles (EVs) selectively concentrates membrane- and vesicle-associated proteins, improving the ability to detect biologically and clinically relevant signatures while keeping sample collection minimally invasive. Automating EV enrichment and downstream sample handling is essential to scale studies for large cohorts with reproducible quantitative performance.

Goals and overview of the study

• Demonstrate an automated, end-to-end workflow that combines Mag-Net EV enrichment with automated Evotip peptide loading, Evosep One separations, and Orbitrap Astral mass spectrometry.
• Evaluate whether this integrated solution increases throughput, sensitivity, dynamic range, and reproducibility for plasma EV proteomics compared with a neat plasma workflow.
• Quantitatively benchmark performance using matrix-matched calibration curves (human:chicken plasma mixtures) across multiple throughput methods (60 SPD and Whisper Zoom 40 SPD) and replicates.

Used instrumentation

• Mag-Net enrichment: MagReSyn magnetic beads (SAX or Hydroxyl variants) for EV/membrane particle capture.
• Liquid handling and automation: OT-2 (Opentrons) platform for Evotip loading and on-deck incubations.
• Chromatography: Evosep One system using Whisper Zoom 40 SPD and 60 SPD methods; Aurora Elite XT analytical column (IonOpticks).
• Mass spectrometry: Thermo Scientific Orbitrap Astral mass spectrometer. Key settings used: spray voltage ~1,900 V; heated capillary ~275 °C; full MS resolution 240,000; scan range 380–980 m/z; full MS AGC 500%; MS/MS isolation 3 Th; maximum ion injection 7 ms; HCD fragmentation at 25% normalized collision energy.
• Data analysis: DIA-NN (v1.9.2) in library-free mode against the reviewed human proteome (Uniprot, Oct 2020), Trypsin/P cleavage rules, up to two missed cleavages, and match-between-runs restricted to replicates within each condition.

Methodology

• Mag-Net EV enrichment (miniaturized & automated): Typical protocol used 4 µL plasma + 4 µL bind buffer (100 mM Bis-Tris Propane, 150 mM NaCl, pH 6.5) + 1 µL MagReSyn SAX beads, diluted with 32 µL wash buffer; performed three sequential 12-minute binding steps and three washes. Proteins were solubilized/reduced/alkylated in a one-pot buffer (1% SDS, 10 mM TCEP, 5 mM CAA in 50 mM Tris-HCl pH 8.5) during a 1 h on-deck incubation. On-bead aggregation was induced with acetonitrile; after a single wash, digestion proceeded for 4 h with LysC and Trypsin (example loads: 75 ng LysC + 300 ng Trypsin). Forty percent of each digest was directly loaded onto Evotips using an OT-2 robot.
• Neat plasma workflow: 1 µL plasma diluted ~180x in buffer (1% SDS, 5 mM TCEP, 10 mM CAA in 50 mM TEAB), ~1.8 µg protein added to 5 µL MagReSyn Hydroxyl beads; digestion and downstream steps performed analogous to Mag-Net but without EV enrichment.
• Calibration/quantitative assessment: Matrix-matched dilutions combining pooled human and chicken plasma at eight ratios (100:0, 70:30, 50:50, 30:70, 10:90, 5:95, 1:99, 0:100) with five replicates per ratio; evaluated empirical vs theoretical log2 ratios to measure accuracy and precision.
• LC-MS acquisition: Evosep One running 60 SPD and Whisper Zoom 40 SPD protocols to assess throughput vs depth trade-offs.
• Typical sample preparation timeline (indicative): capture/wash steps, 1 h solubilization, aggregation ~20 min, wash ~10 min, 4 h digestion, ~1 h Evotip loading.

Main results and discussion

• Proteome depth: The neat plasma workflow (60 SPD) produced >950 protein groups and >7,000 precursors for 100% human plasma, dropping to ~750 protein groups at 1% human content. The automated Mag-Net EV enrichment substantially increased depth: for 100% human plasma, Mag-Net yielded >4,000 protein groups and ~27,000 precursors with 60 SPD, and up to ~5,000 protein groups and ~35,000 precursors using the higher-sensitivity Whisper Zoom 40 SPD method.
• Quantitative precision: Across matrix-matched dilutions and replicate injections, quantitative precision at the protein-group level had median coefficients of variation (CVs) below 16% for the 60 SPD method and below 15% for Whisper Zoom 40 SPD, indicating robust reproducibility suitable for comparative studies.
• Accuracy and dynamic range: The combined workflow (Mag-Net + Orbitrap Astral) demonstrated accurate recovery of expected log2 fold-changes across a broad dynamic range (human:chicken ratios), showing that EV enrichment did not compromise quantitative fidelity and improved detection of low-abundance EV proteins compared with neat plasma.
• Throughput vs identifications: The dataset illustrates that the Orbitrap Astral MS retains high identification performance even at elevated throughputs (e.g., 60 SPD and 100 SPD ranges), with Whisper Zoom 40 SPD delivering the deepest coverage but 60 SPD offering a strong balance of throughput and depth.
• Automation benefits: Miniaturization and robotic handling (OT-2) reduced manual variability, streamlined workflows (on-bead digestion and direct Evotip loading), and increased sample throughput without evident sacrifice of sensitivity or quantitative accuracy.

Benefits and practical applications of the method

• Deep EV proteome coverage from small plasma volumes (µL-scale input), enabling studies where sample amount is limiting (e.g., clinical cohorts, longitudinal sampling).
• High throughput compatible: Evosep One methods and automated Evotip loading support large-scale studies while preserving identification depth when paired with a high-performance instrument (Orbitrap Astral).
• Improved sensitivity and dynamic range: Enrichment of EVs concentrates low-abundance, vesicle-associated proteins that are often masked in whole-plasma analyses.
• Reproducibility and reduced hands-on time: Robotic automation and on-bead digestion reduce operator-dependent variability and simplify sample handling workflows suitable for multi-site studies or clinical translational projects.
• Versatility: The workflow allows users to choose priorities (maximize identifications vs maximize throughput) by selecting Evosep methods while maintaining reliable quantitation.

Future trends and possibilities for application

• Broader clinical adoption: Continued automation and standardization could enable EV-based proteomic assays in large multi-center biomarker discovery and validation studies, and eventually targeted clinical assays.
• Integration with multi-omics: Combining EV proteomics with EV-derived nucleic acids and lipidomics will provide richer, orthogonal biomarkers and mechanistic insight into disease processes.
• Further miniaturization and cost reduction: Advances in bead chemistry, reagent use, and robotic platforms may further reduce input and per-sample cost, increasing accessibility for population-scale studies.
• Targeted follow-up workflows: Deep discovery datasets enabled by this platform can guide development of targeted PRM/SRM or immunoassay panels focused on validated EV markers.
• Method harmonization: Community adoption of matrix-matched calibration strategies and automated EV enrichment protocols will improve cross-study comparability and accelerate translation.

Conclusion

The automated Mag-Net EV enrichment coupled with Evosep One separations and Orbitrap Astral mass spectrometry provides a scalable, sensitive, and reproducible solution for plasma EV proteomics. The integrated workflow delivers substantially deeper proteome coverage than neat plasma workflows while maintaining quantitative accuracy across a broad dynamic range and at high throughputs. Automation reduces variability and hands-on time, making this approach well suited for large-scale translational studies aimed at biomarker discovery and mechanistic investigation.

References

1. Anderson NL, Anderson NG. The human plasma proteome: history, character, and diagnostic prospects. Mol Cell Proteomics. 2002 Nov;1(11):845–67.
2. Xu R, Greening DW, Zhu HJ, Takahashi N, Simpson RJ. Extracellular vesicle isolation and characterization: toward clinical application. J Clin Invest. 2016 Apr 1;126(4):1152–62.
3. Wu CC, Tsantilas KA, Park J, Plubell D, Naicker P, Govender I, et al. Mag-Net: Rapid enrichment of membrane-bound particles enables high coverage quantitative analysis of the plasma proteome. bioRxiv. 2024 Apr 2. Preprint:2023.06.10.544439.
4. Pino LK, Searle BC, Yang HY, Hoofnagle AN, Noble WS, MacCoss MJ. Matrix-matched calibration curves for assessing analytical figures of merit in quantitative proteomics. J Proteome Res. 2020;19(3):1147–1153.