High-throughput plasma proteomics for translational biomarker discovery using PreOmics sample preparation and the Orbitrap Astral mass spectrometer

Importance of the topic

Plasma proteomics is a cornerstone of translational biomarker discovery because blood plasma is minimally invasive to collect and carries a broad representation of systemic and tissue-derived biochemical signals. The principal analytical challenge is the extreme dynamic range of plasma proteins, which necessitates robust, reproducible, and high-sensitivity workflows to detect low-abundance species of clinical relevance. Advances in automated sample preparation and high-sensitivity mass spectrometry are therefore critical to expand proteome coverage, improve quantitation precision, and accelerate discovery in clinical cohorts.

Goals and overview of the study

This technical study evaluated three PreOmics sample preparation workflows—iST-BCT (neat plasma), ENRICH-iST (selective enrichment), and ENRICHplus (advanced enrichment)—in combination with the Thermo Scientific Vanquish Neo UHPLC system and the Orbitrap Astral mass spectrometer. Objectives were to compare analytical reproducibility, depth of proteome coverage, the benefit of gas-phase fractionation (GPF) for spectral library building, and the capacity to detect biologically meaningful differential signals in a small matched cohort comprising healthy donors and patients with Alzheimer’s disease, non-small cell lung cancer (NSCLC), and colorectal cancer.

Methodology

Biological material and sample handling

• K2EDTA human plasma sourced from BioIVT; single-spun plasma for healthy, lung cancer, and Alzheimer’s samples, double-spun for colorectal cancer where available.
• Workflow-specific input volumes: iST-BCT: 1.5 μL; ENRICH-iST: 20 μL; ENRICHplus: 50 μL.
• All digestion steps performed 3 hours in ThermoMixer C; peptides dried, resuspended, quantified and normalized to 100 ng/μL prior to LC-MS.

LC–MS configuration and acquisition strategy

• Vanquish Neo UHPLC in trap-and-elute configuration with EASY-Spray 2 μm C18 analytical column (150 μm × 15 cm) and PepMap Neo trap cartridge.
• Orbitrap Astral mass spectrometer used in data-independent acquisition (DIA) and Gas-Phase Fractionation (GPF) modes. MS1 full scans acquired at high resolution (240k) over segmented m/z ranges for GPF; DIA MS2 used narrow isolation layouts (standard DIA windows and finer 1 m/z windows for GPF-library generation).
• Chromatography and MS parameters optimized for fast, high-throughput runs (short gradients, trap-and-elute, tuned AGC and injection times to match Astral sensitivity).

Data processing

• Biognosys Spectronaut v19 (directDIA and chromatogram/GFP-assisted libraries) used for identification and quantification; human UniProt database (2024-07-29) without isoforms; 1% precursor and protein-group FDR filtering.
• Downstream statistics and visualization in R (RStudio).

Used instrumentation

• Thermo Scientific Vanquish Neo UHPLC system (trap-and-elute).
• Orbitrap Astral mass spectrometer.
• Thermo Scientific EASY-Spray HPLC column (2 μm C18, 150 μm × 15 cm) and PepMap Neo trap cartridge.
• PreOmics iST-BCT, ENRICH-iST and ENRICHplus sample preparation kits (96x formats).
• ThermoMixer C plate shaker, Savant SpeedVac concentrator, Pierce Quantitative Fluorometric Peptide Assay.

Main results and discussion

Proteome depth and reproducibility

• Identified protein groups (average across triplicate technical injections of a pooled healthy sample): iST-BCT: ~1,413; ENRICH-iST: ~2,972; ENRICHplus: ~4,438 — showing stepwise expansion of detectable proteome with enrichment strategies.
• Peptide-level CVs: iST-BCT 12.0%, ENRICH-iST 13.6%, ENRICHplus 17.4%. Protein-group CVs: 9.5% (iST-BCT, ENRICH-iST) and 10.5% (ENRICHplus), indicating overall high quantitative precision compatible with comparative studies.

Dynamic range and enrichment effects

• ENRICH workflows increased detection of low-to-mid abundance proteins relative to neat plasma, shifting identifications toward lower-abundance regions of the dynamic range and revealing complementary subsets of proteins unique to each workflow.
• ENRICH-iST showed a balance between depth gain and preservation of quantitative concordance with neat plasma; ENRICHplus provided the deepest coverage but introduced larger shifts in some fold-change estimates compared with iST-BCT.

GPF-assisted spectral libraries

• Using GPF to build sample-specific chromatogram libraries increased the number of peptides in libraries by ~11–21% and protein groups by ~15–34% depending on preparation type, with the largest relative gains for iST-BCT.
• Implementing combined directDIA + GPF libraries in sample searches yielded 3.7–16.5% more peptide identifications and 4.4–17.5% more protein-group identifications, enhancing proteome depth across sample types.

Differential biological signal (NSCLC vs healthy)

• Number of differentially abundant proteins (Benjamini–Hochberg adjusted): iST-BCT 157; ENRICH-iST 355; ENRICHplus 548 — enrichment increased the number of candidate biomarkers detected.
• Overlap analysis: 24 differential candidates common to all three workflows; larger pairwise overlaps were observed (e.g., ENRICH-iST vs ENRICHplus had 118 shared candidates).
• Log2 fold-change correlations for shared candidates: ENRICH-iST vs iST-BCT = 0.96 (very high concordance); ENRICHplus vs iST-BCT ≈ 0.71 and ENRICHplus vs ENRICH-iST ≈ 0.72, indicating ENRICH-iST preserves biological signal closest to neat plasma while ENRICHplus expands detection at the cost of some quantitative divergence.
• Pathway enrichment (GO, KEGG, Reactome) yielded similar classes of altered pathways across workflows, supporting biological validity of enrichment approaches.

Benefits and practical applications

• Automatable, scalable sample preparation compatible with high-throughput cohort studies; fractionation-free workflows reduce time and complexity.
• ENRICH workflows (especially ENRICHplus) substantially increase the detectable plasma proteome and candidate biomarker yield, improving sensitivity for low-abundance tissue- and signaling-derived proteins.
• High quantitative precision at the protein-group level supports comparative and differential analyses required in translational research.
• GPF-assisted library generation is a pragmatic means to boost DIA identifications without extensive offline fractionation.

Future trends and potential uses

• Scaling to larger and longitudinal clinical cohorts to validate biomarker candidates and assess pre-analytical variability in diverse populations.
• Refinement of enrichment chemistries and workflows to maximize low-abundance capture while minimizing bias in quantitative ratios.
• Integration of AI-driven spectral prediction and hybrid library approaches to further populate libraries and improve peptide detectability in DIA data.
• Combination with orthogonal strategies (targeted MS, immunoassays) for verification and clinical translation of candidates identified by deep discovery workflows.
• Application in precision medicine pipelines for disease stratification, early detection, and therapeutic monitoring where plasma-accessible biomarkers are desirable.

Conclusion

The study demonstrates that PreOmics iST-BCT, ENRICH-iST, and ENRICHplus workflows coupled to Vanquish Neo UHPLC and the Orbitrap Astral MS provide a robust, automatable, and scalable platform for deep plasma proteomics. ENRICH strategies markedly expand proteome coverage and increase the number of differential candidates detected in disease vs healthy comparisons, while ENRICH-iST retains quantitative concordance with neat plasma. GPF-assisted spectral libraries further improve identification rates. Together, these elements form an end-to-end workflow suitable for translational biomarker discovery with high analytical precision and enhanced sensitivity for low-abundance plasma proteins.

Reference

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