Optimizing LC-MS/MS settings for plasma proteomics analysis with cap-flow LC separation and dia-PASEF
Posters | 2025 | Bruker | ASMSInstrumentation
Blood plasma provides a minimally invasive biopsy matrix rich in proteomic information but challenges arise from its high dynamic range and complexity.
High-throughput workflows capable of deep coverage are essential for large-cohort clinical studies.
This study aimed to optimize capillary-flow LC-MS/MS settings using dia-PASEF acquisition for rapid and reproducible plasma proteomics.
Researchers compared neat and bead-based enriched plasma digests, targeting throughput up to 220 samples per day while maintaining depth and quantitative fidelity.
Key procedural steps involved plasma collection, centrifugation (single and double-spun EDTA plasma), enzymatic digestion, and peptide cleanup.
Two workflows were assessed: neat plasma digests via iST-BCT and enriched digests via Enrich-iST kits.
Optimized capillary-flow LC-MS/MS with dia-PASEF balances speed, depth, and reproducibility, supporting high-throughput plasma proteomics for clinical research.
1 Nolte H MacVicar TD Tellkamp F et al Instant Clue: A Software Suite for Interactive Data Visualization and Analysis Sci Rep 8 12648 2018 DOI 10.1038/s41598-018-31154-6
HPLC
IndustriesProteomics
ManufacturerBruker
Summary
Importance of the Topic
Blood plasma provides a minimally invasive biopsy matrix rich in proteomic information but challenges arise from its high dynamic range and complexity.
High-throughput workflows capable of deep coverage are essential for large-cohort clinical studies.
Study Objectives and Overview
This study aimed to optimize capillary-flow LC-MS/MS settings using dia-PASEF acquisition for rapid and reproducible plasma proteomics.
Researchers compared neat and bead-based enriched plasma digests, targeting throughput up to 220 samples per day while maintaining depth and quantitative fidelity.
Used Methodology
Key procedural steps involved plasma collection, centrifugation (single and double-spun EDTA plasma), enzymatic digestion, and peptide cleanup.
Two workflows were assessed: neat plasma digests via iST-BCT and enriched digests via Enrich-iST kits.
Used Instrumentation
- Waters M-Class HPLC system with novel driver for HyStar control
- Capillary-flow chromatography on Bruker Max RP-C18 column (10 cm×150 µm, 1.5 µm) at 60 °C with a Bruker column toaster
- TimsTOF HT mass spectrometer employing optimized dia-PASEF acquisition
Main Results and Discussion
- Cap-LC at 120 SPD (10 min gradient) and 220 SPD (6 min gradient) achieved narrow peaks and high retention time reproducibility
- Neat plasma runs yielded 450–550 protein groups; enriched plasma achieved 670–1150 groups per run
- Cell lysate analysis reported over 6000 protein groups at 120 SPD
- Low carry-over observed even after multiple injections, ensuring data reliability
- Inter-run overlap was ~80% for neat and ~85% for enriched plasma across single and double-spun samples
- High-abundance proteins demonstrated consistent quantitation across 20 donors
Benefits and Practical Applications
- Turnaround times exceeding 100–200 samples/day enable large-scale clinical proteomics
- Reduced overhead for column washing, equilibration, and sample loading improves lab efficiency
- Short acquisition cycles (<0.5 s) yield robust quantitative data for narrow chromatographic peaks
- Bead-based enrichment enhances proteome depth for biomarker discovery
Future Trends and Possibilities
- Integration of faster LC systems and advanced ion mobility for higher throughput
- Multiplexing and automation to support large cohort real-time analysis
- Extension to clinical diagnostics and personalized medicine applications
- Advancements in AI-driven data processing for automated quality control
Conclusion
Optimized capillary-flow LC-MS/MS with dia-PASEF balances speed, depth, and reproducibility, supporting high-throughput plasma proteomics for clinical research.
References
1 Nolte H MacVicar TD Tellkamp F et al Instant Clue: A Software Suite for Interactive Data Visualization and Analysis Sci Rep 8 12648 2018 DOI 10.1038/s41598-018-31154-6
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