High-throughput plasma proteomics: A standardized and scalable workflow for quantitative protein profiling in large sample cohorts

Significance of the topic

Blood plasma proteomics allows the identification of protein biomarkers for diagnostics and disease monitoring. High variability in protein abundance and the need to analyze large sample cohorts demand a standardized, high-throughput workflow to ensure reproducible quantitative data and minimize technical variation.

Objectives and Study Overview

This work aimed to design and validate a scalable plasma and serum proteomics pipeline that balances depth of proteome coverage with the capacity to process hundreds of samples per day. Key goals included automating sample preparation, integrating robust QC standards, and deploying a rapid LC–MS system for translational studies.

Methodology and Instrumentation

• Sample Preparation: Automated digestion and cleanup on a Hamilton Microlab STARLet with a positive air pressure module ([MPE]²) using the EasyPep 96-well kit; peptide recovery assessed by fluorometric assays.
• Chromatography: Evosep One LC with an 8 cm C18 column at throughputs of 30, 60, or 100 samples/day using pre-formed gradients and a 21 min method for 60 samples/day.
• Mass Spectrometry: Q Exactive HF-X Orbitrap operating at 60 000 MS1 and 15 000 MS2 resolution, AGC targets of 3e6/2e4, dynamic exclusion, and data-dependent acquisition.
• Quality Control: Synthetic PRTC peptide mixture, depleted plasma pooled controls (healthy and lung cancer), and HeLa digest standard to monitor retention time, peak area, mass accuracy, and sensitivity.
• Data Analysis: Proteome Discoverer 2.3 for LFQ and identification (UniProt human database, 1% FDR); Skyline for QC metrics; Prism for statistical evaluation.

Main Results and Discussion

• Automation Precision: Robotic pipetting achieved 1.0% CV versus 2.4% manual for 50 µL; peptide recovery reached ~80.7% (CV 7.2%) compared to 77.9% (CV 10.7%) by spin columns.
• LC–MS Reproducibility: Retention time drift <1% CV; peak area variation <20% CV over 100 PRTC runs; mass accuracy ±3 ppm; carryover <1% with interleaved blanks.
• Sensitivity and Coverage: From 50 ng HeLa digest, 1 700 proteins and 7 500 peptides identified at 60 samples/day. Depleted plasma (500 ng) yielded >200 proteins per run.
• Robustness: Across 49 QC runs, protein identifications showed <5% CV. LFQ of pooled plasma had median CV <15%, with >80% of proteins exhibiting <20% CV.
• Cohort Application: Analysis of 4 healthy and 5 lung cancer patients (10 technical replicates each) gave high within-group correlation (R²>0.95). Hierarchical clustering and PCA distinguished healthy from cancer samples. Proteins such as SAA4 and SOD1 were elevated in lung cancer plasma.

Benefits and Practical Applications

• Processes hundreds of clinical samples per day with automated hands-free sample preparation.
• Reduces technical variability and manual labor.
• Integrated QC ensures consistent instrument performance and data quality.
• Compatible with common Orbitrap platforms and adaptable to quantitative labeling workflows.

Future Trends and Potential Applications

• Incorporation of isobaric labeling (TMT) to increase multiplexing and proteome depth.
• Adaptation to next-generation Orbitrap systems for enhanced sensitivity.
• Deployment in large-scale biomarker discovery and precision medicine studies.
• Development of standardized clinical proteomics assays for diagnostics.

Conclusion

The presented workflow offers a standardized, scalable, and robust solution for high-throughput plasma proteomics. Automated sample preparation, rapid LC–MS analysis, and comprehensive QC enable reproducible quantitative profiling, supporting biomarker discovery and translational research in large cohorts.

Reference

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3. Papa L. et al. SOD1, an unexpected novel target for cancer therapy. Genes Cancer. 2014 Jan;5(1-2):15-21.