A MULTI-OMICS APPROACH TO INVESTIGATE THE PLASMA PROTEOME AND DETERMINE THE MECHANISITIC PROCESSES INVOLVED IN DIFFERENT RESPIRATORY DISEASE CONDITIONS

Importance of the Topic

Chronic respiratory diseases such as COPD and asthma pose a significant public health burden and lack comprehensive biomarker panels for early diagnosis and mechanistic insight. A multi‐omics strategy that integrates plasma metabolomics, lipidomics and proteomics can reveal systemic alterations, support disease stratification and accelerate the discovery of molecular pathways involved in respiratory pathology.

Objectives and Study Overview

This study aimed to apply a coordinated LC–MS workflow to analyze plasma from COPD patients, asthmatic individuals and healthy controls. By using the same liquid chromatography configuration, researchers sought to compare polar metabolites, lipids and proteins, identify statistically significant molecular features, and map them to relevant biological pathways.

Methodology and Instrumentation

A unified Acquity I Class UHPLC system was used for all three omics streams with tailored columns and gradients:

• Metabolomics: BEH amide column, HILIC gradient (5–50% aqueous), 10 min run.
• Lipidomics: CSH C18 column, IPA/MeCN gradient, 20 min run.
• Proteomics: CSH130 C18 column, MeCN/formic acid gradient, 15/30/45 min runs at 1 mm scale.

Sample preparation included protein precipitation for metabolites, lipid extraction with IPA and tryptic digestion of plasma spiked with Biognosys PQ500 SIL peptides for proteomics.

Mass spectrometry was performed on a Xevo G2-XS QToF with SONAR data-independent acquisition. Key parameters:

• Quadrupole window: 1–50 Da segments over predefined m/z ranges.
• Alternate low/high collision energy scans to capture precursors and fragments.
• Scan times: 0.1–0.5 s; collision energies optimized per omics stream.

Data processing employed Progenesis QI (metabolites/lipids), Spectronaut Pulsar X (proteomics), EZInfo for PCA and Metacore for pathway mapping.

Main Findings and Discussion

PCA models for metabolites, lipids and peptides consistently separated COPD, asthma and control groups, with quality control samples clustering tightly (CVs < 8%). Volcano and heatmap analyses identified numerous fold-change significant features across all omics layers. Notable observations:

• Polar metabolites and lipid species revealed distinct signatures associated with airway inflammation and oxidative stress.
• Proteomics quantified over 150 heavy/light peptide pairs using stable isotope standards, demonstrating robust quantitation at 10 µg load.
• Pathway analysis highlighted lipoprotein metabolism and inflammatory signalling pathways as dysregulated in both COPD and asthma.

Benefits and Practical Applications

The integrated approach offers:

• A streamlined LC setup for cross‐omics consistency and reduced instrument overhead.
• Robust quantitative performance using DIA and SIL peptides, suitable for large‐scale studies.
• Comprehensive pathway mapping to guide biomarker validation and therapeutic targeting in respiratory diseases.

Future Trends and Applications

Continued development may include higher‐throughput DIA methods, expanded targeted panels for clinical validation, and integration with genomics or transcriptomics. Advances in microflow proteomics and automated data processing will further enable routine deployment in translational research and precision medicine.

Conclusion

This multi‐omics plasma profiling framework successfully discriminated disease states, provided mechanistic insights and established a reproducible platform for comprehensive molecular phenotyping in respiratory conditions.

References

1. Richardson et al, ASMS 2015
2. Moseley et al, J. Proteome Res., 2018, 17(2), 770–779
3. Juvvadi et al, J. Proteome Res., 2018, 17(2), 780–793