Identification of Plasma Biomarkers from Rheumatoid Arthritis Patients Using an Optimized Sequential Window Acquisition of All THeoretical Mass Spectra (SWATH) Proteomics Workflow

Abstract

Rheumatoid arthritis (RA) is a systemic autoimmune and inflammatory disease. Plasma biomarkers are critical for understanding disease mechanisms, treatment effects, and diagnosis. Mass spectrometry-based proteomics is a powerful tool for unbiased biomarker discovery. However, plasma proteomics is significantly hampered by signal interference from high-abundance proteins, low overall protein coverage, and high levels of missing data from data-dependent acquisition (DDA). To achieve quantitative proteomics analysis for plasma samples with a balance of throughput, performance, and cost, we developed a workflow incorporating plate-based high abundance protein depletion and sample preparation, comprehensive peptide spectral library building, and data-independent acquisition (DIA) SWATH mass spectrometry-based methodology. In this study, we analyzed plasma samples from both RA patients and healthy donors. The results showed that the new workflow performance exceeded that of the current state-of-the-art depletion-based plasma proteomic platforms in terms of both data quality and proteome coverage. Proteins from biological processes related to the activation of systemic inflammation, suppression of platelet function, and loss of muscle mass were enriched and differentially expressed in RA. Some plasma proteins, particularly acute-phase reactant proteins, showed great power to distinguish between RA patients and healthy donors. Moreover, protein isoforms in the plasma were also analyzed, providing even deeper proteome coverage. This workflow can serve as a basis for further application in discovering plasma biomarkers of other diseases.

Keywords: DIA; SWATH; biomarker; plasma; protein isoform; proteomics; rheumatoid arthritis.

Figure 1

Schematic of the optimized SWATH proteomics workflow. (A) DDA spectral library construction procedure from pooled plasma samples. (B) plasma sample preparation and DIA proteomics procedure using DDA spectral library from (A).

Figure 2

Proteomics analysis in RA and healthy groups. (A) principal component analysis (PCA) of plasma protein expression pattern from RA and healthy samples. (B) volcano plot highlighting DEPs comparing RA against healthy samples. The horizontal red dashed line indicates p-value = 0.05, and the vertical red dashed line indicates a 1.5-fold change in protein abundance.

Figure 3

Enriched GO biological processes ORA in DEPs. (A) top 15 GO biological processes by in DEPs increasing in abundance ranked by false discovery rate (FDR). (B) top 15 GO biological processes in DEPs decreasing in abundance ranked by FDR.

Figure 4

Biomarker discovery by random forest. (A) top 10 proteins with highest feature importance in determining model accuracy. (B) ROC plots of proteins with AUC greater than 0.8 in distinguishing RA from healthy samples.