Identification of Protein Signatures Reflecting Latent Variation in Aptamer-Based Affinity Proteomics

Area of Science:

• Proteomics
• Systems Biology
• Biomarker Discovery

Background:

• Accurate circulating protein quantification is vital for understanding biological variation and disease mechanisms.
• Preanalytical variability significantly impacts protein measurements, unlike well-controlled technical variations.
• Identifying proteins affected by preanalytical factors can enhance downstream analyses and statistical power.

Purpose of the Study:

• To develop a data-driven framework for detecting latent sources of variation in large-scale proteomic datasets.
• To evaluate the influence of adjusting for all measured proteins on protein quantitative trait loci (pQTL) associations.
• To identify specific proteins that may serve as indicators of preanalytical effects.

Main Methods:

• Application of highly multiplexed aptamer-based affinity proteomics.
• Analysis of plasma samples from three independent cohorts (German, Arab-Asian, Qatari).
• Utilizing the p-gain statistic to assess improvements in association strength after adjustment.

Main Results:

• Identified clusters of proteins whose covariation patterns suggested potential preanalytical effects.
• One cluster contained Heat Shock Protein 90 (HSP90), a marker for white blood cell lysis.
• Other identified clusters were enriched for proteins involved in complement, coagulation, and platelet activation.

Conclusions:

• The study presents a novel framework for detecting hidden confounding factors in large-scale proteomic data.
• This approach can improve the reliability of proteomics data by accounting for preanalytical variability.
• Lays the groundwork for quantifying the impact of unmeasured confounding variables in future proteomic studies.