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

Accurate quantification of circulating proteins is critical
for
assessing biological variation and integrating proteomics with other
omics to understand biological processes and disease mechanisms. Protein
measurements, however, can be substantially influenced by preanalytical
variability arising from differences in sample collection, handling,
and storage, whereas technical variation introduced by the assay and
workflow is typically well controlled through established validation
procedures. Identifying proteins that capture these systematic influences
enables their incorporation into downstream analyzes, thereby improving
statistical power. In this study, we applied highly multiplexed aptamer-based
affinity proteomics to plasma samples from three independent cohortsGerman,
Arab-Asian and Qatari to evaluate how adjusting for all measured proteins
influences protein quantitative trait loci (pQTLs) associations. Using
the p-gain statistic as an indicator of improved association strength,
we identified clusters of proteins whose covariation patterns suggested
potential preanalytical effects. One cluster contained HSP90 (Heat
Shock Protein 90), a marker linked to white blood cell lysis, while
others were enriched for proteins involved in complement and coagulation
cascades or platelet activation. Our work presents a data-driven framework
for detecting latent sources of variation in large-scale proteomic
data sets and lay the groundwork for future efforts to quantify the
impact of hidden confounding factors.