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.精准定量循环蛋白，对于评估生物学变异、整合蛋白质组学与其他组学以解析生物学过程及疾病机制至关重要。然而，蛋白质定量结果极易受样本采集、处理与储存差异引发的分析前变异影响；而由检测实验及实验流程引入的技术变异，通常可通过成熟的验证流程得到良好管控。识别可表征这些系统影响的蛋白，可将其纳入下游分析，从而提升统计效力。本研究针对来自德国、阿拉伯-亚裔及卡塔尔三个独立队列的血浆样本，采用高多重适配体亲和蛋白质组学技术，探究对所有已定量蛋白进行校正后，对蛋白质数量性状位点（protein quantitative trait loci, pQTLs）关联分析的影响。本研究以p增益统计量（p-gain statistic）作为关联强度提升的评估指标，鉴定出若干蛋白簇，其共变异模式提示存在潜在的分析前效应。其中一个蛋白簇包含热休克蛋白90（HSP90，Heat Shock Protein 90）——一种与白细胞裂解相关的标志物，其余蛋白簇则富集了参与补体与凝血级联反应或血小板活化的蛋白。本研究提出了一种可用于大规模蛋白质组数据集潜在变异来源检测的数据驱动框架，为未来量化隐性混杂因素的影响奠定了基础。