Shraim Rawan, Diorio Caroline, Canna Scott W, Macdonald-Dunlop Erin, Bassiri Hamid, Martinez Zachary, Mälarstig Anders, Abbaspour Afrouz, Teachey David T, Lindell Robert B, Behrens Edward M
Division of Oncology, Department of Pediatrics, Children's Hospital of Philadelphia, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA; Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA.
Division of Oncology, Department of Pediatrics, Children's Hospital of Philadelphia, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA.
Mol Cell Proteomics. 2025 May 27;24(7):101000. doi: 10.1016/j.mcpro.2025.101000.
Accurate measurement of secreted proteins in serum and plasma is essential for understanding mechanisms and developing reliable biomarkers. Recent technological advancements, such as proximity extension assay (PEA), have enabled high-throughput multiplex protein analyses from small sample volumes in either serum or plasma. Despite the increasing use of PEA-based proteomics and the generation of extensive datasets, integrated data from these two mediums remains challenging due to inherent differences in sample processing. To address this issue, we developed and validated protein-specific transformation factors using linear modeling to normalize protein measurements between serum and plasma proteins quantified using Olink. Our analysis surveyed 1463 proteins across matched serum and plasma samples, identifying 686 transformation factors. The transformation factors were further validated using independent datasets generated from patients with different disease phenotypes and ages, and 551 of the models and transformation factors were reproducible. These transformation factors provide a valuable resource for normalizing PEA-based proteomic data across serum and plasma, ultimately enhancing the capacity for collaborative analyses and facilitating comprehensive insights across diverse disease phenotypes.
准确测量血清和血浆中的分泌蛋白对于理解机制和开发可靠的生物标志物至关重要。最近的技术进步,如邻位延伸分析(PEA),使得能够从小体积的血清或血浆样本中进行高通量多重蛋白质分析。尽管基于PEA的蛋白质组学的使用越来越多,并且产生了大量数据集,但由于样本处理的固有差异,整合来自这两种介质的数据仍然具有挑战性。为了解决这个问题,我们使用线性建模开发并验证了蛋白质特异性转化因子,以对使用Olink定量的血清和血浆蛋白质之间的蛋白质测量进行标准化。我们的分析调查了匹配的血清和血浆样本中的1463种蛋白质,确定了686种转化因子。使用来自不同疾病表型和年龄患者的独立数据集进一步验证了转化因子,其中551个模型和转化因子是可重复的。这些转化因子为跨血清和血浆标准化基于PEA的蛋白质组学数据提供了宝贵资源,最终增强了协作分析的能力,并促进了对不同疾病表型的全面洞察。