蛋白质组学
国家(计算机科学)
计算生物学
人类血液
数据科学
计算机科学
生物
生理学
遗传学
基因
算法
作者
Zhenyu Sun,T. Mamie Lih,Trung Hoàng,Shao‐Yung Chen,Jin Xu,Donghai Lin,Yuefan Wang,Jongmin Jacob Woo,Yuanyu Huang,Lijun Chen,Hongyi Liu,M. Alpern,Jadranka Milošević,Hong‐Zhang He,Raghothama Chaerkady,Qing Wang,Hui Zhang
标识
DOI:10.1101/2025.05.15.654311
摘要
Blood is a valuable resource for clinical research, offering insight into physiological and pathological states. However, the specific proteins detectable in blood and the optimal proteomic methods for their detection have not been rigorously investigated and documented. To address this, we conducted various blood proteomic strategies, including directly blood proteomic analysis, high-abundance protein depletion, low-abundance protein enrichment, and extracellular vesicle enrichment using data-independent acquisition or targeted proteomics. These approaches identified 11,679 protein groups in plasma from healthy individuals. In 136 pancreatic ductal adenocarcinoma whole blood samples, 6,956 protein groups were found, including 678 not seen in healthy samples, expanding the total to 12,357 blood proteins. This represents the most comprehensive blood proteome to date. To support broader access and analysis, we developed the Human Blood Proteome (HuBP) database, detailing protein detectability, abundance, and reproducibility across workflows, sample types, and disease contexts.
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