Plasma-Derived Extracellular Vesicle Phosphoproteomics through Chemical Affinity Purification

磷酸蛋白质组学 液体活检 细胞外小泡 胞外囊泡 纳米粒子跟踪分析 微泡 蛋白质组学 磷酸化 化学 血液蛋白质类 计算生物学 生物 生物化学 细胞生物学 蛋白质磷酸化 癌症 小RNA 蛋白激酶A 基因 遗传学
作者
Anton Iliuk,Xiaofeng Wu,Li Li,Jie Sun,Marco Hadisurya,Ronald S. Boris,W. Andy Tao
出处
期刊:Journal of Proteome Research [American Chemical Society]
卷期号:19 (7): 2563-2574 被引量:85
标识
DOI:10.1021/acs.jproteome.0c00151
摘要

The invasive nature and the pain caused to patients inhibit the routine use of tissue biopsy-based procedures for cancer diagnosis and surveillance. The analysis of extracellular vesicles (EVs) from biofluids has recently gained significant traction in the liquid biopsy field. EVs offer an essential "snapshot" of their precursor cells in real time and contain an information-rich collection of nucleic acids, proteins, lipids, and so on. The analysis of protein phosphorylation, as a direct marker of cellular signaling and disease progression could be an important stepping stone to successful liquid biopsy applications. Here we introduce a rapid EV isolation method based on chemical affinity called EVtrap (extracellular vesicle total recovery and purification) for the EV phosphoproteomics analysis of human plasma. By incorporating EVtrap with high-performance mass spectrometry (MS), we were able to identify over 16 000 unique peptides representing 2238 unique EV proteins from just 5 μL of plasma sample, including most known EV markers, with substantially higher recovery levels compared with ultracentrifugation. Most importantly, more than 5500 unique phosphopeptides representing almost 1600 phosphoproteins in EVs were identified using only 1 mL of plasma. Finally, we carried out a quantitative EV phosphoproteomics analysis of plasma samples from patients diagnosed with chronic kidney disease or kidney cancer, identifying dozens of phosphoproteins capable of distinguishing disease states from healthy controls. The study demonstrates the potential feasibility of our robust analytical pipeline for cancer signaling monitoring by tracking plasma EV phosphorylation.
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