Plasma-Derived Extracellular Vesicle Phosphoproteomics through Chemical Affinity Purification
Autor: | W. Andy Tao, Marco Hadisurya, Jie Sun, Anton Iliuk, Li Li, Ronald S. Boris, Xiaofeng Wu |
---|---|
Rok vydání: | 2020 |
Předmět: |
Phosphopeptides
0301 basic medicine 030102 biochemistry & molecular biology Chemistry Phosphoproteomics Cancer General Chemistry Extracellular vesicle Phosphoproteins medicine.disease Proteomics Biochemistry Article Mass Spectrometry Microvesicles Extracellular Vesicles 03 medical and health sciences 030104 developmental biology Renal cell carcinoma Chemical affinity medicine Humans Ultracentrifugation Kidney cancer |
Zdroj: | J Proteome Res |
ISSN: | 1535-3907 1535-3893 |
DOI: | 10.1021/acs.jproteome.0c00151 |
Popis: | 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. |
Databáze: | OpenAIRE |
Externí odkaz: |