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A One-pot Analytical Pipeline for Efficient and Sensitive Proteomic Analysis of Extracellular Vesicles

ABSTRACT: Detection of extracellular vesicles (EVs) cargoes from biofluids has drawn more attention in the field of translational and clinical medicine. EV proteomics is particularly a promising tool in discovering potential biomarkers for disease diagnosis, monitoring, and therapeutics. However, the current workflow of mass spectrometry-based EV proteome analysis is not well compatible with clinical settings due to inefficient EV isolation methods and tedious sample preparation processes. To further implement highly efficient EV proteome analysis, this study established a one-pot analytical pipeline based on a robust EV isolation approach, EV total recovery and purification (EVtrap), to detect urinary EV proteome in a rapid and sensitive manner. By incorporating solvent-driven protein capture and on-bead digestion, the one-pot EV proteomics pipeline avoided any sample transfer step and largely reduced the complexity of the peptide preparation process for bottom-up proteomic analysis. Moreover, a shorter digestion time was practicable in this novel pipeline, which enables a whole EV proteome analysis to be completed within one day. In comparison with the more time-consuming conventional workflow, the one-pot pipeline was able to obtain a higher peptide yield and identify similar numbers of unique EV proteins in 1 mL of urine. Finally, we applied the one-pot pipeline to monitor potential biomarkers in urinary EVs of bladder cancer patients. A total of 2,774 proteins and ~20,000 peptides were identified in 53 urine samples using a 15-min gradient direct data-independent acquisition (directDIA). Several known bladder cancer-associated protein markers were successfully identified and showed significantly higher abundance in patient-derived EVs. Taken altogether, our novel one-pot analytical pipeline demonstrated its potential for routine and robust EV proteomics in biomedical applications.

ORGANISM(S): Homo Sapiens (human)

SUBMITTER: W. Andy Tao

PROVIDER: PXD041118 | JPOST Repository | Mon Jul 24 00:00:00 BST 2023

REPOSITORIES: jPOST

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Project description:Objectives: Secretion of extracellular vesicles (EV) and associated micro-RNAs (miR) is altered during cellular stress and may serve as biomarkers of organ injury. We hypothesized that measuring changes in urinary levels of EV and miR will predict the onset of acute kidney injury in cardiac surgery patients. Design: Predictive accuracy biomarker study performed in the cohort of the REVAKI-2 trial Setting: Single center ICU between September 2015 and September 2018 Interventions: Intravenous sildenafil citrate 12.5 mg kg-1 over 150 min or dextrose 5% at the commencement of surgery. Measurements and main results: Urine samples were collected before and 24 hours after the procedure from 93 cardiac surgery patients. Urine EV concentrations and size distribution were assessed using NanoSight. EV derivation and levels were measured using flow cytometry. Samples from 10 selected patients were sequenced to detect differentially expressed miR. Verification was performed with advanced TaqMan assays in samples from all patients. Urine EV concentrations significantly increased in patients with AKI after surgery, with the percentage of EV positive for aquaporin-2 and β1-integrin also increasing. Pre surgery podocalyxin-positive EV were significantly lower, and β1-integrin EV werehigher in patients with AKI. The levels of the former correlated with the severity of theinjury. miR-125a-5p was expressed at higher levels in urine from patients with AKI stage 2/3. Levels of miR-10a-5p decreased after surgery in AKI patients; its levels correlated with the severity of the injury. Preoperative levels of podocalyxin EVs and miR-125a-5p had moderate AKI predictive value and, in a logistic model together with ICU lactate levels, offered good (AUC = 80.9%) AKI prediction. Conclusions: Lower levels of podocalyxin-positive EV at baseline predict the severity of post-surgery AKI. Urine EV concentrations and miR expression offer excellent predictive accuracy when combined with commonly measured biomarkers.

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A One-pot Analytical Pipeline for Efficient and Sensitive Proteomic Analysis of Extracellular Vesicles

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