Membrane Feature-Inspired Profiling of Extracellular Vesicles for Pancreatic Cancer Diagnosis.
Ping LiJie WangMengqiu GaoJue WangYi MaYueqing GuPublished in: Analytical chemistry (2021)
Extracellular vesicles (EVs) have recently emerged as a promising tumor biomarker, and EV phenotyping offers many benefits for cancer diagnosis. However, the practicality of EV assays remains a challenge due to macromolecule disturbances, biomarker heterogeneities, and EV abundance limitations. Here, we demonstrate a membrane-based biosensor for precise and sensitive EV identification. The sensor synergistically integrates EV capture and detection by virtue of EV membrane features (membrane protein and lipid bilayer), comprising antibody-conjugated magnetic beads (AbMBs) and duplex-specific nuclease (DSN)-mediated amplification cycles. Bivalent cholesterol (biChol)-modified RNA-DNA duplexes are designed to insert into the EV membrane, transforming EV signals into RNA signals and initiating the signal amplification. The membrane-based signal production pattern eliminates protein interference. By employing four antibodies specific to PCa-related membrane proteins, the AbMB-biChol platform enables the successful differentiation and monitoring of PCa-related EVs and distinguishes PCa patients from healthy donors with improved efficacy, exhibiting superior efficiency over the analyses based on clinically used biomarker CA19-9 and PCa-related proteins. As such, the developed system has great potential for clinical PCa diagnosis.
Keyphrases
- nucleic acid
- high throughput
- end stage renal disease
- squamous cell carcinoma
- machine learning
- gold nanoparticles
- ejection fraction
- risk assessment
- deep learning
- photodynamic therapy
- peritoneal dialysis
- single cell
- transcription factor
- microbial community
- sensitive detection
- small molecule
- fatty acid
- drug induced
- cell free
- circulating tumor
- real time pcr
- squamous cell