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Published on: August 14, 2019
Temperature-responsive PDDNFs@QDs integrating simplified isolation for efficient tumor-derived small extracellular
Xiangyue Meng1, Zhihao Zhang1, Pengjun Jiang2
1Department of Laboratory Medicine, Med+X Center for Manufacturing, Department of General Surgery, National Clinical Research Center for Geriatrics, State Key Laboratory of Respiratory Health and Multimorbidity, West China Hospital, Sichuan University, Chengdu, Sichuan, 610041, China; Breast Disease Center, West China Hospital, Sichuan University, Chengdu, Sichuan, 610041, China.
Abstract:
Multiplexed small extracellular vesicles (sEVs) biomarker detection holds great promise for breast cancer diagnosis and subtyping; however, its clinical application is often hampered by the complex isolation and detection processes. Herein, we propose a temperature-responsive sensing platform (PDDNFs@QDs) utilizing poly(N-isopropylacrylamide) (PNIPAM)-linked, dumbbell-shaped DNA nanoflowers (DNFs) loading quantum dots (QDs). The sensing mechanism is based on the disassembly of DNFs triggered by aptamer-target recognition, which releases the loaded QDs. This process enables simultaneous quantitative analysis of human epidermal growth factor receptor 2 (HER2) and epithelial cell adhesion molecule (EpCAM) on the surfaces of breast cancer-derived sEVs. Importantly, the PNIPAM-mediated thermo-responsive strategy promotes the aggregation of unreacted probes, thereby reducing matrix-associated background signals. This approach addresses matrix interference in crude membrane filtrates, allowing for the direct detection of sEVs isolated through simple filtration. Additionally, the platform was successfully applied to 45 clinical samples, achieving an area under the curve (AUC) of 0.97 for breast cancer diagnosis and an AUC of 0.85 for HER2-status discrimination, with results closely aligned with clinical imaging findings and pathological assessments. Overall, this strategy provides a simple and sensitive approach with significant potential for breast cancer diagnosis and subtyping.
