Construction of a Glutathione‐Gated Intelligent Nanoclassifier for Spatioselective Visualization of Dual CircRNAs and Synergistic Photodynamic Therapy
Abstract
ABSTRACT Circular RNAs (circRNAs) are key endogenous regulators of tumorigenesis and progression, and their high interspecies diversity and strong sequence homology pose a great challenge for simultaneous, live‐cell differentiation of multiple circRNA variants. Herein, we construct an intelligent nanoclassifier (DB‐CHA@PAN) by assembling disulfide bond (DB)‐modified catalytic hairpin assembly (CHA) probes onto programmable cruciform framework‐based nanoparticles (PAN) for spatioselective visualization of dual circRNAs and synergistic photodynamic therapy. Upon entering cancer cells, GSH cleaves disulfide bonds to initiate multiple rounds of circRNAs‐fueled cyclic CHA cascades, inducing spatial separation of photosensitizer (PS)/BHQ3 and Cy3/BHQ2 pairs and consequently recovery of PS photodynamic activity and Cy3 fluorescence signal. Released PS can generate abundant singlet oxygen ( 1 O 2 ) upon light irradiation, inducing oxidative damage and apoptosis. This intelligent nanoclassifier enables attomolar‐level detection of circCDYL and circHIPK3 in vitro and simultaneous imaging of circCDYL and circHIPK3 in living cells. It can quantify circRNA levels at single‐cell sensitivity, discriminate circRNAs from mismatched variants with single‐base resolution, and even diagnose breast/lung cancer across entire clinical spectrum with 100% accuracy. Moreover, it can real‐time track circRNAs dynamics in living cells and MCF‐7 tumor‐bearing nude mice, and significantly enhance therapeutic efficacy via synergistic photodynamic activation, with promising applications in clinical diagnostics and therapeutics.
Article Details
Authors (4)
Qian Liu
Wenliang Ma
Li‐juan Wang
School of Chemistry and Chemical Engineering State Key Laboratory of Digital Medical Engineering Southeast University Nanjing China
Chun‐yang Zhang
School of Chemistry and Chemical Engineering State Key Laboratory of Digital Medical Engineering Southeast University Nanjing China