Photothermal and colorimetric dual mode assay based on nanozymes for gastric cancer exosomes.
Exosome analysis in gastric cancer (GC) serum requires sensitive readout with reduced matrix interference. Circulating GC-related CD63-positive exosomes can indicate vesicle burden and stress-induced secretion. Herein, we developed a dual-mode photothermal/colorimetric aptasensor using CD63 aptamer-functionalized core-shell AuPt nanozymes for detecting GC-related CD63-positive exosomes in serum samples. The innovation of this platform lies in integrating aptamer recognition, Pt-shell peroxidase-like catalysis, and Au-core near-infrared (NIR) photothermal conversion within one nanoprobe. The porous Pt shell showed high TMB affinity (Km = 0.18 mM), and the Au core enabled 808 nm-responsive photothermal enhancement. Arrhenius analysis showed that 808 nm NIR irradiation decreased the apparent activation energy by 45.9%, while NIR-XPS, photocurrent, impedance, and wavelength-dependent analyses supported localized photothermal heating and plasmon-assisted interfacial charge transfer in the catalytic enhancement. The colorimetric mode achieved a limit of detection of 5.0 × 10² particles/mL and a linear range of 1.0 × 10³ to 1.0 × 10⁶ particles/mL. The photothermal mode covered 1.0 × 10⁵ to 1.0 × 10⁸ particles/mL and reduced hemolysis-induced optical interference. In a preliminary double-blind cohort with 60 serum samples, the assay showed a significant difference between GC patients and healthy donors (P < 0.0001). Biologically, the assay tracked increased particle-level exosome release from SGC-7901 cells under hypoxia and 5-FU stress over 48 h, supporting dynamic monitoring of stress-related vesicle abundance. A portable format combining smartphone RGB analysis and pocket-sized thermal imaging also showed good linearity under fixed imaging conditions. This strategy provides a dual-mode tool for quantitative GC-related exosome analysis and preliminary extracellular vesicle monitoring.
Authors
Chen Chen, Huang Huang, Zeng Zeng, Wang Wang, Chen Chen, Jiang Jiang, Xiao Xiao, Zheng Zheng, Lin Lin, Ye Ye
View on Pubmed