Aptamer recognition driven cascaded biocomputation on surface of serum-derived small extracellular vesicles

Multiplexed profiling of small extracellular vesicle (sEV) surface proteins holds promise for cancer diagnosis and prognosis due to the improved accuracy resulted from probing multiple biomarkers, yet simultaneously identifying these biomarkers on sEVs and directly reporting the analytical results remains a significant challenge. Here, we developed an aptamer-triggered DNA biocomputation platform for multiplexed protein profiling of serum-derived sEVs. Three toehold-extended DNA aptamers target sEV membrane proteins, and a two-layer AND logic gate releases an output strand only when all targets are present. After signal amplification, the system effectively discriminates gastrointestinal cancer patients from healthy donors, achieving 86.7% sensitivity, 80.0% specificity, and 84.0% accuracy for gastric cancer in a validation cohort. By converting protein signals into DNA information, this strategy bypasses complex instrumentation and data processing, offering a modular and clinically adaptable platform for cancer diagnosis and prognosis.