Abstract / Summary
ABSTRACT Accurate and in situ detection of uracil‐DNA glycosylase (UDG) is crucial for clinical diagnosis and prognosis assessment. However, conventional methods show low accuracy, limited sensitivity, and off‐target signal leakage due to flawed signal input/output and amplification mechanisms. Therefore, an engineered allosteric biosensor (UUU‐DZ‐tFNA) was developed, in which UDG specifically recognizes and activates a DNAzyme to achieve signal cycle amplification in a sequentially activatable mode. Experimental results demonstrated that UUU‐DZ‐tFNA enables rapid, highly sensitive (LOD = 0.025 mU/mL), and specific detection of UDG. In addition, UUU‐DZ‐tFNA enables in situ molecular imaging of UDG at both the neuroblastoma (NB) cellular and animal levels, as well as precise in situ detection of UDG in plasma exosomes from NB patients. In particular, the non‐invasive risk stratification model for NB developed using machine learning based on exosomal UDG and clinical multidimensional indicators, including MYCN amplification status, International NB Risk Factors (IDRFs), neuron‐specific enolase (NSE), and lactate dehydrogenase (LDH), showed excellent discriminatory ability, with 85.7% sensitivity, 100.0% specificity, and 92.8% accuracy. UUU‐DZ‐tFNA also enables image‐guided surgical excision in vivo and effective monitoring of drug resistance in NB. In summary, UUU‐DZ‐tFNA enables in situ detection of UDG, facilitating risk stratification and drug resistance monitoring of NB.