Abstract
Accurate identification of single-nucleotide variants (SNVs) is paramount for disease diagnosis. Despite the facile design of DNA hybridization probes, their limited specificity poses challenges in clinical applications. Here, a differential reaction pathway probe (DRPP) based on a dynamic DNA reaction network is presented. DRPP leverages differences in reaction intermediate concentrations between SNV and WT groups, directing them into distinct reaction pathways. This generates a strong pulse-like signal for SNV and a weak unidirectional increase signal for wild-type (WT). Through the application of machine learning to fluorescence kinetic data analysis, the classification of SNV and WT signals is automated with an accuracy of 99.6%, significantly exceeding the 80.7% accuracy of conventional methods. Additionally, sensitivity for variant allele frequency (VAF) is enhanced down to 0.1%, representing a ten-fold improvement over conventional approaches. DRPP accurately identified D614G and N501Y SNVs in the S gene of SARS-CoV-2 variants in patient swab samples with accuracy over 99% (n = 82). It determined the VAF of ovarian cancer-related mutations KRAS-G12R, NRAS-G12C, and BRAF-V600E in both tissue and blood samples (n = 77), discriminating cancer patients and healthy individuals with significant difference (p < 0.001). The potential integration of DRPP into clinical diagnostics, along with rapid amplification techniques, holds promise for early disease diagnostics and personalized diagnostics.