Selective detection of Staphylococcus aureus in food matrices using a chemiluminescent peroxidase-like DNA...

The development of rapid, sensitive, and specific methods for nucleic acid detection is critical for molecular diagnostics and biosafety. Here, we report the design and validation of a peroxidase-like DNA nanomachine (PxDm) that integrates target-templated assembly of a split G-quadruplex (G4) DNAzyme with chemiluminescent (CL) detection via photon counting in a compact, closed-cuvette device for the accurate detection of S. aureus. Following systematic optimization of the CL reaction, the PxDm platform demonstrated robust analytical performance at room temperature, reaching a nanomolar limit of detection (LOD) for single-stranded DNA (ssDNA) approximately 30 min after the start of the analysis. The assay was shown to be able of discriminating target sequences from those bearing single-nucleotide substitutions (SNS) with up to 98 % signal reduction. This CL approach proved to be more sensitive than colorimetric and fluorescent detection modalities for the same PxDm, with a 19.6-fold and 4.3-fold lower LOD, respectively. The platform's versatility was demonstrated through successful adaptation for double-stranded DNA (dsDNA) detection and integration with dual-priming isothermal amplification (DAMP). The integrated DAMP-PxDm assay achieved clinically relevant sensitivity, with a detection limit of 227 genomic equivalents, while maintaining high selectivity, as indicated by reduction factor (RF) values of 62.1 % for non-target DNA amplicons and 63.9 % for simulated target amplicons containing an SNS. The system demonstrated robustness in complex matrices, accurately detecting S. aureus amplicons from bacteria recovered from lettuce and spinach leaves, even in the presence of non-target organisms. The PxDm platform offers a powerful combination of low-nanomolar sensitivity, single-nucleotide specificity, and sufficient operational time, establishing it as a promising tool for on-site pathogen detection and environmental monitoring applications.