A high-throughput platform to select nucleic acid-based bio-recognition elements for electrochemical biosensors

Detecting low-frequency mutations within a high-abundance wild-type (WT) background is essential for precision cancer diagnostics. Standard methods like qPCR and NGS, while effective, are hindered by high costs, complexity, and lengthy workflows. Electrochemical biosensors using mutation-specific capture probes offer a simpler, cost-effective alternative but lack the selectivity needed for detecting low-abundance mutations. To address this, we developed a high-throughput platform for systematically evaluating the hybridization affinity between biorecognition elements (capture probes) and target DNA under varied conditions.

Our method employs hundreds of candidate capture probes, flanked by PCR primer sites, hybridized to biotinylated mutant or WT targets. After streptavidin pull-down, the enriched probes are converted into a probe library and quantified via next-generation sequencing. This platform enables precise evaluation of hybridization parameters such as temperature, salt concentration, probe sequence, and binding position.

We applied the platform to identify capture probes for the KRAS G12C mutation, screening 884 probes in replicates across 12 hybridization conditions (6 temperatures, 2 salt concentrations) in varying WT backgrounds (0%, 25%, 75%). This yielded 84,864 data points. Substantial differences in probe sensitivity and selectivity were observed, with additional probe mismatches further enhancing selectivity. Validation using a photoelectrochemical assay confirmed the robustness of selected probes for KRAS G12C detection.

This high-throughput approach streamlines the optimization of hybridization-based probes, enabling the development of more sensitive and selective biosensors tailored for mutation detection in complex biological samples.