Systematic Evolution of Pattern Recognition Aptamers for Bacterial Identification

The impressive capabilities of natural pattern recognition systems have inspired their synthetic recreation for many chemical and biological applications. However, developing artificial receptors for pattern recognition is currently constrained by a laborious trial-and-error process within a limited selection space of synthetically generated molecules/materials. Here, we propose pattern recognition aptamers (PRAs)─a set of single-stranded nucleic acid ligands with quasi-specificity for multiple targets─that can be evolved through systematic exponential enrichment from a nucleic acid library for high-precision target identification. Our approach allows for the reliable generation of customized artificial receptors over a limited number of selection rounds. Using bacteria as model analytes, we developed 9 PRAs targeting 15 common bacteria through 3 rounds of evolutionary screening, achieving an identification accuracy of 98.5% in blinded unknown bacterial identification. This approach provides a generalized pipeline for creating customized pattern recognition arrays, supporting their potential to meet rapidly increasing application demands.