Identifying Pathoadaptation in <i>Pseudomonas aeruginosa</i> Using Glycopolymer Sensor Arrays

In-host bacterial evolution presents a major barrier to effective infection management, driving phenotypic adaptations such as antibiotic resistance and altered virulence. <i>Pseudomonas aeruginosa</i>, a key opportunistic pathogen, frequently undergoes rapid evolutionary changes during chronic lung infections, complicating diagnosis and treatment. Current strain typing via whole genome sequencing or selective culturing is costly and time-intensive, and the complex relationship between genetic variations and the resulting phenotype makes clinically relevant pathotypes difficult to identify. Here, we report a cross-reactive, glycopolymer-based fluorescent sensor array capable of directly identifying phenotypic changes related to in-host evolution in <i>P. aeruginosa</i>. The sensor array can accurately distinguish phenotypic variations arising from single-gene defects and discriminate clinical isolates with known differences in their evolutionary and pathoadaptive trajectories. Notably, our system is also capable of identifying <i>P. aeruginosa</i> isolates as distinct from other bacterial species commonly found in complex polymicrobial lung infections. Our modular platform presents an opportunity to develop sensor arrays that target carbohydrate recognition in a variety of pathogens, offering potential application as a rapid diagnostic tool to inform clinical treatment decisions based on the direct classification of phenotypic profiles.