Temperature-Enhanced Purine Metabolism-Based Versatile SERS Platform for Rapid Clinical Pathogens Diagnosis and Drug-Resistant Assessment

Label-free surface-enhanced Raman spectroscopy (SERS) combined with machine learning (ML) techniques presents a promising approach for rapid pathogen identification. Previous studies have demonstrated that purine degradation metabolites are the primary contributors to SERS spectra; however, generating these distinguishable spectra typically requires a long incubation time (>10 h) at room temperature. Moreover, the lack of attention to spectral variations between strains of the same bacterial species has limited the generalizability of ML models in real-world applications. To address these issues, we investigated temperature-induced alterations in bacterial purine metabolism and found that robust SERS spectra could be obtained within just 1 h by heating samples to 60 °C. Our study further revealed that pathogens exhibit multiple fingerprint patterns across strains, rather than a uniform spectral signature. To enhance practicality, we optimized ML models by training them on data sets capturing all relevant SERS fingerprints and validated them on separate bacterial strains. The SoftMax classifier achieved 100% accuracy in identifying both laboratory and clinical specimens within 17 h. Additionally, the platform demonstrated over 91% accuracy in distinguishing drug-resistant strains, such as methicillin-resistant <i>Staphylococcus aureus</i> and carbapenem-resistant <i>Klebsiella pneumoniae</i>, and achieved 99.66% accuracy in differentiating specific strains within a species, such as enterohemorrhagic <i>Escherichia coli</i>. This accelerated, purine metabolism-based SERS platform offers a highly promising alternative for the rapid diagnosis of bacterial infections.