摘要： The article describes a SERS-based method for diagnosis of bacterial infections. Positively charged silver nanoparticles (AgNPs+) were employed for identification of methicillin-resistant Staphylococcus aureus (MRSA). It is found that AgNPs+ undergo self-assembly on the surface of bacteria via electrostatic aggregation. The assembled AgNPs+ are excellent SERS substrates. To prove the capability of SERS to differentiate between S. aureus and other microorganisms, six standard strains including S. aureus 29213, S. aureus 25923, C. albicans, B. cereus, E. coli, and P. aeruginosa were tested. To further demonstrate its applicability for the identification of MRSA in clinical samples, 52 methicillin-sensitive S. aureus (MSSA) isolates and 215 MRSA isolates were detected by SERS. The total measurement time (include incubation) is 45 min when using a 3 μL sample. The method gives a strongly enhanced Raman signal (at 730 cm?1 and 1325 cm?1) with good reproducibility and repeatability. It was successfully applied to the discrimination of the six strain microorganisms. The typical Raman peaks of S. aureus at 730, 1154, 1325, and 1457 cm?1 were observed, which were assigned to the bacterial cell wall components (730 cm?1- adenine, glycosidic ring mode, 1154 cm?1- unsaturated fatty acid, 1325 cm?1- adenine, polyadenine, and 1457 cm?1 for -COO- stretching). S. aureus was completely separated from other species by partial least squares discriminant analysis (PLS-DA). Moreover, 52 MSSA isolates and 215 MRSA isolates from clinical samples were identified by PLS-DA. The accuracy was almost 100% when compared to the standard broth microdilution method. A classification based on latent structure discriminant analysis provided spectral variability directly. Conceivably, the method offers a potent tool for the identification of bacteria and antibiotics resistance, and for studies on antibiotic-resistance in general.