Bacterial infections continue to pose a global challenge, particularly in situations where rapid and reliable diagnosis is essential for initiating effective treatment. Among the most clinically important pathogens, Escherichia coli is the leading cause of urinary tract infections, which can progress to severe systemic infections such as sepsis. In these cases, detecting the presence of E. coli is critical, especially during the early stages of infection (1–100 CFU/mL).
Surface-enhanced Raman scattering (SERS) spectroscopy has proven to be a highly useful analytical technique due to its high sensitivity and ability to provide molecular information. However, it faces the challenge of detecting low bacterial concentrations, particularly when bacteria are present in complex biological fluids such as blood or urine.
To address this issue, SERS-based platforms incorporating filters have been developed to facilitate bacterial separation and detection. However, the resulting Raman signals remain weak, limiting detection sensitivity. This limitation is attributed to a scale mismatch between the system components: while the plasmonic hot spots responsible for Raman signal enhancement are located at the nanoscale, bacteria have micrometer-sized dimensions. This size difference hinders interactions between bacteria and the hot spots, consequently reducing SERS signal enhancement.
In this study, a group of researchers from South Korea developed a SERS platform with an integrated filter and an engineering strategy to reconstruct hot spots within the bacterial microenvironment. After selectively capturing bacteria based on their size, Au nanoparticles were grown to form plasmonic structures directly around them. In this way, the hot spots are located at the bacterium–substrate interface, overcoming the scale mismatch between the two and simultaneously increasing the likelihood of interaction and coupling.
The integration of these processes into the proposed platform achieves high sensitivity and selectivity under clinically relevant conditions. Furthermore, since SERS signal intensity is directly correlated with bacterial viability, this system enables rapid antimicrobial susceptibility testing (AST), providing diagnostic information within a significantly shorter time frame.
For further information, see: Small Methods
