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Detecting and Tracking Nosocomial Methicillin-Resistant Staphylococcus aureus Using a Microfluidic SERS Biosensor
- Lu, Xiaonan, Samuelson, Derrick R., Xu, Yuhao, Zhang, Hongwei, Wang, Shuo, Rasco, Barbara A., Xu, Jie, Konkel, Michael E.
- Analytical chemistry 2013 v.85 no.4 pp. 2320-2327
- Raman spectroscopy, antibiotic resistance, bacterial infections, biosensors, clones, cross infection, early diagnosis, genes, humans, least squares, methicillin, methicillin-resistant Staphylococcus aureus, models, monitoring, multilocus sequence typing, polymerase chain reaction, prediction, rapid methods, China, United States
- Rapid detection and differentiation of methicillin-resistant Staphylococcus aureus (MRSA) are critical for the early diagnosis of difficult-to-treat nosocomial and community acquired clinical infections and improved epidemiological surveillance. We developed a microfluidics chip coupled with surface enhanced Raman scattering (SERS) spectroscopy (532 nm) “lab-on-a-chip” system to rapidly detect and differentiate methicillin-sensitive S. aureus (MSSA) and MRSA using clinical isolates from China and the United States. A total of 21 MSSA isolates and 37 MRSA isolates recovered from infected humans were first analyzed by using polymerase chain reaction (PCR) and multilocus sequence typing (MLST). The mecA gene, which refers resistant to methicillin, was detected in all the MRSA isolates, and different allelic profiles were identified assigning isolates as either previously identified or novel clones. A total of 17 400 SERS spectra of the 58 S. aureus isolates were collected within 3.5 h using this optofluidic platform. Intra- and interlaboratory spectral reproducibility yielded a differentiation index value of 3.43–4.06 and demonstrated the feasibility of using this optofluidic system at different laboratories for bacterial identification. A global SERS-based dendrogram model for MRSA and MSSA identification and differentiation to the strain level was established and cross-validated (Simpson index of diversity of 0.989) and had an average recognition rate of 95% for S. aureus isolates associated with a recent outbreak in China. SERS typing correlated well with MLST indicating that it has high sensitivity and selectivity and would be suitable for determining the origin and possible spread of MRSA. A SERS-based partial least-squares regression model could quantify the actual concentration of a specific MRSA isolate in a bacterial mixture at levels from 5% to 100% (regression coefficient, >0.98; residual prediction deviation, >10.05). This optofluidic platform has advantages over traditional genotyping for ultrafast, automated, and reliable detection and epidemiological surveillance of bacterial infections.