Detecting and Tracking Nosocomial Methicillin-Resistant Staphylococcus aureus Using a Microfluidic SERS Biosensor

Abstract

Rapid detection and differentiation of methicillin-resistant Staphylococcus aureus (MRSA) is 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 hours using this optofluidic platform. Intra- and inter-laboratory spectral reproducibility yielded a differentiation index value of 3.43 to 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.

Investigating the Responses of Cronobacter sakazakii to Garlic-Drived Organosulfur Compounds: a Systematic Study of Pathogenic-Bacterium Injury by Use of High-Throughput Whole-Transcriptome Sequencing and Confocal Micro-Raman Spectroscopy

“…The preprocessing of the raw Raman spectra can separate and subsequently eliminate the side effects which may influence the quality of spectroscope-based chemometric models and multivariate analyses. We first conducted a polynomial background fit (45) combined with baseline subtraction using identification and discrimination of minima via adaptive and least-squares thresholding (46) to remove fluorescence background derived from C. sakazakii cells on gold-coated microarray slides, Gaussian noise, white noise, CCD background noise, and cosmic spikes (47)(48)(49). Besides fluorescence background, most of the spectral interference is contributed by CCD background noise, which is generated due to the thermal fluctuations on the CCD detector.…”

“…In general, D y1y2 values of less than 1,000 indicate satisfactory spectral reproducibility (24,48,49). Spectral sensitivity and specificity from three independent experiments were determined using the Wards cluster algorithm at the cutoff value established at 99% similarity for the classification model (i.e., principal component analysis [PCA]) (49).…”

“…The secondderivative transformed Raman spectrum is the result of applying a derivative transform to the raw Raman spectrum, and this algorithm function may swing with greater amplitude than that of the raw spectrum to mag-nify the minor spectral variation and separate out overlapping bands (51). In addition, it could be an ideal noise filter because the changes in the spectral baseline have an insignificant impact on second derivatives (49). It was calculated with the following formula (52):…”

“…The application of Bayesian probability to establish and cross-validate the chemometric classification models for vibrational spectroscopy of bacterial planktonic cells (48,49,53), Bacillus spores (54), and viruses (55) has been recently introduced, while the currently used Bayesian probability model was realized on the basis of the minimum error rate. The Matlab codes for programming the PCA and Bayesian probability approach to establish chemometric models are available in the supplemental material.…”

Biosensors in Antimicrobial Drug Discovery: Since Biology until Screening Platforms

“…On the other hand the combination of microfluidics with optical systems in optofluidic biosensors has been used in the detection of viruses and bacteria, with the advantage to differentiate in multiplex platform virus particles including Vaccinia and Ebola, as well as MRSA in a fast and automated technique useful in epidemiological surveillance [113,114].…”