Journal article
Detecting and tracking nosocomial methicillin-resistant Staphylococcus aureus using a microfluidic SERS biosensor
Analytical chemistry (Washington), Vol.85(4), pp.2320-2327
02/19/2013
Handle:
https://hdl.handle.net/2376/105005
PMCID: PMC3578299
PMID: 23327644
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.
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Details
- Title
- Detecting and tracking nosocomial methicillin-resistant Staphylococcus aureus using a microfluidic SERS biosensor
- Creators
- Xiaonan Lu - School of Molecular Biosciences, College of Veterinary Medicine, Washington State University, Pullman, Washington 99164-7520, United StatesDerrick R Samuelson - School of Molecular Biosciences, College of Veterinary Medicine, Washington State University, Pullman, Washington 99164-7520, United StatesYuhao Xu - Department of Mechanical Engineering, Washington State University, Vancouver, Washington 98686, United StatesHongwei Zhang - Key Laboratory of Food Nutrition and Safety, Ministry of Education of China, Tianjin University of Science and Technology, Tianjin 300457, ChinaShuo Wang - Key Laboratory of Food Nutrition and Safety, Ministry of Education of China, Tianjin University of Science and Technology, Tianjin 300457, ChinaBarbara A Rasco - School of Food Science, Washington State University, Pullman, Washington 99164-6376, United StatesJie Xu - Department of Mechanical Engineering, Washington State University, Vancouver, Washington 98686, United StatesMichael E Konkel - School of Molecular Biosciences, College of Veterinary Medicine, Washington State University, Pullman, Washington 99164-7520, United States
- Publication Details
- Analytical chemistry (Washington), Vol.85(4), pp.2320-2327
- Academic Unit
- Mathematics and Statistics, Department of; Food Science, School of; Molecular Biosciences, School of
- Grant note
- R56 AI088518 || AI / National Institute of Allergy and Infectious Diseases Extramural Activities : NIAID
- Identifiers
- 99900546764301842
- Language
- English
- Resource Type
- Journal article