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

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Title: Detecting and Tracking Nosocomial Methicillin-Resistant Staphylococcus aureus Using a Microfluidic SERS Biosensor.
Authors: Xiaonan Lu1, Samuelson, Derrick R.1, Yuhao Xu2, Hongwei Zhang3, Shuo Wang3, Rasco, Barbara A.4, Jie Xu2 xiaonan.lu@ubc.ca, Konkel, Michael E.1 konkel@vetmed.wsu.edu
Source: Analytical Chemistry. 2/19/2013, Vol. 85 Issue 4, p2320-2327. 8p.
Subjects: Nosocomial infections, Methicillin-resistant staphylococcus aureus, Microfluidics, Surface enhanced Raman effect, Biosensors, Polymerase chain reaction, Optofluidics, Regression analysis
Abstract: 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 [ABSTRACT FROM AUTHOR]
Copyright of Analytical Chemistry is the property of American Chemical Society and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Detecting and Tracking Nosocomial Methicillin-Resistant Staphylococcus aureus Using a Microfluidic SERS Biosensor.
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  Data: <searchLink fieldCode="AR" term="%22Xiaonan+Lu%22">Xiaonan Lu</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Samuelson%2C+Derrick+R%2E%22">Samuelson, Derrick R.</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Yuhao+Xu%22">Yuhao Xu</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Hongwei+Zhang%22">Hongwei Zhang</searchLink><relatesTo>3</relatesTo><br /><searchLink fieldCode="AR" term="%22Shuo+Wang%22">Shuo Wang</searchLink><relatesTo>3</relatesTo><br /><searchLink fieldCode="AR" term="%22Rasco%2C+Barbara+A%2E%22">Rasco, Barbara A.</searchLink><relatesTo>4</relatesTo><br /><searchLink fieldCode="AR" term="%22Jie+Xu%22">Jie Xu</searchLink><relatesTo>2</relatesTo><i> xiaonan.lu@ubc.ca</i><br /><searchLink fieldCode="AR" term="%22Konkel%2C+Michael+E%2E%22">Konkel, Michael E.</searchLink><relatesTo>1</relatesTo><i> konkel@vetmed.wsu.edu</i>
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  Data: <searchLink fieldCode="JN" term="%22Analytical+Chemistry%22">Analytical Chemistry</searchLink>. 2/19/2013, Vol. 85 Issue 4, p2320-2327. 8p.
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  Data: <searchLink fieldCode="DE" term="%22Nosocomial+infections%22">Nosocomial infections</searchLink><br /><searchLink fieldCode="DE" term="%22Methicillin-resistant+staphylococcus+aureus%22">Methicillin-resistant staphylococcus aureus</searchLink><br /><searchLink fieldCode="DE" term="%22Microfluidics%22">Microfluidics</searchLink><br /><searchLink fieldCode="DE" term="%22Surface+enhanced+Raman+effect%22">Surface enhanced Raman effect</searchLink><br /><searchLink fieldCode="DE" term="%22Biosensors%22">Biosensors</searchLink><br /><searchLink fieldCode="DE" term="%22Polymerase+chain+reaction%22">Polymerase chain reaction</searchLink><br /><searchLink fieldCode="DE" term="%22Optofluidics%22">Optofluidics</searchLink><br /><searchLink fieldCode="DE" term="%22Regression+analysis%22">Regression analysis</searchLink>
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  Label: Abstract
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  Data: 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 [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Analytical Chemistry is the property of American Chemical Society and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1021/ac303279u
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        Text: English
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        PageCount: 8
        StartPage: 2320
    Subjects:
      – SubjectFull: Nosocomial infections
        Type: general
      – SubjectFull: Methicillin-resistant staphylococcus aureus
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      – SubjectFull: Microfluidics
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      – SubjectFull: Surface enhanced Raman effect
        Type: general
      – SubjectFull: Biosensors
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      – SubjectFull: Polymerase chain reaction
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      – SubjectFull: Optofluidics
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      – SubjectFull: Regression analysis
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              Text: 2/19/2013
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