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Specificity and Strain-Typing Capabilities of Nanorod Array-Surface Enhanced Raman Spectroscopy for Mycoplasma pneumoniae Detection.

Henderson KC, Benitez AJ, Ratliff AE, Crabb DM, Sheppard ES, Winchell JM, Dluhy RA, Waites KB, Atkinson TP, Krause DC - PLoS ONE (2015)

Bottom Line: At present the most effective means for detection and strain-typing is quantitative polymerase chain reaction (qPCR), which can exhibit excellent sensitivity and specificity but requires separate tests for detection and genotyping, lacks standardization between available tests and between labs, and has limited practicality for widespread, point-of-care use.Here we demonstrate using partial least squares- discriminatory analysis (PLS-DA) of sample spectra that NA-SERS correctly identified M. pneumoniae clinical isolates from globally diverse origins and distinguished these from a panel of 12 other human commensal and pathogenic mycoplasma species with 100% cross-validated statistical accuracy.Furthermore, PLS-DA correctly classified by strain type all 30 clinical isolates with 96% cross-validated accuracy for type 1 strains, 98% cross-validated accuracy for type 2 strains, and 90% cross-validated accuracy for type 2V strains.

View Article: PubMed Central - PubMed

Affiliation: Department of Microbiology, University of Georgia, Athens, GA, United States of America.

ABSTRACT
Mycoplasma pneumoniae is a cell wall-less bacterial pathogen of the human respiratory tract that accounts for > 20% of all community-acquired pneumonia (CAP). At present the most effective means for detection and strain-typing is quantitative polymerase chain reaction (qPCR), which can exhibit excellent sensitivity and specificity but requires separate tests for detection and genotyping, lacks standardization between available tests and between labs, and has limited practicality for widespread, point-of-care use. We have developed and previously described a silver nanorod array-surface enhanced Raman Spectroscopy (NA-SERS) biosensing platform capable of detecting M. pneumoniae with statistically significant specificity and sensitivity in simulated and true clinical throat swab samples, and the ability to distinguish between reference strains of the two main genotypes of M. pneumoniae. Furthermore, we have established a qualitative lower endpoint of detection for NA-SERS of < 1 genome equivalent (cell/μl) and a quantitative multivariate detection limit of 5.3 ± 1 cells/μl. Here we demonstrate using partial least squares- discriminatory analysis (PLS-DA) of sample spectra that NA-SERS correctly identified M. pneumoniae clinical isolates from globally diverse origins and distinguished these from a panel of 12 other human commensal and pathogenic mycoplasma species with 100% cross-validated statistical accuracy. Furthermore, PLS-DA correctly classified by strain type all 30 clinical isolates with 96% cross-validated accuracy for type 1 strains, 98% cross-validated accuracy for type 2 strains, and 90% cross-validated accuracy for type 2V strains.

No MeSH data available.


Related in: MedlinePlus

PLS-DA distinguishing M. pneumoniae strains from other human commensal and pathogenic Mollicutes species.Each panel represents a cross-validated class prediction score for (A) class 1, growth medium control; (B) class 2, all M. pneumoniae strains; and (C) class 3, all other human commensal and pathogenic Mollicutes samples. For panels A-C, each individual shape represents a single pre-processed NA-SERS spectrum. The growth medium control spectra are represented by gray diamonds, the M. pneumoniae spectra by open shapes that differ by cluster to indicate the different individual strains and isolates, and the human commensal and pathogenic Mollicutes species are represented by light gray shapes that differ by cluster to indicate the individual species. The red-dotted line indicates the classification threshold line for positive class prediction, and the black-dotted line indicates the 95% confidence interval. Cross-validated sensitivity, specificity, and class error for the panels were as follows: (A) growth medium control: 1.00, 1.00, and 0, respectively; for (B) All M. pneumoniae samples: 1.00, 1.00, and 0, respectively; and for (C) All 12 Mollicutes species: 1.00, 1.00, and 0, respectively. Cross-validated statistics were obtained using Venetian blinds with 10 data splits to represent the prediction performance of the PLS-DA model for M. pneumoniae detection.
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pone.0131831.g003: PLS-DA distinguishing M. pneumoniae strains from other human commensal and pathogenic Mollicutes species.Each panel represents a cross-validated class prediction score for (A) class 1, growth medium control; (B) class 2, all M. pneumoniae strains; and (C) class 3, all other human commensal and pathogenic Mollicutes samples. For panels A-C, each individual shape represents a single pre-processed NA-SERS spectrum. The growth medium control spectra are represented by gray diamonds, the M. pneumoniae spectra by open shapes that differ by cluster to indicate the different individual strains and isolates, and the human commensal and pathogenic Mollicutes species are represented by light gray shapes that differ by cluster to indicate the individual species. The red-dotted line indicates the classification threshold line for positive class prediction, and the black-dotted line indicates the 95% confidence interval. Cross-validated sensitivity, specificity, and class error for the panels were as follows: (A) growth medium control: 1.00, 1.00, and 0, respectively; for (B) All M. pneumoniae samples: 1.00, 1.00, and 0, respectively; and for (C) All 12 Mollicutes species: 1.00, 1.00, and 0, respectively. Cross-validated statistics were obtained using Venetian blinds with 10 data splits to represent the prediction performance of the PLS-DA model for M. pneumoniae detection.

Mentions: Our goal here was to develop a PLS-DA model that distinguished M. pneumoniae from a panel of closely-related other Mollicutes species that might be found in humans. A total of n = 150 pre-processed NA-SERS spectra were collected on a single nanorod substrate consisting of n = 10 substrate background spectra, n = 10 growth medium control spectra, n = 10 M. pneumoniae spectra, and 10 spectra each per other Mollicutes species. An initial PLS-DA model was generated to discriminate between two classes, the nanorod substrate background and all other biological samples, which it did with 100% cross-validated sensitivity and specificity (data not shown). The purpose of this model was to ensure that the nanorod substrate background signal was significantly different than all other samples in order to exclude the background spectra from our future models. Once we determined that the nanorod substrate background class could be excluded, a second PLS-DA model was generated using the same spectra to distinguish among three classes; the growth medium control, M. pneumoniae, and the other Mollicutes species. This model had a total of n = 140 pre-processed NA-SERS spectra, consisting of n = 10 growth medium control spectra, n = 10 M. pneumoniae spectra, and 10 spectra each per other Mollicutes species. This model distinguished the three classes with 100% cross-validated sensitivity and specificity (data not shown). Upon the successful development of a PLS-DA model to distinguish between the growth medium control, M. pneumoniae, and all other Mollicutes species, a final PLS-DA model was generated using pre-processed NA-SERS spectra from all three nanorod substrates analyzed during these experiments. This model contained a total of n = 495 spectra, consisting of 25 growth medium control spectra, 25 M129 spectra, 25 FH spectra, 10 spectra each per other M. pneumoniae clinical isolates (30 isolates total), and 10 spectra each per other Mollicutes species (12 species total). This model was also categorized into three classes: the growth medium control (Fig 3A); all M. pneumoniae clinical isolates, including reference strains (Fig 3B); and all other Mollicutes species (Fig 3C). PLS-DA distinguished all M. pneumoniae strains from all 12 other human Mollicutes species and the growth medium control with 100% cross-validated sensitivity and specificity (Fig 3A–3C).


Specificity and Strain-Typing Capabilities of Nanorod Array-Surface Enhanced Raman Spectroscopy for Mycoplasma pneumoniae Detection.

Henderson KC, Benitez AJ, Ratliff AE, Crabb DM, Sheppard ES, Winchell JM, Dluhy RA, Waites KB, Atkinson TP, Krause DC - PLoS ONE (2015)

PLS-DA distinguishing M. pneumoniae strains from other human commensal and pathogenic Mollicutes species.Each panel represents a cross-validated class prediction score for (A) class 1, growth medium control; (B) class 2, all M. pneumoniae strains; and (C) class 3, all other human commensal and pathogenic Mollicutes samples. For panels A-C, each individual shape represents a single pre-processed NA-SERS spectrum. The growth medium control spectra are represented by gray diamonds, the M. pneumoniae spectra by open shapes that differ by cluster to indicate the different individual strains and isolates, and the human commensal and pathogenic Mollicutes species are represented by light gray shapes that differ by cluster to indicate the individual species. The red-dotted line indicates the classification threshold line for positive class prediction, and the black-dotted line indicates the 95% confidence interval. Cross-validated sensitivity, specificity, and class error for the panels were as follows: (A) growth medium control: 1.00, 1.00, and 0, respectively; for (B) All M. pneumoniae samples: 1.00, 1.00, and 0, respectively; and for (C) All 12 Mollicutes species: 1.00, 1.00, and 0, respectively. Cross-validated statistics were obtained using Venetian blinds with 10 data splits to represent the prediction performance of the PLS-DA model for M. pneumoniae detection.
© Copyright Policy
Related In: Results  -  Collection

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Show All Figures
getmorefigures.php?uid=PMC4487258&req=5

pone.0131831.g003: PLS-DA distinguishing M. pneumoniae strains from other human commensal and pathogenic Mollicutes species.Each panel represents a cross-validated class prediction score for (A) class 1, growth medium control; (B) class 2, all M. pneumoniae strains; and (C) class 3, all other human commensal and pathogenic Mollicutes samples. For panels A-C, each individual shape represents a single pre-processed NA-SERS spectrum. The growth medium control spectra are represented by gray diamonds, the M. pneumoniae spectra by open shapes that differ by cluster to indicate the different individual strains and isolates, and the human commensal and pathogenic Mollicutes species are represented by light gray shapes that differ by cluster to indicate the individual species. The red-dotted line indicates the classification threshold line for positive class prediction, and the black-dotted line indicates the 95% confidence interval. Cross-validated sensitivity, specificity, and class error for the panels were as follows: (A) growth medium control: 1.00, 1.00, and 0, respectively; for (B) All M. pneumoniae samples: 1.00, 1.00, and 0, respectively; and for (C) All 12 Mollicutes species: 1.00, 1.00, and 0, respectively. Cross-validated statistics were obtained using Venetian blinds with 10 data splits to represent the prediction performance of the PLS-DA model for M. pneumoniae detection.
Mentions: Our goal here was to develop a PLS-DA model that distinguished M. pneumoniae from a panel of closely-related other Mollicutes species that might be found in humans. A total of n = 150 pre-processed NA-SERS spectra were collected on a single nanorod substrate consisting of n = 10 substrate background spectra, n = 10 growth medium control spectra, n = 10 M. pneumoniae spectra, and 10 spectra each per other Mollicutes species. An initial PLS-DA model was generated to discriminate between two classes, the nanorod substrate background and all other biological samples, which it did with 100% cross-validated sensitivity and specificity (data not shown). The purpose of this model was to ensure that the nanorod substrate background signal was significantly different than all other samples in order to exclude the background spectra from our future models. Once we determined that the nanorod substrate background class could be excluded, a second PLS-DA model was generated using the same spectra to distinguish among three classes; the growth medium control, M. pneumoniae, and the other Mollicutes species. This model had a total of n = 140 pre-processed NA-SERS spectra, consisting of n = 10 growth medium control spectra, n = 10 M. pneumoniae spectra, and 10 spectra each per other Mollicutes species. This model distinguished the three classes with 100% cross-validated sensitivity and specificity (data not shown). Upon the successful development of a PLS-DA model to distinguish between the growth medium control, M. pneumoniae, and all other Mollicutes species, a final PLS-DA model was generated using pre-processed NA-SERS spectra from all three nanorod substrates analyzed during these experiments. This model contained a total of n = 495 spectra, consisting of 25 growth medium control spectra, 25 M129 spectra, 25 FH spectra, 10 spectra each per other M. pneumoniae clinical isolates (30 isolates total), and 10 spectra each per other Mollicutes species (12 species total). This model was also categorized into three classes: the growth medium control (Fig 3A); all M. pneumoniae clinical isolates, including reference strains (Fig 3B); and all other Mollicutes species (Fig 3C). PLS-DA distinguished all M. pneumoniae strains from all 12 other human Mollicutes species and the growth medium control with 100% cross-validated sensitivity and specificity (Fig 3A–3C).

Bottom Line: At present the most effective means for detection and strain-typing is quantitative polymerase chain reaction (qPCR), which can exhibit excellent sensitivity and specificity but requires separate tests for detection and genotyping, lacks standardization between available tests and between labs, and has limited practicality for widespread, point-of-care use.Here we demonstrate using partial least squares- discriminatory analysis (PLS-DA) of sample spectra that NA-SERS correctly identified M. pneumoniae clinical isolates from globally diverse origins and distinguished these from a panel of 12 other human commensal and pathogenic mycoplasma species with 100% cross-validated statistical accuracy.Furthermore, PLS-DA correctly classified by strain type all 30 clinical isolates with 96% cross-validated accuracy for type 1 strains, 98% cross-validated accuracy for type 2 strains, and 90% cross-validated accuracy for type 2V strains.

View Article: PubMed Central - PubMed

Affiliation: Department of Microbiology, University of Georgia, Athens, GA, United States of America.

ABSTRACT
Mycoplasma pneumoniae is a cell wall-less bacterial pathogen of the human respiratory tract that accounts for > 20% of all community-acquired pneumonia (CAP). At present the most effective means for detection and strain-typing is quantitative polymerase chain reaction (qPCR), which can exhibit excellent sensitivity and specificity but requires separate tests for detection and genotyping, lacks standardization between available tests and between labs, and has limited practicality for widespread, point-of-care use. We have developed and previously described a silver nanorod array-surface enhanced Raman Spectroscopy (NA-SERS) biosensing platform capable of detecting M. pneumoniae with statistically significant specificity and sensitivity in simulated and true clinical throat swab samples, and the ability to distinguish between reference strains of the two main genotypes of M. pneumoniae. Furthermore, we have established a qualitative lower endpoint of detection for NA-SERS of < 1 genome equivalent (cell/μl) and a quantitative multivariate detection limit of 5.3 ± 1 cells/μl. Here we demonstrate using partial least squares- discriminatory analysis (PLS-DA) of sample spectra that NA-SERS correctly identified M. pneumoniae clinical isolates from globally diverse origins and distinguished these from a panel of 12 other human commensal and pathogenic mycoplasma species with 100% cross-validated statistical accuracy. Furthermore, PLS-DA correctly classified by strain type all 30 clinical isolates with 96% cross-validated accuracy for type 1 strains, 98% cross-validated accuracy for type 2 strains, and 90% cross-validated accuracy for type 2V strains.

No MeSH data available.


Related in: MedlinePlus