PC-DFA score plots of GC-MS profiles.

<p>25 PCs were extracted from PCA and used as inputs to DFA, explaining 99% of the TEV. The legend in the figure shows the 95% CI for the correct classification of the 8 conditions. Significantly altered metabolites were mined through a combination of PC-DFA loadings and univariate significance testing (Student <i>t</i>-test). C, control.</p

Calibration curve for LC-MS.

<p>The curve was built from 20 different gradient concentrations of trimethoprim; see Supporting <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0200272#pone.0200272.s001" target="_blank">S1 Text</a> for information on the concentrations of trimethoprim used to construct the standard curve.</p

Growth characteristics of <i>E</i>. <i>coli</i>.

<p>(a) Growth curves of <i>E</i>. <i>coli</i> K-12 at pH 5 (dashed line) and pH 7 (solid line). For pH 5, the dashed blue line represents control samples, dashed red indicates samples challenged with 0.8 mg L^-1 of trimethoprim added at the beginning of the lag phase (t = 0 h) and dashed green denotes samples challenged with 0.8 mg L^-1 of trimethoprim and added at mid-exponential phase (t = 5 h). For pH 7, the solid purple line represents control samples, solid light blue indicates samples challenged with 0.8 mg L^-1 of trimethoprim added at the beginning of the lag phase (t = 0 h) and solid orange denotes samples challenged with 0.8 mg L^-1 of trimethoprim and added at the exponential phase (t = 5 h). (b) Column chart representing relative <i>E</i>. <i>coli</i> intracellular levels of trimethoprim after challenging with 0.8 mg L^-1 of the drug at pH 5 (red columns) and pH 7 (blue columns) at different growth stages (time = 0 and 5 h) as detected by LC-MS analysis after cells were grown for a total of 18 h. Six replicate growth curves were conducted and a typical growth curve for each condition is shown; the other five growth curves showed similar dynamics.</p

PCA and DFA on FT-IR spectra reveal pH and trimethoprim effects.

<p>(a) PCA scores plot of PC1 <i>vs</i>. PC2 after CO₂ removal around 2350 cm^-1 and EMSC scaling. The total explained variance (TEV) of PC1 is 78.9% and for PC2 is 12.8%. (b) PC-DFA score plots of pH 5 and 7 samples. 20 PCs were extracted from PCA and used as inputs to DFA. These 20 PCs explain 99% of TEV; the legend in the plot shows the 95% confidence interval (CI) for the correct classification of the eight conditions. C, control.</p

Growth curves of <i>E</i>. <i>coli</i> K-12.

<p>(a) Growth curves of <i>E</i>. <i>coli</i> K-12 in three different media. Media: the blue plot indicates LB; red, NB and green, Ψ. (b) Growth curves of E. coli K-12 at four different pH values in the same LB medium. The blue plot indicates pH 3; red pH5; green pH 7 and purple pH 9. Six replicate growth curves were conducted and a typical growth curve for each condition is shown; the other five growth curves showed similar dynamics.</p

Metabolic effects of trimethoprim challenge on <i>E</i>. <i>coli</i> K-12 at pH 5 and pH 7.

<p>When partially ionized at pH 7, trimethoprim is seen to impact on metabolism directly associated with the dihydrofolate pathway, as well as off-target effects upon nucleotide, sugar and amino acid metabolism, glycolysis, the TCA cycle, and up-regulation of osmoprotectants. When trimethoprim is in a poorly ionized state (pH 5), it appears to have a profound effect upon the up-regulation of amino acid metabolism.</p

Optimization of Parameters for the Quantitative Surface-Enhanced Raman Scattering Detection of Mephedrone Using a Fractional Factorial Design and a Portable Raman Spectrometer

A new optimization strategy for the SERS detection of mephedrone using a portable Raman system has been developed. A fractional factorial design was employed, and the number of statistically significant experiments (288) was greatly reduced from the actual total number of experiments (1722), which minimized the workload while maintaining the statistical integrity of the results. A number of conditions were explored in relation to mephedrone SERS signal optimization including the type of nanoparticle, pH, and aggregating agents (salts). Through exercising this design, it was possible to derive the significance of each of the individual variables, and we discovered four optimized SERS protocols for which the reproducibility of the SERS signal and the limit of detection (LOD) of mephedrone were established. Using traditional nanoparticles with a combination of salts and pHs, it was shown that the relative standard deviations of mephedrone-specific Raman peaks were as low as 0.51%, and the LOD was estimated to be around 1.6 μg/mL (9.06 × 10^–6 M), a detection limit well beyond the scope of conventional Raman and extremely low for an analytical method optimized for quick and uncomplicated in-field use

Optimization of Parameters for the Quantitative Surface-Enhanced Raman Scattering Detection of Mephedrone Using a Fractional Factorial Design and a Portable Raman Spectrometer

A new optimization strategy for the SERS detection of mephedrone using a portable Raman system has been developed. A fractional factorial design was employed, and the number of statistically significant experiments (288) was greatly reduced from the actual total number of experiments (1722), which minimized the workload while maintaining the statistical integrity of the results. A number of conditions were explored in relation to mephedrone SERS signal optimization including the type of nanoparticle, pH, and aggregating agents (salts). Through exercising this design, it was possible to derive the significance of each of the individual variables, and we discovered four optimized SERS protocols for which the reproducibility of the SERS signal and the limit of detection (LOD) of mephedrone were established. Using traditional nanoparticles with a combination of salts and pHs, it was shown that the relative standard deviations of mephedrone-specific Raman peaks were as low as 0.51%, and the LOD was estimated to be around 1.6 μg/mL (9.06 × 10^–6 M), a detection limit well beyond the scope of conventional Raman and extremely low for an analytical method optimized for quick and uncomplicated in-field use

Liquid Chromatography–Mass Spectrometry Calibration Transfer and Metabolomics Data Fusion

Metabolic profiling is routinely performed on multiple analytical platforms to increase the coverage of detected metabolites, and it is often necessary to distribute biological and clinical samples from a study between instruments of the same type to share the workload between different laboratories. The ability to combine metabolomics data arising from different sources is therefore of great interest, particularly for large-scale or long-term studies, where samples must be analyzed in separate blocks. This is not a trivial task, however, due to differing data structures, temporal variability, and instrumental drift. In this study, we employed blood serum and plasma samples collected from 29 subjects diagnosed with small cell lung cancer and analyzed each sample on two liquid chromatography–mass spectrometry (LC-MS) platforms. We describe a method for mapping retention times and matching metabolite features between platforms and approaches for fusing data acquired from both instruments. Calibration transfer models were developed and shown to be successful at mapping the response of one LC-MS instrument to another (Procrustes dissimilarity = 0.04; Mantel correlation = 0.95), allowing us to merge the data from different samples analyzed on different instruments. Data fusion was assessed in a clinical context by comparing the correlation of each metabolite with subject survival time in both the original and fused data sets: a simple autoscaling procedure (Pearson’s <i>R</i> = 0.99) was found to improve upon a calibration transfer method based on partial least-squares regression (<i>R</i> = 0.94)

Liquid Chromatography–Mass Spectrometry Calibration Transfer and Metabolomics Data Fusion

Metabolic profiling is routinely performed on multiple analytical platforms to increase the coverage of detected metabolites, and it is often necessary to distribute biological and clinical samples from a study between instruments of the same type to share the workload between different laboratories. The ability to combine metabolomics data arising from different sources is therefore of great interest, particularly for large-scale or long-term studies, where samples must be analyzed in separate blocks. This is not a trivial task, however, due to differing data structures, temporal variability, and instrumental drift. In this study, we employed blood serum and plasma samples collected from 29 subjects diagnosed with small cell lung cancer and analyzed each sample on two liquid chromatography–mass spectrometry (LC-MS) platforms. We describe a method for mapping retention times and matching metabolite features between platforms and approaches for fusing data acquired from both instruments. Calibration transfer models were developed and shown to be successful at mapping the response of one LC-MS instrument to another (Procrustes dissimilarity = 0.04; Mantel correlation = 0.95), allowing us to merge the data from different samples analyzed on different instruments. Data fusion was assessed in a clinical context by comparing the correlation of each metabolite with subject survival time in both the original and fused data sets: a simple autoscaling procedure (Pearson’s <i>R</i> = 0.99) was found to improve upon a calibration transfer method based on partial least-squares regression (<i>R</i> = 0.94)