Flow Injection Analysis

LC-MSmass spectrometry (LC-MS/MS) is increasingly used in clinical settings for quantitative assay of small molecules and peptides such as vitamin D, serum bile acid and parathyroid hormone under Clinical Laboratory Improvement Amendments environments with high sensitivities and specificities³⁴ . In this study, targeted metabolomic analysis of plasma samples was performed using the Biocrates Absolute-IDQ P180 (BIOCRATES, Life Science AG, Innsbruck, Austria). This validated targeted assay allows for simultaneous detection and quantification of metabolites in plasma samples (10 µL) in a high-throughput manner. The methods have been described in detail^{35 ,36} . The plasma samples were processed as per the instructions by the manufacturer and analyzed on a triple-quadrupole mass spectrometer (Xevo TQ-S, Waters Corporation, USA) operating in the MRM mode. The measurements were made in a 96-well format for a total of 148 samples, and seven calibration standards and three quality control samples were integrated in the kit. Briefly, the flow injection analysis tandem mass spectrometry (MS/MS) method was used to quantify a panel of 144 lipids simultaneously by multiple reaction monitoring. The other metabolites are resolved on the UPLC and quantified using scheduled MRMs. The kit facilitates absolute quantitation of 21 amino acids, hexose, carnitine, 39 acylcarnitines, 15 sphingomyelins, 90 phosphatidylcholines and 19 biogenic amines. Data analysis was performed using the MetIQ software (Biocrates), and the statistical analyses included the nonparametric Kruskal-Wallis test with follow-up Mann-Whitney U-tests for pairwise comparisons using the STAT pack module v3 (Biocrates). Significance was adjusted for multiple comparisons using Bonferroni’s method (P < 0.025). The abundance is calculated from area under the curve by normalizing to the respective isotope labeled internal standard. The concentration is expressed as nmol/L. Human EDTA plasma samples spiked with standard metabolites were used as quality control samples to assess reproducibility of the assay. The mean of the coefficient of variation (CV) for the 180 metabolites was 0.08, and 95% of the metabolites had a CV of <0.15.

All chemometric and statistical analyses were carried out in MATLAB (Mathworks, Natick, MA), GraphPad Prism (GraphPad Software Incorporated, La Jolla, CA), Excel (Microsoft Corporation, Redmond, WA) and LabVIEW (National Instruments, Austin, TX). All values are reported as averages ± standard error of the mean (SEM).
Each in vivo training set constructed met specific requirements.[35 , 36 ] First, cyclic voltammograms of all expected analytes were included, generated by electrically stimulating the animal. Second, no more than one cyclic voltammogram for each species was taken per stimulated release event, satisfying the requirement of mutual independence. Third, the training set mimicked the experimental conditions as closely as possible. The same electrode, electronics, and other equipment were used to collect both the training set and the unknown data set. The various cyclic voltammograms in the training set were consistent in shape and representative in noise level to the unknown data set. Cyclic voltammograms of the unknown data set were not used to build the training set. Finally, the training set spanned the concentration range contained in the unknown data set being predicted. Cyclic voltammograms of varying intensity were generated by changing the stimulation parameters (i.e. current, number of pulses, and frequency). Concentrations were estimated using flow injection analysis[37 ] after the experiment was completed.[34 (link)]
In total, 119 training sets were assembled from five users in two different laboratories over the course of several years. These training sets have been used for concentration prediction from a variety of experiments including various behavioral experiments and stimulated release studies. Each training set consisted of five dopamine and five pH change cyclic voltammograms. Any training sets that contained more than five cyclic voltammograms per analyte were truncated to make all training sets consistent in size. However, a uniform distribution of concentration values within the training set was maintained.
PCA was performed using singular value decomposition.[38 ] PCR using residual analysis was performed as described previously.[14 , 15 ] Cyclic voltammetric representations of the training set consisting of only the primary PCs were calculated as follows. First, the primary PCs of the training set determined by either the 99.5% cumulative variance method or Malinowski’s F-test were organized in a matrix, Vc. The projection of the training set onto the primary PCs, A_proj was calculated,
$${\mathbf{A}}_{\mathbf{proj}}=\mathbf{V}{\mathbf{c}}^{\mathbf{T}}\mathbf{A}$$ where the superscript T represents the transpose of the matrix and A contains the training set voltammetric matrix. Finally, the training set consisting of only the primary PCs, A_nPC, was reconstructed by multiplying the retained PCs by the projection of the training set onto the primary PCs[13 ]
To determine the noise discarded in the secondary PCs of a training set, A_jPC, A_nPC was subtracted from the original training set voltammetric matrix A.
The signal-to-noise ratios of the dopamine cyclic voltammograms were calculated by dividing oxidative peak current by the standard deviation of a flat portion of the cyclic voltammogram, specifically from 0.95 V to 0.25 V on the reductive sweep. Root-mean-square (RMS) noise was calculated from A_jPC. The RMS current, i_RMS, was calculated for each cyclic voltammogram in the training set using the following equation
$$i_{\mathit{RMS}}=\sqrt{\frac{{\displaystyle \sum _{x=1}^w}{(i_x)}^2}{w}}$$ where i_x is the current at the x^th point of the voltammetric waveform containing w total points taken from the A_jPC matrix. An average i_RMS value for each analyte using each method of factor selection was calculated for each training set.