Development of an isotope labeling ultra-high performance liquid chromatography mass spectrometric method for quantification of acylglycines in human urine

Acylglycines play a crucial regulatory and detoxification role in the accumulation of the corresponding acyl CoA esters and are an important class of metabolites in the diagnoses of inborn errors of metabolism. Sensitive quantification of a large number of acylglycines not only improves diagnosis but also enables the discovery of potential new biomarkers of diseases. We report an ultra-high performance liquid chromatography tandem mass spectrometry (UPLC–MS) method for quantifying acylglycines in human urine with high sensitivity. This method is based on the use of a newly developed isotope labeling reagent, p-dimethylaminophenacyl (DmPA) bromide, to label acylglycines to improve detection sensitivity. Eighteen acylglycines, namely acetylglycine, propionylglycine, isobutyrylglycine, butyrylglycine, 4-hydroxyphenylacetylglycine, 2-furoylglycine, tiglylglycine, 2-methybutyrylglycine, 3-methylcrotonylglycine, isovalerylglycine, valerylglycine, hexanoylglycine, phenylacetylglycine, phenylpropionylglycine, glutarylglycine, heptanoylglycine, octanoylglycine and suberylglycine, were measured. This method uses calibration standards prepared in surrogate matrix (un-derivatized urine) and stable-isotope labeled analytes as the internal standards. The analysis was carried out in the positive ion detection mode using multiple reaction monitoring (MRM) survey scans. The calibration curves were validated over the range of 1.0–500 nM. The method achieved a lower limit of quantitation (LLOQ) of 1–5 nM for all analytes, as measured by the standard derivations associated with calibration curves and confirmed in surrogate matrix; the signal-to-noise ratio at LLOQ ranged from 12.50 to 156.70. Both accuracy (% RE or relative error) and precision (% CV) were <15%. Matrix effects were minimized using the surrogate matrix. All eighteen analytes were stable in urine for at least 5 h at room temperature, autosampler (4 °C) for 24 h, 7 weeks at −20 °C and after three freeze/thaw cycles. This surrogate matrix approach was validated using a standard addition experiment. As an example of applications, the endogenous concentrations of all eighteen analytes in urine samples of 20 healthy individuals collected in three consecutive days (i.e., 60 samples) were determined; there was no significant correlation found between the acylglycine profile and gender or body mass indices.     

Identification of metabolomic markers of lavender and lavandin essential oils using mid-infrared spectroscopy

Lavender (Lavandula angustifolia) and lavandin (sterile hybrid of L. angustifolia P. Mill. × Lavandula latifolia (L.f.) Medikus) are widely cultivated in the Mediterranean area for produce essential oils. In this study, 80 lavandin and 55 lavender essential oil samples from various varieties were analyzed. Firstly, a chemometric treatment of mid-infrared spectra was used to evaluate the capacity of Partial Least Squares Discriminant Analysis (PLS-DA) regression to discriminate French lavandin and lavender essential oil (EO) samples and their varieties (Abrial, Fine, Grosso, Maillette, Matherone, Sumian and Super), and secondly, to quantify the main compounds such as linalyl acetate, linalool, eucalyptol and camphor by PLS regression using reference data from gas chromatography. The examination of PLS and PLS-DA regression coefficients allowed the identification of metabolomic markers. The lavender/lavandin EOs and their varieties were very well classified (100% for lavender/lavandin EOs and between 98 and 100% for varieties). The calibration models obtained by PLS regression for the determination of the main compound contents revealed good correlation (≥0.86) between the predicted and reference values. This method can be used to control the authenticity and traceability of lavender/lavandin and their varieties. Finally, mid-infrared and Raman spectroscopy results were compared.     

Chemometric methods in data processing of mass spectrometry-based metabolomics: A review

This review focuses on recent and potential advances in chemometric methods in relation to data processing in metabolomics, especially for data generated from mass spectrometric techniques. Metabolomics is gradually being regarded a valuable and promising biotechnology rather than an ambitious advancement. Herein, we outline significant developments in metabolomics, especially in the combination with modern chemical analysis techniques, and dedicated statistical, and chemometric data analytical strategies. Advanced skills in the preprocessing of raw data, identification of metabolites, variable selection, and modeling are illustrated. We believe that insights from these developments will help narrow the gap between the original dataset and current biological knowledge. We also discuss the limitations and perspectives of extracting information from high-throughput datasets.     

Toward automated chromatographic fingerprinting: A non-alignment approach to gas chromatography mass spectrometry data

In contrast to targeted analysis of volatile compounds, non-targeted approaches take information of known and unknown compounds into account, are inherently more comprehensive and give a more holistic representation of the sample composition. Although several non-targeted approaches have been developed, there's still a demand for automated data processing tools, especially for complex multi-way data such as chromatographic data obtained from multichannel detectors. This work was therefore aimed at developing a data processing procedure for gas chromatography mass spectrometry (GC–MS) data obtained from non-targeted analysis of volatile compounds. The developed approach uses basic matrix manipulation of segmented GC–MS chromatograms and PARAFAC multi-way modelling. The approach takes retention time shifts and peak shape deformations between samples into account and can be done with the freely available N-way toolbox for MATLAB. A demonstration of the new fingerprinting approach is presented using an artificial GC–MS data set and an experimental full-scan GC–MS data set obtained for a set of experimental wines.     

Dansylation isotope labeling liquid chromatography mass spectrometry for parallel profiling of human urinary and fecal submetabolomes

Human urine and feces can be non-invasively collected for metabolomics-based disease biomarker discovery research. Because urinary and fecal metabolomes are thought to be different, analysis of both biospecimens may generate a more comprehensive metabolomic profile that can be better related to the health state of an individual. Herein we describe a method of using differential chemical isotope labeling (CIL) liquid chromatography mass spectrometry (LC-MS) for parallel metabolomic profiling of urine and feces. Dansylation labeling was used to quantify the amine/phenol submetabolome changes among different samples based on ¹²C-labeling of individual samples and ¹³C-labeling of a pooled urine or pooled feces and subsequent analysis of the ¹³C-/¹²C-labeled mixture by LC-MS. The pooled urine and pooled feces are further differentially labeled, mixed and then analyzed by LC-MS in order to relate the metabolite concentrations of the common metabolites found in both biospecimens. This method offers a means of direct comparison of urinary and fecal submetabolomes. We evaluated the analytical performance and demonstrated the utility of this method in the analysis of urine and feces collected daily from three healthy individuals for 7 days. On average, 2534 ± 113 (n = 126) peak pairs or metabolites could be detected from a urine sample, while 2507 ± 77 (n = 63) peak pairs were detected from a fecal sample. In total, 5372 unique peak pairs were detected from all the samples combined; 3089 and 3012 pairs were found in urine and feces, respectively. These results reveal that the urine and fecal metabolomes are very different, thereby justifying the consideration of using both biospecimens to increase the probability of finding specific biomarkers of diseases. Furthermore, the CIL LC-MS method described can be used to perform parallel quantitative analysis of urine and feces, resulting in more complete coverage of the human metabolome.     

Potential of monitoring isotopologues by quantitative gas chromatography with time‐of‐flight mass spectrometry for metabolomic assay

Because of the extreme complexity of metabolomic samples, the effectiveness of quantitative gas chromatography with time‐of‐flight mass spectrometry depends substantially on the expansion of the linear dynamic range. Facing the existence of numerous saturated detector signals, a data processing method based on monitoring isotopologues has been developed. The monoisotopic ion kept the high mass spectrometry sensitivity, and the less abundant isotopologue ions extended the linear dynamic range. This alternative method was proved to extend the linear dynamic range to five orders of magnitude successfully and overcome the quantitative problems induced by the ion detector saturation. Finally, to validate the applicability, the method was applied to a metabolomic assay of Alzheimer's disease. Comparing with the traditional monoisotopic method, the use of monitoring isotopologues helped us to discover an additional eight metabolites with significant difference and to conduct a more reliable principal component analysis as well. The results demonstrated that monitoring isotopologues in quantitative gas chromatography with time‐of‐flight mass spectrometry could improve the authenticity of metabolomic analysis.     

Anti-inflammatory effects of Scutellaria baicalensis water extract in LPS-induced THP-1 Macrophages through metabolomics study

(1) Background: Scutellaria baicalensis (Huang Qin) is a traditional Chinese Medicine possess beneficial effects of anti-inflammation in various diseases. In this study, we aimed to use untargeted metabolomics approach to investigate the possible underlying metabolic mechanisms of anti-inflammation effects of Scutellaria baicalensis in LPS-induced macrophages.; (2) Methods: Scutellaria baicalensis water extract (SBE) were applied to the THP-1 cells which were induced by phorbol 12-myristate 13-acetate (PMA) into macrophages under the LPS treated conditions. The cell lysate were collected and metabolites were extracted before characterizing by ultra-performance liquid chromatography (UPLC) combined with Q-Exactive mass/mass spectrometry (LC-MS/MS). The differential accumulated metabolites and related metabolism pathways affected by SBE in LPS-induced macrophages were identified. Further investigation of the secretion and expression of inflammatory cytokines IL-1β, TNF-ɑ and VEGFR were tested by real-time polymerase chain reaction (RT-PCR). (3) Results: The metabolome profile have indicated that retinol metabolism, arachidonic acid metabolism and linoleic acid metaoblism pathways were the most significantly enriched pathways response to SBE in LPS induced inflammatory model. Besides, SBE could inhibit the expression of the pro-inflammatory cytokines IL-1β and TNF-ɑ, and downregulation of the macrophage migration accelerator VEGFR1 in a dose dependent manner; (4) Conclusions: These findings indicated that SBE may exerted anti-inflammatory ability by regulating multiple fatty acids metabolism pathways as well as inhibiting the secretion of pro-inflammatory cytokines and VEGFR. This study provides evidences for Scutellaria baicalensis as the material for developing natural, effective anti-inflammatory products.     

Metabolic fingerprinting of Schistosoma mansoni infection in mice urine with capillary electrophoresis

Schistosoma mansoni infection in mice has been fingerprinted using CE to study the capabilities of this technique as a diagnostic tool for this parasitic disease. Two modes of separation were used in generating the electrophoretic data, with each untreated urine sample the following methods were applied: (i) a fused-silica capillary, operating with an applied potential of 18 kV, in micellar EKC (MEKC) and (ii) a polyacrylamide-coated capillary, operating with an applied potential of -20 kV under zonal CZE conditions. By combining normal and reverse polarities in the data treatment we have extracted more information from the samples, which is a better approach for CE metabolomics. The traditional problems associated with variability in electrophoretic peak migration times for analytes were countered by using a dynamic programming algorithm for the electropherograms alignment. Principal component analyses of these aligned electropherograms and partial least square discriminant analysis (PLS-DA) data are shown to provide a valuable means of rapid and sample classification. This approach may become an important tool for the identification of biomarkers, diagnosis and disease surveillance.     

Effects of the application of different window functions and projection methods on processing of 1H J-resolved nuclear magnetic resonance spectra for metabolomics

Two dimensional (2D) homonuclear ¹H J-resolved (JRES) nuclear magnetic resonance spectroscopy is increasingly used in metabolomics. This approach visualises metabolite chemical shifts and scalar couplings along different spectral dimensions, thereby increasing peak dispersion and facilitating spectral assignments and accurate quantification. Here, we optimise the processing of 2D JRES spectra by evaluating different window functions, a traditional sine-bell (SINE) and a combined sine-bell-exponential (SEM) function. Furthermore, we evaluate different projection methods for generating 1D projected spectra (pJRES). Spectra were recorded from three disparate types of biological samples and evaluated in terms of sensitivity, reproducibility and resolution. Overall, the SEM window function yielded considerably higher sensitivity and comparable spectral reproducibility and resolution compared to SINE, for both 1D pJRES and 2D JRES datasets. Furthermore, for pJRES spectra, the highest spectral quality was obtained using SEM combined with skyline projection. These improvements lend further support to utilising 2D J-resolved spectroscopy in metabolomics.