Search

Quantitative metabolomics has become much more important in clinical research in recent years. Individual differences in matrix effects (MEs) and the injection order effect are two major factors that reduce the quantification accuracy in liquid chromatography-electrospray ionization-mass spectrometry-based (LC-ESI-MS) metabolomics studies. This study proposed a postcolumn infused-internal standard (PCI-IS) combined with a matrix normalization factor (MNF) strategy to improve the analytical accuracy of quantitative metabolomics. The PCI-IS combined with the MNF method was applied for a targeted metabolomics study of amino acids (AAs). D8-Phenylalanine was used as the PCI-IS, and it was postcolumn-infused into the ESI interface for calibration purposes. The MNF was used to bridge the AA response in a standard solution with the plasma samples. The MEs caused signal changes that were corrected by dividing the AA signal intensities by the PCI-IS intensities after adjustment with the MNF. After the method validation, we evaluated the method applicability for breast cancer research using 100 plasma samples. The quantification results revealed that the 11 tested AAs exhibit an accuracy between 88.2 and 110.7%. The principal component analysis score plot revealed that the injection order effect can be successfully removed, and most of the within-group variation of the tested AAs decreased after the PCI-IS correction. Finally, targeted metabolomics studies on the AAs showed that tryptophan was expressed more in malignant patients than in the benign group. We anticipate that a similar approach can be applied to other endogenous metabolites to facilitate quantitative metabolomics studies.

Two-dimensional gas chromatography time-of-flight mass spectrometry (GC×GC/TOF-MS) is superior for chromatographic separation and provides great sensitivity for complex biological fluid analysis in metabolomics. However, GC×GC/TOF-MS data processing is currently limited to vendor software and typically requires several preprocessing steps. In this work, we implement a web-based platform, which we call GC 2 MS, to facilitate the application of recent advances in GC×GC/TOF-MS, especially for metabolomics studies. The core processing workflow of GC 2 MS consists of blob/peak detection, baseline correction, and blob alignment. GC 2 MS treats GC×GC/TOF-MS data as pictures and clusters the pixels as blobs according to the brightness of each pixel to generate a blob table. GC 2 MS then aligns the blobs of two GC×GC/TOF-MS data sets according to their distance and similarity. The blob distance and similarity are the Euclidean distance of the first and second retention times of two blobs and the Pearson's correlation coefficient of the two mass spectra, respectively. GC 2 MS also directly corrects the raw data baseline. The analytical performance of GC 2 MS was evaluated using GC×GC/TOF-MS data sets of Angelica sinensis compounds acquired under different experimental conditions and of human plasma samples. The results show that GC 2 MS is an easy-to-use tool for detecting peaks and correcting baselines, and GC 2 MS is able to align GC×GC/TOF-MS data sets acquired under different experimental conditions. GC 2 MS is freely accessible at http://gc2ms.web.cmdm.tw .

Studies of the cell metabolome greatly improve our understanding of cell biology. Currently, most cellular metabolomics studies control only cell numbers or protein content without adjusting the total metabolite concentration, mainly because of the lack of an effective concentration normalization method for cell metabolites. This study proposed a matrix-induced ion suppression (MIIS) method to measure the total amount of cellular metabolites by utilizing flow injection analysis coupled with electrospray ionization mass spectrometry (FIA-ESI-MS).We used series dilutions of HL-60 cell extracts to establish the relationship between cellular metabolite concentration and the degree of ion suppression of the ion suppression indicator, and a good correlation was obtained between 2- and 12-fold dilutions of cell extracts (R(2) = 0.999). Two lung cancer cells, CL1-0 and CL1-5, were selected as the model cell lines to evaluate the efficacy of the MIIS method and the importance of metabolite concentration normalization. Through MIIS analysis, CL1-0 cells were found to contain metabolites at a concentration 2.1 times higher than in CL1-5, and the metastatic properties of CL1-5 could only be observed after 2.1-fold dilution of CL1-0 before metabolomic analysis. Our results demonstrated that the MIIS method is an effective approach for metabolite concentration normalization and that controlling metabolite concentrations can improve data integrity in cellular metabolomics studies.

Quantification of endogenous metabolites has enabled the discovery of biomarkers for diagnosis and provided for an understanding of disease etiology. The standard addition and stable isotope labeled-internal standard (SIL-IS) methods are currently the most widely used approaches to quantifying endogenous metabolites, but both have some limitations for clinical measurement. In this study, we developed a new approach for endogenous metabolite quantification by the postcolumn infused-internal standard (PCI-IS) method combined with the matrix normalization factor (MNF) method. MNF was used to correct the difference in MEs between standard solution and biofluids, and PCI-IS additionally tailored the correction of the MEs for individual samples. Androstenedione and testosterone were selected as test articles to verify this new approach to quantifying metabolites in plasma. The repeatability (n=4 runs) and intermediate precision (n=3 days) in terms of the peak area of androstenedione and testosterone at all tested concentrations were all less than 11% relative standard deviation (RSD). The accuracy test revealed that the recoveries were between 95.72% and 113.46%. The concentrations of androstenedione and testosterone in fifty plasma samples obtained from healthy volunteers were quantified by the PCI-IS combined with the MNF method, and the quantification results were compared with the results of the SIL-IS method. The Pearson correlation test showed that the correlation coefficient was 0.98 for both androstenedione and testosterone. We demonstrated that the PCI-IS combined with the MNF method is an effective and accurate method for quantifying endogenous metabolites., (Copyright © 2014 Elsevier B.V. All rights reserved.)

Background: As fatty acids play an important role in biological regulation, the profiling of fatty acid expression has been used to discover various disease markers and to understand disease mechanisms. This study developed an effective and accurate comparative fatty acid analysis method using differential labeling to speed up the metabolic profiling of fatty acids., Methods: Fatty acids were derivatized with unlabeled (D0) or deuterated (D3) methanol, followed by GC-MS analysis. The comparative fatty acid analysis method was validated using a series of samples with different ratios of D0/D3-labeled fatty acid standards and with mouse liver extracts., Results: Using a lipopolysaccharide (LPS)-treated mouse model, we found that the fatty acid profiles after LPS treatment were similar between the conventional single-sample analysis approach and the proposed comparative approach, with a Pearson's correlation coefficient of approximately 0.96. We applied the comparative method to investigate voriconazole-induced hepatotoxicity and revealed the toxicity mechanism as well as the potential of using fatty acids as toxicity markers., Conclusions: In conclusion, the comparative fatty acid profiling technique was determined to be fast and accurate and allowed the discovery of potential fatty acid biomarkers in a more economical and efficient manner., (Copyright © 2014 Elsevier B.V. All rights reserved.)

This study developed CE and ultra-high-pressure LC (UHPLC) methods coupled with UV detectors to characterize the metabolomic profiles of different rhubarb species. The optimal CE conditions used a BGE with 15 mM sodium tetraborate, 15 mM sodium dihydrogen phosphate monohydrate, 30 mM sodium deoxycholate, and 30% ACN v/v at pH 8.3. The optimal UHPLC conditions used a mobile phase composed of 0.05% phosphate buffer and ACN with gradient elution. The gradient profile increased linearly from 10 to 21% ACN within the first 25 min, then increased to 33% ACN for the next 10 min. It took another 5 min to reach the 65% ACN, then for the next 5 min, it stayed unchanged. Sixteen samples of Rheum officinale and Rheum tanguticum collected from various locations were analyzed by CE and UHPLC methods. The metabolite profiles of CE were aligned and baseline corrected before chemometric analysis. Metabolomic signatures of rhubarb species from CE and UHPLC were clustered using principle component analysis and distance-based redundancy analysis; the clusters were not only able to discriminate different species but also different cultivation regions. Similarity measurements were performed by calculating the correlation coefficient of each sample with the authentic samples. Hybrid rhizome was clearly identified through similarity measurement of UHPLC metabolite profile and later confirmed by gene sequencing. The present study demonstrated that CE and UHPLC are efficient and effective tools to identify and authenticate herbs even coupled with simple detectors., (© 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Motivation Liquid chromatography coupled with mass spectrometry (LC-MS) is widely used in metabolomics studies, while HILIC LC-MS is particularly suited for polar metabolites. Determining an optimized mobile phase and developing a proper liquid chromatography method tend to be laborious, time-consuming and empirical. Results We developed a containerized web tool providing a workflow to quickly determine the optimized mobile phase by batch-evaluating chromatography peaks for metabolomics LC-MS studies. A mass chromatographic quality value, an asymmetric factor, and the local maximum intensity of the extracted ion chromatogram were calculated to determine the number of peaks and peak retention time. The optimal mobile phase can be quickly determined by selecting the mobile phase that produces the largest number of resolved peaks. Moreover, the workflow enables one to automatically process the repeats by evaluating chromatography peaks and determining the retention time of large standards. This workflow was validated with 20 chemical standards and successfully constructed a reference library of 571 metabolites for the HILIC LC-MS platform. Availability and implementation MetaMOPE is freely available at https://metamope.cmdm.tw. Source code and installation instructions are available on GitHub: https://github.com/CMDM-Lab/MetaMOPE. Supplementary information Supplementary data are available at Bioinformatics Advances online. [ABSTRACT FROM AUTHOR]

Normalizing the total urine concentration is important for minimizing bias in urinary metabolomics analysis comparisons. In this study, we report a matrix-induced ion suppression (MIIS)-based method to normalize concentration using flow injection analysis coupled with electrospray ionization mass spectrometry (FIA-ESI-MS). An ion suppression indicator (ISI) was spiked into urine samples, and the intensity of the extracted ion chromatogram (EIC) for ISI in a urine matrix was subtracted by the EIC for a blank solution and used to calculate the extent to which the signal was reduced by the urine matrix. A series dilution of pooled urine samples was used to correlate the urine concentration and level of ion suppression for ISI. A regression equation was used to estimate the relative concentration of unknown urine samples. The MIIS method was validated for linearity, precision and accuracy. We obtained a good correlation using a quadratic regression model for 1- to 32-fold urine dilutions ( R 2 = 0.998). The reproducibility ( n = 4) and intermediate precision ( n = 3) were below 5% RSD, and the accuracy ranged from 97.15% to 102.10%. The established method was used to estimate the relative concentrations of 16 urine samples, and the results were compared with commonly used normalization methods. Pearson’s correlation test was used to demonstrate that the MIIS method correlated highly with the creatinine and osmolarity methods; the correlation coefficients were 0.93 and 0.99, respectively. We successfully applied this method to a urinary metabolomics study on breast cancer. This study demonstrated the MIIS method is simple, accurate and can contribute to data integrity in urinary metabolomics studies. [ABSTRACT FROM AUTHOR]

Liquid Chromatography - Time of Flight Mass Spectrometry has become an important technique for toxicological screening and metabolomics. We describe TIPick a novel algorithm that accurately and sensitively detects target compounds in biological samples. TIPick comprises two main steps: background subtraction and peak picking. By subtracting a blank chromatogram, TIPick eliminates chemical signals of blank injections and reduces false positive results. TIPick detects peaks by calculating the S(CCINI) values of extracted ion chromatograms (EICs) without considering peak shapes, and it is able to detect tailing and fronting peaks. TIPick also uses duplicate injections to enhance the signals of the peaks and thus improve the peak detection power. Commonly seen split peaks caused by either saturation of the mass spectrometer detector or a mathematical background subtraction algorithm can be resolved by adjusting the mass error tolerance of the EICs and by comparing the EICs before and after background subtraction. The performance of TIPick was tested in a data set containing 297 standard mixtures; the recall, precision and F-score were 0.99, 0.97 and 0.98, respectively. TIPick was successfully used to construct and analyze the NTU MetaCore metabolomics chemical standards library, and it was applied for toxicological screening and metabolomics studies. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]