bims-metlip Biomed News
on Methods and protocols in metabolomics and lipidomics
Issue of 2020‒01‒12
eleven papers selected by
Sofia Costa
Cold Spring Harbor Laboratory

  1. Anal Bioanal Chem. 2020 Jan 04.
      A novel online two-dimensional supercritical fluid chromatography/reversed-phase liquid chromatography-triple-quadrupole mass spectrometry (2D SFC/RPLC-QQQ MS) method based on a vacuum solvent evaporation interface was developed for lipid profiling in human plasma, in which lipid classes were separated by the first-dimension SFC and different lipid molecular species were further separated by the second-dimension RPLC. All separation condition parameters were carefully optimized, and their influence on the chromatographic behavior of lipids is discussed. Finally, the recoveries of 11 lipid standards were all more than 88% for the interface. Besides, the limit of detection for these lipid standards was on the order of nanograms per milliliter, and the relative standard deviations of the peak area and retention time ranged from 1.54% to 19.85% and from 0.00% to 0.10%, respectively. The final 2D SFC/RPLC-QQQ MS method allowed the identification of 370 endogenous lipid species from ten lipid classes, including diacylglycerol, triacylglycerol, ceramide, glucosylceramide, galactosylceramide, lactosylceramide, sphingomyelin, acylcarnitine, phosphatidylcholine, and lysophosphatidylethanolamine, in human plasma within 38 min, which was used for screening potential lipid biomarkers in breast cancer. The 2D SFC/RPLC-QQQ MS method is a potentially useful tool for in-depth studies focused on complex lipid metabolism and biomarker discovery. Graphical Abstract.
    Keywords:  Lipidomics; Mass spectrometry; Supercritical fluid chromatography; Two-dimensional
  2. Anal Chim Acta. 2020 Feb 08. pii: S0003-2670(19)31350-9. [Epub ahead of print]1097 110-119
      Enlightened by the high specificity and reactivity of thiol radical toward allyl, here, we first established a rapid thiol radical-based chemical isotope-labelling (CIL) strategy coupled with high performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS) analysis for the quantitative profiling of sterols. In this strategy, N-(4-(carbazole-9-yl)-phenyl)-N-maleimide labelled derivative of ethylenedithiol (NCPM-d0-SH) and its deuterated analogue NCPM-d2-SH were employed as a novel pair of CIL reagents to efficiently label sterols. Under lighting condition, the thiol radical obtained from NCPM-d0/d2-SH attacks one allyl hydrogen in the B-ring of sterols to produce a reactive radical intermediate which can quickly react with another thiol radical to form the last labelled derivatives. This labelling reaction can rapidly complete only within 1.5 min. Absorbingly, the NCPM-d0-SH and NCPM-d2-SH labelled derivatives of sterols can produce two specific product ions (PIs) containing different isotope tags at m/z of 431.6 and 433.6 via collision induced dissociation, which were employed to develop the multiple reaction monitoring (MRM) mode-based analysis. According to the specific mass differences with a fixed value, the peak pairs with similar retention times can be easily extracted from the two PIs spectrums and designated as the candidates for the identification of sterols. NCPM-d0-SH and NCPM-d2-SH labelled derivatives of sterols can be readily distinguished from their several ion chromatograms. Thus, sterols from two samples labelled by different isotope tags were ionized at the same conditions and measured respectively, providing excellent identification and precise quantitation by compensating the matrix effect and instrument fluctuation during MS-based analysis. The detection sensitivities of thiol-containing drugs improved by 53.5-560.3-fold due to NCPM-labelling. The limits of detection (LODs) and the limits of quantitation (LOQs) were in the range of 0.15-0.40 μg kg-1 and 0.50-1.30 μg kg-1, respectively. Using the developed method, we quantitatively profiled five sterols in vegetable oils with good applicability. As promising, the proposed thiol radical-based CIL strategy is a potential platform for the quantitation of sterols.
    Keywords:  Chemical isotope labelling; Sterols; Thiol radical; Vegetable oils
  3. Metab Eng Commun. 2020 Jun;10 e00120
      13C Metabolic Flux Analysis (13C-MFA) involves the quantification of isotopic enrichment in cellular metabolites and fitting the resultant data to the metabolic network model of the organism. Coverage and resolution of the resultant flux map depends on the total number of metabolites and fragments in which 13C enrichment can be quantified accurately. Experimental techniques for tracking 13C enrichment are evolving rapidly and large volumes of data are now routinely generated through the use of Liquid Chromatography coupled with High-Resolution Mass Spectrometry (HR-LC/MS). Therefore, the current manuscript is focused on the challenges in high-throughput analyses of such large datasets. Current 13C-MFA studies often have to rely on the targeted quantification of a small subset of metabolites, thereby leaving a large fraction of the data unexplored. A number of public domain software tools have been reported in recent years for the untargeted quantitation of isotopic enrichment. However, the suitability of their application across diverse datasets has not been investigated. Here, we test the software tools X13CMS, DynaMet, geoRge, and HiResTEC with three diverse datasets. The tools provided a global, untargeted view of 13C enrichment in metabolites in all three datasets and a much-needed automation in data analysis. Some inconsistencies were observed in results obtained from the different tools, which could be partially ascribed to the lack of baseline separation and potential mass conflicts. After removing the false positives manually, isotopic enrichment could be quantified reliably in a large repertoire of metabolites. Of the software tools explored, geoRge and HiResTEC consistently performed well for the untargeted analysis of all datasets tested.
    Keywords:  13C metabolic flux analysis; Cyanobacteria; Methanolicus; Reticulocytes; Synechococcus sp. PCC 7002; Untargeted analysis
  4. Anal Bioanal Chem. 2020 Jan 10.
      Accurate and precise cortisol measurements are requisite for ensuring appropriate diagnosis and management of diseases related with adrenal or pituitary gland disorders. Prompted by the needs in characterization of certified reference materials and quality assurance for serum cortisol measurements, we developed and evaluated a highly reliable measurement procedure based on isotope dilution liquid chromatography-tandem mass spectrometry (ID LC-MS/MS) combined with dextran sulfate-Mg2+ precipitation as the sample pretreatment. An appropriate amount of serum was accurately weighed and spiked with the isotope labelled internal standard. After precipitation, massive lipids and lipoproteins were removed from serum matrix. The clear supernatant was transferred and extracted with ethyl acetate-hexane solution. The cortisol was analyzed with LC-MS/MS in positive electrospray ionization mode. The within-run and total coefficient of variations (CVs) ranged from 0.3 to 0.6% and 0.7 to 1.2%, respectively, for a concentration range of 76.30 to 768.04 nmol/L. A regression comparison of the results obtained by the present method and the certified values of ERM-DA451 showed agreement with no statistical difference (Y = 1.0092 X-0.7455; 95% CI for the slope, 0.9940 to 1.0212; 95% CI for the intercept, - 3.6575 to 2.6390, r2 = 0.999). All structural analogs of cortisol tested were well resolved from cortisol in 12 min on a phenyl ligand column under an isocratic elution. The limit of quantification was estimated to 5 pg cortisol in absolute amount. This method is accurate and simple and can be served as a candidate reference measurement procedure in establishment of serum cortisol reference system.
    Keywords:  Lipoprotein precipitation; Liquid chromatography-tandem mass spectrometry; Reference method; Serum cortisol
  5. Metabolomics. 2020 Jan 10. 16(1): 14
      INTRODUCTION: Several software packages containing diverse algorithms are available for processing Liquid Chromatography-Mass Spectrometry (LC-MS) chromatographic data and within these deconvolution packages different parameters settings can lead to different outcomes. XCMS is the most widely used peak picking and deconvolution software for metabolomics, but the parameter selection can be hard for inexpert users. To solve this issue, the automatic optimization tools such as Isotopologue Parameters Optimization (IPO) can be extremely helpful.OBJECTIVES: To evaluate the suitability of IPO as a tool for XCMS parameters optimization and compare the results with those manually obtained by an exhaustive examination of the LC-MS characteristics and performance.
    METHODS: Raw HPLC-TOF-MS data from two types of biological samples (liver and plasma) analysed in both positive and negative electrospray ionization modes from three groups of piglets were processed with XCMS using parameters optimized following two different approaches: IPO and Manual. The outcomes were compared to determine the advantages and disadvantages of using each method.
    RESULTS: IPO processing produced the higher number of repeatable (%RSD < 20) and significant features for all data sets and allowed the different piglet groups to be distinguished. Nevertheless, on multivariate level, similar clustering results were obtained by Principal Component Analysis (PCA) when applied to IPO and manual matrices.
    CONCLUSION: IPO is a useful optimization tool that helps in choosing the appropriate parameters. It works well on data with a good LC-MS performance but the lack of such adequate data can result in unrealistic parameter settings, which might require further investigation and manual tuning. On the contrary, manual selection criteria requires deeper knowledge on LC-MS, programming language and XCMS parameter interpretation, but allows a better fine-tuning of the parameters, and thus more robust deconvolution.
    Keywords:  Data treatment; IPO; LC–MS; Metabolomics; XCMS
  6. BMC Bioinformatics. 2020 Jan 09. 21(1): 11
      BACKGROUND: Metabolomics time-course experiments provide the opportunity to understand the changes to an organism by observing the evolution of metabolic profiles in response to internal or external stimuli. Along with other omic longitudinal profiling technologies, these techniques have great potential to uncover complex relations between variations across diverse omic variables and provide unique insights into the underlying biology of the system. However, many statistical methods currently used to analyse short time-series omic data are i) prone to overfitting, ii) do not fully take into account the experimental design or iii) do not make full use of the multivariate information intrinsic to the data or iv) are unable to uncover multiple associations between different omic data. The model we propose is an attempt to i) overcome overfitting by using a weakly informative Bayesian model, ii) capture experimental design conditions through a mixed-effects model, iii) model interdependencies between variables by augmenting the mixed-effects model with a conditional auto-regressive (CAR) component and iv) identify potential associations between heterogeneous omic variables by using a horseshoe prior.RESULTS: We assess the performance of our model on synthetic and real datasets and show that it can outperform comparable models for metabolomic longitudinal data analysis. In addition, our proposed method provides the analyst with new insights on the data as it is able to identify metabolic biomarkers related to treatment, infer perturbed pathways as a result of treatment and find significant associations with additional omic variables. We also show through simulation that our model is fairly robust against inaccuracies in metabolite assignments. On real data, we demonstrate that the number of profiled metabolites slightly affects the predictive ability of the model.
    CONCLUSIONS: Our single model approach to longitudinal analysis of metabolomics data provides an approach simultaneously for integrative analysis and biomarker discovery. In addition, it lends better interpretation by allowing analysis at the pathway level. An accompanying R package for the model has been developed using the probabilistic programming language Stan. The package offers user-friendly functions for simulating data, fitting the model, assessing model fit and postprocessing the results. The main aim of the R package is to offer freely accessible resources for integrative longitudinal analysis for metabolomics scientists and various visualization functions easy-to-use for applied researchers to interpret results.
    Keywords:  Bayesian inference; Biomarker discovery; Integrative analysis; Metabolomics; Pathways
  7. Anal Biochem. 2020 Jan 03. pii: S0003-2697(19)31070-X. [Epub ahead of print] 113558
      Amino acids (AAs) and one-carbon (1-C) metabolism compounds are involved in a range of key metabolic pathways, and mediate numerous health and disease processes in the human body. Previous assays have quantified a limited selection of these compounds and typically require extensive manual handling. Here, we describe the robotic automation of an analytical method for the simultaneous quantification of 37 1-C metabolites, amino acids, and precursors using reversed-phase ultra-high-pressure liquid chromatography coupled with tandem mass spectrometry (UHPLC/MS-MS). Compound extraction from human plasma was tested manually before being robotically automated. The final automated analytical panel was validated on human plasma samples. Our automated and multiplexed method holds promise for application to large cohort studies.
  8. Anal Chim Acta. 2020 Feb 08. pii: S0003-2670(19)31349-2. [Epub ahead of print]1097 49-61
      Clinical metabolomics aims at finding statistically significant differences in metabolic statuses of patient and control groups with the intention of understanding pathobiochemical processes and identification of clinically useful biomarkers of particular diseases. After the raw measurements are integrated and pre-processed as intensities of chromatographic peaks, the differences between controls and patients are evaluated by both univariate and multivariate statistical methods. The traditional univariate approach relies on t-tests (or their nonparametric alternatives) and the results from multiple testing are misleadingly compared merely by p-values using the so-called volcano plot. This paper proposes a Bayesian counterpart to the widespread univariate analysis, taking into account the compositional character of a metabolome. Since each metabolome is a collection of some small-molecule metabolites in a biological material, the relative structure of metabolomic data, which is inherently contained in ratios between metabolites, is of the main interest. Therefore, a proper choice of logratio coordinates is an essential step for any statistical analysis of such data. In addition, a concept of b-values is introduced together with a Bayesian version of the volcano plot incorporating distance levels of the posterior highest density intervals from zero. The theoretical background of the contribution is illustrated using two data sets containing samples of patients suffering from 3-hydroxy-3-methylglutaryl-CoA lyase deficiency and medium-chain acyl-CoA dehydrogenase deficiency. To evaluate the stability of the proposed method as well as the benefits of the compositional approach, two simulations designed to mimic a loss of samples and a systematical measurement error, respectively, are added.
    Keywords:  Bayesian inference; Compositional data; High-dimensional data; Multiple hypotheses testing; Untargeted metabolomics; Volcano plot
  9. J Chromatogr B Analyt Technol Biomed Life Sci. 2019 Dec 06. pii: S1570-0232(19)31313-3. [Epub ahead of print]1138 121925
      An liquid chromatography-mass spectrometry (LC-MS/MS) assay was developed for the combined analysis of the five poly (ADP-ribose) polymerase (PARP) inhibitors niraparib, olaparib, rucaparib talazoparib and veliparib. A simple and fast sample pre-treatment method was used by protein precipitating of plasma samples with acetonitrile and dilution of the supernatant with formic acid (0.1% v/v in water). This was followed by chromatographic separation on a reversed-phase UPLC BEH C18 column and detection with a triple quadrupole mass spectrometer operating in the positive mode. A simplified validation procedure specifically designed for bioanalytical methods for clinical therapeutic drug monitoring (TDM) purposes, was applied. This included assessment of the calibration model, accuracy and precision, lower limit of quantification (LLOQ), specificity and selectivity, carry-over and stability. The validated range was 30-3000 ng/mL for niraparib, 100-10,000 ng/mL for olaparib, 50-5000 ng/mL for rucaparib, 0.5-50 ng/mL for talazoparib and 50-5000 for veliparib. All results were within the criteria of the US Food and Drug Administration (FDA) guidance and European Medicines Agency (EMA) guidelines on method validation. The assay has been successfully implemented in our laboratory.
    Keywords:  LC-MS/MS; Niraparib; Olaparib; PARP-inhibitor; Rucaparib; Talazoparib; Therapeutic drug monitoring; Validation; Veliparib
  10. Rapid Commun Mass Spectrom. 2020 Jan 08. e8722
      RATIONALE: Hyphenation of atmospheric pressure chemical ionization (APCI) mass spectrometry with capillary and micro HPLC is attractive for many applications, but reliable ion sources dedicated to these conditions are still missing. There are a number of aspects to consider when designing such an ion source, including the susceptibility of the ionization processes to ambient conditions. Here we discuss the importance of ion source housing for APCI at low flow rates.METHODS: Selected compounds dissolved in various solvents were used to study ionization reactions at 10 μL/min flow rate. APCI spectra were generated using the Ion Max-S source (Thermo Fisher Scientific) operated with or without the ion source housing.
    RESULTS: The APCI spectra of most compounds measured in the open and enclosed ion sources were markedly different. The differences were explained by water and oxygen molecules that entered the plasma region of the open ion source. Water tended to suppress charge transfer processes while oxygen diminished electron capture reactions and prevented the formation of acetonitrile-related radical cations useful for localizing double bonds in lipids. The effects associated with the ion source housing were significantly less important for compounds that are easy to protonate or deprotonate.
    CONCLUSIONS: The use of ion source housing prevented alternative ionization channels leading to unwanted or unexpected ions. Compared with the conventional flow rate mode (1 mL/min), the effects of ambient air components were significantly higher at 10 μL/min, emphasizing the need for ion source housing in APCI sources dedicated to low flow rates.