bims-metlip Biomed News
on Methods and protocols in metabolomics and lipidomics
Issue of 2020‒12‒06
nine papers selected by
Sofia Costa
Cold Spring Harbor Laboratory


  1. Metabolites. 2020 Dec 02. pii: E495. [Epub ahead of print]10(12):
    Medina J, van der Velpen V, Teav T, Guitton Y, Gallart-Ayala H, Ivanisevic J.
      Expanding metabolome coverage to include complex lipids and polar metabolites is essential in the generation of well-founded hypotheses in biological assays. Traditionally, lipid extraction is performed by liquid-liquid extraction using either methyl-tert-butyl ether (MTBE) or chloroform, and polar metabolite extraction using methanol. Here, we evaluated the performance of single-step sample preparation methods for simultaneous extraction of the complex lipidome and polar metabolome from human plasma. The method performance was evaluated using high-coverage Hydrophilic Interaction Liquid Chromatography-ESI coupled to tandem mass spectrometry (HILIC-ESI-MS/MS) methodology targeting a panel of 1159 lipids and 374 polar metabolites. The criteria used for method evaluation comprised protein precipitation efficiency, and relative MS signal abundance and repeatability of detectable lipid and polar metabolites in human plasma. Among the tested methods, the isopropanol (IPA) and 1-butanol:methanol (BUME) mixtures were selected as the best compromises for the simultaneous extraction of complex lipids and polar metabolites, allowing for the detection of 584 lipid species and 116 polar metabolites. The extraction with IPA showed the greatest reproducibility with the highest number of lipid species detected with the coefficient of variation (CV) < 30%. Besides this difference, both IPA and BUME allowed for the high-throughput extraction and reproducible measurement of a large panel of complex lipids and polar metabolites, thus warranting their application in large-scale human population studies.
    Keywords:  LC-MS/MS; extraction; human plasma; lipidomics; metabolomics; sample preparation
    DOI:  https://doi.org/10.3390/metabo10120495
  2. Metabolites. 2020 Nov 24. pii: E479. [Epub ahead of print]10(12):
    Iyer GR, Wigginton J, Duren W, LaBarre JL, Brandenburg M, Burant C, Michailidis G, Karnovsky A.
      Modern analytical methods allow for the simultaneous detection of hundreds of metabolites, generating increasingly large and complex data sets. The analysis of metabolomics data is a multi-step process that involves data processing and normalization, followed by statistical analysis. One of the biggest challenges in metabolomics is linking alterations in metabolite levels to specific biological processes that are disrupted, contributing to the development of disease or reflecting the disease state. A common approach to accomplishing this goal involves pathway mapping and enrichment analysis, which assesses the relative importance of predefined metabolic pathways or other biological categories. However, traditional knowledge-based enrichment analysis has limitations when it comes to the analysis of metabolomics and lipidomics data. We present a Java-based, user-friendly bioinformatics tool named Filigree that provides a primarily data-driven alternative to the existing knowledge-based enrichment analysis methods. Filigree is based on our previously published differential network enrichment analysis (DNEA) methodology. To demonstrate the utility of the tool, we applied it to previously published studies analyzing the metabolome in the context of metabolic disorders (type 1 and 2 diabetes) and the maternal and infant lipidome during pregnancy.
    Keywords:  differential networks; enrichment analysis; metabolic disorders; metabolomics and lipidomics; partial correlation networks
    DOI:  https://doi.org/10.3390/metabo10120479
  3. J Steroid Biochem Mol Biol. 2020 Nov 28. pii: S0960-0760(20)30322-8. [Epub ahead of print] 105797
    Olesti E, Boccard J, Visconti G, González-Ruiz V, Rudaz S.
      For several decades now, the analysis of steroids has been a key tool in the diagnosis and monitoring of numerous endocrine pathologies. Thus, the available methods used to analyze steroids in biological samples have dramatically evolved over time following the rapid pace of technology and scientific knowledge. This review aims to synthetize the advances in steroids' analysis, from classical approaches considering only a few steroids or a limited number of steroid ratios, up to the new steroid profiling strategies (steroidomics) monitoring large sets of steroids in biological matrices. In this context, the use of liquid chromatography coupled to mass spectrometry has emerged as the technique of choice for the simultaneous determination of a high number of steroids, including phase II metabolites, due to its sensitivity and robustness. However, the large dynamic range to be covered, the low natural abundance of some key steroids, the selectivity of the analytical methods, the extraction protocols, and the steroid ionization remain some of the current challenges in steroid analysis. This review provides an overview of the different analytical workflows available depending on the number of steroids under study. Special emphasis is given to sample treatment, acquisition strategy, data processing, steroid identification and quantification using LC-MS approaches. This work also outlines how the lack of steroid standards, the need for complementary analytical strategies and the improvement of calibration approaches are crucial for achieving complete steroidome quantification.
    Keywords:  Steroid analysis; absolute quantification; challenges; relative estimation; steroidome
    DOI:  https://doi.org/10.1016/j.jsbmb.2020.105797
  4. J Steroid Biochem Mol Biol. 2020 Nov 28. pii: S0960-0760(20)30321-6. [Epub ahead of print] 105796
    Ko DH, Jun SH, Nam Y, Song SH, Han M, Yun YM, Lee K, Song J.
      Bioavailable vitamin D and vitamin D metabolite ratio (VMR) have emerged as potential novel vitamin D markers. We developed a multiplex liquid chromatography-tandem mass spectrometry (LC-MS/MS) method to determine all elements necessary for the calculation of bioavailable vitamin D and VMR, including 25-hydroxyvitamin D [25-(OH)D] and 24,25-dihydroxyvitamin D3 [24,25-(OH)2D3], VDBP and its isoforms, and albumin. Following separate reactions of hexane extraction and trypsin digestion, serum samples were analyzed using LC-MS/MS to measure 25-(OH)D3, 25-(OH)D2, 24,25-(OH)2D3, VDBP and its isoforms, and albumin. Analytical performances were assessed. Korean (n = 229), Arab (n = 98), White (n = 99) and Black American (n = 99) samples were analyzed. Bioavailable vitamin D and VMR were calculated. All target molecules were clearly separated and accurately quantified by LC-MS/MS. Analytical performances, including imprecision, accuracy, ion suppression, limit of quantification, linearity, and comparison with existing methods were within acceptable levels. The allele frequencies of VDBP isoforms in various races resulted similar to previously known values. The levels of bioavailable vitamin D were highest in White Americans and lowest in Black Americans. We have successfully developed a multiplex LC-MS/MS-based assay method that can simultaneously perform the measurement of all parameters needed to calculate bioavailable vitamin D and VMR. Our devised method was robust and reliable in terms of analytical performances and could be applied to routine clinical samples in the future to more accurately assess vitamin D status.
    Keywords:  bioavailable vitamin D; liquid chromatography-tandem mass spectrometry; multiplex assay; vitamin D metabolite ratio; vitamin D-binding protein
    DOI:  https://doi.org/10.1016/j.jsbmb.2020.105796
  5. Anal Chem. 2020 Dec 03.
    Kutuzova S, Colaianni P, Röst H, Sachsenberg T, Alka O, Kohlbacher O, Burla B, Torta F, Schrübbers L, Kristensen M, Nielsen L, Herrgård MJ, McCloskey D.
      Technological advances in high-resolution mass spectrometry (MS) vastly increased the number of samples that can be processed in a life science experiment, as well as volume and complexity of the generated data. To address the bottleneck of high-throughput data processing, we present SmartPeak (https://github.com/AutoFlowResearch/SmartPeak), an application that encapsulates advanced algorithms to enable fast, accurate, and automated processing of capillary electrophoresis-, gas chromatography-, and liquid chromatography (LC)-MS(/MS) data and high-pressure LC data for targeted and semitargeted metabolomics, lipidomics, and fluxomics experiments. The application allows for an approximate 100-fold reduction in the data processing time compared to manual processing while enhancing quality and reproducibility of the results.
    DOI:  https://doi.org/10.1021/acs.analchem.0c03421
  6. Food Chem. 2020 Nov 25. pii: S0308-8146(20)32574-7. [Epub ahead of print] 128712
    Zhao L, Zhao X, Xu Y, Liu X, Zhang J, He Z.
      A sensitive and reliable method was developed and validated for the simultaneous determination of 49 amino acids, B vitamins, flavonoids, and phenolic acids based on a rapid metabolomic extraction procedure combined with ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) in a single chromatographic run and applied in analysis of 26 commonly consumed vegetables. The chromatographic and sample preparation conditions were optimized, given the high diversity of the target analytes. Eight isotope-labeled standards were applied to validate the method in terms of recovery, linearity, matrix effects, precision, and sensitivity. Most recoveries in four vegetable matrices ranged from 65.0% to 105.3% with associated RSDs < 20%. Low LOQs ranged from 0.06 to 17 µg/kg. Linear calibration curves with different ranges were established with R2 > 0.993. Among the 26 vegetables, purple cabbage, broccoli, and red lettuce were found to contain the highest concentrations of free amino acids, B vitamins, and phenolic compounds.
    Keywords:  Amino acids; B vitamins; Phenolic compounds; UPLC-MS/MS; Vegetable
    DOI:  https://doi.org/10.1016/j.foodchem.2020.128712
  7. J Med Biochem. 2020 Sep 02. 39(3): 299-308
    Vladimirov S, Gojković T, Zeljković A, Jelić-Ivanović Z, Spasojević-Kalimanovska V.
      Background: Non-cholesterol sterols (NCS) are promising biomarkers for estimation of cholesterol homeostasis properties. In addition, determination of NCS in high-density lipoprotein (HDL) fraction (HDL-NCS) could provide information on cholesterol efflux. However, matrix effects interfere in liquid chromatography-mass spectrometry (LC-MS) analysis of NCS, thereby impairing the method sensitivity. The aims of this study were development, optimization and validation of LC-MS method for quantification of NCS in serum and HDL-NCS. Additionally, matrix effect interferences and methods application in individual serum samples were examined.Methods: HDL precipitating reagent was used for HDL isolation. Matrix effect was examined by comparing different surrogates by simple regression analysis. Validation was conducted according to the FDA-ICH guideline. 20 healthy volunteers were recruited for testing of method application.
    Results: The observed matrix effect was 30%, and matrix comparison showed that cholesterol was the dominant contributor to the matrix effect. Cholesterol concentration was adjusted by construction of the calibration curve for serum and HDL fraction (5 mmol/L and 2.5 mmol/L, respectively). The intraand interrun variabilities for NCSs were 4.7-10.3% for serum NCS and 3.6-13.6% for HDLNCS and 4.6-9.5% for serum NCSs and 2.5-9.8% for HDL-NCS, respectively. Recovery studies showed satisfactory results for NCSs: 89.8-113.1% for serum NCS and 85.3-95.8% for HDL-NCS.
    Conclusions: The method was successfully developed and optimized. The matrix interference was solved by customising calibration curves for each method and sample type. The measurement of NCS in HDL fraction was proposed for the first time as potentially useful procedure in biomedical researches.
    Keywords:  HPLC-MS/MS; calibration; cholesterol precursors; matrix effect; phytosterols
    DOI:  https://doi.org/10.2478/jomb-2019-0044
  8. Rapid Commun Mass Spectrom. 2020 Dec 04. e9013
    Zhang X, Ren X, Chingin K.
      Direct analysis in real time (DART) combined with mass spectrometry (MS) detection has become one of the most broadly used analytical approaches for the direct molecular characterization of food samples with regard to their chemical quality, safety, origin and authentication. The major merits of DART-MS for food analysis include high chemical sensitivity and specificity, high speed and throughput of analysis, simplicity as well as the obviation of tedious sample preparation and solvents. The recent applications of DART coupled with different mass analyzers, including quadrupole, ion trap, Orbitrap, time of flight and others, are discussed. In addition, sample pretreatment methods that have been coupled with DART-MS are discussed. We summarize the applications of DART-MS in food science and industry published in the period from 2005 to this date. The applications and analytical characteristics are systematically categorized across the three major types of foods: solid foods, liquid foods and viscous foods. DART-MS has proved its high suitability for the direct, rapid and high-throughput molecular analysis of very different food samples with minimal or no sample preparation, thus offering a high-speed alternative to liquid chromatography-mass spectrometry (LC/MS) and gas chromatography-mass spectrometry (GC/MS) approaches that are traditionally employed in food analysis.
    DOI:  https://doi.org/10.1002/rcm.9013
  9. Metabolites. 2020 Nov 25. pii: E482. [Epub ahead of print]10(12):
    Aggarwal P, Baker J, Boyd MT, Coyle S, Probert C, Chapman EA.
      Headspace-solid phase microextraction gas chromatography-mass spectrometry (HS-SPME-GC-MS) can be used to measure volatile organic compounds (VOCs) in human urine. However, there is no widely adopted standardised protocol for the preparation of urine samples for analysis resulting in an inability to compare studies reliably between laboratories. This paper investigated the effect of altering urine sample pH, volume, and vial size for optimising detection of VOCs when using HS-SPME-GC-MS. This is the first, direct comparison of H2SO4, HCl, and NaOH as treatment techniques prior to HS-SPME-GC-MS analysis. Altering urine sample pH indicates that H2SO4 is more effective at optimising detection of VOCs than HCl or NaOH. H2SO4 resulted in a significantly larger mean number of VOCs being identified per sample (on average, 33.5 VOCs to 24.3 in HCl or 12.2 in NaOH treated urine) and more unique VOCs, produced a more diverse range of classes of VOCs, and led to less HS-SPME-GC-MS degradation. We propose that adding 0.2 mL of 2.5 M H2SO4 to 1 mL of urine within a 10 mL headspace vial is the optimal sample preparation prior to HS-SPME-GC-MS analysis. We hope the use of our optimised method for urinary HS-SPME-GC-MS analysis will enhance our understanding of human disease and bolster metabolic biomarker identification.
    Keywords:  H2SO4; HCl; HS-SPME-GC-MS; NaOH; VOCs; hydrochloric acid; sodium hydroxide; vials; volatile organic compounds
    DOI:  https://doi.org/10.3390/metabo10120482