bims-metlip Biomed News
on Methods and protocols in metabolomics and lipidomics
Issue of 2020‒10‒04
fifteen papers selected by
Sofia Costa
Cold Spring Harbor Laboratory


  1. Anal Chim Acta. 2020 Oct 02. pii: S0003-2670(20)30805-9. [Epub ahead of print]1132 74-82
    Lin M, Chen X, Wang Z, Wang D, Zhang JL.
      Bile acids (BAs), as crucial endogenous metabolites, are closely related to cholestasis, metabolic disorders, and cancer. To better understand their function and disease pathogenesis, global profiling of BAs is necessary. Here, multidimensional data mining was developed for the discovery and identification of potentially unknown BAs in cholestasis rats. Based on an in-house theoretical BA database and using a newly established liquid chromatography-tandem high-resolution mass spectrometry (LC-HRMS/MS) method, four-dimensional (4D) data including the retention times (RT), abundances, HRMS, and HRMS/MS spectra were acquired and elucidated. And 491 BAs were totally profiled. Then, the relationships between RT with different conjugation types, different positions and configurations of hydroxyl/ketone groups as well as fragmentation rules of hydroxyl, ortho-hydroxyl, ketone, and conjugated groups of BAs were summarized to assist BA identification for the first time. Finally, 292 BAs were assigned with molecular formulas, 201 of which were putatively identified by integrating the 4D data, applying structure-driven relative retention time rules, and a comparison with synthetic BAs. The estimated concentrations of 201 BAs, including 93 reported and 108 newly identified BAs, were quantified by using surrogate standards with similar structure. Among 201 BAs, 38 BAs were detected in both humans and rats for the first time. Our strategy has expanded the scope of BAs and provides a way to identify a class of metabolites. Compared to normal rats, the significantly increased sulfated and glucuronide conjugated BAs in urine and feces from experimentally cholestatic rats may reveal a way to diagnose intrahepatic cholestasis.
    Keywords:  Bile acids; Identification; Intrahepatic cholestasis; Liquid chromatography–tandem high resolution mass spectrometry; Multidimensional data mining
    DOI:  https://doi.org/10.1016/j.aca.2020.07.067
  2. Mass Spectrom Rev. 2020 Sep 30.
    Hu C, Luo W, Xu J, Han X.
      Lipid research is attracting more and more attention as various key roles and novel biological functions of lipids have been demonstrated and discovered in the organism. Mass spectrometry (MS)-based lipidomics approaches are the most powerful and effective tools for analysis of cellular lipidomes with very high sensitivity and specificity. However, the artifacts generated from in-source fragmentation are always present in all kinds of ion sources, even soft ionization techniques (i.e., electrospray ionization and matrix-assisted laser desorption/ionization [MALDI]). These artifacts can cause many problems for lipidomics, especially when the fragment ions correspond to/are isomeric species of other endogenous lipid species in complex biological samples. These commonly observed artifacts could lead to misannotation, false identification, and consequently, incorrect attribution of phenotypes, and will have negative impact on any MS-based lipidomics research including but not limited to biomarker discovery, drug development, etc. Liquid chromatography-MS, shotgun lipidomics, and MALDI-MS imaging are three representative lipidomics approaches in which ion source-generated artifacts are all manifested and are comprehensively summarized in this article. The strategies on how to avoid/reduce the artifacts of in-source fragmentation on lipidomics analysis are also discussed in detail. We believe that with the recognition and avoidance of ion source-generated artifacts, MS-based lipidomics approaches will provide better accuracy on comprehensive analysis of biological samples and will make greater contribution to the research on metabolism and translational/precision medicine (collectively termed functional lipidomics). © 2020 John Wiley & Sons Ltd. Mass Spec Rev.
    Keywords:  MALDI-MSI; artifacts; functional lipidomics; in-source fragmentation; lipidomics; mass spectrometry
    DOI:  https://doi.org/10.1002/mas.21659
  3. J Anal Toxicol. 2020 Oct 02. pii: bkaa142. [Epub ahead of print]
    Sempio C, Wymore E, Palmer C, Bunik M, Henthorn TK, Christians U, Klawitter J.
      Cannabis is the most commonly used drug of abuse in pregnancy and after delivery. However, little is known regarding the disposition of cannabinoids in breast milk, although delta-9-tetrahydrocannabinol (THC), the main psychoactive component, is highly lipophilic. Quantification of cannabinoids in breastmilk is essential for clinical monitoring and research studies and breastmilk banks mainly rely on ELISA in terms of screening for cannabinoids. To support clinical studies on disposition of cannabinoids in breastmilk, we validated a high-performance liquid chromatography-tandem mass spectrometry (LC-MS-MS) assay for the simultaneous quantification of 12 cannabinoids and their metabolites in human breast milk. Said assay was based upon a simple one-step protein precipitation, online column extraction and detection in the positive multiple reaction monitoring mode. After successful validation, the assay was used to analyze 30 samples from a clinical research study that had tested negative using an ELISA kit that is commonly used by breastmilk banks. In human breast milk, depending on the analyte, the lower limits of quantification of the LC-MS-MS assay ranged from 0.39 to 7.81 ng/mL. Acceptance criteria for intra- and inter-batch accuracy (85-115%) and imprecision (<15%) were met for all compounds. Mean extraction efficiencies were above 60% for all analytes. Mean matrix effect ranged from -12.5% to 44.5% except of THC- glucuronide for which significant matrix effects were noted. No carry-over was detected. Although cannabinoid-negative based on the ELISA, all 30 samples tested positive for THC using LC-MS-MS (0.8-130 ng/mL) and several also for 11-OH-THC, THCCOOH, CBD and CBG. We validated a sensitive and specific assay for the quantification of 12 cannabinoids in human breastmilk that outperformed an ELISA commonly used by breastmilk banks.
    Keywords:  Breast Milk; Cannabinoid; LC–MS-MS; THC
    DOI:  https://doi.org/10.1093/jat/bkaa142
  4. Rapid Commun Mass Spectrom. 2020 Oct 01. e8965
    Yang P, Li X, Yang W, He L, Yang L, Zhang X.
      RATIONALE: Trimethylamine-N-oxide (TMAO) is a potential indicator of cardiovascular disease and chronic kidney disorders. It is important to monitor the TMAO level in plasma or serum in hemodialysis patients. A simple liquid chromatography-differential ion mobility spectrometry-tandem mass spectrometry (HPLC/DMS-MS/MS) method was established and validated for the determination of TMAO in the serum of hemodialysis patients.METHODS: Chromatographic separation was performed on a Waters Atlantis HILIC silica column (2.1 × 50 mm, 3 μm). The gradient mobile phase consisted of 10 mM ammonium formate buffer and acetonitrile with 0.1% formic acid in both solvents. The serum sample was precipitated with acidic acetonitrile prior to HPLC/DMS-MS/MS analysis and trimethylamine N-oxide-d9 was used as the internal standard (IS). Data acquisition was performed in positive ion mode with a differential mobility spectrometry (DMS) system before the electrospray ionization (ESI) source. The multiple reaction monitoring (MRM) transitions were m/z 76.0→58.0 and m/z 85.2→66.1 for TMAO and the IS, respectively.
    RESULTS: Excellent linearity was observed over the calibration range of 0.05~20 μg/mL (r2 >0.995). The method was validated for good specificity and sensitivity. The inter-run and intra-run precision and accuracy were less than 13.6% and 10.7%, respectively.
    CONCLUSIONS: we established a novel and robust HPLC/DMS-MS/MS method for the quantification of TMAO in human serum sample. The validated assay was simple, rapid, sensitive and reliable. The developed method could be applied to the assay of serum samples from the patients with kidney disease who underwent hemodialysis.
    DOI:  https://doi.org/10.1002/rcm.8965
  5. Prostaglandins Other Lipid Mediat. 2020 Sep 27. pii: S1098-8823(20)30076-9. [Epub ahead of print] 106483
    Armstrong M, Manke J, Nkrumah-Elie Y, Shaikh SR, Reisdorph N.
      A liquid chromatography tandem mass spectrometry-based method for the quantitation of 39 lipid mediators in four sample types and in two mouse strains is described. The method builds upon existing methodologies for analysis of lipid mediators by A) utilizing a bead homogenization step for tissue samples; this eliminates the need for homogenization glassware and improves homogenization consistency, B) optimizing the isolation and purification of lipid mediators with polymeric reverse phase SPE columns with lower sorbent masses; this results in lower solvent elution volumes without loss of recovery and C) utilizing an on-column enrichment method to improve analyte focusing before chromatographic separation. The method is linear from 0.25-250 pg on column for low level lipid mediators and from 5-5000 pg on column for high level lipid mediators. The addition of a methyl formate elution step to a previously published method dramatically improved precision and recovery for the cysteinyl leukotrienes. Accuracy and precision for 4 different sample types including human plasma, mouse lung, mouse spleen and mouse liver is demonstrated. Liver samples had extremely high levels of a tentatively identified bile acid which interfered with quantitation of resolvin E1, 11B-prostaglandin F2a and thromboxane A2. Results from 2 different tissue sources from untreated mice (C57BL/6 versus BALB/c) showed dramatically different concentrations of lipid mediators.
    Keywords:  BALB/c; C57BL/6; Lipid mediators; bead mill; mass spectrometry; solid phase extraction; tissue homogenization
    DOI:  https://doi.org/10.1016/j.prostaglandins.2020.106483
  6. Metabolites. 2020 Sep 26. pii: E381. [Epub ahead of print]10(10):
    Eisenbeiss L, Binz TM, Baumgartner MR, Kraemer T, Steuer AE.
      Untargeted metabolomic studies are used for large-scale analysis of endogenous compounds. Due to exceptional long detection windows of incorporated substances in hair, analysis of hair samples for retrospective monitoring of metabolome changes has recently been introduced. However, information on the general behavior of metabolites in hair samples is scarce, hampering correct data interpretation so far. The presented study aimed to investigate endogenous metabolites depending on hair color and along the hair strand and to propose recommendations for best practice in hair metabolomic studies. A metabolite selection was analyzed using untargeted data acquisition in genuine hair samples from different hair colors and after segmentation in 3 cm segments. Significant differences in metabolites among hair colors and segments were found. In conclusion, consideration of hair color and hair segments is necessary for hair metabolomic studies and, subsequently, recommendations for best practice in hair metabolomic studies were proposed.
    Keywords:  LC-MS/MS; endogenous compounds; hair analysis; hair color; hair metabolomics; segmentation
    DOI:  https://doi.org/10.3390/metabo10100381
  7. Anal Bioanal Chem. 2020 Sep 29.
    Xi Y, Tu A, Muddiman DC.
      To better understand cell-to-cell heterogeneity, advanced analytical tools are in a growing demand for elucidating chemical compositions of each cell within a population. However, the progress of single-cell chemical analysis has been restrained by the limitations of small cell volumes and minute cellular concentrations. Here, we present a rapid and sensitive method for investigating the lipid profiles of isolated single cells using infrared matrix-assisted laser desorption electrospray ionization mass spectrometry (IR-MALDESI-MS). In this work, HeLa cells were dispersed onto a glass slide, and the cellular contents were ionized by IR-MALDESI and measured using a Q-Exactive HF-X mass spectrometer. Importantly, this approach does not require extraction and/or enrichment of analytes prior to MS analysis. Using this approach, 45 distinct lipid species, predominantly phospholipids, were detected and putatively annotated from the single HeLa cells. The proof-of-concept study demonstrates the feasibility and efficacy of IR-MALDESI-MS for rapid lipidomic profiling of single cells, which provides an important basis for future work on differentiation between normal and diseased cells at various developmental states, which can offer new insights into cellular metabolic pathways and pathological processes. Although not yet accomplished, we believe this approach can be readily used as an assessment tool to compare the number of identified species during source evolution and method optimization (intra-laboratory), and also disclose the complementary nature of different direct analytical approaches for the coverage of different types of endogenous analytes (inter-laboratory).Graphical abstract.
    Keywords:  IR-MALDESI; Lipidome; Mass spectrometry; Orbitrap; Single-cell analysis
    DOI:  https://doi.org/10.1007/s00216-020-02961-6
  8. Anal Chem. 2020 Sep 28.
    Wilde MJ, Zhao B, Cordell RL, Ibrahim W, Singapuri A, Greening NJ, Brightling CE, Siddiqui S, Monks PS, Free RC.
      Comprehensive two-dimensional gas chromatography (GC×GC) is a powerful analytical tool for both non-targeted and targeted analyses. However, there is a need for more integrated workflows for processing and managing the resultant high complexity datasets. End-to-end workflows for processing GC×GC data are challenging and often require multiple tools or software to process a single dataset. We describe a new approach, which uses an existing underutilized interface within commercial software to integrate free and open-source/external scripts and tools, tailoring the workflow to the needs of the individual researcher within a single software environment. To demonstrate the concept, the interface was successfully used to complete a first-pass alignment on a large-scale GC×GC metabolomics dataset. The analysis was performed by interfacing bespoke and published external algorithms within a commercial software environment, to automatically correct the variation in retention times captured by a routine reference standard. Variation in 1tR and 2tR was reduced on average from 8% and 16% CV pre-alignment, to less than 1% and 2% post-alignment, respectively. The interface enables automation, the creation of new functions, and increases the interconnectivity between chemometric tools, providing a window for integrating data processing software with larger informatics-based data management platforms.
    DOI:  https://doi.org/10.1021/acs.analchem.0c02844
  9. Pak J Biol Sci. 2020 Jan;23(10): 1321-1331
    Starlin Z, Harahap Y, S Sitepu E.
      BACKGROUND AND OBJECTIVE: Acrylamide (AA) is a carcinogenic substance that is easily found in working environment, food, contaminated air and tobacco smoke. This substance can be distributed rapidly through all body compartments. The aim of this study is to get the method for determining acrylamide in dried blood spot.MATERIALS AND METHODS: Dried blood spot was used as the bio-sampling method and was optimized and validated by using propranolol as the internal standard. The sample was prepared using a protein precipitation technique optimized. Reversed-phase chromatography with Acquity® UPLC BEH C18 column (1.7, 2.1× 100 mm) was used for compound separation.
    RESULTS: Optimized analytical condition for this substance was eluted with the flow rate of 0.20 mL/min under a gradient of the mobile phase of 0.1% formic acid in water and acetonitrile within 3 min. Triple quadrupole mass spectrometry with electrospray ionization (ESI) in positive mode was used as quantification analysis. The Multiple Reaction Monitoring (MRM) was set at m/z 71.99>55.23 (m/z) for acrylamide and 260.2>116.2 (m/z) for propranolol. The range of concentration was linear within 2.5-100 μg mL-1.
    CONCLUSION: All the validation parameters were fulfilled the criteria in US FDA Guideline for Bioanalytical Method Validation 2018.
    Keywords:  Acrylamide; electrospray ionization; propranolol; reversed-phase chromatography; validation
    DOI:  https://doi.org/10.3923/pjbs.2020.1321.1331
  10. Anal Chem. 2020 Oct 01.
    Hartler J, Armando AM, Trötzmüller M, Dennis EA, Köfeler HC, Quehenberger O.
      Sphingolipids constitute a heterogeneous lipid category that is involved in many key cellular functions. For high-throughput analyses of sphingolipids, tandem mass spectrometry (MS/MS) is the method of choice, offering sufficient sensitivity, structural information, and quantitative precision for detecting hundreds to thousands of species simultaneously. While glycerolipids and phospholipids are predominantly non-hydroxylated, sphingolipids are typically dihydroxylated. However, species containing one or three hydroxylation sites can be detected frequently. This variability in the number of hydroxylation sites on the sphingolipid long-chain base and the fatty acyl moiety produces many more isobaric species and fragments than for other lipid categories. Due to this complexity, the automated annotation of sphingolipid species is challenging, and incorrect annotations are common. In this study, we present an extension of the Lipid Data Analyzer (LDA) "decision rule set" concept that considers the structural characteristics that are specific for this lipid category. To address the challenges inherent to automated annotation of sphingolipid structures from MS/MS data, we first developed decision rule sets using spectra from authentic standards and then tested the applicability on biological samples including murine brain and human plasma. A benchmark test based on the murine brain samples revealed a highly improved annotation quality as measured by sensitivity and reliability. The results of this benchmark test combined with the easy extensibility of the software to other (sphingo)lipid classes and the capability to detect and correctly annotate novel sphingolipid species make LDA broadly applicable to automated sphingolipid analysis, especially in high-throughput settings.
    DOI:  https://doi.org/10.1021/acs.analchem.0c03016
  11. J Mass Spectrom. 2020 Dec;55(12): e4646
    Margolin Eren KJ, Elkabets O, Amirav A.
      Electron ionization (EI) mass spectra of 46 compounds from several different compound classes were measured. Their molecular ion abundances were compared as obtained with 70-eV EI, with low eV EI (such as 14 eV), and with EI mass spectra of vibrationally cold molecules in supersonic molecular beams (Cold EI). We further compared these mass spectra in their National Institute of Standards and Technology (NIST) library identification probabilities. We found that Low eV EI is not a soft ionization method, and it has little or no influence on the molecular ion relative abundances for large molecules and those with weak or no molecular ions. Low eV EI for compounds with abundant or dominant molecular ions in their 70 eV mass spectra results in the reduction of low mass fragment ions abundances thereby reducing their NIST library identification probabilities thus rarely justifies its use in real-world applications. Cold EI significantly enhances the relative abundance of the molecular ions particularly for large compounds; yet, it retains the low mass fragment ions; hence, Cold EI mass spectra can be effectively identified by the NIST library. Different standard EI ion sources provide different 70 eV EI mass spectra. Among the Agilent technologies ion sources, the "Extractor" exhibits relatively abundant molecular ions compared with the "Inert" ion source, while the "High efficiency source" (HES) provides mass spectra with depleted molecular ions compared with the "Inert" ion source or NIST library mass spectra. These conclusions are demonstrated and supported by experimental data in nine figures and two tables.
    Keywords:  GC-MS; NIST library identification probabilities; cold EI; electron ionization mass spectra; low eV EI; low voltage electron ionization
    DOI:  https://doi.org/10.1002/jms.4646
  12. Anal Chim Acta. 2020 Oct 02. pii: S0003-2670(20)30603-6. [Epub ahead of print]1132 134-155
    Barrientos RC, Zhang Q.
      Aberrant expression of glycosphingolipids has been implicated in a myriad of diseases, but our understanding of the strucural diversity, spatial distribution, and biological function of this class of biomolecules remains limited. These challenges partly stem from a lack of sensitive tools that can detect, identify, and quantify glycosphingolipids at the molecular level. Mass spectrometry has emerged as a powerful tool poised to address most of these challenges. Here, we review the recent developments in analytical glycosphingolipidomics with an emphasis on sample preparation, mass spectrometry and tandem mass spectrometry-based structural characterization, label-free and labeling-based quantification. We also discuss the nomenclature of glycosphingolipids, and emerging technologies like ion mobility spectrometry in differentiation of glycosphingolipid isomers. The intrinsic advantages and shortcomings of each method are carefully critiqued in line with an individual's research goals. Finally, future perspectives on analytical sphingolipidomics are stated, including a need for novel and more sensive methods in isomer separation, low abundance species detection, and profiling the spatial distribution of glycosphingolipid molecular species in cells and tissues using imaging mass spectrometry.
    Keywords:  Ganglioside; Glycosphingolipid; Glycosphingolipidomics; Mass spectrometry
    DOI:  https://doi.org/10.1016/j.aca.2020.05.051
  13. Ther Drug Monit. 2020 Oct 01.
    Shiraiwa K, Suzuki Y, Tanaka K, Kawano M, Iwasaki T, Matsumoto A, Tanaka R, Tatsuta R, Tsumura H, Itoh H.
      BACKGROUND: Pazopanib is widely used to treat renal cell carcinomas and soft tissue tumors in Japan. Pazopanib has significant therapeutic efficacy but it is associated with frequent severe adverse effects. Therapeutic drug monitoring (TDM) may help to prevent adverse effects. A more convenient and rapid pazopanib assay is desirable for the application of TDM in clinical settings. In this study, the authors developed a we aimed to develop a high-throughput method for quantifying pazopanib in human plasma using ultra-high performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS).METHODS: After a simple solid-phase extraction step using a 96-well plate, pazopanib was analyzed by UHPLC-MS/MS in the positive electrospray ionization mode.
    RESULTS: The novel method fulfilled the requirements of the US Food and Drug Administration and the European Medicines Agency guidelines for assay validation, and the lower limit of quantification was 0.5 µg/mL. The calibration curves were linear over the concentration range of 0.5-100 µg/mL. The average recovery rate was 102.0 ± 3.9% (mean ± SD). The precision was below 5.0%, and the accuracy was within 12.0% for all quality control levels. Matrix effect varied between 90.9% and 97.1%. This assay was successfully applied to TDM of pazopanib trough concentrations in three patients treated with the drug for soft tissue tumors.
    CONCLUSIONS: The authors succeeded in developing a novel high-throughput UHPLC-MS/MS method for quantifying pazopanib in human plasma. This method can be applied to TDM of patients receiving pazopanib in clinical settings.
    DOI:  https://doi.org/10.1097/FTD.0000000000000821
  14. Lab Invest. 2020 Sep 29.
    Wang Y, Hui S, Wondisford FE, Su X.
      Metabolic flux analysis (MFA) aims at revealing the metabolic reaction rates in a complex biochemical network. To do so, MFA uses the input of stable isotope labeling patterns of the intracellular metabolites. Elementary metabolic unit (EMU) is the computational framework to simulate the metabolite labeling patterns in a network, which was originally designed for simulating mass isotopomer distributions (MIDs) at the MS1 level. Recently, the EMU framework is expanded to simulate tandem mass spectrometry data. Tandem mass spectrometry has emerged as a new experimental approach to provide information on the positional isotope labeling of metabolites and therefore greatly improves the precision of MFA. In this review, we will discuss the new EMU framework that can accommodate the tandem mass isotopomer distributions (TMIDs) data. We will also analyze the improvement on the MFA precision by using TMID. Our analysis shows that combining the MIDs of the parent and daughter ions and the TMID for the MFA is more powerful than using TMID alone.
    DOI:  https://doi.org/10.1038/s41374-020-00488-z
  15. Metabolomics. 2020 Sep 30. 16(10): 104
    Fan Z, Alley A, Ghaffari K, Ressom HW.
      INTRODUCTION: Metabolite annotation is a critical and challenging step in mass spectrometry-based metabolomic profiling. In a typical untargeted MS/MS-based metabolomic study, experimental MS/MS spectra are matched against those in spectral libraries for metabolite annotation. Yet, existing spectral libraries comprise merely a marginal percentage of known compounds.OBJECTIVE: The objective is to develop a method that helps rank putative metabolite IDs for analytes whose reference MS/MS spectra are not present in spectral libraries.
    METHODS: We introduce MetFID, which uses an artificial neural network (ANN) trained for predicting molecular fingerprints based on experimental MS/MS data. To narrow the search space, MetFID retrieves candidates from metabolite databases using molecular formula or m/z value of the precursor ions of the analytes. The candidate whose fingerprint is most analogous to the predicted fingerprint is used for metabolite annotation. A comprehensive evaluation was performed by training MetFID using MS/MS spectra from the MoNA repository and NIST library and by testing with structure-disjoint MS/MS spectra from the NIST library, the CASMI 2016 dataset, and in-house MS/MS data from a cancer biomarker discovery study.
    RESULTS: We observed that training separate models for distinct ranges of collision energies enhanced model performance compared to a single model that covers a wide range of collision energies. Using MetaboQuest to retrieve candidates, MetFID prioritized the correct putative ID in the first place rank for about 50% of the testing cases. Through the independent testing dataset, we demonstrated that MetFID has the potential to improve the accuracy of ranking putative metabolite IDs by more than 5% compared to other tools such as ChemDistiller, CSI:FingerID, and MetFrag.
    CONCLUSION: MetFID offers a promising opportunity to enhance the accuracy of metabolite annotation by using ANN for molecular fingerprint prediction.
    Keywords:  Artificial neural network; Metabolite identification; Metabolomics; Molecular fingerprint
    DOI:  https://doi.org/10.1007/s11306-020-01726-7