bims-metlip Biomed News
on Methods and protocols in metabolomics and lipidomics
Issue of 2020‒07‒19
eleven papers selected by
Sofia Costa
Cold Spring Harbor Laboratory


  1. Anal Chim Acta. 2020 Aug 15. pii: S0003-2670(20)30605-X. [Epub ahead of print]1125 144-151
    Cao G, Song Z, Hong Y, Yang Z, Song Y, Chen Z, Chen Z, Cai Z.
      Targeted metabolomics has significant advantages for quantification but suffers from reduced metabolite coverage. In this study, we developed a large-scale targeted metabolomics method and expanded its applicability to various human samples. This approach initially involved unbiased identification of metabolites in human cells, tissues and body fluids using ultra high-performance liquid chromatography (UHPLC) coupled to high-resolution Orbitrap mass spectrometry (MS). Targeted metabolomics method was established with utility of UHPLC-triple quadrupole MS, which enables targeted profiling of over 400 biologically important metabolites (e.g., amino acids, sugars, nucleotides, dipeptides, coenzymes, and fatty acids), covering 92 metabolic pathways (e.g., Krebs cycle, glycolysis, amino acids metabolism, ammonia recycling, and one-carbon metabolism). The present method displayed better sensitivity, repeatability and linearity than the Orbitrap MS-based untargeted metabolomics approach and demonstrated excellent performance in lung cancer biomarker discovery, in which 107 differential metabolites were able to discriminate between carcinoma and adjacent normal tissues, implicating the Warburg effect, alteration of redox state, and nucleotide metabolism of lung cancer. This new method is flexible and expandable and offers many advantages for metabolomics analysis, such as wide metabolite coverage, good repeatability and linearity and excellent capability in biomarker discovery, making it useful for both basic and clinical metabolic research.
    Keywords:  Biomarker discovery; Human samples; Mass spectrometry; Targeted metabolomics
    DOI:  https://doi.org/10.1016/j.aca.2020.05.053
  2. Metabolites. 2020 Jul 09. pii: E282. [Epub ahead of print]10(7):
    Gómez C, Stücheli S, Kratschmar DV, Bouitbir J, Odermatt A.
      Bile acids control lipid homeostasis by regulating uptake from food and excretion. Additionally, bile acids are bioactive molecules acting through receptors and modulating various physiological processes. Impaired bile acid homeostasis is associated with several diseases and drug-induced liver injury. Individual bile acids may serve as disease and drug toxicity biomarkers, with a great demand for improved bile acid quantification methods. We developed, optimized, and validated an LC-MS/MS method for quantification of 36 bile acids in serum, plasma, and liver tissue samples. The simultaneous quantification of important free and taurine- and glycine-conjugated bile acids of human and rodent species has been achieved using a simple workflow. The method was applied to a mouse model of statin-induced myotoxicity to assess a possible role of bile acids. Treatment of mice for three weeks with 5, 10, and 25 mg/kg/d simvastatin, causing adverse skeletal muscle effects, did not alter plasma and liver tissue bile acid profiles, indicating that bile acids are not involved in statin-induced myotoxicity. In conclusion, the established LC-MS/MS method enables uncomplicated sample preparation and quantification of key bile acids in serum, plasma, and liver tissue of human and rodent species to facilitate future studies of disease mechanisms and drug-induced liver injury.
    Keywords:  LC-MS; adverse drug effect; bile acid; biomarker; disease; statin myotoxicity
    DOI:  https://doi.org/10.3390/metabo10070282
  3. Anal Chem. 2020 Jul 15.
    Neumann EK, Migas LG, Allen JL, Caprioli RM, Van de Plas R, Spraggins JM.
      Low molecular weight metabolites are essential for defining the molecular phenotypes of cells. However, spatial metabolom-ics tools often lack the sensitivity, specify, and spatial resolution to provide comprehensive descriptions of these species in tissue. MALDI imaging mass spectrometry (IMS) of low molecular weight ions is particularly challenging as MALDI ma-trix clusters are often nominally isobaric with multiple metabolite ions, requiring high resolving power instrumentation or derivatization to circumvent this issue. An alternative to this is to perform ion mobility separation before ion detection, ena-bling the visualization of metabolites without the interference of matrix ions. Additional difficulties surrounding low weight metabolite visualization include high resolution imaging, while maintaining sufficient ion numbers for broad and representa-tive analysis of the tissue chemical complement. Here, we use MALDI timsTOF IMS to image low molecular weight me-tabolites at higher spatial resolution than most metabolite MALDI IMS experiments (20 µm) while maintaining broad cov-erage within the human kidney. We demonstrate that trapped ion mobility spectrometry (TIMS) can resolve matrix peaks from metabolite signal and separate both isobaric and isomeric metabolites with different distributions within the kidney. The added ion mobility data dimension dramatically increased the peak capacity for spatial metabolomics experiments. Through this improved sensitivity, we have found >40 low molecular weight metabolites in human kidney tissue such as arginic acid, acetylcarnitine, and choline that localize to the cortex, medulla, and renal pelvis, respectively. Future work will involve further exploring metabolomic profiles of human kidneys as a function of age, gender, and ethnicity.
    DOI:  https://doi.org/10.1021/acs.analchem.0c02051
  4. Cancer Metab. 2020 ;8 15
    Pietzke M, Vazquez A.
      Background: Metabolomics is gaining popularity as a standard tool for the investigation of biological systems. Yet, parsing metabolomics data in the absence of in-house computational scientists can be overwhelming and time-consuming. As a consequence of manual data processing, the results are often not analysed in full depth, so potential novel findings might get lost.Methods: To tackle this problem, we developed Metabolite AutoPlotter, a tool to process and visualise quantified metabolite data. Other than with bulk data visualisations, such as heat maps, the aim of the tool is to generate single plots for each metabolite. For this purpose, it reads as input pre-processed metabolite-intensity tables and accepts different experimental designs, with respect to the number of metabolites, conditions and replicates. The code was written in the R-scripting language and wrapped into a shiny application that can be run online in a web browser on https://mpietzke.shinyapps.io/autoplotter.
    Results: We demonstrate the main features and the ease of use with two different metabolite datasets, for quantitative experiments and for stable isotope tracing experiments. We show how the plots generated by the tool can be interactively modified with respect to plot type, colours, text labels and the shown statistics. We also demonstrate the application towards 13C-tracing experiments and the seamless integration of natural abundance correction, which facilitates the better interpretation of stable isotope tracing experiments. The output of the tool is a zip-file containing one single plot for each metabolite as well as restructured tables that can be used for further analysis.
    Conclusion: With the help of Metabolite AutoPlotter, it is now possible to simplify data processing and visualisation for a wide audience. High-quality plots from complex data can be generated in a short time by pressing a few buttons. This offers dramatic improvements over manual analysis. It is significantly faster and allows researchers to spend more time interpreting the results or to perform follow-up experiments. Further, this eliminates potential copy-and-paste errors or tedious repetitions when things need to be changed. We are sure that this tool will help to improve and speed up scientific discoveries.
    Keywords:  Automatic; Graphs; Metabolites; Metabolomics; Plots; Processing; Visualisation
    DOI:  https://doi.org/10.1186/s40170-020-00220-x
  5. Rapid Commun Mass Spectrom. 2020 Jul 12.
    Sun Y, Ishikawa NF, Ogawa NO, Kawahata H, Takano Y, Ohkouchi N.
      RATIONALE: To achieve better precision and accuracy for δ13 C analysis of individual amino acids (AAs), we have developed a new analytical method based on multi-dimensional high-performance liquid chromatography (HPLC) and elemental analyzer/isotope ratio mass spectrometry (EA/IRMS). Unlike conventional methods using gas chromatography, this approach omits pre-column chemical derivatization, thus reducing systematic errors associated with the isotopic measurement.METHODS: The separation and isolation of individual AAs in a standard mixture containing 15 AAs and a biological sample, spear squid (Heterololigo bleekeri), were performed. AAs were isolated using a HPLC system equipped with a reversed-phase column and a mixed-mode column and collected using a fraction collector. After the chromatographic separation and further post-HPLC purification, the δ13 C values of AAs were measured by EA/IRMS.
    RESULTS: The complete isolation of all 15 AAs in the standard mixture was achieved. The δ13 C values of these AAs before and after the experiment were in good agreement. Also, 15 AAs in the biological sample, H. bleekeri, were successfully measured. The δ13 C values of AAs in H. bleekeri varied by as much as as 30‰ with glycine being most enriched in 13 C.
    CONCLUSIONS: The consistency between the δ13 C values of reference and processed AAs demonstrates that the experimental procedure generates accurate δ13 C values unaffected by fractionation effects and contamination. This method is therefore suitable for δ13 C analysis of biological samples with higher precision than conventional approaches. We propose this new method as a tool to measure δ13 C values of AAs in biological, ecological and biogeochemical studies.
    DOI:  https://doi.org/10.1002/rcm.8885
  6. Nat Protoc. 2020 Jul 17.
    Garcia-Perez I, Posma JM, Serrano-Contreras JI, Boulangé CL, Chan Q, Frost G, Stamler J, Elliott P, Lindon JC, Holmes E, Nicholson JK.
      Metabolic profiling of biological samples provides important insights into multiple physiological and pathological processes but is hindered by a lack of automated annotation and standardized methods for structure elucidation of candidate disease biomarkers. Here we describe a system for identifying molecular species derived from nuclear magnetic resonance (NMR) spectroscopy-based metabolic phenotyping studies, with detailed information on sample preparation, data acquisition and data modeling. We provide eight different modular workflows to be followed in a recommended sequential order according to their level of difficulty. This multi-platform system involves the use of statistical spectroscopic tools such as Statistical Total Correlation Spectroscopy (STOCSY), Subset Optimization by Reference Matching (STORM) and Resolution-Enhanced (RED)-STORM to identify other signals in the NMR spectra relating to the same molecule. It also uses two-dimensional NMR spectroscopic analysis, separation and pre-concentration techniques, multiple hyphenated analytical platforms and data extraction from existing databases. The complete system, using all eight workflows, would take up to a month, as it includes multi-dimensional NMR experiments that require prolonged experiment times. However, easier identification cases using fewer steps would take 2 or 3 days. This approach to biomarker discovery is efficient and cost-effective and offers increased chemical space coverage of the metabolome, resulting in faster and more accurate assignment of NMR-generated biomarkers arising from metabolic phenotyping studies. It requires a basic understanding of MATLAB to use the statistical spectroscopic tools and analytical skills to perform solid phase extraction (SPE), liquid chromatography (LC) fraction collection, LC-NMR-mass spectroscopy and one-dimensional and two-dimensional NMR experiments.
    DOI:  https://doi.org/10.1038/s41596-020-0343-3
  7. Molecules. 2020 Jul 13. pii: E3192. [Epub ahead of print]25(14):
    Furse S, Watkins AJ, Koulman A.
      Extraction of the lipid fraction is a key part of acquiring lipidomics data. High-throughput lipidomics, the extraction of samples in 96w plates that are then run on 96 or 384w plates, has particular requirements that mean special development work is needed to fully optimise an extraction method. Several methods have been published as suitable for it. Here, we test those methods using four liquid matrices: milk, human serum, homogenised mouse liver and homogenised mouse heart. In order to determine the difference in performance of the methods as objectively as possible, we used the number of lipid variables identified, the total signal strength and the coefficient of variance to quantify the performance of the methods. This showed that extraction methods with an aqueous component were generally better than those without for these matrices. However, methods without an aqueous fraction in the extraction were efficient for milk samples. Furthermore, a mixture containing a chlorinated solvent (dichloromethane) appears to be better than an ethereal solvent (tert-butyl methyl ether) for extracting lipids. This study suggests that a 3:1:0.005 mixture of dichloromethane, methanol and triethylammonium chloride, with an aqueous wash, is the most efficient of the currently reported methods for high-throughput lipid extraction and analysis. Further work is required to develop non-aqueous extraction methods that are both convenient and applicable to a broad range of sample types.
    Keywords:  lipid extraction; lipid metabolism; lipidomics
    DOI:  https://doi.org/10.3390/molecules25143192
  8. J Chromatogr B Analyt Technol Biomed Life Sci. 2020 Jun 26. pii: S1570-0232(20)30276-2. [Epub ahead of print]1152 122257
    van der Aart-van der Beek AB, Wessels AMA, Heerspink HJL, Touw DJ.
      Sodium-glucose cotransporter 2 -inhibitors (SGLT2i) are oral glucose-lowering drugs that have also demonstrated cardioprotective and renoprotective effects. SGLT2i play an increasingly important role in the treatment of type 2 diabetes. Here we report a simple and robust liquid chromatography-tandem mass spectrometry (LC-MS/MS) method for the simultaneous quantification of three SGLT2i (canagliflozin, dapagliflozin and empagliflozin) in human plasma, serum and urine with a runtime of 1 min. Methanol was used as protein precipitating agent. Chromatographic separation was accomplished using a Waters ACQUITY UPLC HSS T3 1.8 μm; 2.1 × 50 mm column with a Waters ACQUITY UPLC HSS T3 1.8 μm VanGuard Pre-column; 2.1 × 5 mm, using gradient elution with ammonium acetate 20 mM (pH 5) and acetonitrile as mobile phase at a flow rate of 0.8 ml/min. Mass spectrometric analysis of the acetate adduct ions was carried out using electrospray with negative ionization and SRM mode. The assay was validated according to FDA and EMA guidelines, including selectivity, linearity, accuracy and precision, dilution integrity, stability and recovery. With a sample volume of 200 µl, linear ranges of 10-5000 µg/L, 1-500 µg/L and 2-1000 µg/L for canagliflozin, dapagliflozin and empagliflozin respectively, were achieved. The assay was successfully applied in two pharmacokinetic studies with dapagliflozin and empagliflozin. In conclusion, we developed and validated a simple, fast and robust LC-MS/MS method for the simultaneous quantification of canagliflozin, dapagliflozin and empagliflozin, that allows rapid analysis of large numbers of samples and can be used for both pharmacokinetic studies and biomedical analysis of canagliflozin, dapagliflozin and empagliflozin.
    Keywords:  Canagliflozin; Dapagliflozin; Empagliflozin; LC-MS/MS; SGLT2i
    DOI:  https://doi.org/10.1016/j.jchromb.2020.122257
  9. J Am Soc Mass Spectrom. 2020 Jul 17.
    Wei P, Hao L, Thomas S, Buchberger AR, Steinke L, Marker PC, Ricke WA, Li L.
      Lower urinary tract symptoms (LUTS) are common in aging males. Disease etiology is largely unknown, but likely includes inflammation and age-related changes in steroid hormones. Diagnosis is currently based on subjective symptom scores, and mainstay treatments can be ineffective and bothersome. Biomarker discovery efforts could facilitate objective diagnostic criteria for personalized medicine and new potential druggable pathways. To identify urine metabolite markers specific to hormone-induced bladder outlet obstruction, we applied our custom synthesized multiplex isobaric tags to monitor the development of bladder outlet obstruction across time in an experimental mouse model of LUTS. Mouse urine samples were collected before treatment and after 2, 4, and 8 weeks of steroid hormone treatment and subsequently analyzed by nanoflow ultrahigh-performance liquid chromatography coupled to tandem mass spectrometry. Accurate and high throughput quantification of amine-containing metabolites was achieved by twelve-plex DiLeu isobaric labeling. Metandem, a novel online software tool for large-scale isobaric labeling-based metabolomics, was used for identification and relative quantification of labeled metabolites. A total of 59 amine-containing metabolites were identified and quantified, 9 of which were changed significantly by the hormone treatment. Metabolic pathway analysis showed that three metabolic pathways were potentially disrupted. Among them, the arginine and proline metabolism pathway was significantly dysregulated both in this model and in a prior analysis of LUTS patient samples. Proline and citrulline were significantly changed in both samples and serve as attractive candidate biomarkers. 12-plex DiLeu isobaric labeling with Metandem data processing presents an accessible and efficient workflow for amine-containing metabolome study in biological specimens.
    DOI:  https://doi.org/10.1021/jasms.0c00110
  10. J Pharm Biomed Anal. 2020 Jul 07. pii: S0731-7085(20)31350-9. [Epub ahead of print]189 113464
    Holleran JL, Parise RA, Guo J, Kiesel BF, Taylor SE, Ivy SP, Chu E, Beumer JH.
      We developed a high-performance liquid chromatography mass spectrometry method for quantitating iohexol in 50 μL human plasma. After acetonitrile protein precipitation, chromatographic separation was achieved with a Shodex Asahipak NH2P-50 2D (5 μm, 2 × 150 mm) column and a gradient of 0.1 % formic acid in acetonitrile and 0.1 % formic acid in water over a 10 min run time. Mass spectrometric detection was performed on a Micromass Quatromicro triple-stage bench-top mass spectrometer with electrospray, positive-mode ionization. The assay was linear from 1 to 500 μg/mL for iohexol, proved to be accurate (101.3-102.1 %) and precise (<3.4 %CV), and fulfilled Food and Drug Administration (FDA) criteria for bioanalytical method validation. Recovery from plasma was 53.1-64.2 % and matrix effect was trivial (-3.4 to -1.3 %). Plasma freeze thaw stability (97.4-99.4 %), stability for 5 months at -80 °C (95.5-103.3 %), and stability for 4 h at room temperature (100.6-103.3 %) were all acceptable. This validated assay using a deuterated internal standard will be an important tool in measuring iohexol clearance and determining glomerular filtration rate (GFR) in patients.
    Keywords:  Assay; Iohexol; Tandem mass spectrometry; Validation
    DOI:  https://doi.org/10.1016/j.jpba.2020.113464
  11. ACS Omega. 2020 Jul 07. 5(26): 16089-16098
    Erny GL, Gomes RA, Santos MSF, Santos L, Neuparth N, Carreiro-Martins P, Marques JG, Guerreiro ACL, Gomes-Alves P.
      Separation techniques hyphenated to high-resolution mass spectrometry are essential in untargeted metabolomic analyses. Due to the complexity and size of the resulting data, analysts rely on computer-assisted tools to mine for features that may represent a chromatographic signal. However, this step remains problematic, and a high number of false positives are often obtained. This work reports a novel approach where each step is carefully controlled to decrease the likelihood of errors. Datasets are first corrected for baseline drift and background noise before the MS scans are converted from profile to centroid. A new alignment strategy that includes purity control is introduced, and features are quantified using the original data with scans recorded as profile, not the extracted features. All the algorithms used in this work are part of the Finnee Matlab toolbox that is freely available. The approach was validated using metabolites in exhaled breath condensates to differentiate individuals diagnosed with asthma from patients with chronic obstructive pulmonary disease. With this new pipeline, twice as many markers were found with Finnee in comparison to XCMS-online, and nearly 50% more than with MS-Dial, two of the most popular freeware for untargeted metabolomics analysis.
    DOI:  https://doi.org/10.1021/acsomega.0c01610