bims-metlip Biomed News
on Methods and protocols in metabolomics and lipidomics
Issue of 2021‒11‒28
thirteen papers selected by
Sofia Costa
Icahn School of Medicine at Mount Sinai

  1. Metabolites. 2021 Oct 20. pii: 713. [Epub ahead of print]11(11):
      Lipidomics is a newly emerged discipline involving the identification and quantification of thousands of lipids. As a part of the omics field, lipidomics has shown rapid growth both in the number of studies and in the size of lipidome datasets, thus, requiring specific and efficient data analysis approaches. This paper aims to provide guidelines for analyzing and interpreting lipidome data obtained using untargeted methods that rely on liquid chromatography coupled with mass spectrometry (LC-MS) to detect and measure the intensities of lipid compounds. We present a state-of-the-art untargeted LC-MS workflow for lipidomics, from study design to annotation of lipid features, focusing on practical, rather than theoretical, approaches for data analysis, and we outline possible applications of untargeted lipidomics for biological studies. We provide a detailed R notebook designed specifically for untargeted lipidome LC-MS data analysis, which is based on xcms software.
    Keywords:  LC-MS; bioinformatics; lipidome
  2. Metabolites. 2021 Oct 21. pii: 720. [Epub ahead of print]11(11):
      Lipidomics is the comprehensive analysis of lipids in a given biological system. This investigation is often limited by the low amount and high complexity of biological samples, therefore highly sensitive lipidomics methods are required. Nanoflow-LC/MS offers extremely high sensitivity; however, it is challenging as a more demanding maintenance is often needed compared to conventional microflow-LC approaches. Here, we developed a sensitive and reproducible lipidomics LC method, termed Opti-nQL, which can be applied to any biological system. Opti-nQL has been validated with cellular lipid extracts of human and mouse origin and with different lipid extraction methods. Among the resulting 4000 detected features, 700 and even more unique lipid molecular species have been identified covering 16 lipid sub-classes, while 400 lipids were uniquely structure defined by MS/MS. These results were obtained by analyzing an amount of lipids extract equivalent to 40 ng of proteins, being highly suitable for low abundant samples. MS analysis showed that theOpti-nQL method increases the number of identified lipids, which is evidenced by injecting 20 times less material than in microflow based chromatography, being more reproducible and accurate thus enhancing robustness of lipidomics analysis.
    Keywords:  lipid species; lipidomics; nano-LC-MS/MS; quantitative analysis; sensitivity
  3. J Mass Spectrom Adv Clin Lab. 2021 Jan;19 34-45
      Background: Nitric oxide (NO) plays an important role in endothelial homeostasis. Asymmetric dimethyl arginine (ADMA), L-N monomethyl arginine (L-NMMA) and symmetric dimethyl arginine (SDMA), which are derivatives of methylarginine, directly or indirectly reduce NO production. Therefore, these metabolites are an important risk factor for various diseases, including cardiovascular diseases. Numerous methods have been developed for the measurement of methylarginine derivatives, but various difficulties have been encountered. This study aimed to develop a reliable, fast and cost-effective method for the analysis and measurement of methylarginine derivatives (ADMA, SDMA, L-NMMA) and related metabolites (arginine, citrulline, homoarginine, ornithine), and to validate this method according to Clinical and Laboratory Standards Institute (CLSI) protocols.Methods: For the analysis of ADMA, SDMA, L-NMMA, arginine, homoarginine, citrulline, ornithine, 200 Âµl of serum were precipitated with methanol, and subsequently derivatized with a butanol solution containing 5% acetyl chloride. Butyl derivatives were separated using a C18 reverse phase column with a 5 min run time. Detection of analytes was achieved by utilising the specific fragmentation patterns identified through tandem mass spectrometry.
    Results: The method was linear for ADMA, SDMA, L-NMMA, ornithine, arginine, homoarginine and citrulline in the ranges of 0.023-6.0, 0.021-5.5, 0.019-5.0, 0.015-250, 0.015-250, 0.019-5 and 0.015-250 µM, respectively. The inter-assay CV% values for all analytes was less than 9.8%.
    Conclusions: Data obtained from method validation studies shows that the developed method is highly sensitive, precise and accurate. Short analysis time, cost-effectiveness, and multiplexed analysis of these metabolites, with the same pretreatment steps, are the main advantages of the method.
    Keywords:  ADMA; ADMA, asymmetric dimethyl arginine; CE, capillary electrophoresis; CE, collision energy; CLSI, The Clinical & Laboratory Standards Institute; CXP, collision cell exit potential; DDAH, dimethylaminohydrolase; DP, declustering potential; EP, enterance potential; FDA, Food and Drug Administration; GC–MS, gas chromatography–mass spectrometry; HPLC, high performance liquid chromatography; L-NMMA, L-N monomethyl arginine; LC-MS, liquid chromatography–mass spectrometry; LC-MS/MS, liquid chromatography tandem-mass spectrometry; MRM, multiple reaction monitoring; Methylarginines; NO, nitric oxide; NOS, nitric oxide synthase; PRMTs, protein arginine methyltransferases; SDMA, symmetric dimethyl arginine; Tandem mass spectrometry; Validation
  4. Metabolites. 2021 Nov 18. pii: 788. [Epub ahead of print]11(11):
      Metabolomics aims to perform a comprehensive identification and quantification of the small molecules present in a biological system. Due to metabolite diversity in concentration, structure, and chemical characteristics, the use of high-resolution methodologies, such as mass spectrometry (MS) or nuclear magnetic resonance (NMR), is required. In metabolomics data analysis, suitable data pre-processing, and pre-treatment procedures are fundamental, with subsequent steps aiming at highlighting the significant biological variation between samples over background noise. Traditional data analysis focuses primarily on the comparison of the features' intensity values. However, intensity data are highly variable between experimental batches, instruments, and pre-processing methods or parameters. The aim of this work was to develop a new pre-treatment method for MS-based metabolomics data, in the context of sample profiling and discrimination, considering only the occurrence of spectral features, encoding feature presence as 1 and absence as 0. This "Binary Simplification" encoding (BinSim) was used to transform several benchmark datasets before the application of clustering and classification methods. The performance of these methods after the BinSim pre-treatment was consistently as good as and often better than after different combinations of traditional, intensity-based, pre-treatments. Binary Simplification is, therefore, a viable pre-treatment procedure that effectively simplifies metabolomics data-analysis pipelines.
    Keywords:  Fourier Transform Ion Cyclotron Resonance mass spectrometry; data analysis; data treatment; metabolomics; multivariate analysis
  5. Clin Mass Spectrom. 2020 Nov;18 48-53
      Background: In tandem mass spectrometry, analyte detection is based on collision-induced fragmentation, which is modulated by the collision energy (CE) setting. Variation in CE leads to differential ion yield, and optimization is usually performed empirically as "tuning" during method development. Our aim was to build a method to objectify the impact of collision energy settings on ion yield for individual compounds.Methods: Collision energy (CE)-breakdown curves were generated based on acquisition files in which a large number of quasi-identical mass transitions were recorded simultaneously, with variation of CE over a defined range within a single injection. Ion yield (normalized to an internal standard recorded with a locked collision energy) was plotted as a curve versus CE settings. Piperacillin and testosterone were studied as exemplary analytes in matrix-free and serum matrix-based liquid chromatography tandem mass spectrometry (LC-MS/MS) measurements. More detailed testosterone CE-breakdown curves were investigated with regard to sample preparation techniques and the isotope labeling pattern of corresponding internal standards.
    Results: CE-breakdown curves were found characteristically for the piperacillin quantifier transition with respect to CE-related maximum ion yield, as well as curve width and shape. A diverging curve profile was observed for the piperacillin qualifier transition. For testosterone analyses, no impact from different sample preparation techniques or the isotope labeling patterns on the selected CE was shown.
    Conclusion: CE-breakdown curves are a convenient and valuable tool to verify LC-MS/MS methods regarding consistent fragmentation characteristics between sample sources or native analytes and isotope-labeled counterparts.
    Keywords:  CE, collision energy; CLSI, Clinical and Laboratory Standards Institute; CXP, cell exit potential; Collision energy-breakdown curves; DP, declustering potential; ISD, internal standard; LC-MS/MS, liquid chromatography tandem mass spectrometry; LLE, liquid–liquid-extraction; MRM, multiple reaction monitoring; Method pre-verification; PP, protein precipitation; SIL, stable isotope labeled; Tandem mass spectrometry (MS/MS); Tes-13C3, testosterone-13C3; Tes-d3, testosterone-d3
  6. Nat Prod Rep. 2021 Nov 17. 38(11): 1967-1993
      Covering: up to the end of 2020Recently introduced computational metabolome mining tools have started to positively impact the chemical and biological interpretation of untargeted metabolomics analyses. We believe that these current advances make it possible to start decomposing complex metabolite mixtures into substructure and chemical class information, thereby supporting pivotal tasks in metabolomics analysis including metabolite annotation, the comparison of metabolic profiles, and network analyses. In this review, we highlight and explain key tools and emerging strategies covering 2015 up to the end of 2020. The majority of these tools aim at processing and analyzing liquid chromatography coupled to mass spectrometry fragmentation data. We start with defining what substructures are, how they relate to molecular fingerprints, and how recognizing them helps to decompose complex mixtures. We continue with chemical classes that are based on the presence or absence of particular molecular scaffolds and/or functional groups and are thus intrinsically related to substructures. We discuss novel tools to mine substructures, annotate chemical compound classes, and create mass spectral networks from metabolomics data and demonstrate them using two case studies. We also review and speculate about the opportunities that NMR spectroscopy-based metabolome mining of complex metabolite mixtures offers to discover substructures and chemical classes. Finally, we will describe the main benefits and limitations of the current tools and strategies that rely on them, and our vision on how this exciting field can develop toward repository-scale-sized metabolomics analyses. Complementary sources of structural information from genomics analyses and well-curated taxonomic records are also discussed. Many research fields such as natural products discovery, pharmacokinetic and drug metabolism studies, and environmental metabolomics increasingly rely on untargeted metabolomics to gain biochemical and biological insights. The here described technical advances will benefit all those metabolomics disciplines by transforming spectral data into knowledge that can answer biological questions.
  7. Talanta. 2021 Nov 17. pii: S0039-9140(21)00981-4. [Epub ahead of print]238(Pt 2): 123059
      Hydrophilic metabolites are essential for all biological systems with multiple functions and their quantitative analysis forms an important part of metabolomics. However, poor retention of these metabolites on reversed-phase (RP) chromatographic column hinders their effective analysis with RPLC-MS methods. Herein, we developed a method for detecting hydrophilic metabolites using the ion-pair reversed-phase liquid-chromatography coupled with mass spectrometry (IPRP-LC-MS/MS) in scheduled multiple-reaction-monitoring (sMRM) mode. We first developed a hexylamine-based IPRP-UHPLC-QTOFMS method and experimentally measured retention time (tR) for 183 hydrophilic metabolites. We found that tRs of these metabolites were dominated by their electrostatic potential depending upon the numbers and types of their ionizable groups. We then systematically investigated the quantitative structure-retention relationship (QSRR) and constructed QSRR models using the measured tR. Subsequently, we developed a retention time predictive model using the random-forest regression algorithm (r2 = 0.93, q2 = 0.70, MAE = 1.28 min) for predicting metabolite retention time, which was applied in IPRP-UHPLC-MS/MS method in sMRM mode for quantitative metabolomic analysis. Our method can simultaneously quantify more than 260 metabolites. Moreover, we found that this method was applicable for multiple major biological matrices including biofluids and tissues. This approach offers an efficient method for large-scale quantitative hydrophilic metabolomic profiling even when metabolite standards are unavailable.
    Keywords:  Ion-pair reversed-phase chromatography; Metabolomics; Quantitative structure-retention relationship; Scheduled MRM; UHPLC-MS
  8. Clin Mass Spectrom. 2020 Nov;18 54-65
    Italian Society of Embryology, Reproduction, Research (SIERR)
      Phthalates and bisphenol A interfere with the synthesis, secretion, transport, binding, metabolism, and excretion of endogenous hormones and, for this reason, are classified as endocrine disruptors. We are here presenting an analytical method for the simultaneous detection of six phthalates metabolites and bisphenol A in different biological fluids (urine, serum and follifular fluid) by liquid chromatography coupled to tandem mass spectrometry. The quantification was carried out in negative electrospray ionization mode using selected reaction monitoring as acquisition mode. Different extraction protocols, using either solid phase or liquid/liquid extraction, were comparatively evaluated to optimize the sample preparation procedure. Solid-phase extraction was chosen as it ensured the best recovery and overall sensitivity. The method was successfully validated: recovery varying in the range 71 ± 2%-107 ± 6% depending on the target analyte and the matrix considered, intra-assay and inter-assay precision ≤ 12% for follicular fluid, ≤11% for serum and ≤ 10% for urine and accuracy ≤ 115% for follicular fluid, ≤113% for serum ≤ 115% for urine , linearity with R2 > 0.99, with the exception of MEP (recovery 64 ± 8%, intra-assay precision ≤ 20%, inter-assay precision ≤ 16% for follicular fluid). The actual applicability of the method developed and validated in this study was assessed by the analysis of real samples, including 10 specimens of follicular fluid, serum and urine samples, that showed the presence of phthalates metabolites and Bisphenol A, and confirming that the newly developed method can be applied in the routine clinical laboratory for the identification and quantitation of these endocrine-disrupting chemicals.
    Keywords:  Bisphenol A; Endocrine-disruptors; Follicular fluid; LC-MS/MS; Phthalates
  9. Metabolites. 2021 Nov 18. pii: 789. [Epub ahead of print]11(11):
      Acoustic ejection mass spectrometry is a novel high-throughput analytical technology that delivers high reproducibility without carryover observed. It eliminates the chromatography step used to separate analytes from matrix components. Fully-automated liquid-liquid extraction is widely used for sample cleanup, especially in high-throughput applications. We introduce a workflow for direct AEMS analysis from phase-separated liquid samples and explore high-throughput analysis from complex matrices. We demonstrate the quantitative determination of fentanyl from urine using this two-phase AEMS approach, with a LOD lower than 1 ng/mL, quantitation precision of 15%, and accuracy better than ±10% over the range of evaluation (1-100 ng/mL). This workflow offers simplified sample preparation and higher analytical throughput for some bioanalytical applications, in comparison to an LC-MS based approach.
    Keywords:  acoustic ejection mass spectrometry; high-throughput analysis; liquid–liquid extraction; sample preparation
  10. Anal Chim Acta. 2022 Jan 02. pii: S0003-2670(21)01044-8. [Epub ahead of print]1189 339218
      Metabolomics, which serves as a readout of biological processes and diseases monitoring, is an informative research area for disease biomarker discovery and systems biology studies. In particular, reversed-phase liquid chromatography-mass spectrometry (RPLC-MS) has become a powerful and popular tool for metabolomics analysis, enabling the detection of most metabolites. Very polar and ionic metabolites, however, are less easily detected because of their poor retention in RP columns. Dansylation of metabolites simplifies the sub-metabolome analysis by reducing its complexity and increasing both hydrophobicity and ionization ability. However, the various metabolite concentrations in clinical samples have a wide dynamic range with highly individual variation in total metabolite amount, such as in saliva. The bicarbonate buffer typically used in dansylation labeling reactions induces solvent stratification, resulting in poor reproducibility, selective sample loss and an increase in false-determined metabolite peaks. In this study, we optimized the dansylation protocol for samples with wide concentration range of metabolites, utilizing diisopropylethylamine (DIPEA) or tri-ethylamine (TEA) in place of bicarbonate buffer, and presented the results of a systemic investigation of the influences of individual processes involved on the overall performance of the protocol. In addition to achieving high reproducibility, substitution of DIPEA or TEA buffer resulted in similar labeling efficiency of most metabolites and more efficient labeling of some metabolites with a higher pKa. With this improvement, compounds that are only present in samples in trace amounts can be detected, and more comprehensive metabolomics profiles can be acquired for biomarker discovery or pathway analysis, making it possible to analyze clinical samples with limited amounts of metabolites.
    Keywords:  Dansylation; Diisopropylethylamine (DIPEA); Metabolites; Metabolomics; Saliva; Triethylamine (TEA)
  11. Clin Mass Spectrom. 2020 Apr;16 1-10
      The accurate measurement of androstenedione in human serum and plasma is required for steroid profiling to assure the appropriate diagnosis and differential diagnosis of hyperandrogenism. In this work, we introduce an isotope dilution liquid chromatography-tandem mass spectrometry (LC-MS/MS) candidate reference measurement procedure for the quantification of androstenedione in human serum and plasma. The performance of the procedure enables its use in the evaluation and standardization of routine assays and for the evaluation of patient samples to ensure the traceability of individual patient results. As the primary standard, a certified reference material from NMIA (National Measurement Institute, Australia) was used. Additionally, a quantitative nuclear magnetic resonance (qNMR) method was developed for the value assignment of the primary reference material, which ensures the direct traceability to SI units, as well as the independence from the availability of reference materials. 13C3-labeled androstenedione was used as the internal standard. The introduced method allows the measurement of androstenedione in the range of 0.05-12 ng/mL, and the assay imprecision was found to be <2% between 5 and 12 ng/mL, 3.5% at 1.5 ng/mL, and 5.2% at 0.05 ng/mL, with an accuracy of 95-105% for the serum and 91-103% for the plasma matrix. The transferability to a second laboratory was validated by method comparison based on 112 patient samples. The comparison of the results obtained from the presented method and an LC-MS/MS routine assay, using 150 native patient samples, showed a good correlation with a bias of the routine method of ≤4.0%.
    Keywords:  Androstenedione; Human serum; LC–MS/MS; Quantitative NMR; Reference measurement procedure
  12. Anal Bioanal Chem. 2021 Nov 25.
      The performance of two different analytical methodologies to investigate the presence of glyphosate (GLY) and aminomethylphosphonic acid (AMPA) residues in wine samples was evaluated. Transformation of compounds in their fluorene-9-methyloxycarbonyl derivatives permitted their separation under reversed-phase liquid chromatography with tandem mass spectrometry (LC-MS/MS) determination. Although the wine matrix severely impaired the efficiency of GLY derivatization, this drawback was solved using a molecularly imprinted sorbent for the previous, selective extraction of GLY and AMPA from wine. Alternatively, the use of a strong anionic exchange, polyvinyl alcohol-based LC column, turned to be the most effective alternative for direct determination of both compounds in diluted wine samples. The chromatographic behavior of this column and the magnitude of matrix effects observed during analysis of diluted wine samples were significantly affected by the composition of the mobile phase. Under final working conditions, this column permitted the separation of AMPA and the fungicide fosetyl (which shows common transitions in tandem MS/MS methods), it improved significantly the sample throughput versus extraction-derivatization-purification method, and it allowed the use of solvent-based calibration standards. Both analytical procedures provided similar limits of quantification (LOQs) for GLY (0.5-1.0 ng mL-1), while the multistep method was 8 times more sensitive to AMPA than the direct procedure. GLY residues stayed above method LOQs in 70% of the processed wines; however, concentrations measured in 95% of positive samples remained 100 times below the maximum residue limit (MRL) set for GLY in vinification grapes.
    Keywords:  Derivatization; Direct injection; Glyphosate; Liquid chromatography tandem mass spectrometry; Wine
  13. Plants (Basel). 2021 Nov 08. pii: 2409. [Epub ahead of print]10(11):
      Metabolomics is now considered a wide-ranging, sensitive and practical approach to acquire useful information on the composition of a metabolite pool present in any organism, including plants. Investigating metabolomic regulation in plants is essential to understand their adaptation, acclimation and defense responses to environmental stresses through the production of numerous metabolites. Moreover, metabolomics can be easily applied for the phenotyping of plants; and thus, it has great potential to be used in genome editing programs to develop superior next-generation crops. This review describes the recent analytical tools and techniques available to study plants metabolome, along with their significance of sample preparation using targeted and non-targeted methods. Advanced analytical tools, like gas chromatography-mass spectrometry (GC-MS), liquid chromatography mass-spectroscopy (LC-MS), capillary electrophoresis-mass spectrometry (CE-MS), fourier transform ion cyclotron resonance-mass spectrometry (FTICR-MS) matrix-assisted laser desorption/ionization (MALDI), ion mobility spectrometry (IMS) and nuclear magnetic resonance (NMR) have speed up precise metabolic profiling in plants. Further, we provide a complete overview of bioinformatics tools and plant metabolome database that can be utilized to advance our knowledge to plant biology.
    Keywords:  analytical tools; data analysis; genetically modified crops; mass spectrometry; metabolomics databases; metabolomics software tools; omics; plant biology