bims-metlip Biomed News
on Methods and protocols in metabolomics and lipidomics
Issue of 2021‒01‒31
thirteen papers selected by
Sofia Costa
Cold Spring Harbor Laboratory


  1. Mass Spectrom Rev. 2021 Jan 25.
    Delvaux A, Rathahao-Paris E, Alves S.
      Metabolomics has become increasingly popular in recent years for many applications ranging from clinical diagnosis, human health to biotechnological questioning. Despite technological advances, metabolomic studies are still currently limited by the difficulty of identifying all metabolites, a class of compounds with great chemical diversity. Although lengthy chromatographic analyses are often used to obtain comprehensive data, many isobar and isomer metabolites still remain unresolved, which is a critical point for the compound identification. Currently, ion mobility spectrometry is being explored in metabolomics as a way to improve metabolome coverage, analysis throughput and isomer separation. In this review, all the steps of a typical workflow for untargeted metabolomics are discussed considering the use of an ion mobility instrument. An overview of metabolomics is first presented followed by a brief description of ion mobility instrumentation. The ion mobility potential for complex mixture analysis is discussed regarding its coupling with a mass spectrometer alone, providing gas-phase separation before mass analysis as well as its combination with different separation platforms (conventional hyphenation but also multidimensional ion mobility couplings), offering multidimensional separation. Various instrumental and analytical conditions for improving the ion mobility separation are also described. Finally, data mining, including software packages and visualization approaches, as well as the construction of ion mobility databases for the metabolite identification are examined.
    Keywords:  Ion mobility-mass spectrometry; hyphenated method; metabolomics; multidimensional data
    DOI:  https://doi.org/10.1002/mas.21685
  2. J Vis Exp. 2021 Jan 05.
    Mohammad K, Jiang H, Titorenko VI.
      Metabolomics is a methodology used for the identification and quantification of many low-molecular-weight intermediates and products of metabolism within a cell, tissue, organ, biological fluid, or organism. Metabolomics traditionally focuses on water-soluble metabolites. The water-soluble metabolome is the final product of a complex cellular network that integrates various genomic, epigenomic, transcriptomic, proteomic, and environmental factors. Hence, the metabolomic analysis directly assesses the outcome of the action for all these factors in a plethora of biological processes within various organisms. One of these organisms is the budding yeast Saccharomyces cerevisiae, a unicellular eukaryote with the fully sequenced genome. Because S. cerevisiae is amenable to comprehensive molecular analyses, it is used as a model for dissecting mechanisms underlying many biological processes within the eukaryotic cell. A versatile analytical method for the robust, sensitive, and accurate quantitative assessment of the water-soluble metabolome would provide the essential methodology for dissecting these mechanisms. Here we present a protocol for the optimized conditions of metabolic activity quenching in and water-soluble metabolite extraction from S. cerevisiae cells. The protocol also describes the use of liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) for the quantitative analysis of the extracted water-soluble metabolites. The LC-MS/MS method of non-targeted metabolomics described here is versatile and robust. It enables the identification and quantification of more than 370 water-soluble metabolites with diverse structural, physical, and chemical properties, including different structural isomers and stereoisomeric forms of these metabolites. These metabolites include various energy carrier molecules, nucleotides, amino acids, monosaccharides, intermediates of glycolysis, and tricarboxylic cycle intermediates. The LC-MS/MS method of non-targeted metabolomics is sensitive and allows the identification and quantitation of some water-soluble metabolites at concentrations as low as 0.05 pmol/µL. The method has been successfully used for assessing water-soluble metabolomes of wild-type and mutant yeast cells cultured under different conditions.
    DOI:  https://doi.org/10.3791/62061
  3. J Lipid Res. 2020 Jan;pii: S0022-2275(20)30020-1. [Epub ahead of print]61(1): 105-115
    Triebl A, Burla B, Selvalatchmanan J, Oh J, Tan SH, Chan MY, Mellet NA, Meikle PJ, Torta F, Wenk MR.
      Quantitative MS of human plasma lipids is a promising technology for translation into clinical applications. Current MS-based lipidomic methods rely on either direct infusion (DI) or chromatographic lipid separation methods (including reversed phase and hydrophilic interaction LC). However, the use of lipid markers in laboratory medicine is limited by the lack of reference values, largely because of considerable differences in the concentrations measured by different laboratories worldwide. These inconsistencies can be explained by the use of different sample preparation protocols, method-specific calibration procedures, and other experimental and data-reporting parameters, even when using identical starting materials. Here, we systematically investigated the roles of some of these variables in multiple approaches to lipid analysis of plasma samples from healthy adults by considering: 1) different sample introduction methods (separation vs. DI methods); 2) different MS instruments; and 3) between-laboratory differences in comparable analytical platforms. Each of these experimental variables resulted in different quantitative results, even with the inclusion of isotope-labeled internal standards for individual lipid classes. We demonstrated that appropriate normalization to commonly available reference samples (i.e., "shared references") can largely correct for these systematic method-specific quantitative biases. Thus, to harmonize data in the field of lipidomics, in-house long-term references should be complemented by a commonly available shared reference sample, such as NIST SRM 1950, in the case of human plasma.
    Keywords:  National Institute of Standards and Technology standard reference material 1950; harmonization; lipids; liquid chromatography; mass spectrometry; plasma; quantitation
    DOI:  https://doi.org/10.1194/jlr.D119000393
  4. Anal Chim Acta. 2021 Feb 22. pii: S0003-2670(20)31218-6. [Epub ahead of print]1147 38-55
    Roca M, Alcoriza MI, Garcia-Cañaveras JC, Lahoz A.
      Metabolomics has become an invaluable tool for both studying metabolism and biomarker discovery. The great technical advances in analytical chemistry and bioinformatics have considerably increased the number of measurable metabolites, yet an important part of the human metabolome remains uncovered. Among the various MS hyphenated techniques available, LC-MS stands out as the most used. Here, we aimed to show the capabilities of LC-MS to uncover part of the metabolome and how to best proceed with sample preparation and LC to maximise metabolite detection. The analyses of various open metabolite databases served us to estimate the size of the already detected human metabolome, the expected metabolite composition of most used human biospecimens and which part of the metabolome can be detected when LC-MS is used. Based on an extensive review and on our experience, we have outlined standard procedures for LC-MS analysis of urine, cells, serum/plasma, tissues and faeces, to guide in the selection of the sample preparation method that best matches with one or more LC techniques in order to get the widest metabolome coverage. These standard procedures may be a useful tool to explore, at a glance, the wide spectrum of possibilities available, which can be a good starting point for most of the LC-MS metabolomic studies.
    Keywords:  Biospecimens; Human metabolome; LC-MS; Metabolome coverage; Metabolomics
    DOI:  https://doi.org/10.1016/j.aca.2020.12.025
  5. J Lipid Res. 2020 Jan;pii: S0022-2275(20)30019-5. [Epub ahead of print]61(1): 95-104
    Schlame M, Xu Y, Erdjument-Bromage H, Neubert TA, Ren M.
      Lipid metabolism plays an important role in the regulation of cellular homeostasis. However, because it is difficult to measure the actual rates of synthesis and degradation of individual lipid species, lipid compositions are often used as a surrogate to evaluate lipid metabolism even though they provide only static snapshots of the lipodome. Here, we designed a simple method to determine the turnover rate of phospholipid and acylglycerol species based on the incorporation of 13C6-glucose combined with LC-MS/MS. We labeled adult Drosophila melanogaster with 13C6-glucose that incorporates into the entire lipidome, derived kinetic parameters from mass spectra, and studied effects of deletion of CG6718, the fly homolog of the calcium-independent phospholipase A2β, on lipid metabolism. Although 13C6-glucose gave rise to a complex pattern of 13C incorporation, we were able to identify discrete isotopomers in which 13C atoms were confined to the glycerol group. With these isotopomers, we calculated turnover rate constants, half-life times, and fluxes of the glycerol backbone of multiple lipid species. To perform these calculations, we estimated the fraction of labeled molecules in glycerol-3-phosphate, the lipid precursor, by mass isotopomer distribution analysis of the spectra of phosphatidylglycerol. When we applied this method to D. melanogaster, we found a range of lipid half-lives from 2 to 200 days, demonstrated tissue-specific fluxes of individual lipid species, and identified a novel function of CG6718 in triacylglycerol metabolism. This method provides fluxomics-type data with significant potential to improve the understanding of complex lipid regulation in a variety of research models.
    Keywords:  genes in lipid dysfunction; lipid metabolism; mass spectrometry; phospholipids/metabolism; stable isotope tracers
    DOI:  https://doi.org/10.1194/jlr.D119000318
  6. Cancer Metab. 2021 Jan 29. 9(1): 9
    Andersen MK, Høiem TS, Claes BSR, Balluff B, Martin-Lorenzo M, Richardsen E, Krossa S, Bertilsson H, Heeren RMA, Rye MB, Giskeødegård GF, Bathen TF, Tessem MB.
      BACKGROUND: Prostate cancer tissues are inherently heterogeneous, which presents a challenge for metabolic profiling using traditional bulk analysis methods that produce an averaged profile. The aim of this study was therefore to spatially detect metabolites and lipids on prostate tissue sections by using mass spectrometry imaging (MSI), a method that facilitates molecular imaging of heterogeneous tissue sections, which can subsequently be related to the histology of the same section.METHODS: Here, we simultaneously obtained metabolic and lipidomic profiles in different prostate tissue types using matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) MSI. Both positive and negative ion mode were applied to analyze consecutive sections from 45 fresh-frozen human prostate tissue samples (N = 15 patients). Mass identification was performed with tandem MS.
    RESULTS: Pairwise comparisons of cancer, non-cancer epithelium, and stroma revealed several metabolic differences between the tissue types. We detected increased levels of metabolites crucial for lipid metabolism in cancer, including metabolites involved in the carnitine shuttle, which facilitates fatty acid oxidation, and building blocks needed for lipid synthesis. Metabolites associated with healthy prostate functions, including citrate, aspartate, zinc, and spermine had lower levels in cancer compared to non-cancer epithelium. Profiling of stroma revealed higher levels of important energy metabolites, such as ADP, ATP, and glucose, and higher levels of the antioxidant taurine compared to cancer and non-cancer epithelium.
    CONCLUSIONS: This study shows that specific tissue compartments within prostate cancer samples have distinct metabolic profiles and pinpoint the advantage of methodology providing spatial information compared to bulk analysis. We identified several differential metabolites and lipids that have potential to be developed further as diagnostic and prognostic biomarkers for prostate cancer. Spatial and rapid detection of cancer-related analytes showcases MALDI-TOF MSI as a promising and innovative diagnostic tool for the clinic.
    Keywords:  Mass spectrometry imaging; Metabolism; Prostate cancer; Tumor heterogeneity
    DOI:  https://doi.org/10.1186/s40170-021-00242-z
  7. Anal Chim Acta. 2021 Feb 22. pii: S0003-2670(20)31236-8. [Epub ahead of print]1147 64-71
    Vrzal T, Malečková M, Olšovská J.
      Retention index in gas chromatographic analyses is an essential tool for appropriate analyte identification. Currently, many libraries providing retention indices for a huge number of compounds on distinct stationary phase chemistries are available. However, situation could be complicated in the case of unknown unknowns not present in such libraries. The importance of identification of these compounds have risen together with a rapidly expanding interest in non-targeted analyses in the last decade. Therefore, precise in silico computation/prediction of retention indices based on a suggested molecular structure will be highly appreciated in such situations. On this basis, a predictive model based on deep learning was developed and presented in this paper. It is designed for user-friendly and accurate prediction of retention indices of compounds in gas chromatography with the semi-standard non-polar stationary phase. Simplified Molecular Input Entry System (SMILES) is used as the model's input. Architecture of the model consists of 2D-convolutional layers, together with batch normalization, max pooling, dropout, and three residual connections. The model reaches median absolute error of prediction of the retention index for validation and test set at 16.4 and 16.0 units, respectively. Median percentage error is lower than or equal to 0.81% in the case of all mentioned data sets. Finally, the DeepReI model is presented in R package, and is available on https://github.com/TomasVrzal/DeepReI together with a user-friendly graphical user interface.
    Keywords:  Artificial intelligence; Convolutional network; Deep learning; Gas chromatography; Retention index
    DOI:  https://doi.org/10.1016/j.aca.2020.12.043
  8. J Forensic Sci. 2021 Jan 29.
    Nowak K, Szpot P, Jurek T, Zawadzki M.
      The paper presents a method for the determination of methadone, EDDP, and EMDP in postmortem biological materials using liquid-liquid extraction with ethyl acetate (pH9) and UHPLC-MS/MS technique. Methadone-d9 and EDDP-d3 were used as the internal standards. The method validation results for blood and urine were as follows: linearity: 0.5-1000 ng/ml; R2  > 0.9993 for methadone, EDDP and R2  > 0.9944 for EMDP. Intra- and inter-day precision: 0.1%-7.5% and 0.3%-8.6%, respectively; intra- and inter-day accuracy: -11.8% to 13.9% and -9.3 to 14.8%, respectively; recovery: 91.5%-123.0%; matrix effect: 83.5%-123.9%. This study also describes 18 postmortem cases, where methadone concentrations ranged 2.3-1180 ng/ml in blood (n = 17), from 11.0 to >10,000 ng/ml in urine (n = 13) and 135.2-409.0 in vitreous humor (VH, n = 3). EDDP concentrations ranged from not detectable to 180 ng/mL in blood, from 42.4 to >10,000 ng/ml in urine and 18.3-36.5 in VH. EMDP concentrations were found in four cases in blood from below LLOQ to 1.8 ng/ml and in seven cases in urine, ranged 2.1-243.0 ng/ml. EMDP was not detected in VH samples. The EDDP/methadone ratios and blood/urine ratios for methadone and EDDP in EMDP-positive and negative cases were performed. The paper presents mass spectra of other methadone metabolites, than EDDP and EMDP (ring hydroxylated methadone, ring hydroxylated EDDP, ring hydroxylated EMDP, methadol, and DDP). Simultaneous determination of methadone and its metabolites in order to unequivocally interpret the results of toxicological tests seems to be useful in cases related to prescription/illicit use of methadone.
    Keywords:  EDDP; EMDP; UHPLC-MS/MS; forensic pathology; forensic toxicology; methadone
    DOI:  https://doi.org/10.1111/1556-4029.14674
  9. Metabolites. 2021 Jan 22. pii: 64. [Epub ahead of print]11(2):
    Paley S, Billington R, Herson J, Krummenacker M, Karp PD.
      Metabolomics, synthetic biology, and microbiome research demand information about organism-scale metabolic networks. The convergence of genome sequencing and computational inference of metabolic networks has enabled great progress toward satisfying that demand by generating metabolic reconstructions from the genomes of thousands of sequenced organisms. Visualization of whole metabolic networks is critical for aiding researchers in understanding, analyzing, and exploiting those reconstructions. We have developed bioinformatics software tools that automatically generate a full metabolic-network diagram for an organism, and that enable searching and analyses of the network. The software generates metabolic-network diagrams for unicellular organisms, for multi-cellular organisms, and for pan-genomes and organism communities. Search tools enable users to find genes, metabolites, enzymes, reactions, and pathways within a diagram. The diagrams are zoomable to enable researchers to study local neighborhoods in detail and to see the big picture. The diagrams also serve as tools for comparison of metabolic networks and for interpreting high-throughput datasets, including transcriptomics, metabolomics, and reaction fluxes computed by metabolic models. These data can be overlaid on the metabolic charts to produce animated zoomable displays of metabolic flux and metabolite abundance. The BioCyc.org website contains whole-network diagrams for more than 18,000 sequenced organisms. The ready availability of organism-specific metabolic network diagrams and associated tools for almost any sequenced organism are useful for researchers working to better understand the metabolism of their organism and to interpret high-throughput datasets in a metabolic context.
    Keywords:  biochemical pathways charts; metabolic charts; metabolic diagrams; metabolic maps; metabolic network diagrams; metabolomics; transcriptomics
    DOI:  https://doi.org/10.3390/metabo11020064
  10. J Chromatogr A. 2021 Jan 05. pii: S0021-9673(20)31141-9. [Epub ahead of print]1638 461867
    Shen J, Wang H, Huang H, Li H, Li C, Yan C, Yu T, Guo H, Hu K, Du Y, Sun H, Xie L, Fang P, Liang Y.
      Considering that neurotransmitters (NTs) and amino acids (AAs) exert pivotal roles in various neurological diseases, global detection of these endogenous metabolites is of great significance for the treatment of nervous system diseases. Herein, a workflow that could cope with various challenges was proposed to establish an extendable all-in-one injection liquid chromatography-tandem mass spectrometry (LC-MS/MS) assay for analyzing these small molecular metabolites with high coverage. To obtain a qualified blank biological matrix for the preparation of standard curves and quality control samples, different absorption solvents, including activated carbon (AC), calcite (Cal) and montmorillonite (Mnt) were systematically evaluated for efficient absorption of endogenous substances with minimum residue. We also firstly proposed a "Collision Energy Defect (CED)" strategy to solve the huge difference of mass signal strength caused by different properties and concentrations of 11 NTs and 17 AAs. The quantitative results were validated by LC-MS/MS. Sensitivity, accuracy, and recovery meeting generally accepted bioanalytic guidelines were observed in a concentration span of at least 100 to 500 times for each analyte. Then the temporal changes of intracerebral and peripheral NTs and AAs in ischemic stroke model and sham operated rats were successfully produced and compared using the described method. All these results suggested that the currently developed assay was powerful enough to simultaneously monitor a large panel of endogenous small molecule metabolites, which was expected to be widely used in the research of various diseases mediated by NTs and AAs.
    Keywords:  Amino acids; Collision energy defect; Multi-dimensional adsorption; Neurotransmitters
    DOI:  https://doi.org/10.1016/j.chroma.2020.461867
  11. Anal Chem. 2021 Jan 26.
    Xiong CF, Ding J, Zhu QF, Bai YL, Yin XM, Ye TT, Yu QW, Feng YQ.
      cis-Diol-containing metabolites are widely distributed in living organisms, and they participate in the regulation of various important biological activities. The profiling of cis-diol-containing metabolites could help us in fully understanding their functions. In this work, based on the characteristic isotope pattern of boron, we employed a boronic acid reagent as the isotope tag to establish a sensitive and selective liquid chromatography-high-resolution mass spectrometry method for the screening and annotation of cis-diol-containing metabolites in biological samples. Boronic acid reagent 2-methyl-4-phenylaminomethylphenylboronic acid was used to label the cis-diol-containing metabolites in biological samples to improve the selectivity and MS sensitivity of cis-diol-containing metabolites. Based on the characteristic 0.996 Da mass difference of precursor ions and the peak intensity ratio of 1:4 originating from 10B and 11B natural isotopes, the potential cis-diol-containing metabolites were rapidly screened from biological samples. Potential cis-diol-containing metabolites were annotated by database searching and analysis of fragmentation patterns obtained by multistage MS (MSn) via collision-induced dissociation. Importantly, the cis-diol position could be readily resolved by the MS3 spectrum. With this method, a total of 45 cis-diol-containing metabolites were discovered in rice, including monoglycerides, polyhydroxy fatty acids, fatty alcohols, and so forth. Furthermore, the established method showed superiority in avoiding false-positive results in profiling cis-diol-containing metabolites.
    DOI:  https://doi.org/10.1021/acs.analchem.0c05037
  12. Anal Chim Acta. 2021 Feb 22. pii: S0003-2670(20)31148-X. [Epub ahead of print]1147 199-210
    Lam SM, Wang Z, Li B, Shui G.
      Rapid advances in front-end separation approaches and analytical technologies have accelerated the development of lipidomics, particularly in terms of increasing analytical coverage to encompass an expanding repertoire of lipids within a single analytical approach. Developments in lipid pathway analysis, however, have somewhat lingered behind, primarily due to (1) the lack of coherent alignment between lipid identifiers in common databases versus that generated from experiments, owing to the differing structural resolution of lipids at molecular level that is specific to the analytical approaches adopted by various laboratories; (2) the immense complexity of lipid metabolic relationships that may entail head group changes, fatty acyls modifications of various forms (e.g. elongation, desaturation, oxidation), as well as active remodeling that demands a multidimensional, panoramic view to take into account all possibilities in lipid pathway analyses. Herein, we discuss current efforts undertaken to address these challenges, as well as alternative form of "pathway analyses" that may be particularly useful for uncovering functional lipid interactions under different biological contexts. Consolidating lipid pathway analyses will be indispensable in facilitating the transition of lipidomics from its prior role of phenotype validation to a hypothesis-generating tool that uncovers novel molecular targets to drive downstream mechanistic pursuits under biomedical settings.
    Keywords:  Functional lipid modules; High-coverage; Lipid interactions; Lipidomics; Mass spectrometry; Pathway analysis
    DOI:  https://doi.org/10.1016/j.aca.2020.11.024
  13. J Anal Toxicol. 2021 Jan 30. pii: bkab009. [Epub ahead of print]
    Cox J, Mathison K, Ott C, DelTondo J, Kraner JC, DeCaprio AP, Arroyo-Mora LE.
      Since 2013, drug overdose deaths involving synthetic opioids (including fentanyl and fentanyl analogs) have increased from 3,105 to 31,335 in 2018. Postmortem toxicological analysis in fentanyl-related overdose deaths is complicated by the high potency of the drug, often resulting in low analyte concentrations and associations with toxicity, multidrug use, novelty of emerging fentanyl analogs and postmortem redistribution. Objectives for this study include the development of a quick, easy, cheap, effective, rugged and safe (QuEChERS) extraction and subsequent liquid chromatography-mass spectrometry/mass spectrometry analysis, validation of the method following the American Academy of Forensic Sciences Standards Board (ASB) standard 036 requirements and application to authentic liver specimens for 34 analytes including fentanyl, metabolites and fentanyl analogs. The bias for all 34 fentanyl analogs did not exceed ±10% for any of the low, medium or high concentrations and the %CV did not exceed 20%. No interferences were identified. All 34 analytes were within the criteria for acceptable percent ionization suppression or enhancement with the low concentration ranging from -10.2% to 23.7% and the high concentration ranging from -7.1% to 11.0%. Liver specimens from 22 authentic postmortem cases were extracted and analyzed with all samples being positive for at least one target analyte from the 34 compounds. Of the 22 samples, 17 contained fentanyl and metabolites plus at least one fentanyl analog. The highest concentration for a fentanyl analog was 541.4 μg/kg of para-fluoroisobutyryl fentanyl (FIBF). The concentrations for fentanyl (n = 20) ranged between 3.6 and 164.9 μg/kg with a mean of 54.7 μg/kg. The fentanyl analog that was most encountered was methoxyacetyl fentanyl (n = 11) with a range of 0.2-4.6 μg/kg and a mean of 1.3 μg/kg. The QuEChERS extraction was fully validated using the ASB Standard 036 requirements for fentanyl, metabolites and fentanyl analogs in liver tissue.
    DOI:  https://doi.org/10.1093/jat/bkab009