bims-metlip Biomed News
on Methods and protocols in metabolomics and lipidomics
Issue of 2020‒01‒05
twenty-two papers selected by
Sofia Costa
Cold Spring Harbor Laboratory


  1. J Food Drug Anal. 2020 Jan;pii: S1021-9498(19)30098-5. [Epub ahead of print]28(1): 60-73
    Chiu HH, Kuo CH.
      Fatty acids play critical roles in biological systems. Imbalances in fatty acids are related to a variety of diseases, which makes the measurement of fatty acids in biological samples important. Many analytical strategies have been developed to investigate fatty acids in various biological samples. Due to the structural diversity of fatty acids, many factors need to be considered when developing analytical methods including extraction methods, derivatization methods, column selections, and internal standard selections. This review focused on gas chromatography-mass spectrometry (GC-MS)-based methods. We reviewed several commonly used fatty acid extraction approaches, including liquid-liquid extraction and solid-phase microextraction. Moreover, both acid and base derivatization methods and other specially designed methods were comprehensively reviewed, and their strengths and limitations were discussed. Having good separation efficiency is essential to building an accurate and reliable GC-MS platform for fatty acid analysis. We reviewed the separation performance of different columns and discussed the application of multidimensional GC for improving separations. The selection of internal standards was also discussed. In the final section, we introduced several biomedical studies that measured fatty acid levels in different sample matrices and provided hints on the relationships between fatty acid imbalances and diseases.
    Keywords:  Biological samples; Fatty acid; Gas chromatography; Mass spectrometry
    DOI:  https://doi.org/10.1016/j.jfda.2019.10.003
  2. J Biomol Tech. 2019 Dec;30(Suppl): S22
    Searle BC, Lippincott JC, Seitzer PM.
      In untargeted metabolomics experiments library search engines detect metabolites using several features, including precursor mass, isotopic distribution, retention time, and MS2 fragmentation. Matching acquired MS2 to library spectra is vital as numerous compounds share molecular formulas, resulting in identical precursor measurements and similar retention times. However, many metabolomics experiments are still collected using LC-MS only, and even in LC-MS/MS experiments many precursors lack MS2 spectra due to the stochastic nature of data dependent acquisition. We observe that when metabolites ionize they can produce unanticipated MS1 features resulting from neutral losses, in-source fragmentation, multimerization, and adducts. Here we present a new approach to leverage these measurements to identify metabolites when MS2 spectra are of low quality or not available. We processing datasets of 75 known standards mixed with whole yeast lysates to strip them of their MS2 scans to produce a gold-standard MS1-only data set of a complex metabolome with known targets. For each dataset we determined the proportion unambiguous annotations (where the correct annotation had a higher score than other potential annotations) and unmistakable annotations (where the correct annotation was the only valid annotation detected). We found that incorporating in-source fragments improved these metrics for both MS1-only (increasing from 60% to 73% unambiguous and 40% to 65% unmistakable matches) and MS2 datasets (from 79% to 84% unambiguous and 41% to 60% unmistakable). Unexpectedly, in these data we observed that the MS2 spectra were less useful than in-source fragment data for improving identification accuracy. We believe this is largely because the low-resolution iontrap MS2 spectra collected in this experiment show significant noise, which diminishes spectral match scores and allows other candidates to outscore the correct identifications. We suspect that noise is less likely to affect MS1 peak groups because they are generated from data aggregated across multiple high-resolution MS1 scans.
  3. Methods Mol Biol. 2020 ;2088 33-50
    Jaiswal D, Mittal A, Nagrath D, Wangikar PP.
      Accurate quantification of mass isotopolog distribution (MID) of intracellular metabolites is a key requirement for 13C metabolic flux analysis (13C-MFA). Liquid chromatography coupled with mass spectrometry (LC/MS) has emerged as a frontrunner technique that combines two orthogonal separation strategies. While metabolomics requires separation of monoisotopic peaks, 13C-MFA imposes additional demands for chromatographic separation as isotopologs of metabolites significantly add to the number of analytes. In this protocol chapter, we discuss two liquid chromatography methods, namely, reverse phase ion-pairing and hydrophilic interaction chromatography (HILIC) that together can separate a wide variety of metabolites that are typically used for 13C metabolic flux analysis.
    Keywords:  HILIC; Metabolic flux analysis; Nucleotides; Reverse phase ion-pairing; Sugar phosphates
    DOI:  https://doi.org/10.1007/978-1-0716-0159-4_3
  4. J Biomol Tech. 2019 Dec;30(Suppl): S39
    Nezami Ranjbar MR, Fan Z, Gao Y, Ressom H.
      Metabolomics plays an indispensable role in the growing systems biology approaches to identify biomarkers for complex diseases such as cancer. Liquid chromatography coupled to mass spectrometry (LC-MS) and gas chromatography coupled to mass spectrometry (GC-MS) have been extensively used for high-throughput comparison of the levels of thousands of metabolites among biological samples. However, the potential values of many disease-associated analytes discovered by these platforms have been inadequately explored in systems biology research due to lack of computational tools. Partly due to these limitations, poor reproducibility of previously identified metabolite biomarker candidates has been observed, especially when they are evaluated through independent platforms and validation sets. Our goal is to provide metabolomics core facilities and research scientists with bioinformatics platforms and expertise that enable them to search for disease-associated metabolites at the systems level through integrative systems metabolomics. To this end, we developed a new browser friendly cloud-based tool (SysMet) to help uncover the relationship of diseases and metabolites by investigating the rewiring and conserved interactions among metabolites and through integrative analysis of multi-omic data. Developed via a modular design and a user-friendly graphical user interface (GUI), SysMet allows users to: (1) import preprocessed metabolomic data for differential analysis of metabolite profiles using a network-based method; (2) import other preprocessed omic data for selection of disease-associated metabolites based on network-based integrative analysis; and (3) visually evaluate the outcome of network-based differential analysis and multi-omic data integration through high-quality figures. We believe SysMet will contribute to improving the ability of researchers to discover disease-associated metabolites by enhancing the role of metabolomics in systems biology research.
  5. Methods Mol Biol. 2020 ;2088 271-298
    Selivanov VA, Marin S, Tarragó-Celada J, Lane AN, Higashi RM, Fan TW, de Atauri P, Cascante M.
      Stable isotope-resolved metabolomics (SIRM), based on the analysis of biological samples from living cells incubated with artificial isotope enriched substrates, enables mapping the rates of biochemical reactions (metabolic fluxes). We developed software supporting a workflow of analysis of SIRM data obtained with mass spectrometry (MS). The evaluation of fluxes starting from raw MS recordings requires at least three steps of computer support: first, extraction of mass spectra of metabolites of interest, then correction of the spectra for natural isotope abundance, and finally, evaluation of fluxes by simulation of the corrected spectra using a corresponding mathematical model. A kinetic model based on ordinary differential equations (ODEs) for isotopomers of metabolites of the corresponding biochemical network supports the final part of the analysis, which provides a dynamic flux map.
    Keywords:  Central energy metabolism; Computational analysis; Isotopolog distribution; Kinetic models of metabolism; Mass spectrometry; Metabolic fluxes; Stable isotope tracing; Stable isotope-resolved metabolomics
    DOI:  https://doi.org/10.1007/978-1-0716-0159-4_12
  6. Methods Mol Biol. 2020 ;2088 73-92
    Grankvist N, Watrous JD, Jain M, Nilsson R.
      The recently developed deep labeling method allows for large-scale profiling of metabolic activities in human cells or tissues using isotope tracing with a highly 13C enriched culture medium in combination with liquid chromatography-high resolution mass spectrometry. This method generates mass spectrometry data sets where endogenous cellular products can be identified, and active pathways can be determined from observed 13C mass isotopomers of the various metabolites measured. Here we describe in detail the experimental procedures for deep labeling experiments in cultured mammalian cells, including synthesis of the deep labeling medium, experimental considerations for cell culture, metabolite extractions and sample preparation, and liquid chromatography-mass spectrometry. We also outline a workflow for the downstream data analysis using publicly available software.
    Keywords:  Cell culture; LC-HRMS; Metabolism; Metabolomics; Stable isotope tracing experiments
    DOI:  https://doi.org/10.1007/978-1-0716-0159-4_5
  7. J Biomol Tech. 2019 Dec;30(Suppl): S38
    Martin R, Midey A, Olivos H, Shrestha B, Claude E.
      Spatial mapping of small molecules, such as neurotransmitters, alongside lipids, can increase our understanding of biological functions of those molecules within the brain. Desorption Electrospray Ionization (DESI) is an ambient ionization technique that can spatially profile the distribution of molecules in research tissue samples. Here we present the utility of DESI imaging to simultaneously detect lipids and neurotransmitters directly in brain tissue samples. Rat brain was harvested and flash-frozen in liquid nitrogen before cryosectioning. Coronal tissue sections (8 microns thick) were mounted on regular glass microscope slides, vacuum dried, and analyzed without any further sample preparation. The DESI imaging platform coupled with a high definition mass spectrometer (HDMS) with ion mobility separation was employed to obtain ion intensities of small molecules and lipids over the entire tissue. DESI Imaging data were collected and processed on a high definition mass spectrometer with ion mobility separation. DESI acquisitions were performed using methanol and water as a DESI spray solvent. The ambient nature of DESI allowed for MS imaging without any matrix application or extensive sample preparation steps. Molecular maps were processed and overlaid with an optical image of the tissue to co-register the molecular distribution based on the anatomical features of the brain, such as the corpus callosum. Small molecules such as amino acids and neurotransmitters were simultaneously detected along with lipids. Molecular identification was aided using high mass accuracy database searches against LipidMaps and HMDB. In addition to the accurate mass and high-fidelity isotopic distribution, collisional cross sections (CCS) or drift time data obtained during ion mobility separation was used to improve confidence in detected molecules. Ion mobility measurements before the MS measurment increased the coverage and added selectivity which helped identification This preliminary work indicated the utility of DESI imaging for clearly distinguishing localized metabolites and lipids to provide insights for neuromolecular research.
  8. Methods Mol Biol. 2020 ;2088 189-204
    Jaiswal D, Wangikar PP.
      Recently, the sequential windowed acquisition of all theoretical fragment ion mass spectra (SWATH) method coupled with liquid chromatography has been demonstrated for the quantification of isotopic 13C enrichment in a large number of cellular metabolites and fragments. SWATH, a data-independent acquisition (DIA) method, alleviates the need for data deconvolution and shows greater accuracy in the quantification of low abundance isotopologs of fragments thereby resulting in a lower systematic error. Here we provide a detailed protocol for the design of Q1 mass isolation windows and the post-acquisition data analysis with emphasis on the untargeted nature of SWATH.
    Keywords:  13C metabolic flux analysis; Liquid chromatography–mass spectrometry; Mass isotopolog distribution; Multiple reaction monitoring; Parallel reaction monitoring
    DOI:  https://doi.org/10.1007/978-1-0716-0159-4_9
  9. J Chromatogr B Analyt Technol Biomed Life Sci. 2019 Dec 15. pii: S1570-0232(19)31435-7. [Epub ahead of print]1137 121936
    Amatya S, Shin Y, Ha JY, Lee SJ, Kang SW, Kwon B, Kim DH.
      A simple, sensitive, and rapid liquid chromatography (LC)-tandem mass spectrometry (MS/MS) method was developed for the simultaneous determination of arginine and its pathway-related metabolites (ornithine, proline, citrulline, glutamate, agmatine, spermidine, and spermine) in cellular extracts. Cells were lysed and cellular proteins precipitated by the addition of acetonitrile followed by ultra-sonication. Supernatants were analyzed using a Chromolith High Resolution RP-18 endcapped column (100 × 4.6 mm, 1.15 μm, 150 Å), with mobile phases of 0.1% formic acid solution and 0.1% formic acid in acetonitrile. Detection was carried out in multiple reaction monitoring (MRM) mode. Calibration curves showed linearity (r2 > 0.99) for all metabolites over the calibration ranges used. The intra- and inter-day precision was less than 13.5%, and the accuracy was between 91.3 and 114.7%. The method developed in this study was successfully applied to measure arginine and its pathway-related metabolites, which are related to nitric oxide synthase/arginase pathways in mouse bone marrow-derived dendritic cells (BMDCs). The ability to simultaneously measure arginine and its pathway-related metabolites is valuable for better understanding local and systemic inflammatory processes.
    Keywords:  Analysis; Arginine and its metabolites; Bone marrow-derived dendritic cells; Liquid chromatography (LC)–tandem mass spectrometry (MS/MS)
    DOI:  https://doi.org/10.1016/j.jchromb.2019.121936
  10. Talanta. 2020 Mar 01. pii: S0039-9140(19)31170-1. [Epub ahead of print]209 120537
    Pichini S, Mannocchi G, Gottardi M, Pérez-Acevedo AP, Poyatos L, Papaseit E, Pérez-Mañá C, Farré M, Pacifici R, Busardò FP.
      Monitoring pharmacological active compounds in pharmaceutical preparations of medical cannabis and in conventional and non-conventional biological matrices of treated individuals use requires both a wide linear range and sensitive detection. We have developed and validated a fast and sensitive method using ultra-high performance liquid chromatography coupled with tandem mass spectrometry (UHPLC-MS/MS) for analysis of Δ-9-tetrahydrocannabinol (THC), cannabidiol (CBD), their acidic precursors Δ-9-tetrahydrocannabinolic acid A (THCA-A) and cannabidiolic acid (CBDA) and some major metabolites of THC such as 11-nor-9-carboxy-THC (THC-COOH), 11-hydroxy-THC (11-OH-THC), Δ-9-THC-Glucuronide (THC-GLUC) and THC-COOH-Glucuronide (THC-COOH-GLUC) in conventional (whole blood and urine) and non-conventional (oral fluid and sweat) of individual treated with medical cannabis preparation. Specifically, THC, THCA-A, CBD and CBD-A were determined in cannabis decoction and oil prepared to treat individuals. The method used positive electrospray ionization (ESI) mode to reach the sensitivity needed to detect minimal amounts of analytes under investigations exposure with limits of quantification ranging from 0.2 to 0.5 ng per milliliter (ng/mL) or ng per patch in case of collected sweat. The validation results indicated this method was accurate (average inter/intra-day error, <10%), precise (inter/intra-day imprecision, <10%), and fast (10 min run time). In addition, time-consuming sample preparation was avoided applying dilute and shoot procedure, meeting the needs for potential large-scale population studies. The analysis of real samples demonstrated a pharmacokinetics of cannabinoids, their precursors and their metabolites dependent from quantity of carboxylated and decarboxylated compounds in pharmaceutical preparations.
    Keywords:  Biological fluids; Cannabinoids; Carboxylated and decarboxylated compounds; Medical cannabis; UHPLC-MS/MS
    DOI:  https://doi.org/10.1016/j.talanta.2019.120537
  11. Talanta. 2020 Mar 01. pii: S0039-9140(19)31206-8. [Epub ahead of print]209 120573
    Cerrato A, Cannazza G, Capriotti AL, Citti C, La Barbera G, Laganà A, Montone CM, Piovesana S, Cavaliere C.
      Polyphenols are a broad class of plant secondary metabolites which carry out several biological functions for plant growth and protection and are of great interest as nutraceuticals for their antioxidant properties. However, due to their structural variability and complexity, the mass-spectrometric analysis of polyphenol content in plant matrices is still an issue. In this work, a novel approach for the identification of several classes of polyphenol derivatives based on ultra-high performance liquid chromatography coupled to high-resolution mass spectrometry was developed. First, mass-spectrometric parameters were optimized in order to obtain a large set of diagnostic product ions for their high-confidence identification. The software Compound Discoverer 3.0 was then implemented with a comprehensive database of 45,567 polyphenol derivatives and with mass-spectrometric data for their building blocks, resulting in a specific tool for the semi-automatic identification of flavonoids, anthocyanins, ellagitannins, proanthocyanidins and phenolic acids. The method was then applied to the identification of polyphenols in industrial hemp (Cannabis sativa), a matrix whose use is recently spreading for pharmaceutical and nutraceutical purposes, resulting in the identification of 147 compounds belonging to the classes of flavonoids, proanthocyanidins and phenolic acids. The proposed method is applicable to the polyphenol profiling of any plant matrix and it is not dependent on data in the literature for their identification, allowing the discovery of compounds which have been never identified before.
    Keywords:  Cannabis sativa; Compound Discoverer; High resolution mass spectrometry; Polyphenols; Ultra-high performance liquid chromatography
    DOI:  https://doi.org/10.1016/j.talanta.2019.120573
  12. Anal Chem. 2019 Dec 30.
    Merder J, Freund JA, Feudel U, Niggemann J, Singer G, Dittmar T.
      Fourier-transform ion cyclotron resonance mass spectrometry (FT-ICR-MS) is one of the state-of-the-art methods to analyze complex natural organic mixtures. The precision of detected masses is crucial for molecular formula attribution. Random errors can be reduced by averaging multiple measurements of the same mass, but because of limited availability of ultrahigh-resolution mass spectrometers, most studies cannot afford analyzing each sample multiple times. Here we show that random errors can be eliminated also by averaging mass spectral data from independent environmental samples. By averaging the spectra of 30 samples of our 15 Tesla instrument we reach a mass precision comparable to a single spectrum of a 21 Tesla instrument. We also show that it is possible to accurately and reproducibly determine isotope ratios with FT-ICR-MS. Intensity ratios of isotopologues were improved to a degree that measured deviations were within the range of natural isotope fractionation effects. In analogy to δ13C in environmental studies, we propose Δ13C as an analytical measure for isotope ratio deviances instead of widely employed C deviances. In conclusion, here we present a simple tool, extensible to Orbitrap-based mass spectrometers, for post-detection data processing that significantly improves mass accuracy and the precision of intensity ratios of isotopologues at no extra cost.
    DOI:  https://doi.org/10.1021/acs.analchem.9b04234
  13. J Biomol Tech. 2019 Dec;30(Suppl): S25-S26
    Dhungana S, Isaac G, Munjoma N, Gethings LA, Plumb RS.
      Lipid metabolism is complex and involves a large number of metabolic reactions resulting in an enormous number and variety of actual lipids within living cells. LIPIDMAPS currently stores more than 40000 lipid structures. The dynamic range of lipid concentrations in biological systems can vary by 106 or more (from nanomolar fatty acids to attomolar eicosanoid lipid mediators). The level of precision of most systems-wide measurements is not yet sufficient to detail specific levels or concentrations of cellular components. Comparing lipidomic data across laboratories requires absolute quantification since relative values can vary widely not only between laboratories and between instruments due to various factors including: analyst errors, sample preparation differences (e.g. extraction methods) and ion suppression when using ESI MS. The lack of accurate characterisation of the lipid species also severely hinders interpretation of lipid metabolism associated with disease and physiological states. The proposed platform involves integrated high throughput analytical tool for accurate and robust measurement (>1500 injections) of lipid from sample preparation through to data handling and pathway elucidation. The platform can also be used for more in depth targeting of specific class of lipids of interest. Validation of the chromatographic method is performed at multiple sites by different analysts to show robustness and ease of method transfer. A rapid total lipid extraction method involving IPA and MTBE for plasma shows promising results. This will grant researchers more flexibility depending on their specific needs and requirements. The calibration, system suitability and QC standards used in this platform is sourced pre-mixed from commercial vendors (Avanti Lipids). SymphonyTM Software is used to automate the entire workflow and integrated with Skyline for data processing to enhance efficiency and flexibility. Once quantitative data has been generated and processed, pathway mapping tools can be used to determine the biological relevance of changes in concentration, and make data comparisons between laboratories.
  14. Methods Mol Biol. 2020 ;2088 17-32
    Dudek CA, Schlicker L, Hiller K.
      Gas chromatography coupled with mass spectrometry can provide an extensive overview of the metabolic state of a biological system. Analysis of raw mass spectrometry data requires powerful data processing software to generate interpretable results. Here we describe a data processing workflow to generate metabolite levels, mass isotopomer distribution, similarity and variability analysis of metabolites in a nontargeted manner, using stable isotope labeling. Using our data analysis software, no bioinformatic or programming background is needed to generate results from raw mass spectrometry data.
    Keywords:  Data analysis; GCMS; Gas chromatography; Mass isotopomer distribution; Mass spectrometry; Metabolism; Nontargeted metabolomics; Stable isotope labeling
    DOI:  https://doi.org/10.1007/978-1-0716-0159-4_2
  15. Talanta. 2020 Mar 01. pii: S0039-9140(19)31191-9. [Epub ahead of print]209 120558
    Canbay E, Sezer ED, Uçar SK, Çoker M, Sözmen EY.
      Cystinosis is an autosomal recessive disorder characterized by the accumulation of cystine in lysosomes, causing irreversible damage to organs, especially the kidneys. Intracellular leukocyte cystine concentrations are used to diagnose cystinosis and to monitor cysteamine treatment. The aim of this study was to develop and validate a liquid chromatography-tandem mass spectrometry (LC-MS/MS) method without derivatization capable of measuring leukocyte intracellular cystine concentrations. During development, the effects of using three different protein precipitation agents were evaluated in terms of sensitivity and the matrix effect, with 12% trichloroacetic acid providing the highest sensitivity. The effects of different blood collection tubes were also assessed in terms of recovery, matrix effect, and protein content. Compared to other methods, our method was quicker (run time of 3 min), was linear over the range 0.078-100 μM, and had lower limits of detection (0.0192 μM) and quantification (0.0582 μM). The intra-day and inter-day reproducibility %CVs were ≤10%. and the method had excellent recovery rates (94%-106%). Other parameters including matrix selectivity, injection carryover, leukocyte lysate stability were also validated and met the acceptance criterias of European Medicines Agency (EMA) Guideline. The assay was successfully applied to quantify cystine leukocyte concentration in healthy and cystinosis patients.
    Keywords:  Cystinosis; LC-MS/MS; Leukocyte cystine; Preanalytic factors; Sample pre-treatment
    DOI:  https://doi.org/10.1016/j.talanta.2019.120558
  16. Metab Eng. 2019 Dec 28. pii: S1096-7176(19)30321-0. [Epub ahead of print]
    Deja S, Fu X, Fletcher JA, Kucejova B, Browning JD, Young JD, Burgess SC.
      Computational models based on the metabolism of stable isotope tracers can yield valuable insight into the metabolic basis of disease. The complexity of these models is limited by the number of tracers and the ability to characterize tracer labeling in downstream metabolites. NMR spectroscopy is ideal for multiple tracer experiments since it precisely detects the position of tracer nuclei in molecules, but it lacks sensitivity for detecting low-concentration metabolites. GC-MS detects stable isotope mass enrichment in low-concentration metabolites, but lacks nuclei and positional specificity. We performed liver perfusions and in vivo infusions of 2H and 13C tracers, yielding complex glucose isotopomers that were assigned by NMR and fit to a newly developed metabolic model. Fluxes regressed from 2H and 13C NMR positional isotopomer enrichments served to validate GC-MS-based flux estimates obtained from the same experimental samples. NMR-derived fluxes were largely recapitulated by modeling the mass isotopomer distributions of six glucose fragment ions measured by GC-MS. Modest differences related to limited fragmentation coverage of glucose C1-C3 were identified, but fluxes such as gluconeogenesis, glycogenolysis, cataplerosis and TCA cycle flux were tightly correlated between the methods. Most importantly, modeling of GC-MS data could assign fluxes in primary mouse hepatocytes, an experiment that is impractical with 2H or 13C NMR.
    DOI:  https://doi.org/10.1016/j.ymben.2019.12.005
  17. J Mass Spectrom. 2020 Jan 02. e4492
    Murphy RC.
      In the middle of the 1960s, I began graduate school and at the same time started on the path of using mass spectrometry to gain insight into various aspects of lipid biochemistry. This was not a straight path but one that went from organic geochemistry, to lunar sample analysis, to a pursuit of the structure of an elusive and very active, lipid mediator slow reacting substance of anaphylaxis (SRS-A). The discovery of the structure of SRS-A opened important questions about phospholipid biochemistry and the arachidonate cycle in cells. I have written this reflection to highlight the various advances in mass spectrometry that occurred during this time that had a great impact on our ability to study lipid biochemistry. I specifically applied these new advances to studies of leukotriene biosynthesis in vivo, leukotriene metabolism, and arachidonate-containing phospholipids that are essential in providing arachidonic acid for the 5-lipoxygenase pathway. Along the way imaging mass spectrometry was shown to be a powerful tool to probe lipids as they exist in tissue slices. We found this as just one of the ways to use the emerging technology of lipidomics to study human pathophysiology. Our studies of neutral lipids and oxidized phospholipids were especially challenging due to the total number of molecular species that could be found in cells. Many challenges remain in using mass spectrometry for lipid studies and a few are presented.
    Keywords:  SRS-A; arachidonic acid; eye; imaging mass spectrometry; ion mobility; leukotriene C4; lipids; lung; neutral lipids; phospholipids
    DOI:  https://doi.org/10.1002/jms.4492
  18. Talanta. 2020 Mar 01. pii: S0039-9140(19)31178-6. [Epub ahead of print]209 120545
    Petrov AP, Sherman LM, Camden JP, Dovichi NJ.
      Although databases are available that provide mass spectra and chromatographic retention information for small-molecule metabolites, no publicly available database provides electrophoretic mobility for common metabolites. As a result, most compounds found in electrophoretic-based metabolic studies are unidentified and simply annotated as "features". To begin to address this issue, we analyzed 460 metabolites from a commercial library using capillary zone electrophoresis coupled with electrospray mass spectrometry. To speed analysis, a sequential injection method was used wherein six compounds were analyzed per run. An uncoated fused silica capillary was used for the analysis at 20 °C with a 0.5% (v/v) formic acid and 5% (v/v) methanol background electrolyte. A Prince autosampler was used for sample injection and the capillary was coupled to an ion trap mass spectrometer using an electrokinetically-pumped nanospray interface. We generated mobility values for 276 metabolites from the library (60% success rate) with an average standard deviation of 0.01 × 10-8 m2V-1s-1. As expected, cationic and anionic compounds were well resolved from neutral compounds. Neutral compounds co-migrated with electro-osmotic flow. Most of the compounds that were not detected were neutral and presumably suffered from adsorption to the capillary wall or poor ionization efficiency.
    Keywords:  Capillary electrophoresis; Metabolite database; Sequential injection
    DOI:  https://doi.org/10.1016/j.talanta.2019.120545
  19. J Biomol Tech. 2019 Dec;30(Suppl): S46-S47
    Ferreira C, Alfaro CM, Pirro V, Cooks RG.
      Desorption electrospray ionization mass spectrometry imaging (DESI-MSI) is an ambient ionization MS method utilized in over 730 peer-reviewed manuscripts. In this technique, a highly charged aerosol of microdroplets is sprayed onto a surface, desorbing and ionizing molecules which are then analyzed by MS. Since samples are analyzed in their native conditions with minimal to no sample preparation (e.g. without the need for separation or an organic matrix), chemicals, drugs, metabolites and lipids are rapidly detected and mapped in diverse types of samples. We focus on three recent DESI-MSI developments: (1) intraoperative brain cancer diagnostics, (2) developmental biology, and (3) high-throughput chemical reaction screening. For intraoperative brain cancer diagnostics, DESI-MS is being used in operating rooms for rapid tissue biopsy smear analysis to screen for diagnostic lipids and oncometabolites indicative of tumor type (e.g. glioma), grade and extent of tumor infiltration at surgical margins. Recently, isocitrate dehydrogenase (IDH) mutation status was determined intraoperatively by DESI in gliomas, offering new tumor management options which may impact extent of resection goals. In developmental biology, DESI-MSI was applied to whole body 2D and 3D swine fetus cryosections, and a range of lipids and metabolites specific to particular organs were spatially mapped and related to organogenesis. Finally, an autonomous system for high-throughput chemical reaction screening by DESI-MSI is an ongoing effort funded by the Defense Advanced Research Projects Agency (DARPA). This system takes advantage of the fact that chemical reactions are accelerated in DESI microdroplets to read over 1,000 unique reaction spots/hour and to explore the high dimensional chemical reaction space. To accomplish that, DESI-MS is integrated with four other commercial instruments all under computer control (a robotic pipetting robot, a robotic arm, a plate hoteling system and a precision solvent delivery system). In addition, future directions and perspectives for DESI-MSI will be presented.
  20. Methods Mol Biol. 2020 ;2088 299-313
    Campit S, Chandrasekaran S.
      The metabolic activity of a mammalian cell changes dynamically over time and is tied to the changing metabolic demands of cellular processes such as cell differentiation and proliferation. While experimental tools like time-course metabolomics and flux tracing can measure the dynamics of a few pathways, they are unable to infer fluxes at the whole network level. To address this limitation, we have developed the Dynamic Flux Activity (DFA) algorithm, a genome-scale modeling approach that uses time-course metabolomics to predict dynamic flux rewiring during transitions between metabolic states. This chapter provides a protocol for applying DFA to characterize the dynamic metabolic activity of various cancer cell lines.
    Keywords:  Cancer metabolism; Constraint-based modeling; Dynamic flux activity; Flux balance analysis; Genome-scale metabolic models; Time-course metabolomics
    DOI:  https://doi.org/10.1007/978-1-0716-0159-4_13
  21. J Biomol Tech. 2019 Dec;30(Suppl): S52-S53
    Schilling B.
      Proteomics workflows in mass spectrometric core facilities require great flexibility and adaptation to the individual projects, providing high quality data sets for diverse sets of projects. We demonstrate the use of data-independent acquisition DIA workflows in our proteomics core for a wide variety of collaborative projects, representing at times small studies or larger scale projects. Depending on the project or species for our DIA data processing we often utilize published large spectral libraries, such as a pan-human spectral library (Rosenberger et al.) or published large mouse spectral libraries (Biognosys). However for other projects prior to DIA acquisitions we apply data-dependent acquisitions (DDA) to build our own spectral libraries that will be used later to process the quantitative DIA data sets, which will be demonstrated for a C. elegans proteostasis project analyzing protein aggregates of young and old worms. Specific challenges and opportunities are also experienced when using DIA for post-translational modifications. All acquisitions implement the use of retention time standards (or iRT) for chromatographic alignments. The presentation discusses practical aspects of using DIA in a Proteomics Core Facility, challenges and solutions use of new technologies and dissemination of data sets to collaborators and core users.