bims-metlip Biomed News
on Methods and protocols in metabolomics and lipidomics
Issue of 2021–06–20
eleven papers selected by
Sofia Costa, Cold Spring Harbor Laboratory
  1. Computational Variation: An Underinvestigated Quantitative Variability Caused by Automated Data Processing in Untargeted Metabolomics.
  2. rMSIannotation: A peak annotation tool for mass spectrometry imaging based on the analysis of isotopic intensity ratios.
  3. AutoCCS: Automated collision cross section calculation software for ion mobility spectrometry-mass spectrometry.
  4. Eight key rules for successful data-dependent acquisition in mass spectrometry-based metabolomics.
  5. A Study of the Identification, Fragmentation Mode and Metabolic Pathways of Imatinib in Rats Using UHPLC-Q-TOF-MS/MS.
  6. A reference map of sphingolipids in murine tissues.
  7. peakPantheR, an R package for large-scale targeted extraction and integration of annotated metabolic features in LC-MS profiling datasets.
  8. Advanced tandem mass spectrometry in metabolomics and lipidomics-methods and applications.
  9. Evaluation of liver tissue extraction protocol for untargeted metabolomics analysis by ultra-high performance liquid chromatography/tandem mass spectrometry.
  10. Analytical considerations for reducing the matrix effect for the sphingolipidome quantification in whole blood.
  11. MolDiscovery: learning mass spectrometry fragmentation of small molecules.
  1. Anal Chem. 2021 Jun 16.
    Computational Variation: An Underinvestigated Quantitative Variability Caused by Automated Data Processing in Untargeted Metabolomics.
    Huaxu Yu, Ying Chen, Tao Huan.
      Computational tools are commonly used in untargeted metabolomics to automatically extract metabolic features from liquid chromatography-mass spectrometry (LC-MS) raw data. However, due to the incapability of software to accurately determine chromatographic peak heights/areas for features with poor chromatographic peak shape, automated data processing in untargeted metabolomics faces additional quantitative variation (i.e., computational variation) besides the well-recognized analytical and biological variations. In this work, using multiple biological samples, we investigated how experimental factors, including sample concentrations, LC separation columns, and data processing programs, contribute to computational variation. For example, we found that the peak height (PH)-based quantification is more precise when MS-DIAL was used for data processing. We further systematically compared the different patterns of computational variation between PH- and peak area (PA)-based quantitative measurements. Our results suggest that the magnitude of computational variation is highly consistent at a given concentration. Hence, we proposed a quality control (QC) sample-based correction workflow to minimize computational variation by automatically selecting PH or PA-based measurement for each intensity value. This bioinformatic solution was demonstrated in a metabolomic comparison of leukemia patients before and after chemotherapy. Our novel workflow can be effectively applied on 652 out of 915 metabolic features, and over 31% (206 out of 652) of corrected features showed distinctly changed statistical significance. Overall, this work highlights computational variation, a considerable but underinvestigated quantitative variability in omics-scale quantitative analyses. In addition, the proposed bioinformatic solution can minimize computational variation, thus providing a more confident statistical comparison among biological groups in quantitative metabolomics.
    DOI:  https://doi.org/10.1021/acs.analchem.0c03381
  2. Anal Chim Acta. 2021 Aug 01. pii: S0003-2670(21)00495-5. [Epub ahead of print]1171 338669
    rMSIannotation: A peak annotation tool for mass spectrometry imaging based on the analysis of isotopic intensity ratios.
    Lluc Sementé, Gerard Baquer, María García-Altares, Xavier Correig-Blanchar, Pere Ràfols.
      Mass spectrometry imaging (MSI) consist of spatially located spectra with thousands of peaks. Only a fraction of these peaks corresponds to unique monoisotopic peaks, as mass spectra include isotopes, adducts and fragments of compounds. Current peak annotation solutions depend on matching MS features to compounds libraries. We present rMSIannotation, a peak annotation algorithm to annotate carbon isotopes and adducts in metabolomics and lipidomics imaging mass spectrometry datasets without using supporting libraries. rMSIannotation measures and evaluates the intensity ratio between carbon isotopic peaks and models their distribution across the m/z axis of the compounds in the Human Metabolome Database. Monoisotopic peak selection is based on the isotopic likelihood score (ILS) made of three components: image morphology correlation, validation of isotopic intensity ratios, and peak centroid mass deviation. rMSIannotation proposes pairs of peaks that can be adducts based on three scores: isotopic pattern coherence, image correlation and mass error. We validated rMSIannotation with three MALDI-MSI datasets which were manually annotated by experts, and compared the annotations obtained with rMSIannotation and with the METASPACE annotation platform. rMSIannotation replicated more than 90% of the manual annotation reported in FT-ICR datasets and expanded the list of annotated compounds with additional monoisotopic peaks and neutral masses. Finally, we evaluated isotopic peak annotation as a data reduction method for MSI by comparing the results of PCA and k-means segmentation before and after removing non-monoisotopic peaks. The results show that monoisotopic peaks retain most of the biologic variance in the dataset.
    DOI:  https://doi.org/10.1016/j.aca.2021.338669
  3. Bioinformatics. 2021 Jun 18. pii: btab429. [Epub ahead of print]
    AutoCCS: Automated collision cross section calculation software for ion mobility spectrometry-mass spectrometry.
    Joon-Yong Lee, Aivett Bilbao, Christopher R Conant, Kent J Bloodsworth, Daniel J Orton, Mowei Zhou, Jesse W Wilson, Xueyun Zheng, Ian K Webb, Ailin Li, Kim K Hixson, John C Fjeldsted, Yehia M Ibrahim, Samuel H Payne, Christer Jansson, Richard D Smith, Thomas O Metz.
       MOTIVATION: Ion mobility spectrometry (IMS) separations are increasingly used in conjunction with mass spectrometry (MS) for separation and characterization of ionized molecular species. Information obtained from IMS measurements includes the ion's collision cross section (CCS), which reflects its size and structure and constitutes a descriptor for distinguishing similar species in mixtures that cannot be separated using conventional approaches. Incorporating CCS into MS-based workflows can improve the specificity and confidence of molecular identification. At present, there is no automated, open-source pipeline for determining CCS of analyte ions in both targeted and untargeted fashion, and intensive user-assisted processing with vendor software and manual evaluation is often required.
    RESULTS: We present AutoCCS, an open-source software to rapidly determine CCS values from IMS-MS measurements. We conducted various IMS experiments in different formats to demonstrate the flexibility of AutoCCS for automated CCS calculation: 1) stepped-field methods for drift tube-based IMS (DTIMS), 2) single-field methods for DTIMS (supporting two calibration methods: a standard and a new enhanced method) and 3) non-linear calibration methods for traveling wave based-IMS (TWIMS) in Waters Synapt and Structures for Lossless Ion Manipulations (SLIM). We demonstrated that AutoCCS offers an accurate and reproducible determination of CCS for both standard and unknown analyte ions in various IMS-MS platforms, IMS-field methods, ionization modes, and collision gases, without requiring manual processing.
    AVAILABILITY: https://github.com/PNNL-Comp-Mass-Spec/AutoCCS.
    SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
    DOI:  https://doi.org/10.1093/bioinformatics/btab429
  4. Mass Spectrom Rev. 2021 Jun 18.
    Eight key rules for successful data-dependent acquisition in mass spectrometry-based metabolomics.
    Emmanuel Defossez, Julien Bourquin, Stephan von Reuss, Sergio Rasmann, Gaétan Glauser.
      In recent years, metabolomics has emerged as a pivotal approach for the holistic analysis of metabolites in biological systems. The rapid progress in analytical equipment, coupled to the rise of powerful data processing tools, now provides unprecedented opportunities to deepen our understanding of the relationships between biochemical processes and physiological or phenotypic conditions in living organisms. However, to obtain unbiased data coverage of hundreds or thousands of metabolites remains a challenging task. Among the panel of available analytical methods, targeted and untargeted mass spectrometry approaches are among the most commonly used. While targeted metabolomics usually relies on multiple-reaction monitoring acquisition, untargeted metabolomics use either data-independent acquisition (DIA) or data-dependent acquisition (DDA) methods. Unlike DIA, DDA offers the possibility to get real, selective MS/MS spectra and thus to improve metabolite assignment when performing untargeted metabolomics. Yet, DDA settings are more complex to establish than DIA settings, and as a result, DDA is more prone to errors in method development and application. Here, we present a tutorial which provides guidelines on how to optimize the technical parameters essential for proper DDA experiments in metabolomics applications. This tutorial is organized as a series of rules describing the impact of the different parameters on data acquisition and data quality. It is primarily intended to metabolomics users and mass spectrometrists that wish to acquire both theoretical background and practical tips for developing effective DDA methods.
    Keywords:  DDA; Q-TOF; cycle time; exclusion list; mass window; precursor selection; tandem mass spectrometry
    DOI:  https://doi.org/10.1002/mas.21715
  5. J Anal Methods Chem. 2021 ;2021 8434204
    A Study of the Identification, Fragmentation Mode and Metabolic Pathways of Imatinib in Rats Using UHPLC-Q-TOF-MS/MS.
    Sijiang Liu, Zhaojin Yu.
      In this study, The metabolites, metabolic pathways, and metabolic fragmentation mode of a tyrosine kinase inhibitor- (TKI-) imatinib in rats were investigated. The samples for analysis were pretreated via solid-phase extraction, and the metabolism of imatinib in rats was studied using ultra-high-performance liquid chromatography-quadrupole-time-of-flight mass spectrometry (UHPLC-Q-TOF-MS/MS). Eighteen imatinib metabolites were identified in rat plasma, 21 in bile, 18 in urine, and 12 in feces. Twenty-seven of the above compounds were confirmed as metabolites of imatinib and 9 of them were newly discovered for the first time. Oxidation, hydroxylation, dealkylation, and catalytic dehydrogenation are the main metabolic pathways in phase I. For phase II, the main metabolic pathways were N-acetylation, methylation, cysteine, and glucuronidation binding. The fragment ions of imatinib and its metabolites were confirmed to be produced by the cleavage of the C-N bond at the amide bond. The newly discovered metabolite of imatinib was identified by UHPLC-Q-TOF-MS/MS. The metabolic pathway of imatinib and its fragmentation pattern were summarized. These results could be helpful to study the safety of imatinib for clinical use.
    DOI:  https://doi.org/10.1155/2021/8434204
  6. Cell Rep. 2021 Jun 15. pii: S2211-1247(21)00615-X. [Epub ahead of print]35(11): 109250
    A reference map of sphingolipids in murine tissues.
    Sneha Muralidharan, Mitsugu Shimobayashi, Shanshan Ji, Bo Burla, Michael N Hall, Markus R Wenk, Federico Torta.
      Sphingolipids (SPs) have both a structural role in the cell membranes and a signaling function that regulates many cellular processes. The enormous structural diversity and low abundance of many SPs pose a challenge for their identification and quantification. Recent advances in lipidomics, in particular liquid chromatography (LC) coupled with mass spectrometry (MS), provide methods to detect and quantify many low-abundant SP species reliably. Here we use LC-MS to compile a "murine sphingolipid atlas," containing the qualitative and quantitative distribution of 114 SPs in 21 tissues of a widely utilized wild-type laboratory mouse strain (C57BL/6). We report tissue-specific SP fingerprints, as well as sex-specific differences in the same tissue. This is a comprehensive, quantitative sphingolipidomic map of mammalian tissues collected in a systematic fashion. It will complement other tissue compendia for interrogation into the role of SP in mammalian health and disease.
    Keywords:  high-fat diet; lipidomics; mass spectrometry; sex-specific; sphingolipids; tissue atlas
    DOI:  https://doi.org/10.1016/j.celrep.2021.109250
  7. Bioinformatics. 2021 Jun 14. pii: btab433. [Epub ahead of print]
    peakPantheR, an R package for large-scale targeted extraction and integration of annotated metabolic features in LC-MS profiling datasets.
    Arnaud M Wolfer, Gonçalo D S Correia, Caroline J Sands, Stephane Camuzeaux, Ada H Y Yuen, Elena Chekmeneva, Zoltán Takáts, Jake T M Pearce, Matthew R Lewis.
        : Untargeted LC-MS profiling assays are capable of measuring thousands of chemical compounds in a single sample, but unreliable feature extraction and metabolite identification remain considerable barriers to their interpretation and usefulness. peakPantheR (Peak Picking and ANnoTation of High-resolution Experiments in R) is an R package for the targeted extraction and integration of annotated features from LC-MS profiling experiments. It takes advantage of chromatographic and spectral databases and prior information of sample matrix composition to generate annotated and interpretable metabolic phenotypic datasets and power workflows for real time data quality assessment.
    AVAILABILITY: peakPantheR is available via Bioconductor (https://bioconductor.org/packages/peakPantheR/). Documentation and worked examples are available at https://phenomecentre.github.io/peakPantheR.github.io/ and https://github.com/phenomecentre/metabotyping-dementia-urine.
    SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
    DOI:  https://doi.org/10.1093/bioinformatics/btab433
  8. Anal Bioanal Chem. 2021 Jun 18.
    Advanced tandem mass spectrometry in metabolomics and lipidomics-methods and applications.
    Sven Heiles.
      Metabolomics and lipidomics are new drivers of the omics era as molecular signatures and selected analytes allow phenotypic characterization and serve as biomarkers, respectively. The growing capabilities of untargeted and targeted workflows, which primarily rely on mass spectrometric platforms, enable extensive charting or identification of bioactive metabolites and lipids. Structural annotation of these compounds is key in order to link specific molecular entities to defined biochemical functions or phenotypes. Tandem mass spectrometry (MS), first and foremost collision-induced dissociation (CID), is the method of choice to unveil structural details of metabolites and lipids. But CID fragment ions are often not sufficient to fully characterize analytes. Therefore, recent years have seen a surge in alternative tandem MS methodologies that aim to offer full structural characterization of metabolites and lipids. In this article, principles, capabilities, drawbacks, and first applications of these "advanced tandem mass spectrometry" strategies will be critically reviewed. This includes tandem MS methods that are based on electrons, photons, and ion/molecule, as well as ion/ion reactions, combining tandem MS with concepts from optical spectroscopy and making use of derivatization strategies. In the final sections of this review, the first applications of these methodologies in combination with liquid chromatography or mass spectrometry imaging are highlighted and future perspectives for research in metabolomics and lipidomics are discussed.
    Keywords:  Biopolymers/lipids; HPLC; Lipidomics; Mass spectrometry imaging; Metabolomics; Tandem mass spectrometry
    DOI:  https://doi.org/10.1007/s00216-021-03425-1
  9. J Sep Sci. 2021 Jun 15.
    Evaluation of liver tissue extraction protocol for untargeted metabolomics analysis by ultra-high performance liquid chromatography/tandem mass spectrometry.
    Zhipeng Liu, Peng Wang, Zengjiao Liu, Chunbo Wei, Liyan Liu, Ying Li.
      The aim of untargeted metabolomics study is to obtain a global metabolome coverage from biological samples. Therefore, a comprehensive and systematic protocol for tissue metabolite extraction is highly desirable. In this study, we evaluated a comprehensive liver pretreatment strategy based on ultra-high performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry to obtain more metabolites using four different protocols. These protocols included (A) methanol protein precipitation, (B) two-step extraction of dichloromethane-methanol followed by methanol-water, (C) two-step extraction of methyl tert-butyl ether-methanol followed by methanol-water, and (D) two-step extraction of isopropanol-methanol followed by methanol-water. Our results showed that protocol D was superior to the others due to more extracted features, annotated metabolites and better reproducibility. And then, the stability and extraction sequence of protocol D were evaluated. The results showed that extraction with isopropanol-methanol followed by methanol-water was the optimum preparation sequence, which offered higher extraction efficiency, satisfactory repeatability and acceptable stability. Furthermore, the optimal protocol was successfully applied by liver samples of rats after high-fat intervention. In summary, our protocol enabled a comprehensive and systematic evaluation of liver pretreatment to obtain more medium-polar and nonpolar metabolites and was suitable for high-throughput metabolomics analysis. This article is protected by copyright. All rights reserved.
    Keywords:  Metabolic profiles; Metabolomics; Sample pretreatment; Tandem mass spectrometry; Ultra-high performance liquid chromatography
    DOI:  https://doi.org/10.1002/jssc.202100051
  10. Bioanalysis. 2021 Jun 10.
    Analytical considerations for reducing the matrix effect for the sphingolipidome quantification in whole blood.
    Dezhen Wang, Peining Xu, Clementina Mesaros.
      Aim: Plasma and serum are widely used blood-derived biofluids for metabolomics and lipidomics assays, but analytes that are present in high concentrations in blood cells cannot be evaluated in those samples and isolating serum or plasma could introduce additional variability in the data. Materials & methods: In this study, we provide a comprehensive method for quantification of the whole blood (WB) sphingolipidome, combining a single-phase extraction method with LC-high-resolution mass spectrometry. Results: We were able to quantify more than 150 sphingolipids, and when compared with paired plasma, WB contained higher concentration of most sphingolipids and individual variations were lower. These findings suggest that WB could be a better alternative to plasma, and potentially guide the evaluation of the sphingolipidome for biomarker discovery.
    Keywords:  ceramides; high-resolution mass spectrometry; plasma; sphingolipids; whole blood
    DOI:  https://doi.org/10.4155/bio-2021-0098
  11. Nat Commun. 2021 06 17. 12(1): 3718
    MolDiscovery: learning mass spectrometry fragmentation of small molecules.
    Liu Cao, Mustafa Guler, Azat Tagirdzhanov, Yi-Yuan Lee, Alexey Gurevich, Hosein Mohimani.
      Identification of small molecules is a critical task in various areas of life science. Recent advances in mass spectrometry have enabled the collection of tandem mass spectra of small molecules from hundreds of thousands of environments. To identify which molecules are present in a sample, one can search mass spectra collected from the sample against millions of molecular structures in small molecule databases. The existing approaches are based on chemistry domain knowledge, and they fail to explain many of the peaks in mass spectra of small molecules. Here, we present molDiscovery, a mass spectral database search method that improves both efficiency and accuracy of small molecule identification by learning a probabilistic model to match small molecules with their mass spectra. A search of over 8 million spectra from the Global Natural Product Social molecular networking infrastructure shows that molDiscovery correctly identify six times more unique small molecules than previous methods.
    DOI:  https://doi.org/10.1038/s41467-021-23986-0
This page is being maintained by Thomas Krichel. It was last updated on 2024‒11‒22 at 4:21.