LC-MS-Based Metabolomics of Biofluids Using All-Ion Fragmentation (AIF) Acquisition

  • Romanas Chaleckis
  • Shama Naz
  • Isabel Meister
  • Craig E. WheelockEmail author
Part of the Methods in Molecular Biology book series (MIMB, volume 1730)


The field of liquid chromatography-mass spectrometry (LC-MS)-based nontargeted metabolomics has advanced significantly and can provide information on thousands of compounds in biological samples. However, compound identification remains a major challenge, which is crucial in interpreting the biological function of metabolites. Herein, we present a LC-MS method using the all-ion fragmentation (AIF) approach in combination with a data processing method using an in-house spectral library. For the purposes of increasing accuracy in metabolite annotation, up to four criteria are used: (1) accurate mass, (2) retention time, (3) MS/MS fragments, and (4) product/precursor ion ratios. The relative standard deviation between ion ratios of a metabolite in a biofluid vs. its analytical standard is used as an additional metric for confirming metabolite identity. Furthermore, we include a scheme to distinguish co-eluting isobaric compounds. Our method enables database-dependent targeted as well as nontargeted metabolomics analysis from the same data acquisition, while simultaneously improving the accuracy in metabolite identification to increase the quality of the resulting biological information.

Key words

Metabolomics Liquid chromatography-mass spectrometry (LC-MS) All-ion fragmentation (AIF) Metabolite annotation 





All-ion fragmentation


Accurate mass


Collision-induced dissociation


Extracted ion chromatogram


Hydrophilic interaction liquid chromatography


Liquid chromatography-mass spectrometry




Reverse phase


Retention time



We acknowledge the support of the Swedish Heart Lung Foundation (HLF 20140469), the Swedish Research Council (2016-02798), the Swedish Foundation for Strategic Research, the Karolinska Institutet and AstraZeneca Joint Research Program in Translational Science (ChAMP; Centre for Allergy Research Highlights Asthma Markers of Phenotype), the Novo Nordisk Foundation (TrIC NNF15CC0018486 and MSAM NNF15CC0018346), and Gunma University Initiative for Advanced Research (GIAR). This work was supported in part by The Environment Research and Technology Development Fund (ERTDF) (Grant No 5-1752). CEW was supported by the Swedish Heart Lung Foundation (HLF 20150640).


  1. 1.
    Dunn WB, Broadhurst DI, Atherton HJ et al (2011) Systems level studies of mammalian metabolomes: the roles of mass spectrometry and nuclear magnetic resonance spectroscopy. Chem Soc Rev 40:387–426. Scholar
  2. 2.
    Kell DB, Oliver SG (2016) The metabolome 18 years on: a concept comes of age. Metabolomics 12:148. Scholar
  3. 3.
    Beger RD, Dunn W, Schmidt MA et al (2016) Metabolomics enables precision medicine: “a white paper, community perspective”. Metabolomics 12:149. Scholar
  4. 4.
    Viant MR, Kurland IJ, Jones MR, Dunn WB (2017) How close are we to complete annotation of metabolomes? Curr Opin Chem Biol 36:64–69. Scholar
  5. 5.
    Dias DA, Jones OAH, Beale DJ et al (2016) Current and future perspectives on the structural identification of small molecules in biological systems. Meta 6:46. Scholar
  6. 6.
    Sumner LW, Amberg A, Barrett D et al (2007) Proposed minimum reporting standards for chemical analysis chemical analysis working group (CAWG) metabolomics standards initiative (MSI). Metabolomics 3:211–221. Scholar
  7. 7.
    Dunn WB, Broadhurst D, Begley P et al (2011) Procedures for large-scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry. Nat Protoc 6:1060–1083. Scholar
  8. 8.
    Plumb RS, Johnson KA, Rainville P et al (2006) UPLC/MS(E); a new approach for generating molecular fragment information for biomarker structure elucidation. Rapid Commun Mass Spectrom 20:1989–1994. Scholar
  9. 9.
    Naz S, Gallart-Ayala H, Reinke SN et al (2017) Development of an LC-HRMS metabolomics method with high specificity for metabolite identification using all ion fragmentation (AIF) acquisition. Anal Chem 89(15):7933–7942. Scholar
  10. 10.
    Edmands WMB, Ferrari P, Scalbert A (2014) Normalization to specific gravity prior to analysis improves information recovery from high resolution mass spectrometry metabolomic profiles of human urine. Anal Chem 86:10925–10931. Scholar
  11. 11.
    Martínez-López S, Sarriá B, Baeza G et al (2014) Pharmacokinetics of caffeine and its metabolites in plasma and urine after consuming a soluble green/roasted coffee blend by healthy subjects. Food Res Int 64:125–133. Scholar
  12. 12.
    Tsugawa H, Cajka T, Kind T et al (2015) MS-DIAL: data-independent MS/MS deconvolution for comprehensive metabolome analysis. Nat Methods 12:523–526. Scholar

Copyright information

© Springer Science+Business Media, LLC 2018

Authors and Affiliations

  • Romanas Chaleckis
    • 1
    • 2
  • Shama Naz
    • 1
  • Isabel Meister
    • 1
    • 2
  • Craig E. Wheelock
    • 1
    • 2
    Email author
  1. 1.Division of Physiological Chemistry 2, Department of Medical Biochemistry and BiophysicsKarolinska InstitutetStockholmSweden
  2. 2.Gunma University Initiative for Advanced Research (GIAR), Gunma UniversityGunmaJapan

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