Abstract
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.
Romanas Chaleckis and Shama Naz contributed equally to this work.
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Abbreviations
- ACN:
-
Acetonitrile
- AIF:
-
All-ion fragmentation
- AM:
-
Accurate mass
- CID:
-
Collision-induced dissociation
- EIC:
-
Extracted ion chromatogram
- HILIC:
-
Hydrophilic interaction liquid chromatography
- LC-MS:
-
Liquid chromatography-mass spectrometry
- MeOH:
-
Methanol
- RP:
-
Reverse phase
- RT:
-
Retention time
References
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. https://doi.org/10.1039/b906712b
Kell DB, Oliver SG (2016) The metabolome 18 years on: a concept comes of age. Metabolomics 12:148. https://doi.org/10.1007/s11306-016-1108-4
Beger RD, Dunn W, Schmidt MA et al (2016) Metabolomics enables precision medicine: “a white paper, community perspective”. Metabolomics 12:149. https://doi.org/10.1007/s11306-016-1094-6
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. https://doi.org/10.1016/j.cbpa.2017.01.001
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. https://doi.org/10.3390/metabo6040046
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. https://doi.org/10.1007/s11306-007-0082-2
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. https://doi.org/10.1038/nprot.2011.335
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. https://doi.org/10.1002/rcm.2550
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. https://doi.org/10.1021/acs.analchem.7b00925.
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. https://doi.org/10.1021/ac503190m
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. https://doi.org/10.1016/j.foodres.2014.05.043
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. https://doi.org/10.1038/nmeth.3393
Acknowledgments
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).
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Chaleckis, R., Naz, S., Meister, I., Wheelock, C.E. (2018). LC-MS-Based Metabolomics of Biofluids Using All-Ion Fragmentation (AIF) Acquisition. In: Giera, M. (eds) Clinical Metabolomics. Methods in Molecular Biology, vol 1730. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-7592-1_3
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DOI: https://doi.org/10.1007/978-1-4939-7592-1_3
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