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
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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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