» Articles » PMID: 36409330

Compound Identification Strategies in Mass Spectrometry-Based Metabolomics and Pharmacometabolomics

Overview
Specialty Pharmacology
Date 2022 Nov 21
PMID 36409330
Authors
Affiliations
Soon will be listed here.
Abstract

The metabolome is composed of a vast array of molecules, including endogenous metabolites and lipids, diet- and microbiome-derived substances, pharmaceuticals and supplements, and exposome chemicals. Correct identification of compounds from this diversity of classes is essential to derive biologically relevant insights from metabolomics data. In this chapter, we aim to provide a practical overview of compound identification strategies for mass spectrometry-based metabolomics, with a particular eye toward pharmacologically-relevant studies. First, we describe routine compound identification strategies applicable to targeted metabolomics. Next, we discuss both experimental (data acquisition-focused) and computational (software-focused) strategies used to identify unknown compounds in untargeted metabolomics data. We then discuss the importance of, and methods for, assessing and reporting the level of confidence of compound identifications. Throughout the chapter, we discuss how these steps can be implemented using today's technology, but also highlight research underway to further improve accuracy and certainty of compound identification. For readers interested in interpreting metabolomics data already collected, this chapter will supply important context regarding the origin of the metabolite names assigned to features in the data and help them assess the certainty of the identifications. For those planning new data acquisition, the chapter supplies guidance for designing experiments and selecting analysis methods to enable accurate compound identification, and it will point the reader toward best-practice data analysis and reporting strategies to allow sound biological and pharmacological interpretation.

Citing Articles

Pharmacometabolomics Enables Real-World Drug Metabolism Sciences.

Nijdam F, Hof M, Blokzijl H, Bakker S, Hak E, Hopfgartner G Metabolites. 2025; 15(1).

PMID: 39852382 PMC: 11767479. DOI: 10.3390/metabo15010039.


Rapid LA-REIMS-based metabolic fingerprinting of serum discriminates aflatoxin-exposed from non-exposed pregnant women: a prospective cohort from the Butajira Nutrition, Mental Health, and Pregnancy (BUNMAP) Study in rural Ethiopia.

Tesfamariam K, Plekhova V, Gebreyesus S, Lachat C, Alladio E, Argaw A Mycotoxin Res. 2024; 40(4):681-691.

PMID: 39259493 PMC: 11480126. DOI: 10.1007/s12550-024-00558-x.

References
1.
Alka O, Shanthamoorthy P, Witting M, Kleigrewe K, Kohlbacher O, Rost H . DIAMetAlyzer allows automated false-discovery rate-controlled analysis for data-independent acquisition in metabolomics. Nat Commun. 2022; 13(1):1347. PMC: 8924252. DOI: 10.1038/s41467-022-29006-z. View

2.
Alley Jr W, Mechref Y, Novotny M . Characterization of glycopeptides by combining collision-induced dissociation and electron-transfer dissociation mass spectrometry data. Rapid Commun Mass Spectrom. 2008; 23(1):161-70. DOI: 10.1002/rcm.3850. View

3.
Anderson B, Raskind A, Habra H, Kennedy R, Evans C . Modifying Chromatography Conditions for Improved Unknown Feature Identification in Untargeted Metabolomics. Anal Chem. 2021; 93(48):15840-15849. PMC: 10634695. DOI: 10.1021/acs.analchem.1c02149. View

4.
Beyoglu D, Zhou Y, Chen C, Idle J . Mass isotopomer-guided decluttering of metabolomic data to visualize endogenous biomarkers of drug toxicity. Biochem Pharmacol. 2018; 156:491-500. DOI: 10.1016/j.bcp.2018.09.022. View

5.
Bocker S, Letzel M, Liptak Z, Pervukhin A . SIRIUS: decomposing isotope patterns for metabolite identification. Bioinformatics. 2008; 25(2):218-24. PMC: 2639009. DOI: 10.1093/bioinformatics/btn603. View