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Finding correspondence between metabolomic features in untargeted liquid chromatography-mass spectrometry metabolomics datasets.

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Title: Finding correspondence between metabolomic features in untargeted liquid chromatography-mass spectrometry metabolomics datasets.
Authors: Climaco Pinto, R
Karaman, I
Lewis, MR
Hällqvist, J
Kaluarachchi, M
Graça, G
Chekmeneva, E
Durainayagam, B
Ghanbari, M
Ikram, MA
Zetterberg, H
Griffin, J
Elliott, P
Tzoulaki, I
Dehghan, A
Herrington, D
Ebbels, T
Item Type: Journal Article
Abstract: Integration of multiple datasets can greatly enhance bioanalytical studies, for example, by increasing power to discover and validate biomarkers. In liquid chromatography-mass spectrometry (LC-MS) metabolomics, it is especially hard to combine untargeted datasets since the majority of metabolomic features are not annotated and thus cannot be matched by chemical identity. Typically, the information available for each feature is retention time (RT), mass-to-charge ratio (m/z), and feature intensity (FI). Pairs of features from the same metabolite in separate datasets can exhibit small but significant differences, making matching very challenging. Current methods to address this issue are too simple or rely on assumptions that cannot be met in all cases. We present a method to find feature correspondence between two similar LC-MS metabolomics experiments or batches using only the features' RT, m/z, and FI. We demonstrate the method on both real and synthetic datasets, using six orthogonal validation strategies to gauge the matching quality. In our main example, 4953 features were uniquely matched, of which 585 (96.8%) of 604 manually annotated features were correct. In a second example, 2324 features could be uniquely matched, with 79 (90.8%) out of 87 annotated features correctly matched. Most of the missed annotated matches are between features that behave very differently from modeled inter-dataset shifts of RT, MZ, and FI. In a third example with simulated data with 4755 features per dataset, 99.6% of the matches were correct. Finally, the results of matching three other dataset pairs using our method are compared with a published alternative method, metabCombiner, showing the advantages of our approach. The method can be applied using M2S (Match 2 Sets), a free, open-source MATLAB toolbox, available at https://github.com/rjdossan/M2S.
Issue Date: 12-Apr-2022
Date of Acceptance: 21-Mar-2022
URI: http://hdl.handle.net/10044/1/96770
DOI: 10.1021/acs.analchem.1c03592
ISSN: 0003-2700
Publisher: American Chemical Society
Start Page: 5493
End Page: 5503
Journal / Book Title: Analytical Chemistry
Volume: 94
Issue: 14
Copyright Statement: © 2022 American Chemical Society. This work is published with CC BY licence.
Sponsor/Funder: Home Office
Commission of the European Communities
Imperial College Healthcare NHS Trust- BRC Funding
European Molecular Biology Laboratory
Health Data Research Uk
Funder's Grant Number: PG0484
305422
RDF03
654241
3336025
Keywords: Biomarkers
Chromatography, Liquid
Mass Spectrometry
Metabolomics
Chromatography, Liquid
Mass Spectrometry
Metabolomics
Biomarkers
Biomarkers
Chromatography, Liquid
Mass Spectrometry
Metabolomics
Analytical Chemistry
0301 Analytical Chemistry
0399 Other Chemical Sciences
Publication Status: Published
Conference Place: United States
Online Publication Date: 2022-03-31
Appears in Collections:Department of Metabolism, Digestion and Reproduction
Faculty of Medicine
School of Public Health



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