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DecoID improves identification rates in metabolomics through database-assisted MS/MS deconvolution

Abstract

Chimeric MS/MS spectra contain fragments from multiple precursor ions and therefore hinder compound identification in metabolomics. Historically, deconvolution of these chimeric spectra has been challenging and relied on specific experimental methods that introduce variation in the ratios of precursor ions between multiple tandem mass spectrometry (MS/MS) scans. DecoID provides a complementary, method-independent approach where database spectra are computationally mixed to match an experimentally acquired spectrum by using LASSO regression. We validated that DecoID increases the number of identified metabolites in MS/MS datasets from both data-independent and data-dependent acquisition without increasing the false discovery rate. We applied DecoID to publicly available data from the MetaboLights repository and to data from human plasma, where DecoID increased the number of identified metabolites from data-dependent acquisition data by over 30% compared to direct spectral matching. DecoID is compatible with any user-defined MS/MS database and provides automated searching for some of the largest MS/MS databases currently available.

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Fig. 1: Deconvolution with DecoID to identify metabolites with chimeric MS/MS spectra.
Fig. 2: Orphan isotopologue contamination and MS/MS spectrum prediction.
Fig. 3: DecoID improves metabolite identification in the DDA and DIA IROA datasets.
Fig. 4: DecoID improves identification rates in NIST SRM 1950.
Fig. 5: DecoID increases the identification rate in a human plasma DIA dataset.
Fig. 6: DecoID increases the identification rate of metabolites in a publicly available mouse xenograft RPLC/MS/MS dataset.

Data availability

All MS/MS data used in the evaluation of DecoID have been uploaded to the MetaboLights repository as study MTBLS2207 and is also available on the DecoID GitHub release (https://github.com/e-stan/DecoID/releases/). The publicly available dataset analyzed is available on MetaboLights as study MTBLS1066 (all reversed-phase, negative-mode data files were used). The MS/MS databases applied can be obtained at the curators’ websites (https://mona.fiehnlab.ucdavis.edu/, https://www.mzcloud.org/ and https://hmdb.ca/). The in-house IROA metabolite database is available within the DecoID release on GitHub (https://github.com/e-stan/DecoID/releases/), and the reference spectra have been uploaded to MoNA (submitter: E.S.; origin file: IROA_DB_for_mona_filtered_exported_addedInfo.msp).

Code availability

Source code is available on Zenodo40 and GitHub (https://github.com/e-stan/DecoID/). Included is an example dataset along with documentation for both the DecoID Python package and user interface. A standalone executable built for Windows can alternatively be downloaded from the Patti Lab website (http://pattilab.wustl.edu/software/DecoID/).

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Acknowledgements

This work was supported by funding from the National Institutes of Health grants U01 CA235482 (to G.J.P.), R35 ES028365 (to G.J.P.) and R24 OD024624 (to G.J.P.).

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Authors

Contributions

G.J.P. and E.S. conceptualized the project. E.S. wrote the source code and performed data analysis with input from G.J.P. and M.S.-H. M.S. and M.S.-H. acquired the MS/MS data and prepared all samples. E.S. and G.J.P. wrote the manuscript. All authors provided comments during manuscript preparation.

Corresponding author

Correspondence to Gary J. Patti.

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Competing interests

G.J.P. is a scientific advisory board member for Cambridge Isotope Laboratories and has a research collaboration agreement with Thermo Fisher Scientific.

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Peer review information Nature Methods thanks Mingxun Wang and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Arunima Singh was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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Stancliffe, E., Schwaiger-Haber, M., Sindelar, M. et al. DecoID improves identification rates in metabolomics through database-assisted MS/MS deconvolution. Nat Methods 18, 779–787 (2021). https://doi.org/10.1038/s41592-021-01195-3

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