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Comparing Targeted vs. Untargeted MS2 Data-Dependent Acquisition for Peak Annotation in LC-MS Metabolomics

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Comparing Targeted vs. Untargeted MS2 Data-Dependent Acquisition for Peak Annotation in LC-MS Metabolomics

dc.contributor.author	Moreno Torres, Marta
dc.contributor.author	Ten Doménech, Isabel
dc.contributor.author	Martínez-Sena, Teresa
dc.contributor.author	Sanjuan Herráez, Juan Daniel
dc.contributor.author	Castell, José V.
dc.contributor.author	Parra Llorca, Anna
dc.contributor.author	Vento Torres, Máximo
dc.contributor.author	Quintás Soriano, Guillermo
dc.contributor.author	Kuligowski, Julia
dc.date.accessioned	2023-05-19T11:10:09Z
dc.date.available	2023-05-19T11:10:09Z
dc.date.issued	2020
dc.identifier.citation	Moreno Torres, Marta Ten Doménech, Isabel Martínez-Sena, Teresa Sanjuan Herráez, Juan Daniel Castell, José V. Parra Llorca, Anna Vento Torres, Máximo Quintás Soriano, Guillermo Kuligowski, Julia 2020 Comparing Targeted vs. Untargeted MS2 Data-Dependent Acquisition for Peak Annotation in LC-MS Metabolomics Metabolites 10 4 126 136
dc.identifier.uri	https://hdl.handle.net/10550/86714
dc.description.abstract	One of the most widely used strategies for metabolite annotation in untargeted LCMS is based on the analysis of MSn spectra acquired using data-dependent acquisition (DDA), where precursor ions are sequentially selected from MS scans based on user-selected criteria. However, the number of MSn spectra that can be acquired during a chromatogram is limited and a trade-off between analytical speed, sensitivity and coverage must be ensured. In this research, we compare four different strategies for automated MS2 DDA, which can be easily implemented in the frame of standard QA/QC workflows for untargeted LC-MS. These strategies consist of (i) DDA in the MS working range; (ii) iterated DDA split into several m/z intervals; (iii) dynamic iterated DDA of (pre)selected potentially informative features; and (iv) dynamic iterated DDA of (pre)annotated metabolic features using a reference database. Their performance was assessed using the analysis of human milk samples as model example by comparing the percentage of LC-MS features selected as the precursor ion for MS2, the number, and class of annotated features, the speed and confidence of feature annotation, and the number of LC runs required.
dc.language.iso	eng
dc.relation.ispartof	Metabolites, 2020, vol. 10, num. 4, p. 126-136
dc.subject	Bioquímica
dc.subject	Biologia
dc.title	Comparing Targeted vs. Untargeted MS2 Data-Dependent Acquisition for Peak Annotation in LC-MS Metabolomics
dc.type	journal article
dc.date.updated	2023-05-19T11:10:09Z
dc.identifier.doi	10.3390/metabo10040126
dc.identifier.idgrec	159095
dc.rights.accessRights	open access