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.
Highlights
Metabolomics is a rapidly evolving field in biomedical research that targets the analysis of the low molecular weight metabolites within a biological system
Initial XCMS data pre-processing of data acquired from the ‘initial batch’ comprising the analysis of two blanks and three QCs described in the sample analysis in Section 3.4, identified 8971 liquid chromatography mass spectrometry (LC–MS)
We developed and compared targeted and untargeted dependent acquisition (DDA) methods for metabolite annotation and compared results obtained in the frame of a QA/QC pipeline
Summary
Metabolomics is a rapidly evolving field in biomedical research that targets the analysis of the low molecular weight metabolites within a biological system. The analysis of untargeted LC–MS data requires the identification or annotation of the metabolites prior to further analysis such as pathway, metabolite enrichment or overrepresentation analysis [1]. The putative identification of a metabolic feature for which the assignment of its structure is highly likely, but not validated through chemical-reference standards, is defined as ‘annotation’ [2]. The comparison of experimentally acquired MS data of a given metabolic feature against a spectral database such as the HMDB (www.hmdb.ca), METLIN (metlin.scripps.edu) or the Kyoto Encyclopedia of Genes and Genomes (KEGG, www.genome.jp) can be used for metabolite annotation. MS-based approaches typically lead to multiple molecular formulae for each feature and multiple hits in spectral databases may be obtained.
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