Abstract
Unknown features in untargeted metabolomics and non-targeted analysis (NTA) are identified using fragment ions from MS/MS spectra to predict the structures of the unknown compounds. The precursor ion selected for fragmentation is commonly performed using data dependent acquisition (DDA) strategies or following statistical analysis using targeted MS/MS approaches. However, the selected precursor ions from DDA only cover a biased subset of the peaks or features found in full scan data. In addition, different statistical analysis can select different precursor ions for MS/MS analysis, which make the post-hoc validation of ions selected following a secondary analysis impossible for precursor ions selected by the original statistical method. Here we propose an automated, exhaustive, statistical model-free workflow: paired mass distance-dependent analysis (PMDDA), for reproducible untargeted mass spectrometry MS2 fragment ion collection of unknown compounds found in MS1 full scan. Our workflow first removes redundant peaks from MS1 data and then exports a list of precursor ions for pseudo-targeted MS/MS analysis on independent peaks. This workflow provides comprehensive coverage of MS2 collection on unknown compounds found in full scan analysis using a “one peak for one compound” workflow without a priori redundant peak information. We compared pseudo-spectra formation and the number of MS2 spectra linked to MS1 data using the PMDDA workflow to that obtained using CAMERA and RAMclustR algorithms. More annotated compounds, molecular networks, and unique MS/MS spectra were found using PMDDA compared with CAMERA and RAMClustR. In addition, PMDDA can generate a preferred ion list for iterative DDA to enhance coverage of compounds when instruments support such functions. Finally, compounds with signals in both positive and negative modes can be identified by the PMDDA workflow, to further reduce redundancies. The whole workflow is fully reproducible as a docker image xcmsrocker with both the original data and the data processing template.Graphical
Highlights
Metabolomics often aims at revealing changes in levels of all possible metabolites in biological samples [1] and nontargeted analysis (NTA) usually aims at comprehensive profiling of compounds in environmental samples [2]
Both approaches use high-resolution mass spectrometry (HRMS) to perform unbiased measurement of small molecules followed by identification of unknowns [3]
We developed a reproducible untargeted metabolomics data analysis workflow called pairedmass distance dependent analysis (PMDDA) which creates a study-specific list of independent peaks as precursor ions for MS/MS annotation based on MS1 full scan data
Summary
Metabolomics often aims at revealing changes in levels of all possible metabolites in biological samples [1] and nontargeted analysis (NTA) usually aims at comprehensive profiling of compounds in environmental samples [2] To achieve these goals, both approaches use high-resolution mass spectrometry (HRMS) to perform unbiased measurement of small molecules followed by identification of unknowns [3]. For biological studies comparing subject groups, statistical analysis, machine learning algorithms, and annotation of isotopes, adducts, and neutral losses can be performed to subset the features into peaks of interest [6, 7] Those selected peaks are targeted for MS/MS fragmentation for identification. An exhaustive MS2 collection strategy of all possible small molecules with reliable MS1 measures needs to be developed to maximize potential metabolite annotations, as well as increasing the reproducibility between the MS1 measurements and MS2 acquisition
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