Post-acquisition spectral stitching. An alternative approach for data processing in untargeted metabolomics by UHPLC-ESI(−)-HRMS

•A new strategy was developed for the detection of chromatographically and spectroscopically reliable features during a metabolomic study.•The shredding of the LC-MS chromatograms into multiple m/z ranges increased the number of identified chromatographically and spectroscopically reliable features.•Using either the statistical significant features from the unpaired t-test or the variables after VIPs scoring a valid model can be produced.

IntroductionIn the case of the MS-based metabolomics, the large number of false positives remains a fundamental issue.ObjectiveThe aim of this study was to develop a new strategy, which highlights the number of the reliable features i.e. the detected features that correspond to a consistent peak according to chromatographic and mass spectrometric criteria.MethodFor the analysis blood samples from 20 chickens, which were administrated with naringin and 9 samples from control, were analyzed by UHPLC-HRMS (Orbitrap Velos). Two methodologies have been compared for data processing. In the first one (classical approach), all data in the 100-900 m/z mass-to charge range were included for the data processing procedure whereas for the newly developed methodology, the data were shred in 100 Da slices generating 8 datasets, which have been then subjected to the downstream MS data processing. Each dataset was treated separately and the m/z_tR features obtained by either VIP's or t-test values were merged and used as the input for the construction of the general model.ResultsThe new methodology resulted to a 4-fold increase of the peaks that could be considered chromatographically and mass spectrometrically valid.ConclusionA new strategy was reported on the detection of chromatographically reliable features during a metabolomic approach. The shredding of the LC-MS chromatograms into multiple m/z ranges increased the number of the identified chromatographically reliable features.