Targeting unique biological signals on the fly to improve MS/MS coverage and identification efficiency in metabolomics

• Published in: Analytica chimica acta Vol. 1149; p. 338210
• Main Authors: Cho, Kevin, Schwaiger-Haber, Michaela, Naser, Fuad J., Stancliffe, Ethan, Sindelar, Miriam, Patti, Gary J.
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 08.03.2021
• Subjects: Chromatography, Liquid; Credentialing; Escherichia coli; Liquid chromatography; Mass spectrometry; Metabolite identification; Metabolomics; Tandem Mass Spectrometry; Untargeted metabolomics; Workflow; Credentialing; Liquid chromatography; Metabolite identification; Mass spectrometry; Untargeted metabolomics
• ISSN: 0003-2670; 1873-4324
• DOI: 10.1016/j.aca.2021.338210

When using liquid chromatography/mass spectrometry (LC/MS) to perform untargeted metabolomics, it is common to detect thousands of features from a biological extract. Although it is impractical to collect non-chimeric MS/MS data for each in a single chromatographic run, this is generally unnecessary because most features do not correspond to unique metabolites of biological relevance. Here we show that relatively simple data-processing strategies that can be applied on the fly during acquisition of data with an Orbitrap ID-X, such as blank subtraction and well-established adduct or isotope calculations, decrease the number of features to target for MS/MS analysis by up to an order of magnitude for various types of biological matrices. We demonstrate that annotating these non-biological contaminants and redundancies in real time during data acquisition enables comprehensive MS/MS data to be acquired on each remaining feature at a single collision energy. To ensure that an appropriate collision energy is applied, we introduce a method using a series of hidden ion-trap scans in an Orbitrap ID-X to find an optimal value for each feature that can then be applied in a subsequent high-resolution Orbitrap scan. Data from 100 metabolite standards indicate that this real-time optimization of collision energies leads to more informative MS/MS patterns compared to using a single fixed collision energy alone. As a benchmark to evaluate the overall workflow, we manually annotated unique biological features by independently subjecting E. coli samples to a credentialing analysis. While credentialing led to a more rigorous reduction in feature number, on-the-fly annotation with blank subtraction on an Orbitrap ID-X did not inappropriately discard unique biological metabolites. Taken together, our results reveal that optimal fragmentation data can be obtained in a single LC/MS/MS run for >90% of the unique biological metabolites in a sample when features are annotated during acquisition and collision energies are selected by using parallel mass spectrometry detection. [Display omitted] •Blank subtraction does not unfaithfully remove credentialed features.•Filtering common redundancies and contaminants reduces the MS/MS burden up to 90%.•Collision energies can be optimized in real time with dual MS detection.•Optimal MS/MS data for >90% of credentialed metabolites can be acquired in one run.