ArticleAnalytical biochemistry2023
Systematic analysis of in-source modifications of primary metabolites during flow-injection time-of-flight mass spectrometry.
Article in Analytical biochemistry, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Rapid analysis of terpenes produced by fermentation using flow injection analysis coupled to APCI MS.Analytical methods : advancing methods and applications · 2026Article
- Intact protein barcoding enables one-shot identification of CRISPRi strains and their metabolic state.Cell reports methods · 2024Article
- Mapping temperature-sensitive mutations at a genome scale to engineer growth switches in Escherichia coli.Molecular systems biology · 2023Article
- Identification of Metabolite Interference Is Necessary for Accurate LC-MS Targeted Metabolomics Analysis.Analytical chemistry · 2023Article
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5 authors.
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Abstract
Flow-injection mass spectrometry (FI-MS) enables metabolomics studies with a very high sample-throughput. However, FI-MS is prone to in-source modifications of analytes because samples are directly injected into the electrospray ionization source of a mass spectrometer without prior chromatographic separation. Here, we spiked authentic standards of 160 primary metabolites individually into an Escherichia coli metabolite extract and measured the thus derived 160 spike-in samples by FI-MS. Our results demonstrate that FI-MS can capture a wide range of chemically diverse analytes within 30 s measurement time. However, the data also revealed extensive in-source modifications. Across all 160 spike-in samples, we identified significant increases of 11,013 ion peaks in positive and negative mode combined. To explain these unknown m/z features, we connected them to the m/z feature of the (de-)protonated metabolite using information about mass differences and MS2 spectra. This resulted in networks that explained on average 49 % of all significant features. The networks showed that a single metabolite undergoes compound specific and often sequential in-source modifications like adductions, chemical reactions, and fragmentations. Our results show that FI-MS generates complex MS1 spectra, which leads to an overestimation of significant features, but neutral losses and MS2 spectra explain many of these features.
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