Systematic analysis of in-source modifications of primary metabolites during flow-injection time-of-flight mass spectrometry

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.

Automated summary

Flow-injection mass spectrometry (FI-MS) allows high sample-throughput metabolomics studies. However, FI-MS is susceptible to in-source modifications of analytes due to the direct injection of samples without prior chromatographic separation. In this study, we spiked authentic standards of 160 primary metabolites into an Escherichia coli metabolite extract and analyzed the spike-in samples using FI-MS. The results showed that FI-MS can detect a wide range of chemically diverse analytes within a short measurement time, but extensive in-source modifications were observed. We used mass differences and MS2 spectra to connect unknown m/z features to (de-)protonated metabolites, which explained a significant portion of all features.