henhaobio
Sign in

bioRxivBiochemistry

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

N. Farke, T. Schramm, A. Verhuelsdonk, H. Link

PDFDOIPreprint

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 divers analytes within 30 seconds 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. HighlightsO_LIFI-MS enables measurements of chemically divers metabolites. C_LIO_LIExtensive in-source modifications during electrospray ionization are detected by FI-MS. C_LIO_LIA network approach explains 49 % of all recorded in-source modifications. C_LI

Reproducibility ledger

Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.

Sign in to file a reproducibility report.

Similarity Map
SelectedSame fieldCross-field
THISSeitzer '18Kiuchi '24Misra '19Bjärterot '25Wang '26luan '20
63% semantic matchBioinformatics
Phillip Seitzer, Brian C. Searle·Journal of Proteome Research·Oct 8, 2018· 38 citations