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Abstract:
Introduction Stable isotopic labeling experiments are powerful tools to study metabolic pathways, to follow tracers and
fluxes in biotic and abiotic transformations and to elucidate molecules involved in metal complexing.
Objective To introduce a software tool for the identification of isotopologues from mass spectrometry data.
Methods DeltaMS relies on XCMS peak detection and X13CMS
isotopologue grouping and then analyses data for specific
isotope ratios and the relative error of these ratios. It provides pipelines for recognition of isotope patterns in three experiment
types commonly used in isotopic labeling studies: (1) search for isotope signatures with a specific mass shift and intensity
ratio in one sample set, (2) analyze two sample sets for a specific mass shift and, optionally, the isotope ratio, whereby one
sample set is isotope-labeled, and one is not, (3) analyze isotope-guided perturbation experiments with a setup described
in X13CMS.
Results To illustrate the versatility of DeltaMS, we analyze data sets from case-studies that commonly pose challenges in
evaluation of natural isotopes or isotopic signatures in labeling experiment. In these examples, the untargeted detection
of sulfur, bromine and artificial metal isotopic patterns is enabled by the automated search for specific isotopes or isotope
signatures.
Conclusion DeltaMS provides a platform for the identification of (pre-defined) isotopologues in MS data from single samples or comparative metabolomics data sets.
