An untargeted metabolomics workflow for the detection of metabolites derived from endogenous or exogenous tracer substances is presented. To this end, a recently developed stable isotope assisted LC-HRMS based metabolomics workflow for the global annotation of biological samples has been further developed and extended. For untargeted detection of metabolites arising from labeled tracer substances, isotope pattern recognition has been adjusted to account for non-labeled moieties conjugated to the native and labeled tracer molecules. Furthermore, the workflow has been extended by (i) an optional ion intensity ratio check, (ii) the automated combination of positive and negative ionization mode mass spectra derived from fast polarity switching and (iii) metabolic feature annotation. These extensions enable the automated, unbiased and global detection of tracer derived metabolites in complex biological samples. The workflow is demonstrated with the metabolism of 13C9-phenylalanine in wheat cell suspension cultures in the presence of the mycotoxin deoxynivalenol (DON). In total 341 metabolic features (150 in positive and 191 in negative ionization mode) corresponding to 139 metabolites were detected. The benefit of fast polarity switching was evident with 32 and 58 of these metabolites having exclusively been detected in the positive and negative mode respectively. Moreover, for 19 of the remaining 49 phenylalanine derived metabolites the assignment of ion species and thus molecular weight was only possible by the use of complementary features of the two ion polarity modes. Statistical evaluation showed that treatment with DON increased or decreased the abundances of many detected metabolites.