Comprehensive and Quantitative Profiling of the Human Sweat Submetabolome Using High-Performance Chemical Isotope Labeling LC–MS

Human sweat can be noninvasively collected and used as a media for diagnosis of certain diseases as well as for drug detection. However, because of very low concentrations of endogenous metabolites present in sweat, metabolomic analysis of sweat with high coverage is difficult, making it less widely used for metabolomics research. In this work, a high-performance method for profiling the human sweat submetabolome based on chemical isotope labeling (CIL) liquid chromatography–mass spectrometry (LC–MS) is reported. Sweat was collected using a gauze sponge style patch, extracted from the gauze by centrifugation, and then derivatized using CIL. Differential 12C- and 13C-dansylation labeling was used to target the amine/phenol submetabolome. Because of large variations in the total amount of sweat metabolites in individual samples, sample amount normalization was first performed using liquid chromatography with UV detection (LC–UV) after dansylation. The 12C-labeled individual sample was then mixed with an equal amount of 13C-labeled pooled sample. The mixture was subjected to LC–MS analysis. Over 2707 unique metabolites were detected across 54 sweat samples collected from six individuals with an average of 2002 ± 165 metabolites detected per sample from a total of 108 LC–MS runs. Using a dansyl standard library, we were able to identify 83 metabolites with high confidence; many of them have never been reported to be present in sweat. Using accurate mass search against human metabolome libraries, we putatively identified an additional 2411 metabolites. Uni- and multivariate analyses of these metabolites showed significant differences in the sweat submetabolomes between male and female, as well as between early and late exercise. These results demonstrate that the CIL LC–MS method described can be used to profile the human sweat submetabolome with high metabolomic coverage and high quantification accuracy to reveal metabolic differences in different sweat samples, thereby allowing the use of sweat as another human biofluid for comprehensive and quantitative metabolomics research.