Gina Tan1, Svetlana Rezinciuc2, Heather Smallwood2, Andreas FR Hühmer1
ASMS 2017 Poster
Purpose: Untargeted metabolomics approach using an integrated MS platform and software solution to potentially identify disease biomarkers and demonstrate the possible applicability of this workflow to metabolite profiling studies on clinical research samples.
Methods: Donor nasal washes were analyzed using an LC-MS approach on a Thermo Scientific™ Orbitrap Fusion™ Tribrid™ mass spectrometer and the data generated were processed on a single software application that performed metabolite identification and differential analysis.
Results: The proposed workflow allowed the confident identification of over 600 compounds and the observation of sample variation in this study, enabling us to analyze statistically significant metabolites that could potentially be biomarkers for the disease research. All of this was achieved by using a suite of statistical tools within a single software application.
1 Thermo Fisher Scientific, San Jose, CA, USA
2 University of Tennessee Health Science Center, Memphis, TN, USA
United States
