A major impediment to mass spectrometry based metabolomics unleashing its full potential is the complexity of the data which is cluttered with solvent and salt adducts. This is called degeneracy and gives multiple peaks from one analytes which diminish analyte signal and need to be discarded using bioinformatic tools. In response to PAR-17-045 which calls for ?focused technology research and development,? a multi-PI team will develop a series of three distinct chemical tagging platforms based on our recent universal proton affinity tags. These tags react with virtually all metabolites and eliminate degeneracy, increase signal, allow for multi-charging, and analysis of ultra-small samples.
Aim 1 will develop a universal proton affinity tagging scheme with multi-dimensional liquid chromatography mass spectrometry platform which allows for pre- concentrating all metabolites and minimal degeneracy.
Aim 2 will synthesize and develop two sets of isotope labeled tags for ~$2/sample. The first set are isobaric tags for targeted analyses using low resolution mass spectrometry. The second set are neucode based tags for high resolution mass spectrometry capable of analyzing up to 60 samples simultaneously.
Aim 3 uses a novel tag which fragments across the carbon-carbon backbone to allow identification of new metabolites using fragmentation modeling. In the final aim of the proposal we will leverage the increase in sensitivity and multiplexing of the previous aims to analyze small samples. The methods developed here will be evaluated for robustness and transferability by comparing performance across multiple independent laboratories. The outcomes for this proposal are three distinct technologies which solve multiple critical barriers in metabolomics.
Understanding how the human body processes and metabolizes nutrients is critical to developing new paradigms and treatments for many diseases including diabetes, cancer, heart disease, and digestive disorders. Current technologies for analyzing metabolism generate large data sets which can contain misleading data. A multi-investigator team will develop innovative technologies to clarify these data sets and streamline metabolomics based investigations.
