New Tools for Metabolite Identification and Quantitation

Georgetown University, Washington DC

Investigators

Linked publications, trials & patents

Abstract

PROJECT SUMMARY Targeted metabolomics offers a sensitive and accurate platform to compare the levels of pre-specified metabolites in biological samples. In particular, the use of selected ion monitoring (SIM) and selected reaction monitoring (SRM) has become an ideal method for quantitative analysis of metabolite targets using gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS), respectively. Although targeted metabolomics allows more sensitive and accurate measurements than untargeted metabolomics, the ability to determine the identity of most of the potential targets from untargeted studies as well as the lack of tools for designing targeted experiments have been a challenge for researchers aiming to conduct large-scale targeted metabolomic studies. Our team recently developed a software tool (MetaboSearch) for metabolite identification and an R-package SIMAT for designing targeted metabolomics experiments by GC-SIM-MS and analyzing the resulting data. In this application, we propose to develop: (1) an R-package metID for a network-based prioritization of putative metabolite IDs identified by mass-based search tools such as MetaboSearch; and (2) an R-package SRMAT that selects optimal transitions for targeted metabolomic experiments by LC-SRM-MS. Together with MetaboSearch, metID will enable users to prioritize putative metabolite IDs for analytes that may be associated with disease based on findings from previous untargeted metabolomic studies. For highly-scored putative metabolite IDs, SIMAT and SRMAT will choose transitions and retention times/indices for targeted experiments by GC-SIM-MS and LC-SRM-MS, respectively, and perform quantitation of the levels of target metabolites. Successful implementation of the proposed suite of tools will enable researchers to capitalize on the power of targeted metabolomics for accurate evaluation of the levels of promising metabolites in biological samples to uncover the relationships of the metabolites with the phenotypes of the samples.