Expedient Data Mining for Nontargeted High-Resolution LC–MS Profiles of Biological Samples

Abstract

The application of high-resolution LC-MS metabolomics for drug candidate toxicity screening reflects phenotypic changes of an organism caused by induced chemical interferences. Its success depends not only on the ability to translate the acquired analytical information into biological knowledge, but also on the timely delivery of the results to aid the decision making process in drug discovery and development. Recent improvements in analytical instrumentation have resulted in the ability to acquire extremely information-rich datasets. These new data collection abilities have shifted the bottleneck in the timeline of metabolomic studies to the data analysis step. This paper describes our approach to expedient data analysis of nontargeted high-resolution LC-MS profiles of biological samples. The workflow is illustrated with the example of metabolomics study of time-dependent fasting in male rats. The results from measurement of 220 endogenous metabolites in urine samples illustrate significant biochemical changes induced by fasting. The developed software enables the reporting of relative quantities of annotated components while maintaining practical turnaround times. Each component annotation in the report is validated using both calculated isotopic peaks patterns and experimentally determined retention time data on standards.