Professors Sarah Rutan at Virginia Commonwealth University (VCU) and Peter Carr at the University of Minnesota are supported by the Analytical and Surface Chemistry Program in the Division of Chemistry to develop quantitative aspects of two-dimensional liquid chromatography, with a focus on applications in metabolomics. Specifically, this work addresses the optimization of the separation conditions and exploration of novel means to accelerate both the second dimension separation and the analysis of the resulting data. The aim is to transform a time-consuming process that is semi-quantitative at best into a rapid and efficient method that will provide precise quantitative results for applications such as biomarker identification.
If successful, the work will provide important new tools for biological scientists and will contribute towards improved understanding of complex biological systems. In order to make the concepts of metabolomics more accessible to a broader audience, a website with online simulators for metabolism and separations will be developed, along with associated activities designed for use in the high school classroom.
It is well known that the quantitative analysis of biological fluids can provide important information for the early diagnosis and treatment of disease thereby improving health. It is becoming increasingly clear that it is important to characterize a large number of interacting compounds, such as DNA, proteins and low molecular weight metabolites (e.g., glucose, cholesterol) that are essential to increasing our understanding of disease processes and treatment outcomes. In particular, this research has focused on the development of two –dimensional liquid separation methods that allow for the separation and analysis of multiple metabolites present in biological fluids, such as blood, serum and urine. This cutting edge, transformative technology needs further development before it can be implemented in a clinical setting, and the research carried out with support from this this grant has addressed some of the most important intellectual challenges involved in developing this technology to the point where it will become practical for real-world analyses. The software concepts produced during this work permits the resolution of analytes present at low concentrations in exceedingly complex matrices, such as urine and even wastewater treatment plant effluents. Chemical components that are not evident upon inspection of the original data can be revealed upon utilization of the algorithms developed here, as shown in the figure where peak N16 has been detected. This project has also trained three undergraduate students, two graduate students and a post-doctoral associate on these important methodologies.
