Biochemical Profiling Research Group Implements New Software for Metabolomics

2 Sept 2007
Greg Smith
Analyst / Analytical Chemist

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Researchers at the Virginia Bioinformatics Institute (VBI) at Virginia Tech are examining ways in which metabolomics can be applied to the study of systems biology. Recently, they invested in new technology, ACD/IntelliXtract, to further enhance their ability to handle the data obtained by liquid chromatography/mass spectrometry (LC/MS).

Metabolomics involves the systematic study of the metabolic processes of living cells and requires the high-throughput analysis of a large number of small-molecule cellular metabolites. While current instrument technology enables samples to be analyzed more and more quickly, the resulting avalanche of data must then be managed—often an overwhelming task.

ACD/IntelliXtract quickly and accurately extracts all chromatographic components, assigning adducts and interpreting mass spectra for each component. Components of interest can then be labeled and isolated from other components for identification, helping researchers to gain a quicker understanding of their results. Moreover, this process can be carried out as part of manual data interpretation for a few samples, or can be automated for large numbers of samples.

“We were looking for software that could help alleviate the pressure of data interpretation we were experiencing. There is an inevitable backlog of information that occurs with high-throughput analysis and ACD/IntelliXtract is able to automate some of that work,” said Dr. Vladimir Shulaev, Associate Professor at VBI and the head of the Biochemical Profiling Research Group. “Having already used ACD/MS Manager extensively, we were familiar with ACD/Labs software and their customer service, and confident about the quality of the results IntelliXtract would generate.”

One application—especially useful to VBI’s metabolomics studies—uses ACD/IntelliXtract to assist with related fragment screening, taking advantage of the fact that many intermediates in a metabolic pathway have similar structures. Exploiting this knowledge in identifying and elucidating metabolites seems intuitive; however, in practice, organizing the data and visualizing relationships is much more difficult. With the help of ACD/IntelliXtract, LC/MS data is automatically analyzed and organized, presenting the relevant data for related metabolites in a clear and concise report.

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