Final ProgramCPSA 2011
Science and Technology Coming Together to Make a Difference
October 3 - 6, 2011
Bucks County Sheraton Hotel
Langhorne, PA
Poster Abstract #36
Advances in the Automated Data Analysis of High Resolution Accurate Mass LC-MS Metabolomics
1) Bristol-Myers Squibb Co., Rt. 206 & Provinceline Rd, Lawrenceville, NJ, 08543, USA; 2) Bristol-Myers Squibb Co., 311 Pennington-Rocky Hill Road, Pennington, NJ, 08534, USA; 3) Thermo Fisher Scientific, Somerset, NJ, USA
High resolution accurate mass (HRAM) LC-MS metabolomics provides a unique approach for evaluation of perturbations in biochemical pathways. HRAM LC-MS couples high sensitivity detection with measurement accuracy and wide dynamic range, thus producing an enormous amount of information. Previously presented at CPSA 2010, our approach for reduction of the complexity of HRAM LC-MS dataset and condensing the results into biologically meaningful information* has been amended with many improvements and expanded to the new Thermo Fisher Q-Exactive platform.
The improved workflow for automated data analysis using Component Elucidator (CE), in-house software written specifically for data processing of HRAM metabolomics data, is illustrated with the metabolomics experiment investigating the effects of fasting on metabolic changes in male rats. All HRAM LC-MS data were collected on a Thermo Fisher Q-Exactive mass spectrometer coupled to a uHPLC Acella chromatographic system. Raw-files containing HRAM LC-MS profiles were automatically processed with CE software which includes data reduction, signal annotation and statistical analysis. The data reduction module converts raw data points into tables of components, where each pair of accurate mass and retention time corresponds to a unique analyte. Subsequent quantifiability filtering, alignment and annotation further reduces the number of components and assigns molecular identities, resulting in an output table of annotated components along with relative abundances in each sample. Finally, in the statistical module, univariate statistics were applied to reveal changes and trends which correlate with the duration of animal fasting.
* Serhiy Hnatyshyn, Tom McClure, Michael Reily, Mark Sanders. Component Elucidator the Software for Automated Analysis of High Resolution Accurate Mass LC-MS Datasets in Metabolomics. CPSA 2010, Longhorn, PA, October 2010
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