pap_Smith_2006

pap_Smith_2006 - Anal Chem 2006 78 779-787 XCMS Processing...

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XCMS: Processing Mass Spectrometry Data for Metabolite Profiling Using Nonlinear Peak Alignment, Matching, and Identification Colin A. Smith, Elizabeth J. Want, Grace O’Maille, Ruben Abagyan, and Gary Siuzdak* The Scripps Center for Mass Spectrometry and Department of Molecular Biology, The Scripps Research Institute, 10550 North Torrey Pines Road, BCC-007, La Jolla, California 92037 Metabolite profiling in biomarker discovery, enzyme sub- strate assignment, drug activity/specificity determination, and basic metabolic research requires new data prepro- cessing approaches to correlate specific metabolites to their biological origin. Here we introduce an LC/MS-based data analysis approach, XCMS, which incorporates novel nonlinear retention time alignment, matched filtration, peak detection, and peak matching. Without using internal standards, the method dynamically identifies hundreds of endogenous metabolites for use as standards, calculat- ing a nonlinear retention time correction profile for each sample. Following retention time correction, the relative metabolite ion intensities are directly compared to identify changes in specific endogenous metabolites, such as potential biomarkers. The software is demonstrated using data sets from a previously reported enzyme knockout study and a large-scale study of plasma samples. XCMS is freely available under an open-source license at http:// metlin.scripps.edu/download/. Recent advances in analytical technology have enabled the high-throughput analysis of many of nature’s biological building blocks. DNA microarrays can measure the transcription of the entire human genome using a single chip. 1 Liquid chromatography coupled to tandem mass spectrometry (LC - MS/MS) can be used to identify thousands of proteins from a complex mixture. 2 More recently, metabolite profiling has gained popularity using a number of techniques including nuclear magnetic resonance (NMR) or different combinations of liquid chromatography (LC), gas chro- matography (GC), and mass spectrometry (MS). 3 - 5 One particu- larly popular platform for untargeted metabolite profiling is LC/ MS using electrospray ionization (LC/ESI-MS). Unlike NMR, LC/ ESI-MS resolves individual chemical components into separate peaks, where NMR provides only a chemical fingerprint. Unlike GC/MS, it additionally detects nonvolatile compounds, which make up a large proportion of metabolites. Finally, LC separation provides a means for resolving isobaric compounds and reducing signal suppression. 3,6 The simultaneous separation and detection of metabolite analytes using both LC and MS produces complex data sets that require significant preprocessing before multiple samples can be analyzed statistically. In preprocessing spectral and separation data, two general strategies can be taken: (1) Divide the signal into bins, incorporating all data into a recognition profile for each sample. (2) Identify and individually quantify significant features, discarding data not deemed to be part of a feature. Variations and
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This note was uploaded on 02/08/2010 for the course ECEN 689-601 taught by Professor Staff during the Spring '10 term at Texas A&M.

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pap_Smith_2006 - Anal Chem 2006 78 779-787 XCMS Processing...

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