Categorizing Ion ?Features in Liquid Chromatography/Mass Spectrometry Metobolomics DataAnne M. Evans*, Matthew W. Mitchell, Hongping Dai and Corey D. DeHaven
Metabolon, Incorporated, 617 Davis Drive, Suite 400, Durham, 27713 North Carolina
- *Corresponding Author:
- Anne M. Evans
Metabolon, Incorporated, 617 Davis Drive
Suite 400, Durham, 27713 North Carolina
Fax: 919-572- 1721
E-mail: [email protected]
Received date: March 09, 2012; Accepted date: March 29, 2012; Published date: March 31, 2012
Citation: Evans AM, Mitchell MW, Dai H, DeHaven CD (2012) Categorizing Ion -Features in Liquid Chromatography/Mass Spectrometry Metobolomics Data. Metabolomics 2:110. doi:10.4172/2153-0769.1000110
Copyright: © 2012 Evans AM, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Mass spectrometry based metabolomics experiments generate copious amounts of signal data which in turn is processed to ultimately convert the signal data into identified metabolites so that biological interpretation and pathway analysis can be performed. The actual number of biochemicals detected in global biochemical profiling studies utilizing liquid chromatography coupled to mass spectrometry (LC/MS) is much lower than the total number of mass spectral ion-features detected, particularly when using positive electrospray ionization (ESI+). Given the conflicting numbers of detected metabolites reported in literature, a detailed analysis of the ion-feature composition is warranted. Ultrahigh pressure liquid chromatography (UHPLC)/Ion-trap MS and fragmentation (MS2) nominal mass data from 10 human plasma samples were analyzed in triplicate. The resulting detected ion-features were analyzed for ion-feature reproducibility, type and source. It was found that nearly 70% of all ion-features detected were non-reproducible, that 22% were from chemicals contributed to the samples due to storage and processing and that only 25% of the reproducible and annotatable ion-features could be determined to be protonated molecular ions. In addition, a previously undocumented ion-feature type; amalgam adducts, and ion-feature source; ions arising from chemistry of compounds occurring within extracted samples is reported. Ultimately, this analysis demonstrated that from an average of 10,000 ion-features detected in a human plasma sample ultimately only 220 compounds of biological origin were detected and identified from a positive ion analysis only.