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Zhou B, Xiao JF, Tuli L, et al. LC-MS-based metabolomics. Mol Biosyst. 2011;8(2):470-81doi: 10.1039/c1mb05350g.
Zhou, B., Xiao, J. F., Tuli, L., & Ressom, H. W. (2012). LC-MS-based metabolomics. Molecular bioSystems, 8(2), 470-81. https://doi.org/10.1039/c1mb05350g
Zhou, Bin, et al. "LC-MS-based metabolomics." Molecular bioSystems vol. 8,2 (2012): 470-81. doi: https://doi.org/10.1039/c1mb05350g
Zhou B, Xiao JF, Tuli L, Ressom HW. LC-MS-based metabolomics. Mol Biosyst. 2012 Feb;8(2):470-81. doi: 10.1039/c1mb05350g. Epub 2011 Nov 01. PMID: 22041788; PMCID: PMC3699692.
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Mol Biosyst. 2012 Feb;8(2):470-81. doi: 10.1039/c1mb05350g. Epub 2011 Nov 01.

LC-MS-based metabolomics.

Molecular bioSystems

Bin Zhou, Jun Feng Xiao, Leepika Tuli, Habtom W Ressom

Affiliations

  1. Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC 20057, USA.

PMID: 22041788 PMCID: PMC3699692 DOI: 10.1039/c1mb05350g

Abstract

Metabolomics aims at identification and quantitation of small molecules involved in metabolic reactions. LC-MS has enjoyed a growing popularity as the platform for metabolomic studies due to its high throughput, soft ionization, and good coverage of metabolites. The success of a LC-MS-based metabolomic study often depends on multiple experimental, analytical, and computational steps. This review presents a workflow of a typical LC-MS-based metabolomic analysis for identification and quantitation of metabolites indicative of biological/environmental perturbations. Challenges and current solutions in each step of the workflow are reviewed. The review intends to help investigators understand the challenges in metabolomic studies and to determine appropriate experimental, analytical, and computational methods to address these challenges.

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