Standardizing GC-MS metabolomics.
Metabolomics being the most recently introduced "omic" analytical platform is currently at its development phase. For the metabolomics to be broadly deployed to biological and clinical research and practice, issues regarding data validation and reproducibility need to be resolved. Gas chromatography-mass spectrometry (GC-MS) will remain integral part of the metabolomics laboratory. In this paper, the sources of biases in GC-MS metabolomics are discussed and experimental evidence for their occurrence and impact on the final results is provided. When available, methods to correct or account for these biases are presented towards the standardization of a systematic methodology for quantitative GC-MS metabolomics.
Kanani
Harin
H
Metabolic Engineering and Systems Biology Laboratory, Department of Chemical and Biomolecular Engineering, University of Maryland, MD 20742, USA.
Chrysanthopoulos
Panagiotis K
PK
Klapa
Maria I
MI
D000081:Acetamides; D000818:Animals; D017360:Arabidopsis / Q000737:chemistry; D001923:Brain Chemistry; D019295:Computational Biology / Q000379:methods*; D005463:Fluoroacetates; D008401:Gas Chromatography-Mass Spectrometry / Q000379:methods / Q000592:standards*; D008297:Male; D008660:Metabolism; D051379:Mice; D008807:Mice, Inbred BALB C; D014269:Trifluoroacetic Acid / Q000737:chemistry; D014297:Trimethylsilyl Compounds / Q000737:chemistry
D000081:Acetamides; D005463:Fluoroacetates; D014297:Trimethylsilyl Compounds; C086665:N-methyl-N-(trimethylsilyl)trifluoroacetamide; D014269:Trifluoroacetic Acid