Closed-loop, multiobjective optimization of two-dimensional gas chromatography/mass spectrometry for serum metabolomics.
Metabolomics seeks to measure potentially all the metabolites in a biological sample, and consequently, we need to develop and optimize methods to increase significantly the number of metabolites we can detect. We extended the closed-loop (iterative, automated) optimization system that we had previously developed for one-dimensional GC-TOF-MS (O'Hagan, S.; Dunn, W. B.; Brown, M.; Knowles, J. D.; Kell, D. B. Anal. Chem. 2005, 77, 290-303) to comprehensive two-dimensional (GCxGC) chromatography. The heuristic approach used was a multiobjective version of the efficient global optimization algorithm. In just 300 automated runs, we improved the number of metabolites observable relative to those in 1D GC by some 3-fold. The optimized conditions allowed for the detection of over 4000 raw peaks, of which some 1800 were considered to be real metabolite peaks and not impurities or peaks with a signal/noise ratio of less than 5. A variety of computational methods served to explain the basis for the improvement. This closed-loop optimization strategy is a generic and powerful approach for the optimization of any analytical instrumentation.
O'Hagan
Steve
S
School of Chemistry, The University of Manchester, Faraday Building, Sackville Street, Manchester M60 1QD, UK.
Dunn
Warwick B
WB
Knowles
Joshua D
JD
Broadhurst
David
D
Williams
Rebecca
R
Ashworth
Jason J
JJ
Cameron
Maureen
M
Kell
Douglas B
DB
D015415:Biomarkers / Q000097:blood* / Q000378:metabolism; D008401:Gas Chromatography-Mass Spectrometry / Q000379:methods* / Q000592:standards*; D006801:Humans
D015415:Biomarkers