Closed-loop, multiobjective optimization of two-dimensional gas chromatography/mass spectrometry for serum metabolomics
Closed-loop, multiobjective optimization of two-dimensional gas chromatography/mass spectrometry for serum metabolomics
复制标题
DOI：
10.1021/ac061443
复制
发表时间：
2007-01-15
影响因子：
7.4
通讯作者：
Kell, Douglas B.
中科院分区：
文献类型：
--
作者：
Hagan, Steve O';Dunn, Warwick B.;Kell, Douglas B.
代谢组学旨在测量生物样品中潜在的所有代谢物，因此，我们需要开发和优化方法，以显着增加我们可以检测到的代谢物的数量。我们扩展了我们以前为一维GC-TOF-MS开发的闭环（迭代的、自动的）优化系统（O ′ Hagan，S.;邓恩，W。B.;布朗，M.; Knowles，J. D.; Kell，D. B。Anal. 2005，77，290-303）至综合二维（GCxGC）色谱法。所使用的启发式方法是一个多目标版本的有效的全局优化算法。在仅仅300次自动化运行中，我们将相对于1D GC中可观察到的代谢物数量提高了约3倍。优化条件允许检测超过4000个原始峰，其中约1800个被认为是真实的代谢物峰，而不是杂质或信噪比小于5的峰。各种计算方法用来解释改进的基础。这种闭环优化策略是优化任何分析仪器的通用和强大的方法。
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.