Koek Maud M, van der Kloet Frans M, Kleemann Robert, Kooistra Teake, Verheij Elwin R, Hankemeier Thomas
Metabolomics. 2011 Mar;7(1):1-14. doi: 10.1007/s11306-010-0219-6. Epub 2010 Jul 15.
Due to the complexity of typical metabolomics samples and the many steps required to obtain quantitative data in GC × GC-MS consisting of deconvolution, peak picking, peak merging, and integration, the unbiased non-target quantification of GC × GC-MS data still poses a major challenge in metabolomics analysis. The feasibility of using commercially available software for non-target processing of GC × GC-MS data was assessed. For this purpose a set of mouse liver samples (24 study samples and five quality control (QC) samples prepared from the study samples) were measured with GC × GC-MS and GC-MS to study the development and progression of insulin resistance, a primary characteristic of diabetes type 2. A total of 170 and 691 peaks were quantified in, respectively, the GC-MS and GC × GC-MS data for all study and QC samples. The quantitative results for the QC samples were compared to assess the quality of semi-automated GC × GC-MS processing compared to targeted GC-MS processing which involved time-consuming manual correction of all wrongly integrated metabolites and was considered as golden standard. The relative standard deviations (RSDs) obtained with GC × GC-MS were somewhat higher than with GC-MS, due to less accurate processing. Still, the biological information in the study samples was preserved and the added value of GC × GC-MS was demonstrated; many additional candidate biomarkers were found with GC × GC-MS compared to GC-MS. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11306-010-0219-6) contains supplementary material, which is available to authorized users.
由于典型代谢组学样品的复杂性以及在气相色谱-质谱联用（GC×GC-MS）中获取定量数据所需的多个步骤，包括去卷积、峰识别、峰合并和积分，GC×GC-MS数据的无偏非靶向定量在代谢组学分析中仍然是一个重大挑战。评估了使用商业软件对GC×GC-MS数据进行非靶向处理的可行性。为此，使用GC×GC-MS和GC-MS对一组小鼠肝脏样品（24个研究样品和从研究样品中制备的5个质量控制（QC）样品）进行测量，以研究2型糖尿病的主要特征——胰岛素抵抗的发展和进程。在所有研究和QC样品的GC-MS和GC×GC-MS数据中，分别对170个和691个峰进行了定量。将QC样品的定量结果进行比较，以评估半自动GC×GC-MS处理与靶向GC-MS处理的质量，靶向GC-MS处理涉及对所有错误积分的代谢物进行耗时的手动校正，被视为金标准。由于处理不够准确，GC×GC-MS获得的相对标准偏差（RSD）比GC-MS略高。尽管如此，研究样品中的生物学信息得以保留，并且证明了GC×GC-MS的附加价值；与GC-MS相比，GC×GC-MS发现了许多额外的候选生物标志物。电子补充材料：本文的在线版本（doi:10.1007/s11306-010-0219-6）包含补充材料，授权用户可获取。
