High-throughput data analysis for detecting and identifying differences between samples in GC/MS-based metabolomic analyses
High-throughput data analysis for detecting and identifying differences between samples in GC/MS-based metabolomic analyses
复制标题
DOI：
10.1021/ac050601e
复制
发表时间：
2005-09-01
影响因子：
7.4
通讯作者：
Moritz, T
中科院分区：
文献类型：
--
作者：
Jonsson, P;Johansson, AI;Moritz, T
在代谢组学中，目标是确定样品之间代谢物谱的差异。代谢组学研究中广泛使用的工具是气相色谱-质谱法 (GC/MS)。如果对重叠的 GC/MS 峰进行解卷积，则可在单次分析中检测到 400 多种化合物。然而，反卷积过程非常耗时且难以自动化，并且需要额外的处理来比较样本。因此，需要改进和自动化基于GC/MS的代谢组学中生成的数据的数据处理策略；否则，处理步骤将成为高通量分析的主要瓶颈。在这里，我们描述了一种新的半自动化策略，使用分层多元曲线分辨率方法来同时处理所有样本。所提出的策略生成（经过适当处理，例如多变量分析）所有检测到的代谢物的表格，这些代谢物在样品之间的相对浓度不同。 70 个样品的处理时间与样品的 GC/TOFMS 分析时间相似。该策略已使用两组不同的样品进行了验证：标准化合物和拟南芥样品的复杂混合物。
In metabolomics, the objective is to identify differences in metabolite profiles between samples. A widely used tool in metabolomics investigations is gas chromatography-mass spectrometry (GC/MS). More than 400 compounds can be detected in a single analysis, if overlapping GC/ MS peaks are deconvoluted. However, the deconvolution process is time-consuming and difficult to automate, and additional processing is needed in order to compare samples. Therefore, there is a need to improve and automate the data processing strategy for data generated in GC/MS-based metabolomics; if not, the processing step will be a major bottleneck for high-throughput analyses. Here we describe a new semiautomated strategy using a hierarchical multivariate curve resolution approach that processes all samples simultaneously. The presented strategy generates (after appropriate treatment, e.g., multivariate analysis) tables of all the detected metabolites that differ in relative concentrations between samples. The processing of 70 samples took similar time to that of the GC/TOFMS analyses of the samples. The strategy has been validated using two different sets of samples: a complex mixture of standard compounds and Arabidopsis samples.