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Analysis of Four-Way Two-Dimensional Liquid Chromatography-Diode Array Data: Application to Metabolomics

机译:四向二维液相色谱-二极管阵列数据的分析:在代谢组学中的应用

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Two-dimensional liquid chromatography (2D-LC) is rapidly gaining popularity for the analysis of very complex mixtures, including proteomic and metabolomic samples. It provides an effective strategy for separating such samples, because the resolving power of 2D-LC is far superior to that of traditional single-dimension separations. The present work focuses on the development of data analysis methods for the extremely large data sets, on the order of 10 million data points, generated by 2D-LC with diode-array detection (DAD). Specifically, we have applied and adapted chemometric methods to the analysis of maize seedling digests, focusing on compounds related to the biosynthetic pathways of indole-3-acetic acid, the primary growth regulator in plants. The chemometric techniques of window target testing factor analysis (WTTFA), along with parallel factor analysis - alternating least squares (PARAFAC-ALS) were used to analyze 2D-LC-DAD chromatograms of a sample composed of 26 indolic standards, 2 extracts of mutant orange pericarp maize seedlings, 2 extracts of wild-type maize seedlings, and a blank sample. The indolic compounds studied belonged to six spectrally unique groups, and WTTFA was able to specifically identify the presence or absence of any of the 26 indolic standards in the mutant and wild-type samples. A PARAFAC-ALS algorithm and an ALS algorithm with flexible constraints were successfully applied to resolve the spectrally rank deficient data and to demonstrate the quantitative potential of multivariate curve resolution methods. Using this procedure, 95 total peaks were resolved in the data set analyzed. Of those 95 peaks, 45 were found in both the mutant and wild-type maize samples, 16 peaks were unique to the mutant maize samples, 13 peaks were unique to the wild-type maize samples, and 15 peaks were unique to the standard chromatograms. Of the 26 standards included in the data set, several indole acetic acid conjugates were identified and quantified in the maize samples at levels of approximately 0.3-2 (mu)g/g plant material.
机译:二维液相色谱(2D-LC)迅速用于分析非常复杂的混合物,包括蛋白质组学和代谢组学样品。它提供了一种分离此类样品的有效策略,因为2D-LC的分离能力远远优于传统的一维分离。本工作着重于开发具有2D-LC和二极管阵列检测(DAD)的超大型数据集(约一千万个数据点)的数据分析方法。具体来说,我们已经应用化学计量学方法并将其应用于玉米幼苗消化物的分析,重点研究与吲哚-3-乙酸(植物中的主要生长调节剂)的生物合成途径有关的化合物。窗口目标测试因子分析(WTTFA)的化学计量学技术以及平行因子分析-交替最小二乘(PARAFAC-ALS)用于分析由26种靛蓝标准品,2种突变体提取物组成的样品的2D-LC-DAD色谱图橙色果皮玉米幼苗,2种野生型玉米幼苗提取物和空白样品。研究的吲哚化合物属于六个光谱独特的组,并且WTTFA能够特异性鉴定突变体和野生型样品中26种吲哚标准物中是否存在。成功地将具有柔性约束的PARAFAC-ALS算法和ALS算法应用于解决光谱秩不足的数据并证明了多元曲线解析方法的定量潜力。使用此过程,在分析的数据集中解析了95个总峰。在这95个峰中,在突变型和野生型玉米样品中均发现45个峰,突变型玉米样品独有的16个峰,野生型玉米样品独有的13个峰,标准色谱图的唯一15个峰。在数据集中包含的26个标准物中,几种玉米吲哚乙酸共轭物已被鉴定并定量,其水平约为0.3-2μg/ g植物材料。

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