Capellades Jordi, Navarro Miriam, Samino Sara, Garcia-Ramirez Marta, Hernandez Cristina, Simo Rafael, Vinaixa Maria, Yanes Oscar
Centre for Omic Sciences, Universitat Rovira i Virgili , Avinguda Universitat 1, 43204 Reus, Spain.
Institut d'Investigacio Sanitaria Pere i Virgili (IISPV), Avinguda Universitat 1, 43204 Reus, Spain.
Anal Chem. 2016 Jan 5;88(1):621-8. doi: 10.1021/acs.analchem.5b03628. Epub 2015 Dec 18.
Studying the flow of chemical moieties through the complex set of metabolic reactions that happen in the cell is essential to understanding the alterations in homeostasis that occur in disease. Recently, LC/MS-based untargeted metabolomics and isotopically labeled metabolites have been used to facilitate the unbiased mapping of labeled moieties through metabolic pathways. However, due to the complexity of the resulting experimental data sets few computational tools are available for data analysis. Here we introduce geoRge, a novel computational approach capable of analyzing untargeted LC/MS data from stable isotope-labeling experiments. geoRge is written in the open language R and runs on the output structure of the XCMS package, which is in widespread use. As opposed to the few existing tools, which use labeled samples to track stable isotopes by iterating over all MS signals using the theoretical mass difference between the light and heavy isotopes, geoRge uses unlabeled and labeled biologically equivalent samples to compare isotopic distributions in the mass spectra. Isotopically enriched compounds change their isotopic distribution as compared to unlabeled compounds. This is directly reflected in a number of new m/z peaks and higher intensity peaks in the mass spectra of labeled samples relative to the unlabeled equivalents. The automated untargeted isotope annotation and relative quantification capabilities of geoRge are demonstrated by the analysis of LC/MS data from a human retinal pigment epithelium cell line (ARPE-19) grown on normal and high glucose concentrations mimicking diabetic retinopathy conditions in vitro. In addition, we compared the results of geoRge with the outcome of X(13)CMS, since both approaches rely entirely on XCMS parameters for feature selection, namely m/z and retention time values. To ensure data traceability and reproducibility, and enabling for comparison with other existing and future approaches, raw LC/MS files have been deposited in MetaboLights (MTBLS213) and geoRge is available as an R script at https://github.com/jcapelladesto/geoRge.
研究化学基团在细胞内发生的一系列复杂代谢反应中的流动，对于理解疾病状态下体内稳态的改变至关重要。最近，基于液相色谱-质谱联用（LC/MS）的非靶向代谢组学和同位素标记代谢物已被用于促进通过代谢途径对标记基团进行无偏映射。然而，由于所得实验数据集的复杂性，用于数据分析的计算工具很少。在此，我们介绍geoRge，一种能够分析来自稳定同位素标记实验的非靶向LC/MS数据的新型计算方法。geoRge用开放语言R编写，并在广泛使用的XCMS软件包的输出结构上运行。与现有的少数工具不同，这些工具通过使用轻、重同位素之间的理论质量差迭代所有质谱信号来使用标记样本跟踪稳定同位素，而geoRge使用未标记和标记的生物学等效样本比较质谱中的同位素分布。与未标记化合物相比，同位素富集化合物会改变其同位素分布。这直接反映在标记样本的质谱中相对于未标记等效物出现一些新的质荷比（m/z）峰和更高强度的峰。通过对在体外模拟糖尿病视网膜病变条件的正常和高葡萄糖浓度下生长的人视网膜色素上皮细胞系（ARPE - 19）的LC/MS数据进行分析，证明了geoRge的自动非靶向同位素注释和相对定量能力。此外，我们将geoRge的结果与X(13)CMS的结果进行了比较，因为这两种方法完全依赖XCMS参数进行特征选择，即m/z和保留时间值。为确保数据的可追溯性和可重复性，并便于与其他现有和未来方法进行比较，原始LC/MS文件已存入MetaboLights（MTBLS213），geoRge可作为R脚本在https://github.com/jcapelladesto/geoRge获取。
