Center for Metabolomics and Mass Spectrometry, The Scripps Research Institute, La Jolla, California, USA.
Department of Chemistry and Biochemistry, San Diego State University, San Diego, California, USA.
Nat Protoc. 2018 Apr;13(4):633-651. doi: 10.1038/nprot.2017.151. Epub 2018 Mar 1.
Systems biology is the study of complex living organisms, and as such, analysis on a systems-wide scale involves the collection of information-dense data sets that are representative of an entire phenotype. To uncover dynamic biological mechanisms, bioinformatics tools have become essential to facilitating data interpretation in large-scale analyses. Global metabolomics is one such method for performing systems biology, as metabolites represent the downstream functional products of ongoing biological processes. We have developed XCMS Online, a platform that enables online metabolomics data processing and interpretation. A systems biology workflow recently implemented within XCMS Online enables rapid metabolic pathway mapping using raw metabolomics data for investigating dysregulated metabolic processes. In addition, this platform supports integration of multi-omic (such as genomic and proteomic) data to garner further systems-wide mechanistic insight. Here, we provide an in-depth procedure showing how to effectively navigate and use the systems biology workflow within XCMS Online without a priori knowledge of the platform, including uploading liquid chromatography (LC)-mass spectrometry (MS) data from metabolite-extracted biological samples, defining the job parameters to identify features, correcting for retention time deviations, conducting statistical analysis of features between sample classes and performing predictive metabolic pathway analysis. Additional multi-omics data can be uploaded and overlaid with previously identified pathways to enhance systems-wide analysis of the observed dysregulations. We also describe unique visualization tools to assist in elucidation of statistically significant dysregulated metabolic pathways. Parameter input takes 5-10 min, depending on user experience; data processing typically takes 1-3 h, and data analysis takes ∼30 min.
系统生物学是研究复杂的生物体的，因此，在系统范围内的分析涉及到收集代表整个表型的信息密集型数据集。为了揭示动态的生物学机制，生物信息学工具已成为大规模分析中促进数据解释的必要手段。全局代谢组学就是进行系统生物学的一种方法，因为代谢物代表了正在进行的生物过程的下游功能产物。我们开发了 XCMS Online，这是一个能够进行在线代谢组学数据处理和解释的平台。最近在 XCMS Online 中实现的系统生物学工作流程，能够使用原始代谢组学数据快速进行代谢途径映射，从而研究失调的代谢过程。此外，该平台还支持整合多组学（如基因组和蛋白质组学）数据，以获得更广泛的系统机制见解。在这里，我们提供了一个深入的程序，展示了如何在没有平台先验知识的情况下有效地在 XCMS Online 中导航和使用系统生物学工作流程，包括从代谢物提取的生物样本上传液相色谱（LC）-质谱（MS）数据、定义识别特征的作业参数、校正保留时间偏差、对样本类别之间的特征进行统计分析以及进行预测代谢途径分析。可以上传额外的多组学数据，并与先前鉴定的途径叠加，以增强对观察到的失调的系统分析。我们还描述了独特的可视化工具，以协助阐明具有统计学意义的失调代谢途径。参数输入需要 5-10 分钟，具体取决于用户的经验；数据处理通常需要 1-3 小时，数据分析需要约 30 分钟。
