Data processing, multi-omic pathway mapping, and metabolite activity analysis using XCMS Online

Abstract

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 (LCLC)–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.

Metabolomics reveals that dietary xenoestrogens alter cellular metabolism induced by palbociclib/letrozole combination cancer therapy

“…Incubation with 10 nM LET resulted in a similar number of dysregulated features (459). These distinct effects of the agents is reflected by the pathway cloud plots shown in Figure S2, which report predicted modified metabolic pathways based on the recently implemented systems biology functionality within XCMS Online (Forsberg et al, 2017; Huan et al, 2017). This tool allows for the rapid prediction of dysregulated pathways without time consuming metabolite identification.…”

Metabolic profile analysis of Zhi-zi-chi decoction in feces of normal and chronic unpredictable mild stress-induced depression rats based on UHPLC-ESI-Q-TOF-MS/MS and multiple analytical strategies

“…Due to the ability of the XCMS online platform for processing and visualizing mass-spectrometry-based and entire untargeted metabolomic data, an Excel le comprising the information of peak codes, m/ z-retention time (t R ) pairs and ion intensity in both blank sample and drug-dose sample could be given as the result of the online analysis. 31,41 Then, the SIMCA-P soware was used for multivariate data analysis. According to the results of PCA analysis, CUMS + ZZCD samples, normal + ZZCD samples and control samples were divided into three detached groups in score plots, which indicate that the chemical constituents of rat feces changed due to the dose of ZZCD, as shown in Fig.…”

Intestinal peroxisomal fatty acid β-oxidation regulates neural serotonin signaling through a feedback mechanism

“…Compound identities were confirmed based on their high-resolution masses (accuracy < 1 ppm), MS/MS fragmentation spectra, and/or comparison with authentic standards. Data analysis was carried out using the Bioconductor package XCMS [65,144]. The matched filter algorithm in XCMS for peak picking in the profile data was used.…”