Interactive XCMS Online: Simplifying Advanced Metabolomic Data Processing and Subsequent Statistical Analyses

Abstract

XCMS Online () is a cloud-based informatic platform designed to process and visualize mass-spectrometry-based, untargeted metabolomic data. Initially, the platform was developed for two-group comparisons to match the independent, “control” versus “disease” experimental design. Here, we introduce an enhanced XCMS Online interface that enables users to perform dependent (paired) two-group comparisons, meta-analysis, and multigroup comparisons, with comprehensive statistical output and interactive visualization tools. Newly incorporated statistical tests cover a wide array of univariate analyses. Multigroup comparison allows for the identification of differentially expressed metabolite features across multiple classes of data while higher order meta-analysis facilitates the identification of shared metabolic patterns across multiple two-group comparisons. Given the complexity of these data sets, we have developed an interactive platform where users can monitor the statistical output of univariate (cloud plots) and multivariate (PCA plots) data analysis in real time by adjusting the threshold and range of various parameters. On the interactive cloud plot, metabolite features can be filtered out by their significance level (p-value), fold change, mass-to-charge ratio, retention time, and intensity. The variation pattern of each feature can be visualized on both extracted-ion chromatograms and box plots. The interactive principal component analysis includes scores, loadings, and scree plots that can be adjusted depending on scaling criteria. The utility of XCMS functionalities is demonstrated through the metabolomic analysis of bacterial stress response and the comparison of lymphoblastic leukemia cell lines.

1H-NMR and HPLC–MS/MS-based global/targeted metabolomic evaluation of Hypericum perforatum L. intervention for menopause

“…These compounds were identified by matching an online mass spectrometry database with a selfbuilt standard compound database, and the results were verified ( Fig. 1C) (Gowda et al, 2014;Tautenhahn, Patti, Rinehart, & Siuzdak, 2012). …”

Comparative and integrative metabolomics reveal that S-nitrosation inhibits physiologically relevant metabolic enzymes

“…LC-MS analysis of each sample was performed at the Scripps Center for Metabolomics and Mass Spectrometry using negative and positive mode MS for the C18 column and negative mode MS for the HILIC column. Resulting MS spectra and LC peak integrations were collected and analyzed using XCMS Online (27)(28)(29).…”

“…The combined data narrows down the metabolic enzyme targets of S-nitrosation to only those S-nitrosation sites that impact enzymatic activity. After obtaining a list of 176,035 mass features (observed monoisotopic m/z values with consistent retention times across all samples) of eluents from a C18 HPLC column analyzed separately by positive and negative mode mass spectrometry as well as a hydrophilic interaction chromatography (HILIC) column analyzed by negative mode mass spectrometry, the chromatograms of six biological replicates of each WT and eNOS -/-organ were analyzed and mass feature variations were compared using XCMS online (27)(28)(29). It is important to note that many of the observed mass features could match multiple metabolites, isotopes, or salt forms.…”

Non-targeted screening approaches for contaminants and adulterants in food using liquid chromatography hyphenated to high resolution mass spectrometry

“…A similar workflow was also demonstrated in honey where a banned pesticide was detected and identified in one of the samples by mining the data for chlorine-containing molecular species using partial least squares discriminant analysis (PLS-DA) and PCA [74]. Most instrument vendors offer chemometric software tools to analyze their own data formats, although vendor neutral software packages are also available, such as XCMS [75]. Developing a statistical workflow with known standard mixtures in complex matrices can be beneficial in addressing challenges, such as sampling and data processing, before receiving a potentially hazardous set of samples.…”