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Capillary reverse phase liquid chromatography (4 μL/min) was interfaced to a TOF mass spectrometer, and data were processed using nonlinear alignment software (XCMS) and an online database (METLIN) to find and identify metabolites differentially regulated in disease. Methods: We used a screening platform that used untargeted, mass-based metabolomics of methanol-extracted plasma to find significantly different molecular features in human plasma samples from MMA and PA patients and from healthy individuals. « lessĪbstract Background: We applied untargeted mass spectrometry-based metabolomics to the diseases methylmalonic acidemia (MMA) and propionic acidemia (PA). The utility of XCMS functionalities is demonstrated through the metabolomic analysis of bacterial stress response and the comparison of lymphoblastic leukemia cell lines. The interactive principal component analysis includes scores, loadings, and scree plots that can be adjusted depending on scaling criteria. The variation pattern of each feature can be visualized on both extracted-ion chromatograms and box plots. 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. 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. 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 more » patterns across multiple two-group comparisons. Newly incorporated statistical tests cover a wide array of univariate analyses. 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. Initially, the platform was developed for two-group comparisons to match the independent, “control” versus “disease” experimental design. XCMS Online () is a cloud-based informatic platform designed to process and visualize mass-spectrometry-based, untargeted metabolomic data.

(LBNL), Berkeley, CA (United States) Sponsoring Org.: USDOE Office of Science (SC) OSTI Identifier: 1788445 Grant/Contract Number: AC02-05CH11231 Resource Type: Accepted Manuscript Journal Name: Analytical Chemistry Additional Journal Information: Journal Volume: 85 Journal Issue: 2 Journal ID: ISSN 0003-2700 Publisher: American Chemical Society (ACS) Country of Publication: United States Language: English Subject: 59 BASIC BIOLOGICAL SCIENCES 47 OTHER INSTRUMENTATION 37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY Lipids Metabolomics Metabolism Biological databases Rodent = , Publication Date: Mon Dec 03 00:00: Research Org.: Lawrence Berkeley National Lab. Skaggs School of Pharmacy and Pharmaceutical Sciences of California, San Diego, CA (United States). Departments of Chemistry and Biology Univ. Departments of Chemistry, Molecular Biology, and Center for Metabolomics The Scripps Research Inst., La Jolla, CA (United States).Departments of Chemistry, Genetics, and Medicine

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The cloud plot is an effective representation of global mass spectrometry-based metabolomic data, and we describe how to extract it as standard output from our XCMS metabolomic software. While much attention has been dedicated to lipid compounds as potential biomarkers for sepsis, the cloud plot shows that alterations in hydrophilic metabolites may provide an early signature of the disease prior to the onset of clinical symptoms. Here, we present such a visualization scheme of global metabolomic data using a so-called “cloud plot” to represent multidimensional data from septic mice. Given that metabolite identification through tandem mass spectrometry data acquisition is a time-limiting step of the untargeted metabolomic workflow, simultaneous visualization of these parameters from large sets of data could facilitate compound identification and data interpretation. Although several visualization schemes have been used for metabolomic data, most commonly used representations exclude important data dimensions and therefore limit interpretation of global data sets. With mass spectrometry-based methods, global metabolomic data sets typically comprise thousands of peaks, each of which is associated with a mass-to-charge ratio, retention time, fold change, p-value, and relative intensity. Global metabolomics describes the comprehensive analysis of small molecules in a biological system without bias.

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