Journal
ANALYTICAL CHEMISTRY
Volume 85, Issue 2, Pages 798-804Publisher
AMER CHEMICAL SOC
DOI: 10.1021/ac3029745
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Funding
- NCI NIH HHS [R01 CA170737] Funding Source: Medline
- NIEHS NIH HHS [R01 ES022181] Funding Source: Medline
- NIMH NIH HHS [P30 MH062261] Funding Source: Medline
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Global metabolomics describes the comprehensive analysis of small molecules in a biological system without bias. 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. 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. 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. Here, we present such a visualization scheme of global metabolomic data using a so-called cloud plot to represent multidimensional data from septic mice. 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. 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.
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