Data Processing for GC-MS- and LC-MS-Based Untargeted Metabolomics
Gas chromatography and liquid chromatography coupled to mass spectrometry are used extensively in untargeted metabolomics, which involves the profiling of small metabolites in biological samples. The complex raw dataset produced from untargeted metabolomi
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ction Metabolomics involves broadscale profiling of small molecule metabolites (also termed “the metabolome”). The discovery of significant metabolic changes within a biological system in response to diseases, metabolic pathway alterations, and genetic and environmental changes provides insight for fundamental understanding of biological mechanisms. Applications of metabolomics are described for the fields of plant biology, biomedical research and pharmacology, nutritional studies, and others [1–4]. Among the various modern analytical techniques, gas chromatography and liquid chromatography coupled to mass spectrometry (GC-MS and LC-MS, respectively) are the most commonly used for acquiring metabolomics data. Prior to data acquisition, metabolites are extracted using aqueous and/or organic solvents from biological samples such as cells, blood, urine, tissues, and other biofluids. Chemical derivatization is usually required to increase the volatility of compounds for separation and detection by GC-MS. GC-MS and LC-MS are complementary techniques, which together can yield Angelo D’Alessandro (ed.), High-Throughput Metabolomics: Methods and Protocols, Methods in Molecular Biology, vol. 1978, https://doi.org/10.1007/978-1-4939-9236-2_18, © Springer Science+Business Media, LLC, part of Springer Nature 2019
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broad coverage of metabolites and generate a large amount of highly dimensional complex data. This data complexity is intensified by additional acquisition options sometimes specific to one vendor’s technology offerings. For example, instrumentation produced by Waters Corporation provides the option of acquiring MSE-containing LC-MS data, which records data for every detectable compound and its fragment ions, i.e., all precursor ions and product ions are collected without preselection or discrimination [5]. Proper data processing is a key step in the analysis of such comprehensive datasets and requires knowledge of both bioinformatics and analytical chemistry in addition to the field of interest. Although a wide selection of data processing tools are available, either freely or commercially, a generalized data processing workflow is needed for better utilization of the data by a broader field of scientists. Here we describe a data processing workflow routinely used in our laboratory for GC-MS- and LC-MS-based untargeted global metabolite profiling studies. This workflow utilizes three freely available tools, XCMS [6], RAMClustR [7], and RAMSearch [8], to achieve feature detection and alignment, data reduction, and annotation, respectively. An overview of the data processing workflow is shown in Fig. 1. We have included applications of our workflow to MSE-containing LC-MS data as an example of how to accommodate more complex data from optional acquisition modes. The benefits of this workflow include more effective utilization of instrumentation through efficient use of all available MS data, reduced feature redundancy for downstream statistical analysis, reduction of analytical variance, a
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