Pre-analytic Considerations for Mass Spectrometry-Based Untargeted Metabolomics Data
Metabolomics is the science of characterizing and quantifying small molecule metabolites in biological systems. These metabolites give organisms their biochemical characteristics, providing a link between genotype, environment, and phenotype. With these o
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oduction Metabolomics is concerned with the comprehensive characterization and quantification of small molecule metabolites in biological systems; the metabolites present in a sample or system are referred to as the metabolome (see [1, 2] and http://www.metabolomicssociety.org/). Metabolomics allows for an assessment of a cellular state within the context of the environment (see [2–5] and references therein) and gives complementary insight into biological processes as compared to genomics and proteomics (see [2, 3, 6]). Metabolites can also serve as markers of disease severity and drug sensitivity [2, 7–10]. Thus, metabolites give organisms their
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_20, © Springer Science+Business Media, LLC, part of Springer Nature 2019
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iochemical characteristics, providing a link between genotype, b environment, and phenotype. Metabolomics can be classified as targeted or untargeted. Targeted metabolomics measures only smaller, defined groups of chemically characterized and biochemically annotated metabolites [11], while untargeted metabolomics is designed to comprehensively measure analytes in a sample, including chemical unknowns. In addition, internal standards can be included in both approaches for absolute quantification and/or to evaluate technical variation. In the following, we focus on untargeted metabolomics and will refer to it simply as metabolomics. We will also use the terms metabolites and compounds interchangeably. We focus on liquid chromatography mass spectrometry (LC- MS), but much of the discussion applies to other MS applications, such as gas chromatography and LC-MS-based proteomics data. The main alternative to MS is nuclear magnetic resonance (NMR) spectroscopy [12, 13]. Both MS and NMR technologies have their strengths and weaknesses, supplementing and complementing each other [12–14]: For example, MS is more sensitive than NMR and can detect lower-abundance metabolites. NMR, on the other hand, is better at determining the structure of unknown metabolites, is nondestructive, and can be used in vivo. While MS-based metabolomics brings many opportunities, the complexity of the metabolome and technical limitations also bring challenges. The challenges we focus on here are missing values and unwanted variation, such as batch effects; annotation of compounds is another challenge that we only discuss in the context of missing values. These challenges occur also in other omics fields such as transcriptomics, and tools that have been developed there are often adapted or directly used for metabolomics data. For example, ComBat was developed to “combat” batch effects in gene expression microarray data [15–17], but it has been used in a variety of fields, including metabolomics (see [18] and references therein). On the other hand, there are also many MS-specific tools for metabolomics data. An example for a normalization
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