Organization of GC/MS and LC/MS metabolomics data into chemical libraries

By Corey D. DeHaven, A. M. Evans, Hongping Dai, Kay A. Lawton. Journal of Cheminformatics, 2010

593 citations recorded by the source corpus.

BackgroundMetabolomics experiments involve generating and comparing small molecule (metabolite) profiles from complex mixture samples to identify those metabolites that are modulated in altered states (e.g., disease, drug treatment, toxin exposure). One non-targeted metabolomics approach attempts to identify and interrogate all small molecules in a sample using GC or LC separation followed by MS or MSn detection. Analysis of the resulting large, multifaceted data sets to rapidly and accurately identify the metabolites is a challenging task that relies on the availability of chemical libraries of metabolite spectral signatures. A method for analyzing spectrometry data to identify and Quantify Individual Components in a Sample, (QUICS), enables generation of chemical library entries from known standards and, importantly, from unknown metabolites present in experimental samples but without a corresponding library entry. This method accounts for all ions in a sample spectrum, performs library matches, and allows review of the data to quality check library entries. The QUICS method identifies ions related to any given metabolite by correlating ion data across the complete set of experimental samples, thus revealing subtle spectral trends that may not be evident when viewing individual samples and are likely to be indicative of the presence of one or more otherwise obscured metabolites.ResultsLC-MS/MS or GC-MS data from 33 liver samples were analyzed simultaneously which exploited the inherent biological diversity of the samples and the largely non-covariant chemical nature of the metabolites when viewed over multiple samples. Ions were partitioned by both retention time (RT) and covariance which grouped ions from a single common underlying metabolite. This approach benefitted from using mass, time and intensity data in aggregate over the entire sample set to reject outliers and noise thereby producing higher quality chemical identities. The aggregated data was matched to reference chemical libraries to aid in identifying the ion set as a known metabolite or as a new unknown biochemical to be added to the library.ConclusionThe QUICS methodology enabled rapid, in-depth evaluation of all possible metabolites (known and unknown) within a set of samples to identify the metabolites and, for those that did not have an entry in the reference library, to create a library entry to identify that metabolite in future studies.

Subfields: Analytical Chemistry, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/069703d1a19979a262df144c83fc448b42c91a64

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Citations in the corpus

The fecal metabolome as a functional readout of the gut microbiome (2018, unknown): “…experimental samples to a reference library of chemical standard entries that included retention time, molecular weight (m/z), preferred adducts, and in-source fragments as well as associated MS spectra and curated by visual inspection for quality control using software developed at Metabolon35,36.”
Early infancy microbial and metabolic alterations affect risk of childhood asthma (2015, methodology): “Metabolites were identified by automated comparison of the ion features in the experimental samples to a reference library of chemical standard entries that included retention time, molecular weight (m/z), preferred adducts, and in-source fragments, as well as associated MS spectra, and curated by v”
Impact of heat stress during seed development on soybean seed metabolome (2016, methodology): “Metabolites in seed were identified by automated analysis of ion features in comparison with reference libraries as previously described (DeHaven et al. 2010, 2012).”
Metagenomic Shotgun Sequencing and Unbiased Metabolomic Profiling Identify Specific Human Gut Microbiota and Metabolites Associated with Immune Checkpoint Therapy Efficacy in Melanoma Patients (2017, methodology): “Compounds were identified by automated comparison to reference chemical library entries with subsequent visual inspection for quality control as previously described [20].”
Glycogen synthesis is a required component of the nitrogen stress response in Synechococcus elongatus PCC 7942 (2013, methodology): “The identity of metabolites was determined by matching the combination of chromatographic retention index and mass spectra signatures to reference library entries based on analysis of authentic chemical standards [26].”