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

Abstract

Background: Metabolomics 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.

Results: LC-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.

Conclusion: The 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.

Figure 1

A single scan from an EI GC/MS analysis of a liver biopsy.

Figure 2

The selected ion chromatogram (SIC) for two different ions from leucine (A) and two ions from glycerol (B) as measured from 4 different liver sample analyses. The ions from leucine, 158 and isotope 232 m/z, trend across the different liver samples and the ions related to glycerol share a different trend.

Figure 3

Correlation of m/z 158 and 232, two ions related to leucine, across all injections in a study. All ions were pulled from all sample injections in a study and analyzed all for correlation. Ions are then grouped based on a user-specified correlation limit.

Figure 4

The creation of 3 different groups of correlating ions, A,C, and E, and their respective authentic standard library entries are shown for comparison, B, D, and F. The ions within the groups correlated with a minimum of 0.8. From the single scan in Figure 1, 3 different compounds are present; phosphate, leucine and glycerol.

Figure 5

(A) An ion group including the protonated molecular ion, isotopes, adducts, and multimers of inosine (m + H⁺ 269) based on correlation across a 33 sample set study. (B) The authentic standard spectrum of inosine. (C) The correlation between the protonated molecular ion of inosine at 269 m/z and an in-source fragment at 137 m/z.

Figure 6

(A) An ion group created from an EI-GC/MS analysis of urine that when created was an unknown. (B) The authentic standard spectrum of equol, later permitted the identification of the unknown.