Automated Screening and Filtering Scripts for GC×GC-TOFMS Metabolomics Data

Abstract

Comprehensive two-dimensional gas chromatography mass spectrometry (GC×GC-MS) is a powerful tool for the analysis of complex mixtures, and it is ideally suited to discovery studies where the entire sample is potentially of interest. Unfortunately, when unit mass resolution mass spectrometers are used, many detected compounds have spectra that do not match well with libraries. This could be due to the compound not being in the library, or the compound having a weak/nonexistent molecular ion cluster. While high-speed, high-resolution mass spectrometers, or ion sources with softer ionization than 70 eV electron impact (EI) may help with some of this, many GC×GC systems presently in use employ low-resolution mass spectrometers and 70 eV EI ionization. Scripting tools that apply filters to GC×GC-TOFMS data based on logical operations applied to spectral and/or retention data have been used previously for environmental and petroleum samples. This approach rapidly filters GC×GC-TOFMS peak tables (or raw data) and is available in software from multiple vendors. In this work, we present a series of scripts that have been developed to rapidly classify major groups of compounds that are of relevance to metabolomics studies including: fatty acid methyl esters, free fatty acids, aldehydes, alcohols, ketones, amino acids, and carbohydrates.

Hyphenated Mass Spectrometry versus Real-Time Mass Spectrometry Techniques for the Detection of Volatile Compounds from the Human Body

“…Therefore, the qualitative data from the library coupled with retention indices need to be carefully analysed or ideally confirmed with standards to confirm metabolite identification. In contrast, many techniques such as the GC-TOFMS, GC × GC and MS/MS techniques were limited in the past when compared to quadrupole GC-MS by the mass spectral libraries that were available although recent software developments [ 81 ] have enabled improvements [ 82 ]. As an example, it was possible to detect 1000 s of chromatographic peaks when analysing breath samples by GC × GC-TOFMS [ 83 ], but naming even a fraction of these proved difficult.…”

Foods and Contaminants Analysis Using Multidimensional Gas Chromatography: An Update of Recent Studies, Technology, and Applications

“…In the case of food-omics or metabolomics applications, dealing with several hundreds or thousands of compounds, an automated screening and filtering script for compound identification may be used. 48 In this study, the approach is based on assessment of MS data (no retention time data needed) with the following steps: (1) calculation of expected molecular ions of the compound family (e.g., saturated or unsaturated FAME) based on its structure; (2) neutral loss may be subtracted from the calculated molecular ion; (3) evaluation of other prominent features such as mass fragment intensity. For example, in the case of saturated FAME analysis, (1) confirmation that the molecule is a FAME.…”

Antibacterial Activity and Untargeted Metabolomics Profiling of Acalypha arvensis Poepp

“…The resulting chromatograms were processed with automated filtering scripts and displayed as a contour map [47]. The color scale on the map indicated the intensity or concentration of the peaks, with red denoting high intensity and dark blue representing the baseline or background (refer to part 2 in Supplementary Materials).…”