Improving the quality of biomarker candidates in untargeted metabolomics via peak table-based alignment of comprehensive two-dimensional gas chromatography–mass spectrometry data

Abstract

The potential of high-resolution analytical technologies like GC×GC/TOF MS in untargeted metabolomics and biomarker discovery has been limited by the development of fully automated software that can efficiently align and extract information from multiple chromatographic data sets. In this work we report the first investigation on a peak-by-peak basis of the chromatographic factors that impact GC×GC data alignment. A representative set of 16 compounds of different chromatographic characteristics were followed through the alignment of 63 GC×GC chromatograms. We found that varying the mass spectral match parameter had a significant influence on the alignment for poorly- resolved peaks, especially those at the extremes of the detector linear range, and no influence on well- chromatographed peaks. Therefore, optimized chromatography is required for proper GC×GC data alignment. Based on these observations, a workflow is presented for the conservative selection of biomarker candidates from untargeted metabolomics analyses.

Metabolomics toward personalized medicine

“…The GC‐MS method has undergone a few modifications over the past decade, with respect to the mass analyzers used such as TOF and the triple quadruple (QqQ). The latest advancement is two‐dimensional chromatographic separation system (GCxGC‐MS), which has been used recently for biomarker selection of candidates from untargeted metabolomics analyses . TOF analyzers are preferred because of their higher scan speed, sensitivity, compatibility with GC and high resolution as a new analyzer, resulting in better characterization of unknown metabolites by using accurate masses.…”

“…The similarity of molecular fragmentation patterns for structural isomers may make compound identification difficult in GC-MS [34][35][36]. For example, Miyamoto et al 37 used GChas been used recently for biomarker selection of candidates from untargeted metabolomics analyses 38. TOF analyzers are preferred because of their higher scan speed, sensitivity, compatibility with GC and high resolution as a new analyzer, resulting in better characterization of unknown metabolites by using accurate masses.…”

Chemical characterization of the acid alteration of diesel fuel: Non-targeted analysis by two-dimensional gas chromatography coupled with time-of-flight mass spectrometry with tile-based Fisher ratio and combinatorial threshold determination

“…The tilebased F-ratio software enables analysis of GC × GC-TOFMS data with minimal user intervention, and is able to discover analytes even at low absolute concentration and a low concentration ratio. Further, the tile-based algorithm mitigates recognized challenges in the analysis of GC × GC-TOFMS data, including the impacts of minor chromatographic retention time shifts and ensuring all of the column 2 peaklets for a given modulated analyte are accurately combined for 2D peak quantification [38][39][40]. By improving the signal-to-noise ratio (S/N) and reducing the impact of spurious chromatographic and detector variations prior to the F-ratio calculation, the tile-based algorithm maximizes the discovery of significant features while minimizing features of lesser value [21,22].…”

Optimizing 2D gas chromatography mass spectrometry for robust tissue, serum and urine metabolite profiling

“…GC-MS has been particularly useful for the analysis of low-polarity volatile metabolites of fats and esters, but also high-polarity molecules such as amino- and organic acids that need to be converted into volatile derivatives [8] , [9] , [10] , [11] . Limitations in resolving power of GC-MS have been improved by the development of two-dimensional workflows (GCxGC) [12] combined with high-speed scan single quadrupole (qMS) [13] , [14] , [15] , [16] , [17] and high-resolution mass spectrometry (GCxGC-TOF MS) [18] , [19] , [20] , [21] , [22] . In a GCxGC configuration, two columns with different properties (apolar vs polar) are connected through a modulator, allowing further separation of compounds that co-elute from the first column, thereby giving rise to enhanced resolution and peak capacity.…”

“…Technically, this has been achieved by either closed cycle refrigerated loop modulation [23] or flow based modulation [24] , [25] . Challenges in the analysis of complex GCxGC-MS data has so far precluded the use of this approach for the analysis of complex biological samples, but more recent developments in GCxGC-MS workflows and software tools have now made this approach more feasible for metabolomics experiments including biomarker discovery [19] , [22] , [26] , [27] . In this study, we describe a robust GCxGC-qMS platform applied to the profiling of a panel of metabolites covering different chemical classes present in clinically relevant samples such as mammalian cells, tissue, serum and urine.…”