Abstract
IntroductionDirect injection Fourier-transform mass spectrometry (FT-MS) allows for the high-throughput and high-resolution detection of thousands of metabolite-associated isotopologues. However, spectral artifacts can generate large numbers of spectral features (peaks) that do not correspond to known compounds. Misassignment of these artifactual features creates interpretive errors and limits our ability to discern the role of representative features within living systems.ObjectivesOur goal is to develop rigorous methods that identify and handle spectral artifacts within the context of high-throughput FT-MS-based metabolomics studies.ResultsWe observed three types of artifacts unique to FT-MS that we named high peak density (HPD) sites: fuzzy sites, ringing and partial ringing. While ringing artifacts are well-known, fuzzy sites and partial ringing have not been previously well-characterized in the literature. We developed new computational methods based on comparisons of peak density within a spectrum to identify regions of spectra with fuzzy sites. We used these methods to identify and eliminate fuzzy site artifacts in an example dataset of paired cancer and non-cancer lung tissue samples and evaluated the impact of these artifacts on classification accuracy and robustness.ConclusionOur methods robustly identified consistent fuzzy site artifacts in our FT-MS metabolomics spectral data. Without artifact identification and removal, 91.4% classification accuracy was achieved on an example lung cancer dataset; however, these classifiers rely heavily on artifactual features present in fuzzy sites. Proper removal of fuzzy site artifacts produces a more robust classifier based on non-artifactual features, with slightly improved accuracy of 92.4% in our example analysis.
Highlights
Direct injection Fourier-transform mass spectrometry (FT-MS) allows for the high-throughput and high-resolution detection of thousands of metabolite-associated isotopologues
High peak density artifacts can be found through the comparison of the actual peak density at a given m/z to the expected peak density derived from surrounding regions (Step 2)
All three artifacts are obvious upon manual inspection with fuzzy sites being far more common than ringing and partial ringing, the high-throughput use of FT-MS-based experiments necessitates automated methods for their efficient identification
Summary
Direct injection Fourier-transform mass spectrometry (FT-MS) allows for the high-throughput and high-resolution detection of thousands of metabolite-associated isotopologues. Multiple isotope-labeling experiments provide necessary information to elucidate unknown metabolic pathways (Creek et al 2012; Higashi et al 2014), quantify relative fluxes through connected metabolic pathways (Hiller et al 2010), identify multiple pools of a given metabolite in different compartments (Fan et al 2012), and identify active metabolic pathways under various cellular conditions (Moseley et al 2011; Sellers et al 2015; Verdegem et al 2017) These informational gains enable more complete modeling of cellular metabolism and better understanding of physiological and pathological processes at a mechanistic level, facilitating the identification of potential therapeutic targets (Fan et al 2009) and the quantification of differential drug response (Harris et al 2012). Without automation, rigorous assignment of nonpolymeric biomolecules in untargeted MS analyses remains difficult even with FT-MS’s high resolution
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