Metabolite discovery through global annotation of untargeted metabolomics data

Abstract

A primary goal of metabolomics is to identify all biologically important metabolites. One powerful approach is liquid chromatography-high resolution mass spectrometry (LC-MS), yet most LC-MS peaks remain unidentified. Here, we present a global network optimization approach, NetID, to annotate untargeted LC-MS metabolomics data. We consider all experimentally observed ion peaks together, and assign annotations to all of them simultaneously so as to maximize a score that considers properties of peaks (known masses, retention times, MS/MS fragmentation patterns) as well network constraints that arise based on mass difference between peaks. Global optimization results in accurate peak assignment and trackable peak-peak relationships. Applying this approach to yeast and mouse data, we identify a half-dozen novel metabolites, including thiamine and taurine derivatives. Isotope tracer studies indicate active flux through these metabolites. Thus, NetID applies existing metabolomic knowledge and global optimization to annotate untargeted metabolomics data, revealing novel metabolites.

Metabolomics analysis reveals changes related to pseudocyst formation induced by iron depletion in Trichomonas vaginalis

“…Among them, 824 metabolites were annotated after MS/MS analysis (~ 25% of total compounds). The annotation rate of human metabolomics analysis is approximately 10% [21,22], suggesting that our annotation rate was reasonable and suitable for further investigations. The annotated compounds are listed in Supplementary le 1.…”

Selected ion monitoring for orbitrap-based metabolomics

“…LC-MS analysis was performed on a Vanquish UHPLC system coupled with an Exploris 480 orbitrap mass spectrometer. LC separation was achieved using a Waters XBridge BEH Amide column (2.1x150mm, 2.5 µm particle size) with a 25-min gradient (Figure S1) [4,22,27]. The retention times of ~600 metabolites using authentic standards are provided in Table S1.…”

Reference data-set driven metabolomics

“…Source data includes MS/MS spectra of multiple ion forms of both known and unknown molecules, isotopes, adducts, in-source fragments, and multimers. 7,8 The curated reference dataset can be matched in human biospecimens via direct matching of the MS/MS spectra or by sophisticated approaches such as molecular networking. We have created a step-by-step tutorial on how to perform an RDD analysis using the GNPS ecosystem (https://ccms-ucsd.github.io/GNPSDocumentation/tutorials/rdd/).…”