Metabolite Identification Using Automated Comparison of High-Resolution Multistage Mass Spectral Trees

Abstract

Multistage mass spectrometry (MS(n)) generating so-called spectral trees is a powerful tool in the annotation and structural elucidation of metabolites and is increasingly used in the area of accurate mass LC/MS-based metabolomics to identify unknown, but biologically relevant, compounds. As a consequence, there is a growing need for computational tools specifically designed for the processing and interpretation of MS(n) data. Here, we present a novel approach to represent and calculate the similarity between high-resolution mass spectral fragmentation trees. This approach can be used to query multiple-stage mass spectra in MS spectral libraries. Additionally the method can be used to calculate structure-spectrum correlations and potentially deduce substructures from spectra of unknown compounds. The approach was tested using two different spectral libraries composed of either human or plant metabolites which currently contain 872 MS(n) spectra acquired from 549 metabolites using Orbitrap FTMS(n). For validation purposes, for 282 of these 549 metabolites, 765 additional replicate MS(n) spectra acquired with the same instrument were used. Both the dereplication and de novo identification functionalities of the comparison approach are discussed. This novel MS(n) spectral processing and comparison approach increases the probability to assign the correct identity to an experimentally obtained fragmentation tree. Ultimately, this tool may pave the way for constructing and populating large MS(n) spectral libraries that can be used for searching and matching experimental MS(n) spectra for annotation and structural elucidation of unknown metabolites detected in untargeted metabolomics studies.

Systematic Structural Characterization of Metabolites in Arabidopsis via Candidate Substrate-Product Pair Networks

“…The most appropriate correlation threshold can be estimated based on the data obtained from the MS 2 spectral similarity matching (see Methods). Whenever the "substrate" and "product" in a CSPP have similar MS 2 spectra, they usually have similar molecular structures (Rasche et al, 2012;Rojas-Cherto et al, 2012), conferring a high probability that the CSPP represents a "true" metabolic reaction. Therefore, the MS 2 spectral similarity is a measure for judging the likelihood that a CSPP for which a moderate to high correlation coefficient was obtained represents a "true" metabolic reaction.…”

“…It should be stressed that, in the discussion above, MS 2 spectral similarities are used to assess the validity of including correlation coefficients as a filter to select CSPPs that are more likely associated with "true" biochemical conversions. Logically, adding the MS 2 spectral similarity itself as a second filter will, in combination with the correlation coefficient, diminishes the chance of a false positive even more (Figure 1) (Rasche et al, 2012;Rojas-Cherto et al, 2012). However, calculating the chance of a false positive using the CSPP algorithm with the inclusion of both filters is impossible as it would need the unambiguous structural identification of all characterized molecules.…”

Primer on Agar-Based Microbial Imaging Mass Spectrometry

“…The challenge of annotating mass spectrometry data also applies to proteomics, metabolomics, and lipidomics. These advanced "omics" tools, even when combined with the power of genome mining, peptidogenomics, and search algorithms, annotate only a small percentage of what are presumably some of the most abundant ions, as observed in an imaged sample (32), indicating that there are still many opportunities in mass spectrometry to develop novel approaches to identify (ID) molecules (8,24,42,46). We encourage the scientific community to develop systematic, integrated workflows for handling unknowns in IMS data that may capture more than 50% of the IMS signals.…”

An Integrated Approach for Studying Exposure, Metabolism, and Disposition of Multiple Component Herbal Medicines Using High-Resolution Mass Spectrometry and Multiple Data Processing Tools

“…It was demonstrated previously that MTSF can quantitatively evaluate structural similarity among multiple unknown components by comparing their fragmentation patterns and product ions (Sheldon et al, 2009;van der Hooft et al, 2011;Ridder et al, 2012;Rojas-Cherto et al, 2012). Furthermore, a biotransformation-matching method was employed to define the relationship between two TCM components after high structural similarity was determined by MTSF.…”