Retrieving and Utilizing Hypothetical Neutral Losses from Tandem Mass Spectra for Spectral Similarity Analysis and Unknown Metabolite Annotation

Abstract

Spectral similarity comparison through tandem mass spectrometry (MS2) is a powerful approach to annotate known and unknown metabolic features in mass spectrometry (MS)-based untargeted metabolomics. In this work, we proposed the concept of hypothetical neutral loss (HNL), which is the mass difference between a pair of fragment ions in a MS2 spectrum. We demonstrated that HNL values contain core structural information that can be used to accurately assess the structural similarity between two MS2 spectra. We then developed the Core Structure-based Search (CSS) algorithm based on HNL values. CSS was validated with sets of hundreds of randomly selected metabolites and their reference MS2 spectra, showing significantly improved correlation between spectral and structural similarities. Compared to state-of-the-art spectral similarity algorithms, CSS generates better ranking of structurally relevant chemicals among false positives. Combining CSS, HNL library, and biotransformation database, we further developed Metabolite core structure-based Search (McSearch), a novel computational solution to facilitate the annotation of unknown metabolites using the reference MS2 spectra of their structural analogs. McSearch generates better results in the Critical Assessment of Small Molecule Identification (CASMI) 2017 data set than conventional unknown feature annotation programs. McSearch was also tested in experimental MS2 data of xenobiotic metabolite derivatives belonging to three different metabolic pathways. Our results confirmed that McSearch can better capture the underlying structural similarity between MS2 spectra. Overall, this work provides a novel direction for metabolite annotation via HNL values, paving the way for annotating metabolites using their structurally similar compounds.

Global-Scale Metabolomic Profiling of Human Hair for Simultaneous Monitoring of Endogenous Metabolome, Short- and Long-Term Exposome

“…Following metabolite extraction, we applied the LC-MS platform to profile metabolites in both ESI(+) and ESI(–) modes. The collected metabolomic data were further processed using recently developed feature extraction program targeting low-abundant metabolic features (Hu et al, 2019 ) and metabolite annotation software (Xing et al, 2020 ) aiming to annotate “unknown unknown” metabolites that are not archived in a spectral database. The annotated metabolic feature table can then be used for downstream data interpretation to understand how short- and long-term exposures as well as endogenous metabolites are changed over different exposure situations.…”

“…Detailed statistical analysis was performed using MetaboAnalyst (Xia et al, 2015 ) following the in-depth spectral annotation. Metabolite annotation was performed using dot-product based exact match against MoNA MS 2 spectra library and McSearch (Xing et al, 2020 ) with one biotransformation reaction (level 2 annotation) (Schymanski et al, 2014 ). In the cases where multiple hits are returned in the search, we manually checked the results to see which one is more reasonable.…”

“…Orthogonal LC-MS analyses were performed to detect and quantify a wide range of metabolites. To effectively process LC-MS data, we applied a recently developed integrated feature extraction algorithm to capture both high and low abundant metabolic features (Hu et al, 2019 ), followed by metabolite annotation using McSearch to capture novel metabolites that are not archived in a spectral library (Xing et al, 2020 ). For data interpretation, we manually sourced and resolved the chemicals on hair surfaces and in hair strands by searching against the latest version of Human Metabolome Database (HMDB; Wishart et al, 2018 ).…”

Spec2Vec: Improved mass spectral similarity scoring through learning of structural relationships

“…In addition, one could think of adding relevant mass differences as input to train the model, for example following the approach of Kreitzberg et al [29]. In future work we are also keen to explore how Spec2Vec can be combined with the concept of hypothetical neutral losses as proposed by [30].…”

METLIN Neutral Loss Database Enhances Similarity Analysis

“…Neutral losses 1,2 constitute a rich resource, and have already been widely used in proteomics, pharmacology, and metabolomics for over three decades. 1,2,[8][9][10][11][12] Yet, even though mass spectrometry-based neutral loss (NL) analysis has been extensively applied, with hundreds to thousands of papers on the topic, no comprehensive small molecule library of neutral loss data exists.…”