MassIVE MSV000088215

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GNPS Uncovering Xenobiotics in the Dark Metabolome using Ion Mobility Spectrometry, Mass Defect Analysis and Machine Learning

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Description

The identification of xenobiotics through nontargeted analysis is a vital step in understanding human exposure, however metabolism, excretion, and co-existence with other endogenous molecules in the metabolome greatly complicate their measurements. Xenobiotics are therefore commonly undetected and exist as part of the dark metabolome. While mass spectrometry (MS)-based platforms are commonly used in metabolomic measurements, deconvoluting endogenous metabolites and xenobiotics is often limited by a lack of xenobiotic parent and metabolite standards as well as the numerous isomers possible for each m/z feature. Here we evaluated the ability of ion mobility spectrometry (IMS) and mass defect filtering techniques to narrow large metabolomic feature lists to xenobiotics of interest. Due to the lack of IMS collision cross section (CCS) values for per- and polyfluoroalkyl substances (PFAS), we initially evaluated 87 PFAS standards with IMS-MS to develop a PFAS CCS library. The detected PFAS CCS and m/z values were then compared to other biomolecule and xenobiotic classes, illustrating clear differentiation between the biomolecules and the halogenated xenobiotics. To address the lack of xenobiotic standards, machine learning was then utilized to predict CCS values. Ultimately, a xenobiotic selection workflow combining experimental and theoretical CCS values and mass defect filtering was employed to evaluate PFAS features in NIST human serum. This workflow reduced the 2,423 LC-IMS-MS features to 98 possible PFAS, and 23 were identified using homologous series information. [doi:10.25345/C5FV9K] [dataset license: CC0 1.0 Universal (CC0 1.0)]

Keywords: Perfluoroalkyl Substances ; Polyfluoroalkyl Substances ; PFAS

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Principal Investigators:
(in alphabetical order) 		Erin Baker, North Carolina State University, United States
Submitting User: mrfoster
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GNPS content goes here (MSV000088215 [task=2e56d6e15e9b4446a648aa0ee9f5c2b2])
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Distinct condition labels are counted across all files submitted in the "Metadata" category having a "Condition" column in this dataset.

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Number of distinct biological replicates across all analyses (original submission and reanalyses) associated with this dataset.

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Number of distinct technical replicates across all analyses (original submission and reanalyses) associated with this dataset.

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Originally identified proteins that were automatically remapped by MassIVE to proteins in the SwissProt human reference database.

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Number of distinct protein accessions reported across all analyses (original submission and reanalyses) associated with this dataset.

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Number of distinct unmodified peptide sequences reported across all analyses (original submission and reanalyses) associated with this dataset.

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Number of distinct peptide sequences (including modified variants or peptidoforms) reported across all analyses (original submission and reanalyses) associated with this dataset.

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Total number of peptide-spectrum matches (i.e. spectrum identifications) reported across all analyses (original submission and reanalyses) associated with this dataset.

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Number of distinct proteins quantified across all analyses (original submission and reanalyses) associated with this dataset.

Distinct protein accessions are counted across all files submitted in the "Statistical Analysis of Quantified Analytes" category having a "Protein" column in this dataset.

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Number of distinct proteins found to be differentially abundant in at least one comparison across all analyses (original submission and reanalyses) associated with this dataset.

A protein is differentially abundant if its change in abundance across conditions is found to be statistically significant with an adjusted p-value <= 0.05 and lists no issues associated with statistical tests for differential abundance.

Distinct protein accessions are counted across all files submitted in the "Statistical Analysis of Quantified Analytes" category having a "Protein" column in this dataset.

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