A structural examination and collision cross section database of over 500 metabolities and xenobiotics using drift tube ion mobility
Ontology highlight
ABSTRACT:
ORGANISM(S): None
SUBMITTER: 
Erin Baker
PROVIDER: ST000619 | MetabolomicsWorkbench | Sat May 27 00:00:00 BST 2017
REPOSITORIES: MetabolomicsWorkbench
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| mwtab | Other |
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Project description:Standards of xenobiotics were analyzed with Agilent 6560 Ion mobility QTOF MS platform to find Collision Cross Section values. All measurements were performed in triplicate in both positive and negative polarities with nitrogen gas and at seven different electric fields, so that values could be directly measured and random standard deviations (RSD) assessed for each molecule.
2018-03-01 | ST000938 | MetabolomicsWorkbench
Project description:In this work we have established a collision cross section (CCS) library of primary metabolites based on analytical standards in the Mass Spectrometry Metabolite Library of Standards (MSMLS). Using a commercially available ion mobility-mass spectrometer (IM-MS, Agilent 6560), from the 554 unique compounds in the MSMLS plate library we obtained a total of 1246 CCS measurements over a wide range of biochemical classes and adduct types. Resulting data analysis showed that the curated CCS library provides broad molecular coverage of metabolic pathways and highlights intrinsic mass/mobility relationships for specific metabolite superclasses. The separation and characterization of isomeric metabolites were assessed as well as an investigation to determine the analytical separation efficiency in both the mass (m/z) and mobility (CCS/ΔCCS) dimension required for untargeted metabolomic analyses. To further demonstrate the analytical utility of CCS as an additional molecular descriptor, a well characterized biological sample of human plasma serum (NIST 1950) was examined by LC-IM-MS and a detailed analysis of carbohydrate isomers by ion mobility is presented.
2018-07-27 | ST001030 | MetabolomicsWorkbench
Project 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.
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Project description:Ion mobility can add a dimension to LC-MS based shotgun proteomics which has the potential to boost proteome coverage, quantification accuracy and dynamic range. Required for this is suitable software that extracts the information contained in the four-dimensional (4D) data space spanned by m/z, retention time, ion mobility and signal intensity. Here we describe the ion mobility enhanced MaxQuant software, which utilizes the added data dimension. It offers an end to end computational workflow for the identification and quantification of peptides, proteins and posttranslational modification sites in LC-IMS-MS/MS shotgun proteomics data. We apply it to trapped ion mobility spectrometry (TIMS) coupled to a quadrupole time-of-flight (QTOF) analyzer. A highly parallelizable 4D feature detection algorithm extracts peaks which are assembled to isotope patterns. Masses are recalibrated with a non-linear m/z, retention time, ion mobility and signal intensity dependent model, based on peptides from the sample. A new matching between runs (MBR) algorithm that utilizes collisional cross section (CCS) values of MS1 features in the matching process significantly gains specificity from the extra dimension. Prerequisite for using CCS values in MBR is a relative alignment of the ion mobility values between the runs. The missing value problem in protein quantification over many samples is greatly reduced by CCS aware MBR.MS1 level label-free quantification is also implemented which proves to be highly precise and accurate on a benchmark dataset with known ground truth. MaxQuant for LC-IMS-MS/MS is part of the basic MaxQuant release and can be downloaded from http://maxquant.org.
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Project 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.
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