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Collision cross section compendium to annotate and predict multi-omic compound identities

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Abstract

Ion mobility mass spectrometry (IM-MS) expands the analyte coverage of existing multi-omic workflows by providing an additional separation dimension as well as a parameter for characterization and identification of molecules – the collision cross section (CCS). This work presents a large, Unified CCS compendium of >3800 experimentally acquired CCS values obtained from traceable molecular standards and measured with drift tube ion mobility-mass spectrometers. An interactive visualization of this compendium along with data analytic tools have been made openly accessible. Represented in the compendium are 14 structurally-based chemical super classes, consisting of a total of 80 classes and 157 subclasses. Using this large data set, regression fitting and predictive statistics have been performed to describe mass-CCS correlations specific to each chemical ontology. These structural trends provide a rapid and effective filtering method in the traditional untargeted workflow for identification of unknown biochemical species. The utility of the approach is illustrated by an application to metabolites in human serum, quantified trends of which were used to assess the probability of an unknown compound belonging to a given class. CCS-based filtering narrowed the chemical search space by 60% while increasing the confidence in the remaining isomeric identifications from a single class, thus demonstrating the value of integrating predictive analyses into untargeted experiments to assist in identification workflows. The predictive abilities of this compendium will improve in specificity and expand to more chemical classes as additional data from the IM-MS community is contributed. Instructions for data submission to the compendium and criteria for inclusion are provided.

Graphical abstract: Collision cross section compendium to annotate and predict multi-omic compound identities

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Publication details

The article was received on 03 Oct 2018, accepted on 21 Nov 2018 and first published on 27 Nov 2018


Article type: Edge Article
DOI: 10.1039/C8SC04396E
Citation: Chem. Sci., 2019, Advance Article
  • Open access: Creative Commons BY-NC license
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    Collision cross section compendium to annotate and predict multi-omic compound identities

    J. A. Picache, B. S. Rose, A. Balinski, Katrina L. Leaptrot, S. D. Sherrod, J. C. May and J. A. McLean, Chem. Sci., 2019, Advance Article , DOI: 10.1039/C8SC04396E

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