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

Jaqueline A Picache1, Bailey S Rose1, Andrzej Balinski1

  • 1Department of Chemistry , Center for Innovative Technology , Vanderbilt Institute of Chemical Biology , Vanderbilt Institute for Integrative Biosystems Research and Education , Vanderbilt-Ingram Cancer Center , Vanderbilt University , Nashville , Tennessee 37235 , USA .

Chemical Science
|February 19, 2019
PubMed
Summary
This summary is machine-generated.

A new unified compendium of over 3800 collision cross section (CCS) values from ion mobility mass spectrometry (IM-MS) aids in identifying unknown biochemicals. This resource enhances multi-omic workflows by improving molecule characterization and identification.

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Area of Science:

  • Analytical Chemistry
  • Biochemistry
  • Computational Biology

Background:

  • Multi-omic workflows benefit from expanded analyte coverage and molecular characterization.
  • Ion mobility mass spectrometry (IM-MS) offers an additional separation dimension and collision cross section (CCS) for molecule identification.

Purpose of the Study:

  • To present a large, unified compendium of experimentally acquired CCS values.
  • To provide interactive visualization and data analytic tools for accessible use.
  • To enable improved identification of unknown biochemical species in untargeted workflows.

Main Methods:

  • Acquisition of >3800 CCS values from traceable molecular standards using drift tube IM-MS.
  • Development of an interactive visualization and data analytic tools for the compendium.
  • Regression fitting and predictive statistics to establish mass-CCS correlations across chemical ontologies.

Main Results:

  • The compendium includes 14 super classes, 80 classes, and 157 subclasses of molecules.
  • Mass-CCS correlations were identified specific to chemical ontologies.
  • CCS-based filtering reduced the chemical search space by 60% in human serum metabolite analysis, increasing identification confidence.

Conclusions:

  • Integrating predictive CCS analyses into untargeted experiments significantly aids in identifying unknown biochemical species.
  • The compendium and associated tools enhance multi-omic workflows by improving molecule characterization and identification.
  • Future contributions from the IM-MS community will further enhance the compendium's predictive capabilities.