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Title: Mapping Lipid Fragmentation for Tailored Mass Spectral Libraries

Abstract

Libraries of simulated lipid fragmentation spectra enable the identification of hundreds of unique lipids from complex lipid extracts, even when the corresponding lipid reference standards do not exist. Often, these in silico libraries are generated through expert annotation of spectra to extract and model fragmentation rules common to a given lipid class. Although useful for a given sample source or instrumental platform, the time-consuming nature of this approach renders it impractical for the growing array of dissociation techniques and instrument platforms. Here, we introduce Library Forge, a unique algorithm capable of deriving lipid fragment mass-to-charge (m/z) and intensity patterns directly from high-resolution experimental spectra with minimal user input. Library Forge exploits the modular construction of lipids to generate m/z transformed spectra in silico which reveal the underlying fragmentation pathways common to a given lipid class. By learning these fragmentation patterns directly from observed spectra, the algorithm increases lipid spectral matching confidence while reducing spectral library development time from days to minutes. We embed the algorithm within the preexisting lipid analysis architecture of LipiDex to integrate automated and robust library generation within a comprehensive LC-MS/MS lipidomics workflow.

Authors:
 [1];  [2]; ORCiD logo [3]
  1. Univ. of Wisconsin, Madison, WI (United States). Dept. of Chemistry; Genome Center of Wisconsin, Madison WI (United States)
  2. Genome Center of Wisconsin, Madison WI (United States); Morgridge Inst. for Research, Madison, WI (United States)
  3. Univ. of Wisconsin, Madison, WI (United States). Dept. of Chemistry; Genome Center of Wisconsin, Madison WI (United States); Morgridge Inst. for Research, Madison, WI (United States; Univ. of Wisconsin, Madison, WI (United States). Dept. of Biomolecular Chemistry
Publication Date:
Research Org.:
Univ. of Wisconsin, Madison, WI (United States). Great Lakes Bioenergy Research Center
Sponsoring Org.:
USDOE
Contributing Org.:
Pagliarini Lab at the Morgridge Institute for Research
OSTI Identifier:
1494816
Grant/Contract Number:  
SC0018409
Resource Type:
Accepted Manuscript
Journal Name:
Journal of the American Society for Mass Spectrometry
Additional Journal Information:
Journal Volume: 30; Journal Issue: 4; Journal ID: ISSN 1044-0305
Publisher:
American Society for Mass Spectrometry
Country of Publication:
United States
Language:
English
Subject:
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY; Lipidomics; Mass spectrometry; Spectral libraries; In silico fragmentation modeling; Lipid identifications

Citation Formats

Hutchins, Paul D., Russell, Jason D., and Coon, Joshua J. Mapping Lipid Fragmentation for Tailored Mass Spectral Libraries. United States: N. p., 2019. Web. doi:10.1007/s13361-018-02125-y.
Hutchins, Paul D., Russell, Jason D., & Coon, Joshua J. Mapping Lipid Fragmentation for Tailored Mass Spectral Libraries. United States. doi:10.1007/s13361-018-02125-y.
Hutchins, Paul D., Russell, Jason D., and Coon, Joshua J. Tue . "Mapping Lipid Fragmentation for Tailored Mass Spectral Libraries". United States. doi:10.1007/s13361-018-02125-y.
@article{osti_1494816,
title = {Mapping Lipid Fragmentation for Tailored Mass Spectral Libraries},
author = {Hutchins, Paul D. and Russell, Jason D. and Coon, Joshua J.},
abstractNote = {Libraries of simulated lipid fragmentation spectra enable the identification of hundreds of unique lipids from complex lipid extracts, even when the corresponding lipid reference standards do not exist. Often, these in silico libraries are generated through expert annotation of spectra to extract and model fragmentation rules common to a given lipid class. Although useful for a given sample source or instrumental platform, the time-consuming nature of this approach renders it impractical for the growing array of dissociation techniques and instrument platforms. Here, we introduce Library Forge, a unique algorithm capable of deriving lipid fragment mass-to-charge (m/z) and intensity patterns directly from high-resolution experimental spectra with minimal user input. Library Forge exploits the modular construction of lipids to generate m/z transformed spectra in silico which reveal the underlying fragmentation pathways common to a given lipid class. By learning these fragmentation patterns directly from observed spectra, the algorithm increases lipid spectral matching confidence while reducing spectral library development time from days to minutes. We embed the algorithm within the preexisting lipid analysis architecture of LipiDex to integrate automated and robust library generation within a comprehensive LC-MS/MS lipidomics workflow.},
doi = {10.1007/s13361-018-02125-y},
journal = {Journal of the American Society for Mass Spectrometry},
number = 4,
volume = 30,
place = {United States},
year = {2019},
month = {2}
}

Journal Article:
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This content will become publicly available on February 12, 2020
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