Skip to main content
U.S. flag

An official website of the United States government

Official websites use .gov
A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS
A lock ( ) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.

Expanding lipidome coverage using LC-MS/MS data-dependent acquisition with automated exclusion list generation



John Bowden, Jeremy P. Koelmel, Nicholas M. Kroeger, Emily L. Gill, Candice Z. Ulmer, Rainey E. Patterson, Richard A. Yost, Timothy J. Garrett


Untargeted omics analyses aim to comprehensively characterize biomolecules within a biological system. Changes in the presence or quantity of these biomolecules can indicate important biological perturbations, such as those caused by disease. With current technological advancements, the entire genome can now be sequenced, however, in the burgeoning fields of metabolomics and lipidomics, only a subset of metabolites and lipids can be identified. The recent emergence of high resolution tandem mass spectrometry (HR-MS/MS), in combination with ultra-high performance liquid chromatography, has resulted in an increased coverage of the metabolome and lipidome. Yet, identifications from MS/MS are generally limited by the number of precursors which can be selected for fragmentation during chromatographic elution. Therefore, we developed the software IE-Omics to automate iterative exclusion (IE), where selected precursors using data-dependent topN analyses are excluded in sequential injections. In each sequential injection, unique precursors are fragmented until HR-MS/MS spectra of all ions above a user-defined intensity threshold are acquired. IE-Omics was applied to metabolomic and lipidomic analyses in Red Cross plasma and substantia nigra tissue. The methodology provided minimal increases in identification for metabolomics; however, improved identification was observed in lipidomic analyses. When applying IE-Omics to Red Cross plasma and substantia nigra extracts in positive ion mode, 104 % and 52 % more lipid identifications were obtained, respectively. In addition, applying IE-Omics increased the coverage of trace species, including odd-chained and short-chained DAGs, which were not observed without using IE, and oxidized lipid species.
Journal of the American Society for Mass Spectrometry


Bowden, J. , Koelmel, J. , Kroeger, N. , Gill, E. , Ulmer, C. , Patterson, R. , Yost, R. and Garrett, T. (2017), Expanding lipidome coverage using LC-MS/MS data-dependent acquisition with automated exclusion list generation, Journal of the American Society for Mass Spectrometry, [online], (Accessed July 25, 2024)


If you have any questions about this publication or are having problems accessing it, please contact

Created March 6, 2017, Updated November 10, 2018