Increasing the Coverage, Sensitivity and Specificity of Rapid Lipidomic Measurements

PROJECT SUMMARY/ABSTRACT
Lipids are a vital class of molecules that play countless important and varied roles in biological processes. Fully
understanding lipid roles, however, is difficult since the number and diversity of lipid species is immense with
cells expressing tens of thousands of diverse lipid species. While recent advances in chromatography and high-
resolution mass spectrometry have greatly improved our understanding of the potential lipid species present in
many different sample types, effectively separating the numerous lipids still remains problematic due to the many
isomeric lipids. Isomeric lipid species such as those resulting from subclass isomers, distinct acyl chains
connectivity (sn-1, sn-2, or sn-3), different double bond positions and orientations (cis or trans), and unique
functional group stereochemistry (R versus S) have made lipid characterization especially difficult due to many
having the exact same mass. To address this challenge, ion mobility spectrometry separations, ion-molecule
reactions and fragmentation techniques have increasingly been added to lipid analysis workflows to allow both
species separation and improved characterization. However, currently these analyses are still not able to fully
assess the number of lipid species present in complex lipid mixtures or provide an in-depth analysis of molecular
differences based on their spatial position in tissues and organs. Furthermore, when several analytical
techniques are utilized separately, experimental and data analysis times are greatly extended, making largescale
evaluations difficult or impossible. The overall objective of this research is to develop a new analytical platform
and corresponding data analysis and visualization methods to increase the coverage, throughput and spatial
assessment of lipidomic analyses. The use of a combinatorial approach of analytical methods including
traditional chromatographic methods, chiral separations, automated solid phase extractions (SPE), gas phase
chemical derivatizations, multiplexed ion mobility spectrometry-mass spectrometry (IMS-MS) separations and
automated data analysis will provide unprecedented coverage for the numerous lipid isomers and species
present in complex samples. This highly specific and sensitive, automated platform will then be applied to the
targeted quantification of various lipid species in largescale tissue screening analyses to assess over a 1000
lipidomic samples per day.

Grant Number: 5R01GM141277-04
NIH Institute/Center: NIH
Principal Investigator: Erin Baker