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.