Targeted and untargeted lipidomic and metabolomic analyses are conducted on GC/MS, LC/MS, and LC/hybrid MS platforms that are integrated among several laboratories in the MS Research Resource. Drs. John Turk, Fong-Fu Hsu, Haowei Song and Mr. Alan Bohrer have pioneered the use of contemporary chromatography systems and high resolution mass spectrometers to identify and characterize several lipid classes. These resources and expertise have advanced our understanding of at least 2 major complex lipid classes that exist in biological organisms; glycerolipids and sphingolipids. By developing innovative approaches that exploit electrospray ionization (ESI) coupled to tandem quadrupole and quadrupole linear ion trap mass spectrometers, and more recently ESI-tandem mass spectrometers with electron transfer dissociation capabilities, Resource personnel have expanded our understanding of complex lipids that act as signaling molecules and transcriptional regulators in mammalian tissues, complex lipids in pathogenic microorganisms that have the potential to become therapeutic targets for new antimicrobial drugs, and complex lipids involved in insulin secretory pathways in pancreatic islets that are important for understanding the pathogenesis of diabetes. Recently, Drs. Fong-Fu Hsu and Weidong Cui successfully applied novel top-down MS approaches and tested the potential for using charge remote fragmentation strategies to advance sphingolipid characterization capabilities.
Staff personnel have developed a software platform (LipidQA Analysis Website) that uses ESI-MS data and provides qualitative and quantitative analysis of complex lipids. LipidQA utilizes a custom-designed de-isotoping algorithm, internal standard normalization, linear regression analysis of standard curves, and phosphate content calibration to deliver fast, accurate, simultaneous identification and quantitation of complex lipid classes and species present in lipid mixtures extracted from biological organisms. A Web-based version is being developed and tested locally and will be made available to all lipid researchers for MS data uploading once its robustness is demonstrated.
Smaller polar metabolites can be identified, characterized, and quantified using targeted (GC/MS, LC/MS/MS) and untargeted profiling MS-based systems (GC-QTOF-MS, UPLC-QTOF-MS). Jan Crowley, Robert Chott, and Dr. Kevin Yarasheski have a long history of developing and applying traditional GC/MS and LC/MS/MS systems for quantifying amino acids, fatty acids, carbohydrates, sterol lipids, and prostanoids in complex biological matrices (e.g., serum, plasma, urine, tissues, breath, cell extracts). Resource personnel utilize these instruments to quantify stable isotope abundance in biomolecules (protein, peptides, lipids, glucose) isolated during in vivo and in vitro metabolic labeling studies. In 2012, GC- and UPLC-QTOF-MS were added to the Resource, along with a suite of bioinformatics packages, databases, and libraries that are used for comparative metabolomics profiling experiments. These screening platforms are capable of fast scanning speeds and accurate mass measurements (2-5ppm); enough information to assign putative identifications to unknown metabolites and compounds that are differentially expressed between two experimental conditions (e.g., healthy vs diseased, wild-type vs genetically modified mice, before vs after intervention). The putative identifications made using screening/profiling/discovery MS-based analyses are followed up with targeted tandem MS analyses to further characterize, confirm the chemical identity, and quantify the differentially expressed metabolites. Staff scientist Dr. Daryl Giblin employs density functional theory to help understand the complex fragmentation of metabolites and lipids.