Amie M. Solosky   Amie M. Solosky completed her B.S. in Chemistry in 2019 at The College of Saint Rose in Albany, NY. In 2020, she began her work in analytical chemistry at the New York State Department of Health. Here, she routinely ran GC-MS methods and developed LC-MS biomonitoring methods. Currently, she is an analytical chemistry Ph.D. candidate at the University of North Carolina at Chapel Hill in Prof. Erin Baker’s laboratory. Her research focuses on optimizing lipid analyses for more sensitive studies and applying the measurements to answer research questions involving marine animals including California sea lions and various species of sharks. Amie also utilizes multiple separation types (e.g., GC, LC, IMS, etc) and various mass spectrometry platforms to dive into her research questions. Furthermore, she can code and apply Skyline software to expedite her studies.

Developing and Applying a Multidimensional Lipid Database Containing Liquid Chromatography, Ion Mobility Spectrometry, and Mass Spectrometry Separation Characteristics in Skyline

Lipids are critical for biological function in the body as they are responsible for cell membrane structure, cell signaling, lipid biosynthesis, and energy storage. Additionally, lipid dysregulation has been observed in disorders such as cancer, Alzheimer’s disease, and diabetes and has been closely linked to different environmental exposures. These functional and disease associations have motivated researchers to evaluate lipidomic changes in numerous matrices. Read More
To date, lipidomic studies have several limitations due to differing instrumentation capabilities, extraction methods and the many isomeric and isobaric lipids that complicate annotation efforts. Accurate lipid annotation is therefore crucial for biological interpretation, however, lipids can only be annotated as far as the capabilities of each analysis platform. For example, the minimum annotations are class level (headgroup) or species level (class and fatty acyl sum composition, e.g., PC(36:4)), but analyses such as chromatography and ion mobility spectrometry coupled to mass spectrometry precursor and fragment measurements allow for fatty acyl chain compositions (e.g., PC(16:0_20:4)) and positions (sn-1 or sn-2, e,g., PC(16:0/20:4)) to be reported as well. Here, we utilize a platform combining liquid chromatography, ion mobility spectrometry, collision-induced dissociation, and mass spectrometry (LC-IMS-CID-MS) to collect untargeted lipidomic data from various sample types and populate a lipid database for use in Skyline. This database contains LC retention times, IMS collision cross section (CCS) values, and m/z precursor and fragment information for 877 lipids resulting in over 1500 features, and even contains spectral data for ~500 lipids. Skyline was used to manually process this data, calculate the CCS values, and store spectra for future library matching. A plasma subsection of the library created from Avanti standards, collected human plasma, and sea lion plasma was then utilized to evaluate potential lipid biomarkers in California sea lion suffering from domoic acid toxicosis (DAT). Fifteen triglycerides were found to distinguish the DAT sea lions from other conditions and healthy animals. A rapid triglyceride diagnostic test is now under evaluation by veterinarians.


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