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OBJECTIVE: The aim of this study was to identify a signature lipid profile from cerebral thrombi in acute ischemic stroke (AIS) patients at the time of ictus. METHODS: We performed untargeted lipidomics analysis using liquid chromatography-mass spectrometry on cerebral thrombi taken from a nonprobability, convenience sampling of adult subjects (≥18 years old, n = 5) who underwent thrombectomy for acute cerebrovascular occlusion. The data were classified using random forest, a machine learning algorithm. RESULTS: The top 10 metabolites identified from the random forest analysis were of the glycerophospholipid species and fatty acids. CONCLUSION: Preliminary analysis demonstrates feasibility of identification of lipid metabolomic profiling in cerebral thrombi retrieved from AIS patients. Recent advances in omic methodologies enable lipidomic profiling, which may provide insight into the cellular metabolic pathophysiology caused by AIS. Understanding of lipidomic changes in AIS may illuminate specific metabolite and lipid pathways involved and further the potential to develop personalized preventive strategies.

Background and Purpose: The University of Washington Clot Bank (UWCB) has established a tissue banking protocol optimized for “omic” analysis for cerebral thrombi retrieved during standard-of-care thrombectomy. Thrombi from cerebral arteries of acute ischemic stroke (AIS) patients are a novel resource that allow for the evaluation of lipid biological markers present at the time of ictus. The pathophysiological processes following AIS are complex, and involve ischemia, inflammation, and disturbances of lipid metabolism. The purpose of this study was to characterize the lipid profile from cerebral thrombi in AIS patients. Methods: The UWCB study is a non-probability, convenience sampling of adult subjects (> 18 years of age) who undergo thrombectomy for vascular occlusion. Lipidomics analysis was performed using LC-MS of untargeted lipids on thrombi taken from cerebral arteries of AIS subjects who underwent thrombectomy. Random forest, a supervised machine learning method, was employed to identify lipids associated with cerebral thrombi. Results: Five subjects (age = 53 ± 26.4, 3 males) were included from the UWCB registry. The most common comorbid conditions were atrial fibrillation (40%) and hypertension (40%). The top 10 metabolites identified from the random forest analysis, were of phosphatidylethanolamine molecular species. Phospholipids are involved in structural and functional integrity of cellular membranes and mediate host inflammatory responses. They are a key component of bacterial membranes. Changes in the structure or function of membrane phospholipids of host or relevant human microbiota may lead to alterations in membrane permeability and contribute to pathology of diseases such as neurodegeneration. Conclusion: An untargeted LC-MS lipidomics approach was used to identify lipids in cerebral thrombi at the time of mechanical thrombectomy. The top 10 lipids present in cerebral thrombi were identified, which included significant concentrations of phosphatidylethanolamine species. Because phospholipids play a key regulatory role in complex downstream cellular function, these findings may be of great significance in understanding AIS pathophysiology that has the potential to inform personalized preventive strategies.