Computational Exploration of Phospholipid Alterations in a Human Neuroblastoma Cell Model Exposed to Amyloid-Beta

Lipidomics identifies and analyzes the composition of intact lipid molecules within biological systems. Techniques including mass spectrometry enhance lipid coverage; however, the loss of ion connectivity and the generation of large datasets complicate precise feature annotation. Given the complexity of lipidomic data, advanced computational methodologies are essential for precise analysis. Herein, we efficiently explored lipidome alterations in human neuroblastoma SK-N-SH cells exposed to amyloid-beta, a peptide that appears to play a crucial role in the pathology of Alzheimer's disease. The derived datasets were analyzed using the R programming language, employing tools such as lipidr for lipid species identification and significance and ggplot2 for data visualization. Our results revealed significant changes in phospholipid levels under amyloid-beta exposure with emphasis on PE 16:0-18:1, PC 18:2-16:1, and PC 20:2-18:0, highlighting the complex and multifaceted interactions of lipid markers in the pathophysiology of Alzheimer's disease and providing deeper insights into their potential roles in disease progression.