Project description:Sample collection can significantly affect measurements of relative lipid concentrations in cell line panels, hiding intrinsic biological properties of interest between cell lines. Most quality control steps in lipidomic data analysis focus on controlling technical variation. Correcting for the total amount of biological material remains an additional challenge for cell line panels. Here, we investigated how we can normalize lipidomic data acquired from multiple cell lines to correct for differences in sample biomass.We studied how commonly used data normalization and transformation steps during analysis influenced the resulting lipid data distributions. We compared normalization by biological properties such as cell count or total protein concentration, to statistical and data-based approaches, such as median, mean or probabilistic quotient-based normalization and used intraclass correlation to estimate how similarity between replicates changed after normalization.Normalizing lipidomic data by cell count improved similarity between replicates, but only for a study with cell lines with similar morphological phenotypes. For cell line panels with multiple morphologies collected over a longer time, neither cell count nor protein concentration was sufficient to increase the similarity of lipid abundances between replicates of the same cell line. Data-based normalizations increased these similarities, but also created artifacts in the data caused by a bias towards the large and variable lipid class of triglycerides. This artifact was reduced by normalizing for the abundance of only structural lipids. We conclude that there is a delicate balance between improving the similarity between replicates and avoiding artifacts in lipidomic data and emphasize the importance of an appropriate normalization strategy in studying biological phenomena using lipidomics. __________________________________________ Rewiring of lipid metabolism is a hallmark of cancer, supporting tumor growth, survival, and therapy resistance. However, lipid metabolic heterogeneity in breast cancer remains poorly understood. In this study, we systematically profiled the lipidome of 52 breast cancer cell lines using liquid chromatography-mass spectrometry to uncover lipidomic signatures associated with tumor subtype, proliferation, and epithelial-to-mesenchymal (EMT) state. A total of 806 lipid species were identified and quantified across 21 lipid classes. The main lipidomic heterogeneity was associated with the EMT state, with lower sphingolipid, phosphatidylinositol and phosphatidylethanolamine levels and higher cholesterol ester levels in aggressive mesenchymal-like cell lines compared to epithelial-like cell lines. In addition, cell lines with higher proliferation rates had lower levels of sphingomyelins and polyunsaturated fatty acid (PUFA) side chains in phospholipids. Next, changes in the lipidome over time were analyzed for three fast-proliferating mesenchymal-like cell lines MDA-MB-231, Hs578T, and HCC38. Triglycerides decreased over time, leading to a reduction in lipid droplet levels, and especially PUFA-containing triglycerides and -phospholipids decreased during proliferation. These findings underscore the role of EMT in metabolic plasticity and highlight proliferation-associated lipid dependencies that may be exploited for therapeutic intervention. In conclusion, our study reveals that EMT-driven metabolic reprogramming is a key factor in lipid heterogeneity in breast cancer, providing new insights into tumor lipid metabolism and potential metabolic vulnerabilities.

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Homo sapiens

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