Machine-learning assisted confocal imaging of intracellular sites of triglycerides and cholesteryl esters formation and storage

Abstract

All living systems are maintained by a constant flux of metabolic energy and, among the different reactions, the process of lipids storage and lipolysis is of fundamental importance. Current research has focused on the investigation of lipid droplets (LD) as a powerful biomarker for the early detection of metabolic and neurological disorders. Efforts in this field aim at increasing selectivity for LD detection by exploiting existing or newly synthesized probes. However, LD constitute only the final product of a complex series of reactions during which fatty acids are transformed into triglycerides and cholesterol is transformed in cholesteryl esters. These final products can be accumulated in intracellular organelles or deposits other than LD. A complete spatial mapping of the intracellular sites of triglycerides and cholesteryl esters formation and storage is, therefore, crucial to highlight any potential metabolic imbalance, thus predicting and counteracting its progression. Here, we present a machine learning assisted, polarity-driven segmentation which enables to localize and quantify triglycerides and cholesteryl esters biosynthesis sites in all intracellular organelles, thus allowing to monitor in real-time the overall process of the turnover of these non-polar lipids in living cells. This technique is applied to normal and differentiated PC12 cells to test how the level of activation of biosynthetic pathways changes in response to the differentiation process.