Pratsch, C. | bioRxiv Science

bioRxiv2026

Multimodal 3D imaging reveals a central role for lysosomes indissolution of cholesterol crystals by macrophages

Formation of cholesterol crystals (CCs) is a key event during the development of atherosclerosis, but the molecular mechanisms of their degradation within cells are poorly understood. By incorporating the fluorescent cholesterol analogue TopFluor-Cholesterol (TF-Chol) into CCs, we were able to visualize the uptake of CCs in macrophages using correlative fluorescence and soft X-ray microscopy. Using quantitative 3D live-cell imaging, we show that CCs are processed in late endosomes and lysosomes (LE/Lys), resulting in formation of TF-Chol containing lipid droplets (LDs) over time. Inhibition of lysosomal sterol export with U18666A caused accumulation of TF-Chol in LE/Lys, and inhibition of lysosomal acidification with bafilomycin A1 led to reduced dissolution of the CCs. Using a novel assay combined with 3D image processing, we show that large CCs in contact with macrophages are processed via lysosomal exocytosis followed by extracellular and intracellular degradation of CCs. Treating macrophages with a fluorescent version of cyclodextrin (CD) promoted the dissolution of CCs and enhanced the formation of LDs enriched with TF-Chol. The majority of fluorescent CD co-localized with a marker for LE/Lys during this process, suggesting that intracellular delivery to LE/Lys may contribute to the dissolution of CCs. Dehydroergosterol (DHE) is an intrinsically fluorescent sterol closely mimicking the properties and behavior of cholesterol. DHE is known to self-associate into aggregates and crystals, and by using fluorescence spectroscopy and specialized ultraviolet (UV) microscopy, we found that CD enhances the dissolution of DHE crystals in vitro and in cells. Together, our findings highlight the lysosomal pathway as responsible for dissolution of CCs, cholesterol trafficking, and efflux in macrophages.

biochemistry↗

bioRxiv2023

Automated quantification of lipophagy in Saccharomyces cerevisiae from fluorescence and cryo-soft X-ray microscopy data using deep learning

Lipophagy is a form of autophagy by which lipid droplets (LDs) become digested to provide nutrients as a cellular response to starvation. Lipophagy is often studied in yeast, Saccharomyces cerevisiae, in which LDs become internalized into the vacuole. There is a lack of tools to quantitatively assess lipophagy in intact cells with high resolution and throughput. Here, we combine soft X-ray tomography (SXT) with fluorescence microscopy and use a deep learning computational approach to visualize and quantify lipophagy in yeast. We focus on yeast homologs of mammalian Niemann Pick type C proteins, whose dysfunction leads to Niemann Pick type C disease in humans, i.e., NPC1 (named NCR1 in yeast) and NPC2. We developed a convolutional neural network (CNN) model which classifies ring-shaped versus lipid-filled or fragmented vacuoles containing ingested LDs in fluorescence images from wild-type yeast and from cells lacking NCR1 ({Delta}ncr1 cells) or NPC2 ({Delta}npc2 cells). Using a second CNN model, which performs automated segmentation of LDs and vacuoles from high-resolution reconstructions of X-ray tomograms, we can obtain 3D renderings of LDs inside and outside of the vacuole in a fully automated manner and additionally measure droplet volume, number, and distribution. We find that cells lacking functional NPC proteins can ingest LDs into vacuoles normally but show compromised degradation of LDs and accumulation of lipid vesicles inside vacuoles. This phenotype is most severe in{Delta} npc2 cells. Our new method is versatile and allows for automated high-throughput 3D visualization and quantification of lipophagy in intact cells.

bioinformatics↗