Reduced lipolysis in lipoma phenocopies lipid accumulation in obesity.

BACKGROUND: Elucidation of lipid metabolism and accumulation mechanisms is of paramount importance to understanding obesity and unveiling therapeutic targets. In vitro cell models have been extensively used for these purposes, yet, they do not entirely reflect the in vivo setup. Conventional lipomas, characterized by the presence of mature adipocytes and increased adipogenesis, could overcome the drawbacks of cell cultures. Also, they have the unique advantage of easily accessible matched controls in the form of subcutaneous adipose tissue (SAT) from the same individual. We aimed to determine whether lipomas are a good model to understand lipid accumulation. METHODS: We histologically compared lipomas and control SAT, followed by assessment of the lipidome using high-resolution 1H NMR spectroscopy and ESI-IT mass spectrometry. RNA-sequencing was used to obtain the transcriptome of lipomas and the matched SAT. RESULTS: We found a significant increase of small-size (maximal axis < 70 µm) and very big (maximal axis > 150 µm) adipocytes within lipomas. This suggests both enhanced adipocyte proliferation and increased lipid accumulation. We further show that there is no significant change in the lipid composition compared to matched SAT. To better delineate the pathophysiology of lipid accumulation, we considered two groups with different genetic backgrounds: (1) lipomas with HMGA2 fusions and (2) without gene fusions. To reduce the search space for genes that are relevant for lipid pathophysiology, we focused on the overlapping differentially expressed (DE) genes between the two groups. Gene Ontology analysis revealed that DE genes are enriched in pathways related to lipid accumulation. CONCLUSIONS: We show that the common shared lipid accumulation mechanism in lipoma is a reduction in lipolysis, with most gene dysregulations leading to a reduced cAMP in the adipocyte. Superficial lipomas could thus be used as a model for lipid accumulation through altered lipolysis as found in obese patients.

sNucConv: A bulk RNA-seq deconvolution method trained on single-nucleus RNA-seq data to estimate cell-type composition of human adipose tissues.

Deconvolution algorithms mostly rely on single-cell RNA-sequencing (scRNA-seq) data applied onto bulk RNA-sequencing (bulk RNA-seq) to estimate tissues' cell-type composition, with performance accuracy validated on deposited databases. Adipose tissues' cellular composition is highly variable, and adipocytes can only be captured by single-nucleus RNA-sequencing (snRNA-seq). Here we report the development of sNucConv, a Scaden deep-learning-based deconvolution tool, trained using 5 hSAT and 7 hVAT snRNA-seq-based data corrected by (i) snRNA-seq/bulk RNA-seq highly correlated genes and (ii) individual cell-type regression models. Applying sNucConv on our bulk RNA-seq data resulted in cell-type proportion estimation of 15 and 13 cell types, with accuracy of R = 0.93 (range: 0.76-0.97) and R = 0.95 (range: 0.92-0.98) for hVAT and hSAT, respectively. This performance level was further validated on an independent set of samples (5 hSAT; 5 hVAT). The resulting model was depot specific, reflecting depot differences in gene expression patterns. Jointly, sNucConv provides proof-of-concept for producing validated deconvolution models for tissues un-amenable to scRNA-seq.