Systematic evaluation of transcriptomics-based deconvolution methods and references using thousands of clinical samples.

Estimating cell type composition of blood and tissue samples is a biological challenge relevant in both laboratory studies and clinical care. In recent years, a number of computational tools have been developed to estimate cell type abundance using gene expression data. Although these tools use a variety of approaches, they all leverage expression profiles from purified cell types to evaluate the cell type composition within samples. In this study, we compare 12 cell type quantification tools and evaluate their performance while using each of 10 separate reference profiles. Specifically, we have run each tool on over 4000 samples with known cell type proportions, spanning both immune and stromal cell types. A total of 12 of these represent in vitro synthetic mixtures and 300 represent in silico synthetic mixtures prepared using single-cell data. A final 3728 clinical samples have been collected from the Framingham cohort, for which cell populations have been quantified using electrical impedance cell counting. When tools are applied to the Framingham dataset, the tool Estimating the Proportions of Immune and Cancer cells (EPIC) produces the highest correlation, whereas Gene Expression Deconvolution Interactive Tool (GEDIT) produces the lowest error. The best tool for other datasets is varied, but CIBERSORT and GEDIT most consistently produce accurate results. We find that optimal reference depends on the tool used, and report suggested references to be used with each tool. Most tools return results within minutes, but on large datasets runtimes for CIBERSORT can exceed hours or even days. We conclude that deconvolution methods are capable of returning high-quality results, but that proper reference selection is critical.

NOTUM promotes thermogenic capacity and protects against diet-induced obesity in male mice.

We recently showed that NOTUM, a liver-secreted Wnt inhibitor, can acutely promote browning of white adipose. We now report studies of chronic overexpression of NOTUM in liver indicating that it protects against diet-induced obesity and improves glucose homeostasis in mice. Adeno-associated virus (AAV) vectors were used to overexpress GFP or mouse Notum in the livers of male C57BL/6J mice and the mice were fed an obesifying diet. After 14 weeks of high fat, high sucrose diet feeding, the AAV-Notum mice exhibited decreased obesity and improved glucose tolerance compared to the AAV-GFP mice. Gene expression and immunoblotting analysis of the inguinal fat and brown fat revealed increased expression of beige/brown adipocyte markers in the AAV-Notum group, suggesting enhanced thermogenic capacity by NOTUM. A ß3 adrenergic receptor agonist-stimulated lipolysis test suggested increased lipolysis capacity by NOTUM. The levels of collagen and C-C motif chemokine ligand 2 (CCL2) in the epididymal white adipose tissue of the AAV-Notum mice were significantly reduced, suggesting decreased fibrosis and inflammation, respectively. RNA sequencing analysis of inguinal white adipose of 4-week chow diet-fed mice revealed a highly significant enrichment of extracellular matrix (ECM) functional cluster among the down-regulated genes in the AAV-Notum group, suggesting a potential mechanism contributing to improved glucose homeostasis. Our in vitro studies demonstrated that recombinant human NOTUM protein blocked the inhibitory effects of WNT3A on brown adipocyte differentiation. Furthermore, NOTUM attenuated WNT3A's effects on upregulation of TGF-ß signaling and its downstream targets. Overall, our data suggest that NOTUM modulates adipose tissue function by promoting thermogenic capacity and inhibiting fibrosis through inhibition of Wnt signaling.