Trajectory inference (TI) or pseudotime analysis has dramatically extended the analytical framework of single-cell RNA-seq data, allowing regulatory genes contributing to cell differentiation and those involved in various dynamic cellular processes to be identified. However, most TI analysis procedures deal with individual genes independently while overlooking the regulatory relations between genes. Integrating information from gene regulatory networks (GRNs) at different pseudotime points may lead to more interpretable TI results. To this end, we introduce scInTime—an unsupervised machine learning framework coupling inferred trajectory with single-cell GRNs (scGRNs) to identify master regulatory genes. We validated the performance of our method by analyzing multiple scRNA-seq data sets. In each of the cases, top-ranking genes predicted by scInTime supported their functional relevance with corresponding signaling pathways, in line with the results of available functional studies. Overall results demonstrated that scInTime is a powerful tool to exploit pseudotime-series scGRNs, allowing for a clear interpretation of TI results toward more significant biological insights.

Obesity is a global health concern affecting more than one-third of the adult population worldwide. Epidemiological studies indicate that body fat distribution, more than overall adiposity, is a key risk factor for obesity-related diseases. Genome-wide association studies revealed that RSPO3 is associated with body fat distribution. Additionally, marked sex differences in body fat distribution are largely influenced by estrogen, suggesting that exploring the relationship between estrogen and RSPO3 expression may illuminate the mechanisms underlying the sexual dimorphism of fat distribution. We revisited a comprehensive set of gene expression data from humans and mice to explore these mechanisms. We assessed RSPO3 expression in male and female adipose tissues and utilized single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics to examine RSPO3’s function within different tissues. Our findings confirmed significantly higher RSPO3 expression in male adipose tissue compared to female. Furthermore, our data for the first time suggest that estrogen plays a crucial role in fat distribution, as blocking estrogen signaling upregulates RSPO3 expression. In mouse liver, we observed a correlation between sex-specific ADK overexpression and RSPO3 expression, resulting in increased obesity and hepatocyte expansion via WNT/β-catenin signaling in male mice. This study sheds light on how estrogen influences sexual dimorphism in fat distribution and underscores RSPO3’s vital role in this process. These insights open new avenues for targeted therapeutic interventions addressing obesity and metabolic diseases with a focus on sex-specific factors.