Integrated analysis of motif activity and gene expression changes of transcription factors.

The ability to predict transcription factors based on sequence information in regulatory elements is a key step in systems-level investigation of transcriptional regulation. Here, we have developed a novel tool, IMAGE, for precise prediction of causal transcription factors based on transcriptome profiling and genome-wide maps of enhancer activity. High precision is obtained by combining a near-complete database of position weight matrices (PWMs), generated by compiling public databases and systematic prediction of PWMs for uncharacterized transcription factors, with a state-of-the-art method for PWM scoring and a novel machine learning strategy, based on both enhancers and promoters, to predict the contribution of motifs to transcriptional activity. We applied IMAGE to published data obtained during 3T3-L1 adipocyte differentiation and showed that IMAGE predicts causal transcriptional regulators of this process with higher confidence than existing methods. Furthermore, we generated genome-wide maps of enhancer activity and transcripts during human mesenchymal stem cell commitment and adipocyte differentiation and used IMAGE to identify positive and negative transcriptional regulators of this process. Collectively, our results demonstrate that IMAGE is a powerful and precise method for prediction of regulators of gene expression.

Isolation of nuclei from mouse white adipose tissues for single-nucleus genomics.

Lipid-filled adipocytes are incompatible with droplet-based single-cell methods, such as 10x Genomics-based technology, thus restricting droplet-based single-cell analyses of adipose tissues to the stromal vascular fraction. To overcome this limitation and obtain cellular and molecular insight into adipose tissue composition and plasticity, single-nucleus sequencing-based technologies can be applied. Here, we provide an optimized protocol for nuclei isolation from mouse adipose tissues suitable for single-nucleus RNA sequencing. This allows for transcriptomic profiling of the entire adipose tissue at single-cell resolution. For complete details on the use of this protocol, please refer to Sárvári et al., 2021.

Plasticity of Epididymal Adipose Tissue in Response to Diet-Induced Obesity at Single-Nucleus Resolution.

Adipose tissues display a remarkable ability to adapt to the dietary status. Here, we have applied single-nucleus RNA-seq to map the plasticity of mouse epididymal white adipose tissue at single-nucleus resolution in response to high-fat-diet-induced obesity. The single-nucleus approach allowed us to recover all major cell types and to reveal distinct transcriptional stages along the entire adipogenic trajectory from preadipocyte commitment to mature adipocytes. We demonstrate the existence of different adipocyte subpopulations and show that obesity leads to disappearance of the lipogenic subpopulation and increased abundance of the stressed lipid-scavenging subpopulation. Moreover, obesity is associated with major changes in the abundance and gene expression of other cell populations, including a dramatic increase in lipid-handling genes in macrophages at the expense of macrophage-specific genes. The data provide a powerful resource for future hypothesis-driven investigations of the mechanisms of adipocyte differentiation and adipose tissue plasticity.