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.
Summary
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
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