Minaeva Mariia, Domingo Júlia, Rentzsch Philipp, Lappalainen Tuuli
Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Tomtebodavägen 23A, 17165 Solna, Sweden.
New York Genome Center, 101 Avenue of the Americas, New York, NY 10013, USA.
NAR Genom Bioinform. 2025 Jan 7;7(1):lqae178. doi: 10.1093/nargab/lqae178. eCollection 2025 Mar.
Understanding the role of transcription and transcription factors (TFs) in cellular identity and disease, such as cancer, is essential. However, comprehensive data resources for cell line-specific TF-to-target gene annotations are currently limited. To address this, we employed a straightforward method to define regulons that capture the cell-specific aspects of TF binding and transcript expression levels. By integrating cellular transcriptome and TF binding data, we generated regulons for 40 common cell lines comprising both proximal and distal cell line-specific regulatory events. Through systematic benchmarking involving TF knockout experiments, we demonstrated performance on par with state-of-the-art methods, with our method being easily applicable to other cell types of interest. We present case studies using three cancer single-cell datasets to showcase the utility of these cell-type-specific regulons in exploring transcriptional dysregulation. In summary, this study provides a valuable pipeline and a resource for systematically exploring cell line-specific transcriptional regulations, emphasizing the utility of network analysis in deciphering disease mechanisms.
了解转录和转录因子（TFs）在细胞特性以及诸如癌症等疾病中的作用至关重要。然而，目前用于细胞系特异性TF到靶基因注释的综合数据资源有限。为了解决这个问题，我们采用了一种简单的方法来定义调控子，该调控子能够捕捉TF结合和转录表达水平的细胞特异性方面。通过整合细胞转录组和TF结合数据，我们为40种常见细胞系生成了调控子，包括近端和远端细胞系特异性调控事件。通过涉及TF敲除实验的系统基准测试，我们证明了该方法的性能与最先进的方法相当，并且我们的方法易于应用于其他感兴趣的细胞类型。我们使用三个癌症单细胞数据集进行了案例研究，以展示这些细胞类型特异性调控子在探索转录失调方面的实用性。总之，本研究提供了一个有价值的流程和资源，用于系统地探索细胞系特异性转录调控，强调了网络分析在解读疾病机制中的实用性。
