Computational Biology Group, Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, 6, avenue du Swing, L-4367, Belvaux, Luxembourg.
Department of Medical Biochemistry and Biophysics, Laboratory of Molecular Neurobiology, Biomedicum 6C, Solnavägen 9, Karolinska Institutet, 17177, Stockholm, Sweden.
Nat Commun. 2018 Jul 3;9(1):2595. doi: 10.1038/s41467-018-05016-8.
Single-cell RNA sequencing allows defining molecularly distinct cell subpopulations. However, the identification of specific sets of transcription factors (TFs) that define the identity of these subpopulations remains a challenge. Here we propose that subpopulation identity emerges from the synergistic activity of multiple TFs. Based on this concept, we develop a computational platform (TransSyn) for identifying synergistic transcriptional cores that determine cell subpopulation identities. TransSyn leverages single-cell RNA-seq data, and performs a dynamic search for an optimal synergistic transcriptional core using an information theoretic measure of synergy. A large-scale TransSyn analysis identifies transcriptional cores for 186 subpopulations, and predicts identity conversion TFs between 3786 pairs of cell subpopulations. Finally, TransSyn predictions enable experimental conversion of human hindbrain neuroepithelial cells into medial floor plate midbrain progenitors, capable of rapidly differentiating into dopaminergic neurons. Thus, TransSyn can facilitate designing strategies for conversion of cell subpopulation identities with potential applications in regenerative medicine.
单细胞 RNA 测序允许定义分子上不同的细胞亚群。然而，确定定义这些亚群的特定转录因子 (TF) 集仍然是一个挑战。在这里，我们提出亚群身份是由多个 TF 的协同活性产生的。基于这个概念，我们开发了一个计算平台（TransSyn），用于识别确定细胞亚群身份的协同转录核心。TransSyn 利用单细胞 RNA-seq 数据，并使用协同的信息论度量标准，对最佳协同转录核心进行动态搜索。大规模的 TransSyn 分析确定了 186 个亚群的转录核心，并预测了 3786 对细胞亚群之间的身份转换 TF。最后，TransSyn 的预测可以实现将人类后脑神经上皮细胞转化为中脑基板祖细胞的实验，这些细胞能够快速分化为多巴胺能神经元。因此，TransSyn 可以促进设计细胞亚群身份转换的策略，在再生医学中有潜在的应用。
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