Okawa Satoshi, Angarica Vladimir Espinosa, Lemischka Ihor, Moore Kateri, Del Sol Antonio
Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg.
Ichan School of Medicine at Mount Sinai, New York, NY, USA.
NPJ Syst Biol Appl. 2015 Nov 12;1:15012. doi: 10.1038/npjsba.2015.12. eCollection 2015.
Stem cell differentiation is a complex biological process. Cellular heterogeneity, such as the co-existence of different cell subpopulations within a population, partly hampers our understanding of this process. The modern single-cell gene expression technologies, such as single-cell RT-PCR and RNA-seq, have enabled us to elucidate such heterogeneous cell subpopulations. However, the identification of a transcriptional regulatory network (TRN) for each cell subpopulation within a population and genes determining specific cell fates (lineage specifiers) remains a challenge due to the slower development of appropriate computational and experimental workflows. Here, we propose a computational differential network analysis approach for predicting lineage specifiers in binary-fate differentiation events.
The proposed method is based on a model that considers each stem cell subpopulation being in a stable state maintained by its specific TRN stability core, and cell differentiation involves changes in these stability cores between parental and daughter cell subpopulations. The method first reconstructs topologically different cell-subpopulation specific TRNs from single-cell gene expression data, literature knowledge and transcription factor (TF)-DNA binding-site prediction. Then, it systematically predicts lineage specifiers by identifying genes in the TRN stability cores in both parental and daughter cell subpopulations.
Application of this method to different stem cell differentiation systems was able to predict known and putative novel lineage specifiers. These examples include the differentiation of inner cell mass into either primitive endoderm or epiblast, different progenitor cells in the hematopoietic system, and the lung alveolar bipotential progenitor into either alveolar type 1 or alveolar type 2.
The method is generally applicable to any binary-fate differentiation system, for which single-cell gene expression data are available. Therefore, it should aid in understanding stem cell lineage specification, and in the development of experimental strategies for regenerative medicine.
干细胞分化是一个复杂的生物学过程。细胞异质性，例如群体内不同细胞亚群的共存，在一定程度上阻碍了我们对这一过程的理解。现代单细胞基因表达技术，如单细胞逆转录聚合酶链反应（RT-PCR）和RNA测序（RNA-seq），使我们能够阐明这种异质性细胞亚群。然而，由于合适的计算和实验工作流程发展较为缓慢，确定群体内每个细胞亚群的转录调控网络（TRN）以及决定特定细胞命运的基因（谱系决定因子）仍然是一个挑战。在此，我们提出一种计算性差异网络分析方法，用于预测二元命运分化事件中的谱系决定因子。
所提出的方法基于一个模型，该模型认为每个干细胞亚群处于由其特定的TRN稳定核心维持的稳定状态，细胞分化涉及亲代和子代细胞亚群之间这些稳定核心的变化。该方法首先从单细胞基因表达数据、文献知识和转录因子（TF）-DNA结合位点预测中重建拓扑结构不同的细胞亚群特异性TRN。然后，通过识别亲代和子代细胞亚群的TRN稳定核心中的基因，系统地预测谱系决定因子。
将该方法应用于不同的干细胞分化系统，能够预测已知的和推定的新型谱系决定因子。这些例子包括内细胞团分化为原始内胚层或外胚层、造血系统中的不同祖细胞，以及肺泡双潜能祖细胞分化为1型肺泡细胞或2型肺泡细胞。
该方法普遍适用于任何可获得单细胞基因表达数据的二元命运分化系统。因此，它应有助于理解干细胞谱系特化，并有助于再生医学实验策略的开发。
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