A blood cell lineage consists of several consecutive developmental stages from the pluri- or multipotent stem cell to a state of terminal differentiation. Despite their importance for human biology, the regulatory pathways and gene networks that govern these differentiation processes are not yet fully understood. This is in part due to challenges associated with delineating the interactions between transcription factors (TFs) and their target genes. A possible path forward in this issue is provided by increasingly available expression data as a basis for linking differentiation stages and gene activities. Here, we present a novel hierarchical approach to identify characteristic expression peak patterns that global regulators expose along the differentiation path of cell lineages. Based on such simple patterns, we identify cell state-specific marker genes and extract TFs that likely drive their differentiation. Integration of the mean expression values of stage-specific key player genes yields a distinct peaking pattern for each lineage that is used to identify further genes in the dataset behaving similarly. Incorporating the set of TFs which regulate these genes incurred at a set of stage-specific regulators controlling the biological process of cell fate. As proof of concept, we consider two expression datasets covering key differentiation events in blood cell formation of mice.
翻译:血细胞系系由几个连续的发育阶段组成,从多功能或多能干干细胞到最终分化状态。尽管这些分化过程对人类生物学很重要,但管理这些分化过程的监管路径和基因网络尚未完全理解。这部分是由于分解转录因数及其目标基因之间的相互作用方面的挑战。这个问题的一个可能前进的道路是,越来越多的表达数据作为将差异阶段和基因活动联系起来的基础,为这一问题提供了一种可能的前进道路。在这里,我们提出了一个新的等级分级办法,以确定全球监管机构在细胞系分化路径上暴露的特征表达峰值模式。根据这些简单模式,我们确定特定细胞的标记基因,并提取可能驱动其分化的TF。整合特定阶段关键玩基因的平均表达值为每个分级提供了一种不同的峰值模式,用来确定数据集中的更多基因。在控制细胞命运生物过程的一组分级监管机构中,将管理这些基因的一组调控。作为概念的证明,我们考虑两种表达式数据结构,包括关键血细胞事件。