Abstract
A blood cell lineage consists of several consecutive developmental stages starting 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 corresponding target genes. A possible step forward in this case is provided by the increasing amount of 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 excert along the differentiation path of cell lineages. Based on such simple patterns, we identified cell state-specific marker genes and extracted TFs that likely drive their differentiation. Integration of the mean expression values of stage-specific “key player” genes yielded a distinct peaking pattern for each lineage that was used to identify further genes in the dataset which behave similarly. Incorporating the set of TFs that regulate these genes led to a set of stage-specific regulators that control the biological process of cell fate. As proof of concept, we considered two expression datasets covering key differentiation events in blood cell formation of mice.
Highlights
Cell fate describes a biological program, which determines how a less specialized cell type develops into a more specialized one
A regulatory network was constructed from the correlated genes following the integrated expression pattern with a set of transcription factors (TFs) that regulate them which forms the candidates in the fourth layer
We derived a shortest regulatory path that connects the set of correlated genes that are regulated by multiple TFs (PathDevFate, see Methods)
Summary
Cell fate describes a biological program, which determines how a less specialized cell type develops into a more specialized one. For each transition out of a particular state, this involves a decision between either self-renewal or differentiation into daughter cells (Garcia-Ojalvo and Martinez Arias, 2012). Knowing the “key players” involved in these events may serve as a predictive marker to help determining differentiation stages of cells, but could potentially be useful for clinical purposes, for example by aiding in the search for therapeutic targets across different diseases involving aberrations in the composition of cell types or stages, respectively (An et al, 2014). One of the best-studied examples are blood cells, which are already widely used in diagnostics. In complex blood-related diseases such as leukemia, understanding the manifestation of the disease and monitoring its progression and response to treatment could greatly benefit from a deeper
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