Frontiers in ImmunologyCell BiologyPeer-reviewed journal article
Transcriptional Regulatory Logic Orchestrating Lymphoid and Myeloid Cell Fate Decisions
Abstract
The differentiation of hematopoietic stem cells (HSCs) into diverse blood and immune cells is a complex, highly hierarchical process characterized by a series of tightly regulated steps. It involves a sequence of intermediate oligo-potent progenitors making successive binary decisions. This process gradually narrows down lineage possibilities until a final fate is reached. This step-wise process is tightly controlled by Transcription Factors (TFs) and their associated regulome ultimately resulting the differentiation of both lymphoid and myeloid compartments. Here, we set to unravel the lineage-specific gene regulatory circuitry controlling the development of B cells, T cells, Innate Lymphoid Cells (ILCs), and Dendritic Cells (DCs). We employ Weighted Gene Co-expression Network Analysis (WGCNA) to characterize gene modules associated to the lymphoid or myeloid cell fate, enabling the identification of lineage restricted TFs based on their expression patterns. By identifying TFs whose expression is subset-restricted or those with a broader expression in the hematopoietic compartment we construct a regulatory logic that potentially controls the development of these key immune cells. Our results point to conserved regulatory elements between ILCs, Natural Killer cells, and DCs. This analysis unravels an intricate relationship between each cell types and how the expression of key TFs dictate lineage specificity. We particularly dissect the elements associated to conventional DCs and plasmacytoid DCs. In conclusion, our findings shed new lights on regulatory mechanisms controlling blood cell development and offer a blueprint that can be leveraged to better understand the molecular mechanisms underpinning blood cell development.
- assay: bulk RNA-seq
- research focus: cell type annotation
- research focus: gene regulatory network inference
- research focus: differential expression
- approach: statistical model
- Citations
- 5
- FWCI
- 1.38
- Field percentile
- top 21%
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