Su, E. Y. | Search bioRxiv

bioRxiv2021

Signaling and transcriptional networks governing late synovial joint development

BackgroundDuring synovial joint development, cavitation marks the end of the emergence of new cell types and the onset of the consolidation of cell type specific programs. However, the transcriptional programs that regulate this crucial stage prior to joint maturation are incompletely characterized. Gdf5-lineage cells give rise to the majority of joint constituents such as articular cartilage, meniscus, ligament, and tendon. Therefore, to explore pre-maturation of the synovial joint, we performed single cell RNA-Seq analysis of 1,306 Gdf5-lineage cells from the murine knee joint at E17.5. ResultsUsing computational analytics and in situ hybridization, we identified nine sub-states contributing to articular cartilage, meniscus, cruciate ligament, synovium, lining, and surrounding fibrous tissue. We identified a common progenitor population that is predicted to give rise to ligamentaocytes, articular chondrocytes, and lining cells. We further found that while a large number of signaling pathways orchestrate the differentiation of this progenitor to either ligamentocytes or to lining cells, only continued FGF signaling guides these cells to a default chondrocyte fate. ConclusionsOur single cell transcriptional atlas is a resource that can be used to better understand and further study synovial joint development and the reactivation of embryonic programs in diseases such as osteoarthritis.

developmental biology↗

bioRxiv2021

Reconstruction of dynamic regulatory networks reveals signaling-induced topology changes associated with germ layer specification

Elucidating regulatory relationships between transcription factors (TFs) and target genes is fundamental to understanding how cells control their identity and behavior. Computational gene regulatory network (GRN) reconstruction methods aim to map this control by inferring relationships from transcriptomic data. Unfortunately, existing methods are imprecise, may be computationally burdensome, and do not uncover how networks transition from one topology to another. Here we present Epoch, a computational network reconstruction tool that leverages single cell transcriptomics to infer dynamic network structures. Epoch performs favorably when benchmarked using data derived from in vivo, in vitro, and in silico sources. To illustrate the usefulness of Epoch, we applied it to identify the dynamic networks underpinning directed differentiation of mouse embryonic stem cells (ESC) guided by multiple primitive streak induction treatments. Our analysis demonstrates that modulating signaling pathways drives topological network changes that shape cell fate potential. We also find that Peg3 is a central contributor to the rewiring of the pluripotency network to favor mesoderm specification. By integrating signaling pathways with GRN structures, we traced how Wnt activation and PI3K suppression govern mesoderm and endoderm specification, respectively. Finally, we compare the networks established in in vitro directed differentiation of ESCs to those in in vivo gastrulation and mesoderm specification. The methods presented here are available in the R package Epoch, and provide a foundation for future work in understanding the biological implications of dynamic regulatory structures.

systems biology↗