DevelopmentDevelopmental BiologyPeer-reviewed journal article
Defining developmental diversification of diencephalon neurons through single-cell gene expression profiling
Abstract
The embryonic diencephalon gives rise to diverse neuronal cell types, which form complex integration centers and intricate relay stations of the vertebrate forebrain. Prior anecdotal gene expression studies suggest several developmental compartments within the developing diencephalon. In the current study, we utilized single-cell RNA sequencing to profile transcriptomes of dissociated cells from the diencephalon of E12.5 mouse embryos. Through analysis of unbiased transcriptional data, we identified the divergence of different progenitors, intermediate progenitors, and emerging neuronal cell types. After mapping the identified cell groups to their spatial origins, we were able to characterize the molecular features across different cell types and cell states, arising from various diencephalic compartments. Furthermore, we reconstructed the developmental trajectory of different cell lineages within the diencephalon. This allowed the identification of the genetic cascades and gene regulatory networks underlying the progression of the cell cycle, neurogenesis, and cellular diversification. The analysis provides new insights into the molecular mechanism underlying the specification and amplification of thalamic progenitor cells. In addition, the single-cell-resolved trajectories not only confirm a close relationship between the rostral thalamus and prethalamus, but also uncover an unexpected close relationship between the caudal thalamus, epithalamus and rostral pretectum. Our data provide a useful resource for the systematic study of cell heterogeneity and differentiation kinetics within the developing diencephalon.
- organism: mouse
- assay: scRNA-seq
- research focus: clustering
- research focus: trajectory inference
- research focus: gene regulatory network inference
- research focus: cell type annotation
- approach: statistical model
- approach: machine learning (classical)
- Citations
- 30
- FWCI
- 1.41
- Field percentile
- top 19%
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