R data package for the 2017 Cell Reports paper Developmental emergence of adult neural stem cells as revealed by single cell transcriptional profiling by Yuzwa, Borrett, et al.
Adult neural stem cells (NSCs) derive from embryonic precursors, but little is known about how or when this occurs. We have addressed this issue using single-cell RNA sequencing at multiple developmental time points to analyze the embryonic murine cortex, one source of adult forebrain NSCs. We computationally identify all major cortical cell types, including the embryonic radial precursors (RPs) that generate adult NSCs. We define the initial emergence of RPs from neuroepithelial stem cells at E11.5. We show that, by E13.5, RPs express a transcriptional identity that is maintained and reinforced throughout their transition to a non-proliferative state between E15.5 and E17.5. These slowly proliferating late embryonic RPs share a core transcriptional phenotype with quiescent adult forebrain NSCs. Together, these findings support a model wherein cortical RPs maintain a core transcriptional identity from embryogenesis through to adulthood and wherein the transition to a quiescent adult NSC occurs during late neurogenesis.
There are four datasets representing scRNAseq data from mouse embryonic
cortically-derived cells from embryonic days 11.5 (e11), 13.5 (e13), 15.5 (e15), and 17.5 (e17).
You can install this package by running:
It takes a while for this command to run, since data files are larger than your usual github code.
If you get an error about not being able to install
multtest, try installing it directly from Bioconductor with
BiocManager::install("multtest"). This error has occured during
Seurat installation, but may be resolved in newer versions.
Then the data can be viewed in the scClustViz Shiny app by running:
MouseCortex::viewMouseCortex("e11") # or "e13", "e15", "e17" for your timepoint of choice.
Installing org.Mm.eg.db from Bioconductor is also suggested for annotation purposes:
scClustViz is a visualization tool for single-cell RNAseq designed to assess clustering results for biological relevance using a metric based on differential gene expression between clusters. It also has figures designed for the identification of clusters and their marker genes. See the website and upcoming paper for more details.