GEO series
Multipotent neural stem cells originating from neuroepithelium exist outside of the mouse central nervous system
GSE213158
Mus musculus
Expression profiling by high throughput sequencing
28 samples
2025/01/10
GPL19057
Summary
It has been widely accepted that mammalian neural stem cells (NSCs) exist only in the central nervous system. Here, we report that peripheral NSCs (pNSCs) exist outside of the CNS and can be isolated—from mouse embryonic limb, postnatal lung, tail, dorsal root ganglia, and adult lung tissues. pNSCs are from Sox1+ cells and originate from neuroepithelial cells. Many pNSCs that migrate out of the neural tube can differentiate into neurons and limited glia cells during embryo development. We found the existence of pNSCs provides previously unidentified insight of the mammalian nervous system development and presents an alternative potential strategy for neural regenerative therapy. Our study demonstrates that directly converted cells can be a promising source for generating transplantable assembloids. Mice and Cells: RNA-seq : RNA quality was verified on an Agilent Bioanalyzer Nano Eukaryote chip. 1 mg of total RNA was used for poly-A selection with NEBNext PolyA mRNA magnetic isolation module and subsequent cDNA synthesis, Illumina Tru-seq adaptor ligation and library preparation were performed with NEBNext Ultra RNA Library Prep Kit (NEB) according to the manufacturer’s instructions. Library quality and concentration were determined using an Agilent Bioanalyzer DNA1000 chip and a Qubit fluorometer. Libraries were sequenced on Illumina NextSeq500 in single-end mode. HISAT24 was used to align the RNA‐seq reads for each of the samples to the mouse reference genome GRCm38, and Cufflinks to annotate them. The counts of aligned reads to each gene were calculated with HTSeq. In‐house software was used to merge the expression results into a single text file used in the downstream analysis in Matlab (MathWorks). We performed quantile normalization to equalize the data and stabilized them through the log2 transform of the data plus one. The heatmap of the most highly variable transcripts, the hierarchical clustering dendrograms (calculated using the unweighted pair group method with arithmetic mean and Euclidean distance measure), and the PCAs were performed using in‐home functions developed in Matlab (MathWorks).
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Paper (PMID 40211073) ↗
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