Abstract
During brain development, neural stem cells (NSCs) undergo multiple fate-switches to generate various neuronal subtypes and glial cells, exhibiting distinct transcriptomic profiles at different stages. However, full-length transcriptomic datasets of NSCs across different neurodevelopmental stages under similar experimental settings are lacking, which is essential for uncovering stage-specific transcriptional and post-transcriptional mechanisms underlying the fate commitment of NSCs. Here, we report the full-length transcriptome of mouse NSCs at five different stages during embryonic and postnatal development. We used fluorescent-activated cell sorting (FACS) to isolate CD133+Blbp+ NSCs from C57BL/6 transgenic mice that express enhanced green fluorescent protein (EGFP) under the control of a Blbp promoter. By integrating short- and long-read full-length RNA-seq, we created a transcriptomic dataset of gene and isoform expression profiles in NSCs at embryonic days 15.5, 17.5, and postnatal days 1.5, 8, and 60. This dataset provides a detailed characterization of full-length transcripts in NSCs at distinct developmental stages, which could be used as a resource for the neuroscience community to study NSC fate determination, neural development, and disease.
Highlights
Background & SummaryDuring mammalian brain development, neural stem cells (NSCs) give rise to major cell types in various brain regions, including neurons and glial cells
A subset of radial glial cells transform into postnatal NSCs in the subventricular zone (SVZ) and subgranular zone (SGZ) in the hippocampus, which continue to generate interneurons and glia[2]
The majority of NSCs in the SVZ and SGZ are committed to neuronal fate[3,4]
Summary
Background & SummaryDuring mammalian brain development, neural stem cells (NSCs) give rise to major cell types in various brain regions, including neurons and glial cells (astrocytes and oligodendrocytes). This protocol can be adapted for full-length bulk RNA-seq of rare cell populations, such as NSCs. for the conventional 2nd-generation RNA-seq, cDNA generated from Smart-seq[2] is fragmented before sequencing, resulting in accurate short-read raw data, which complicates the task of reconstructing and quantifying transcript isoforms. Reads’ distribution over genome feature, and RNA integrity at cDNA level of short-read sequencing data were calculated by geneBody_coverage.py, read_distribution.py, and tin.py from RSeQC16, respectively.
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