GliaNeurosciencePeer-reviewed journal article
Transcriptomic heterogeneity of Sox2-expressing pituitary cells
Patrick A. FletcheriD, Kosara SmiljanićiD, Rafael Maso PrévideiD, Stéphanie ConstantiniD, Arthur ShermaniD, Steven L. CooniD, Stanko S. StojilkovićiD
Abstract
The mammalian pituitary gland is a complex organ consisting of hormone-producing cells (HPC), nonhormonal folliculostellate cells (FSC) and pituicytes, vascular pericytes and endothelial cells, and putative Sox2-expressing stem cells. Here, we used scRNAseq analysis of adult female rat pituitary cells to study the heterogeneity of pituitary cells with a focus on evaluating the transcriptomic profile of the Sox2-expressing population. Samples containing whole pituitary and separated anterior and posterior lobe cells allowed the identification of all expected pituitary resident cell types and lobe-specific subpopulations of vascular cells. Sox2 was expressed uniformly in all FSC, pituicytes, and a fraction of HPC. FSC comprised two subclusters; FSC1 contained more cells but expressed less genetic diversity compared to FSC2. The latter contained proliferative cells, expressed genes consistent with stem cell niche formation, including tight junctions, and shared genes with HPC. The FSC2 transcriptome profile was also consistent with the activity of pathways regulating cell proliferation and stem cell pluripotency, including the Hippo and Wnt pathways. The expression of other stem cell marker genes was common for FSC and pituicytes (Sox9, Cd9, Hes1, Vim, S100b) or cell type-specific (FSC: Prop1, Prrx1, Pitx1, Pitx2, Lhx3; pituicytes: Fgf10, Tbx3, Lhx2, Nkx2-1, Rax). FSC and pituicytes also expressed other astroglial marker genes, some common and other distinct, consistent with their identities as astroglial cells of the pituitary. These data suggest functional heterogeneity of FSC, with a larger fraction representing classical FSC, and a smaller fraction containing active stem-like cells and HPC-committed progenitors.
- organism: rat
- assay: scRNA-seq
- research focus: cell type annotation
- research focus: clustering
- research focus: differential expression
- approach: statistical model
- Citations
- 28
- FWCI
- 5.05
- Field percentile
- top 4%
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