The nature of cell-state transitions during the transit-amplifying phases of many developmental processes-hematopoiesis in particular-is unclear. Here, we use single-cell RNA sequencing to demonstrate a continuum of transcriptomic states in committed transit-amplifying erythropoietic progenitors, which correlates with a continuum of proliferative potentials in these cells. We show that glucocorticoids enhance erythrocyte production by slowing the rate of progression through this developmental continuum of transit-amplifying progenitors, permitting more cell divisions prior to terminal erythroid differentiation. Mechanistically, glucocorticoids prolong expression of genes that antagonize and slow induction of genes that drive terminal erythroid differentiation. Erythroid progenitor daughter cell pairs have similar transcriptomes with or without glucocorticoid stimulation, indicating largely symmetric cell division. Thus, the rate of progression along a developmental continuum dictates the absolute number of erythroid cells generated from each transit-amplifying progenitor, suggesting a paradigm for regulating the total output of differentiated cells in numerous other developmental processes.
Pubmed ID: 30827895 RIS Download
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Statistical analysis software that combines scientific graphing, comprehensive curve fitting (nonlinear regression), understandable statistics, and data organization. Designed for biological research applications in pharmacology, physiology, and other biological fields for data analysis, hypothesis testing, and modeling.
View all literature mentionsSoftware package for analyzing single cell gene expression, classifying and counting cells, performing differential expression analysis between subpopulations of cells, and reconstructing cellular trajcectories. Works well with very large single-cell RNA-Seq experiments containing tens of thousands of cells or more. Used in computational analysis of gene expression data in single cell gene expression studies to profile transcriptional regulation in complex biological processes and highly heterogeneous cell populations.
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