bioRxivSystems BiologyPreprint
Designing signaling environments to steer transcriptional diversity in neural progenitor cell populations
Sisi CheniD, Jong H. Park, Jialong JiangiD, Tiffany TsouiD, Paul RivaudiD, Matt W. ThomsoniD
Abstract
Stem and progenitor populations within developing embryos are diverse, composed of different subpopulations of precursor cells with varying developmental potential. How these different subpopulations are coordinately regulated by their signaling environments is not well understood. In this paper we develop a framework for controlling progenitor population structure in cell culture using high-throughput single cell mRNA-seq and computational analysis. We find that the natural transcriptional diversity of neural stem cell populations from the developing mouse brain collapses during in vitro culture. Cell populations are depleted of committed neuroblast progenitors and become dominated by a single pre-astrocytic cell population. By analyzing the response of neural stem cell populations to forty combinatorial signaling conditions, we demonstrate that signaling environments can restructure cell populations by modulating the relative abundance of pre-astrocytic and pre-neuronal subpopulations according to a simple log-linear model. Our work demonstrates that single-cell RNA-seq can be applied to learn how to modulate the diversity of stem cell populations, providing a new strategy for population-level stem cell control. HighlightsO_LINatural progenitor diversity in the brain collapses during in vitro culture to a single progenitor type C_LIO_LILoss of progenitor diversity alters fate potential of cells during differentiation C_LIO_LILarge scale single-cell signaling screen identifies signals that reshape population structure towards neuronal cell types C_LIO_LISignals regulate population structure according to a simple log-linear model C_LI GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=89 SRC="FIGDIR/small/890087v3_ufig1.gif" ALT="Figure 1"> View larger version (29K): org.highwire.dtl.DTLVardef@e5286corg.highwire.dtl.DTLVardef@c0e69borg.highwire.dtl.DTLVardef@640b20org.highwire.dtl.DTLVardef@16779e_HPS_FORMAT_FIGEXP M_FIG C_FIG
- organism: mouse
- assay: scRNA-seq
- research focus: cell type annotation
- research focus: clustering
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 0
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