bioRxivDevelopmental BiologyPreprint
Smarcc1 is essential for the patterning of the optic stalk and differentiation of the optic nerve head astrocytes
Nitay Zuk-Bar, Shai OvadiaiD, Guizhong CuiiD, Alexey Obolensky, Eyal BaniniD, Ron OfriiD, Naihe JingiD, Ruth Ashery‐PadaniD
Abstract
The optic nerve is essential for vision, and its development depends on the transient neuroectodermal optic stalk, which forms alongside the optic cup through coordinated morphogenesis and differentiation into optic nerve astrocytes that provide lifelong support for retinal ganglion cell (RGC) axons. Here, we delineate key steps in astrocyte formation from the optic stalk and uncover stage- and lineage-specific roles of Smarcc1 (Baf155) and Smarcc2 (Baf170), scaffolding subunits of SWI/SNF chromatin remodeling complexes. Both factors are co-expressed in early retinal pigment epithelium (RPE) and optic stalk progenitors, with Smarcc2 persisting in differentiated derivatives, including RPE and optic nerve astrocytes. Conditional deletion using Dct-Cre revealed reciprocal compensation in pigmented lineages, whereas Smarcc1 loss uniquely caused optic nerve head (ONH) morphogenetic failure, characterized by glial lamina collapse, RGC degeneration, and progressive visual decline. Spatial transcriptomics and functional assays demonstrate that Smarcc1 drives the transition of dorsal optic stalk progenitors from a pigmented, RPE-like state to astrocyte progenitors by repressing pigment gene programs and enabling Pax2/Sox2 activity. After specification, Smarcc1 is required for glial lamina assembly, essential for ONH integrity and RGC survival. These findings establish a mechanism in which Smarcc1-mediated chromatin remodeling couples astrocyte progenitor specification and differentiation with local morphogenesis, ensuring long-term retinal function.
- organism: mouse
- assay: bulk RNA-seq
- research focus: cell type annotation
- research focus: differential expression
- research focus: gene regulatory network inference
- research focus: spatial domain detection
- approach: statistical model
- Citations
- 0
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