Genes & DevelopmentDevelopmental BiologyPeer-reviewed journal article
Sharp cell type boundaries emerge from coordinated morphogen signaling
Ruiqi LiiD, Yiqun Jiang, Sarah Platt, Tianchi XiniD, Shagun Lukkad, Sarah Van, Kevin A. PetersoniD, Ryan R. DriskelliD, Jonathan Zhu, Hainan Lam, Eva-LaRue Barber, David H. WangiD, Chae Ho LimiD, Makoto Mark TaketoiD, Yuval KlugeriD, Peggy S. MyungiD
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Abstract
Classic models of the French flag problem depict sharp cell-type boundaries emerging from threshold responses to morphogen gradients. How discrete cell-type boundaries arise from morphogen signals that vary continuously across developing tissues is not completely understood. We use hair follicle dermal condensate formation to study a sharp developmental transition in which proliferative progenitors undergo cell-cycle exit concurrent with molecular differentiation. Using genetic and genomic approaches, we show that Wnt and Hedgehog signaling interact to coordinate the timing of these two processes. We identify a division of labor between the pathways: Wnt signaling promotes cell-cycle exit by regulating chromatin binding of the Hedgehog mediator GLI3, while Hedgehog signaling induces differentiation genes in a Wnt-dependent manner and simultaneously elevates Wnt activity. When Wnt and Hedgehog activities are temporally aligned, differentiation and cell-cycle exit occur within the same developmental window, restricting both the duration and abundance of intermediate states and producing a sharp cell-type boundary. When these signals are misaligned, intermediate states persist and expand, producing fuzzy boundaries. These findings reveal a mechanism in which interacting morphogen signals regulate the duration and abundance of intermediate states during a developmental transition, thereby controlling how continuous cell-state progression is translated into discrete tissue patterning.
- organism: mouse
- assay: bulk RNA-seq
- research focus: cell type annotation
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: statistical model
- approach: experimental assay
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