eLifeCell BiologyPeer-reviewed journal article
Anti-resonance in developmental signaling regulates cell fate decisions
Samuel J RoseniD, Olivier WitteveeniD, Naomi J. BaxteriD, Ryan S. LachiD, Erik HopkinsiD, Marianne BaueriD, Maxwell Z. WilsoniD
Abstract
Cells process dynamic signaling inputs to regulate fate decisions during development. While oscillations or waves in key developmental pathways, such as Wnt, have been widely observed, the principles governing how cells decode these signals remain unclear. By leveraging optogenetic control of the Wnt signaling pathway in both HEK293T cells and H9 human embryonic stem cells, we systematically map the relationship between signal frequency and downstream pathway activation. We find that cells exhibit a minimal response to Wnt at certain frequencies, a behavior we term anti-resonance. We developed both detailed biochemical and simplified hidden variable models that explain how anti-resonance emerges from the interplay between fast and slow pathway dynamics. Remarkably, we find that frequency directly influences cell fate decisions involved in human gastrulation; signals delivered at anti-resonant frequencies result in dramatically reduced mesoderm differentiation. Our work reveals a previously unknown mechanism of how cells decode dynamic signals and how anti-resonance may filter against spurious activation. These findings establish new insights into how cells decode dynamic signals with implications for tissue engineering, regenerative medicine, and cancer biology. Significance StatementWnt signaling is responsible for driving stem cell differentiation. Our study explores a wide range of temporal patterns of Wnt activation using state-of-the-art optogenetics, advanced imaging and modeling. We identify anti-resonant frequencies that suppress mesoderm differentiation. We confirm this anti-resonant suppression for two human cell lines, HEK and embryonic stem cells, and expect that it also occurs in other pathways, as it arises from the interplay of different timescales along the pathway. Our work opens new avenues for systematically exploring signal spaces that natural systems can robustly respond to, and has broader implications for tissue engineering, regenerative medicine, and cancer biology.
- organism: human
- assay: bulk RNA-seq
- research focus: cell type annotation
- research focus: differential expression
- approach: deep learning
- approach: statistical model
- Citations
- 2
- FWCI
- 2.09
- Field percentile
- top 21%
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