bioRxivDevelopmental BiologyPreprint
Single-cell transcriptomics reveal ddx43+ cells as regulators of slow-cycling neoblasts in planaria
Nikhil Kumar JaligamiD, Mohamed Mohamed HarooniD, M. Basu, Atriya Mazumdar, Swathi Pavithran, Vinay Kumar DubeyiD, Praveen Kumar VemulaiD, Ankit Ved AroraiD, Dasaradhi PalakodetiiD
Abstract
Cell cycle dynamics are fundamental to stem cell maintenance and differentiation. While the G1 phase is known to influence stem cell fate decisions, the mechanisms regulating its duration remain poorly understood. Using Schmidtea mediterranea, a highly tractable model for studying regeneration, we uncovered how the interplay of systemic and local cues from specialized cell types regulates G1 phase progression, thereby priming the cells in a slow-cycling state poised to differentiate. Single-cell transcriptomics identified G1-enriched, non-committed neoblasts that give rise to a previously uncharacterized ddx43 cell type, localized near sub-epidermal muscle and closely associated with surrounding neoblasts. Functional analyses revealed that ddx43 cells act as gatekeepers, maintaining adjacent neoblasts in an extended G1 phase and a differentiation-primed state. ddx43 knockdown led to neoblast hyperproliferation, highlighting their niche-like regulatory role. Following injury, extracellular signal-regulated kinase (Erk) signalling that propagates to distal regions via subepidermal muscle cells is critical for suppression of ddx43 cells by downregulating Notch signalling. This establishes a critical cross-talk between sub-epidermal muscle and ddx43 cells, mediated by the Erk-Notch axis, essential for maintaining G1 phase extension. In summary, we reveal a ddx43-dependent, non-cell autonomous mechanism that regulates a transient, slow-cycling neoblast population, offering new insights into how extrinsic cues orchestrate stem cell cycling during tissue homeostasis and regeneration.
- organism: plant (other)
- assay: scRNA-seq
- research focus: cell type annotation
- research focus: differential expression
- research focus: cell-cell communication
- approach: statistical model
- approach: experimental assay
- Citations
- 0
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