A ligand/receptor trafficking clock governs self-renewal and abscission dynamics in pluripotent stem cells
Bibliographic record
Abstract
Summary/Abstract How extracellular cues are temporally integrated to regulate self-renewal and differentiation propensities across the cell cycle remains largely unresolved. We identify a ligand/receptor trafficking clock in rodent and human pluripotent stem cells (PSCs) in which the cyclic turnover of Netrin-1 and its receptors Neo1 and Unc5b (NNU) governs self-renewal capacity and abscission dynamics. In G1, NNU complexes undergo Clathrin-mediated internalization and lysosomal degradation, a process required for timely post-mitotic bridge abscission. At later stages of the cycle, NNU activate Src within early endosomes, inducing a genome-wide redistribution of the transcriptional co-activator Yap1. This reshapes gene regulatory networks by activating stemness- and ectoderm-associated transcriptional programs enriched for Sox2/Nanog binding and by repressing mesodermal- and cell cycle–related targets enriched for Sox2 and Tcf3. Functionally, recombinant Netrin-1 reduces functional heterogeneity and enhances clonogenicity in G1, uncovering a tractable strategy to canalize stem cell behavior. Collectively, our results reveal cell cycle-dependent ligand/receptor trafficking as a temporal clock that directly links membrane dynamics to epigenetic regulation and stem cell fate, opening new avenues for regenerative medicine.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".