Numb is Required for the Production of Terminal Asymmetric Cell Divisions in the Developing Mouse Retina
Bibliographic record
Abstract
In the developing nervous system, cell diversification depends on the ability of neural progenitor cells to divide asymmetrically to generate daughter cells that acquire different identities. While much work has recently focused on the mechanisms controlling self-renewing asymmetric divisions producing a differentiating daughter and a progenitor, little is known about mechanisms regulating how distinct differentiating cell types are produced at terminal divisions. Here we study the role of the endocytic adaptor protein Numb in the developing mouse retina. Using clonal numb inactivation in retinal progenitor cells (RPCs), we show that Numb is required for normal cell-cycle progression at early stages, but is dispensable for the production of self-renewing asymmetric cell divisions. At late stages, however, Numb is no longer required for cell-cycle progression, but is critical for the production of terminal asymmetric cell divisions. In the absence of Numb, asymmetric terminal divisions that generate a photoreceptor and a non-photoreceptor cell are decreased in favor of symmetric terminal divisions generating two photoreceptors. Using live imaging in retinal explants, we show that a Numb fusion protein is asymmetrically inherited by the daughter cells of some late RPC divisions. Together with our finding that Numb antagonizes Notch signaling in late-stage RPCs, and that blocking Notch signaling in late RPCs almost completely abolishes the generation of terminal asymmetric divisions, these results suggest a model in which asymmetric inheritance of Numb in sister cells of terminal divisions might create unequal Notch activity, which in turn drives the production of terminal asymmetric divisions.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".