Mapping differentiation kinetics in the mouse retina reveals an extensive period of cell cycle protein expression in post‐mitotic newborn neurons
Bibliographic record
Abstract
BACKGROUND: Knowledge of gene expression kinetics around neuronal cell birth is required to dissect mechanisms underlying progenitor fate. Here, we timed cell cycle and neuronal protein silencing/induction during cell birth in the developing murine retina. RESULTS: The pan-cell cycle markers Pcna and Mcm6 were present in the post-mitotic ganglion cell layer. Although confined to the neuroblastic layer (NBL), 6-7% of Ki67(+) cells lacked six progenitor/cell cycle markers, and expressed neuronal markers. To define protein extinction/induction timing, we defined G2/M length throughout retinogenesis, which was typically 1-2 h, but <10% cells took double this time. BrdU-chase analyses revealed that at E12.5, Tubb3 (Tuj1) appeared at M-phase, followed by Calb2 and Dcx at ~2 h, Elavl2/3/4 at ~4 h, and Map2 at ~6 h after cell birth, and these times extended with embryonic age. Strikingly, Ki67 was not extinguished until up to a day after cell cycle exit, coinciding with exit from the NBL and induction of late markers such as Map1b/Uchl1/Rbfox3. CONCLUSIONS: A minor population of progenitors transits slowly through G2/M and, most importantly, some cell cycle proteins are retained for an unexpectedly long period in post-mitotic neurons. The high-resolution map of cell birth kinetics reported here provides a framework to better define mechanisms that regulate neurogenesis.
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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".