Chd4 remodels chromatin to control retinal cell type specification and lineage termination
Bibliographic record
Abstract
During development, neural progenitor cells modify their output over time to produce different types of neurons and glia in chronological sequences. Epigenetic processes have been shown to regulate neural progenitor potential, but the underlying mechanisms are not well understood. Here, we generated retina-specific conditional mouse knockouts (cKOs) in the key nucleosome remodeller Chd4. Chd4 cKOs overproduced early-born retinal ganglion and amacrine cells. Postnatally, later-born rod photoreceptors were drastically underproduced. Progenitors failed to differentiate into Müller glia on schedule and continued to proliferate beyond their normal developmental window. Next, to determine how Chd4 regulates the genome, we performed CUT&RUN-seq and ATAC-seq, revealing that genome accessibility was significantly increased at ∼10,000 regulatory elements. Accordingly, multiplexed single-cell transcriptomics demonstrated that deletion of Chd4 led to corresponding increases in transcription. These results suggest that Chd4 restricts the genome to repress progenitor identity and promote differentiation. Taken together, our data suggest that Chd4-dependent nucleosome remodelling plays a crucial role in the temporal transition that governs lineage termination, but does not regulate earlier temporal transitions.
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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".