Single-cell ATAC-seq of fetal human retina and stem-cell-derived retinal organoids shows changing chromatin landscapes during cell fate acquisition
Bibliographic record
Abstract
We previously used single-cell transcriptomic analysis to characterize human fetal retinal development and assessed the degree to which retinal organoids recapitulate normal development. We now extend the transcriptomic analyses to incorporate single-cell assay for transposase-accessible chromatin sequencing (scATAC-seq), a powerful method used to characterize potential gene regulatory networks through the changes in accessible chromatin that accompany cell-state changes. The combination of scATAC-seq and single-cell RNA sequencing (scRNA-seq) provides a view of developing human retina at an unprecedented resolution. We identify key transcription factors relevant to specific fates and the order of the transcription factor cascades that define each of the major retinal cell types. The changing chromatin landscape is largely recapitulated in retinal organoids; however, there are differences in Notch signaling and amacrine cell gene regulation. The datasets we generated constitute an excellent resource for the continued improvement of retinal organoid technology and have the potential to inform and accelerate regenerative medicine approaches to retinal diseases.
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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".