Spatiotemporally resolved transcriptomics reveals the cellular dynamics of human retinal development
Bibliographic record
Abstract
The morphogenesis and cellular interactions in developing retina are incompletely characterized. The full understanding needs a precise mapping of the gene expression with a single-cell spatial resolution. Here, we present a spatial transcriptomic (ST) resource for the developing human retina at six developmental stages. Combining the spatial and single-cell transcriptomic data enables characterization of the cell-type-specific expression profiles at distinct anatomical regions at each developmental stage, highlighting the spatiotemporal dynamics of cellular composition during retinal development. All the ST spots are catalogued into consensus spatial domains, which are further associated to their specific expression signatures and biological functions associated with neuron and eye development. We prioritize a set of critical regulatory genes for the transitions of spatial domains during retinal development. Differentially expressed genes from different spatial domains are associated with distinct retinal diseases, indicating the biological relevance and clinical significance of the spatially defined gene expression. Finally, we reconstruct the spatial cellular communication networks, and highlight critical ligand-receptor interactions during retinal development. Overall, our study reports the spatiotemporal dynamics of gene expression and cellular profiles during retinal development, and provides a rich resource for the future studies on retinogenesis.
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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 | 0.001 | 0.001 |
| 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".