Gene family evolution underlies cell type diversification in the hypothalamus of teleosts
Bibliographic record
Abstract
ABSTRACT Hundreds of cell types form the vertebrate brain, but it is largely unknown how similar these cellular repertoires are between or within species, or how cell type diversity evolves. To examine cell type diversity across and within species, we performed single-cell RNA sequencing of ∼130,000 hypothalamic cells from zebrafish ( Danio rerio ) and surface- and cave-morphs of Mexican tetra ( Astyanax mexicanus ). We found that over 75% of cell types were shared between zebrafish and Mexican tetra, which last shared a common ancestor over 150 million years ago. Orthologous cell types displayed differential paralogue expression that was generated by sub-functionalization after genome duplication. Expression of terminal effector genes, such as neuropeptides, was more conserved than the expression of their associated transcriptional regulators. Species-specific cell types were enriched for the expression of species-specific genes, and characterized by the neo-functionalization of members of recently expanded or contracted gene families. Within species comparisons revealed differences in immune repertoires and transcriptional changes in neuropeptidergic cell types associated with genomic differences between surface- and cave-morphs. The single-cell atlases presented here are a powerful resource to explore hypothalamic cell types, and reveal how gene family evolution and the neo- and sub-functionalization of paralogs contribute to cellular diversity.
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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".