Single-cell transcriptomic analysis of neuroepithelial cells and other cell types of the gills of zebrafish (Danio rerio) exposed to hypoxia
Bibliographic record
Abstract
Abstract The fish gill is a multifunctional organ involved in numerous physiological processes, such as gas exchange and sensing of hypoxia by respiratory chemoreceptors, called neuroepithelial cells (NECs). Many studies have focused on zebrafish (Danio rerio) to investigate the structure, function and development of the gills, yet the transcriptomic profile of most gill cells remains obscure. We present the results of a comprehensive transcriptomic analysis of the gills of zebrafish using single-cell RNA sequencing (scRNA‐seq). Gill cells from ETvmat2:EGFP zebrafish were individually labelled before scRNA‐seq library construction using 10× Genomics Chromium technology. 12,819 cells were sequenced with an average depth of over 27,000 reads per cell. We identified a median of 485 genes per cell and 16 cell clusters, including NECs, neurons, pavement cells, endothelial cells and mitochondrion-rich cells. The identity of NECs was confirmed by expression of slc18a2, encoding the vesicular monoamine transporter, Vmat2. Highly differentially-expressed genes in NECs included tph1a, encoding tryptophan hydroxylase, sv2 (synaptic vesicle protein), and proteins implicated in O2 sensing (ndufa4l2a, cox8al and epas1a). In addition, NECs and neurons expressed genes encoding transmembrane receptors for serotonergic, cholinergic or dopaminergic neurotransmission. Differential expression analysis showed a clear shift in the transcriptome of NECs following 14 days of acclimation to hypoxia. NECs in the hypoxia group showed high expression of genes involved in cell cycle control and proliferation. The present article provides a complete cell atlas for the zebrafish gill and serves as a platform for future studies investigating the molecular biology and physiology of this organ.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".