Transcriptomic characterization of transitioning cell types in the skin of Atlantic salmon
Bibliographic record
Abstract
BACKGROUND: The skin maintains the body's integrity and serves as the first line of defence against pathogens, stressors and mechanical injuries. Despite the global significance of salmon in aquaculture, how the transcriptomic profile of cells varies during wound healing remains unexplored. Teleost's skin contains adult pluripotent cells that differentiate into various tissues, including bone, cartilage, tendon, ligament, adipose, dermis, muscle and connective tissue within the skin. These cells are pivotal for preserving the integrity of skin tissue throughout an organism's lifespan and actively participate in the wound healing processes. In this study, we characterize the transcriptomic profiles of putative mesenchymal stromal cells (fibroblast-like adult stem cells) in healthy Atlantic salmon tissue and during the wound healing process. RESULTS: Single-nucleus sequencing and spatial transcriptomics were used to detect transcriptomic changes occurring during wound healing that are commonly associated with mesenchymal stromal cells. We followed the transcriptomic activity of these cells during an in vivo wound healing time course study showing that these cells become more transcriptionally active during the remodelling stage of wound healing. The changes detected give insights into the potential differentiation pathways leading to osteogenic and fibroblast lineages in the skin of Atlantic salmon. CONCLUSIONS: We chart the transcriptomic activity of subclusters of putative differentiating stromal cells during the process of wound healing for the first time, revealing different spatial niches of the various putative MSC subclusters, and setting the stage for further investigation of the manipulation of transitioning cell types to improve fish health.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".