Characterisation of mesenchymal stromal cells in the skin of Atlantic salmon
Bibliographic record
Abstract
ABSTRACT Background The skin serves as the first line of defence for an organism against the external environment. Despite the global significance of salmon in aquaculture, a critical component of this first line of defence, mesenchymal stromal cells, remains unexplored. These pluripotent cells can differentiate into various tissues, including bone, cartilage, tendon, ligament, adipocytes, 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 wound healing processes. Results In this study, we characterise mesenchymal stromal cells in detail for the first time in healthy Atlantic salmon tissue and during the wound healing process. Single-nucleus sequencing and spatial transcriptomics revealed the transcriptional dynamics of these cells, elucidating the differentiation pathways leading to osteogenic and fibroblast lineages in the skin of Atlantic salmon. We charted their activity during an in vivo wound healing time course, showing clear evidence of their active role during this process, as they become transcriptionally more active during the remodelling stage of wound healing. Conclusions For the first time, we chart the activity of sub-clusters of differentiating stromal cells during the process of wound healing, revealing different spatial niches of the various MSC subclusters, and setting the stage for investigations into the manipulation of MSCs to improve fish health.
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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 | 0.001 |
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".