A novel tissue culture method to produce professional antigen-presenting cells from rainbow trout in vitro
Bibliographic record
Abstract
• A tissue culture method to generate fish antigen presenting cells was developed. • Large numbers of antigen presenting cells were produced from trout fin explants. • Trout fin leukocyte-like cells express biomarkers of antigen presenting cells. In this study, we describe a tissue culture method to identify and produce large quantities of professional antigen presenting cells (pAPCs) including dendritic cells (DCs) using fin explant cultures from rainbow trout. After several months of in vitro incubation with a routine schedule of selective cell enrichment and specifically formulated medium nutrient feeding, flasks of fin explant cultures produced a heterogenous population of cells whose morphologies resembled those of monocytes, macrophages, melanomacrophages, and DCs, with DCs being present in a large quantity. These cells were collectively referred to as fin leukocyte-like cells (fin-LLCs). The RT-PCR result showed that the fin-LLCs expressed abundant transcript levels of four markers of pAPCs (Major Histocompatibility (MH) IIα, MH IIβ, S25–7, INXV), three transcript markers of DCs (CD83, CD205, CD209), one transcript marker for macrophages (CSF1R), and one transcript marker for pro-inflammatory responses (IL-1β). Using affinity-purified anti-trout MH IIα and MH IIβ primary antibodies that were generated in house, abundant levels of MH IIα and MH IIβ polypeptides were detected in fin-LLCs by Western blotting. Immunocytochemistry in combination with confocal microscopy showed that DC-like cells had more cell-surface MH II proteins than macrophage-like cells. These results suggest the fin-LLCs had characteristics and markers of pAPCs. As fin clipping does not threaten the survival of fish and fins are natural regenerative appendages, our method allows the large production of autologous fish APCs in vitro , which can be used for many other research purposes in the future.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".