Development of a novel method for in vitro analysis of γc-cytokine effects on CD8 T cells positive selection (111.15)
Bibliographic record
Abstract
Abstract Interleukin (IL)7 is the only γc-cytokine known to support double-positive thymocytes differentiation to the single-positive (SP) stage. To determine if additional γc-cytokines can support T-cell development, we studied their differential effects on positive selection in vitro using a monolayer of OP9 bone marrow-derived stromal cells. We chose the OTI transgenic TCR as our working model due to the previous identification of positively selecting peptides in FTOC assays. In addition to rIL7, rIL4 and rIL13 (but not rIL2, rIL9, rIL15 or rIL21) were able to induce SP CD8 T-cell development. Interestingly, rIL4 induces the de novo expression of EOMES and triggers the development of 2 distinct SP CD8 T cell populations, which we termed CD8int and CD8hi. Flow-cytometry analysis showed that ex vivo generated CD8int, but not CD8hi, expressed high levels of CD69, PD-L1 and CD44 whereas rIL7 or rIL13 treatments did not up regulate these markers. Furthermore, rIL4 has a unique ability to enhance expression of FOXO1, KLF2 and S1PR1. Flow-cytometry analysis of signaling events demonstrated that every γc-cytokine leads to differential activation of AKT, ERK, STAT3, STAT5 and STAT6. Taken together, our findings show that CD8 T-cell positive selection can be studied with an in vitro model that is more convenient than FTOC assays and can be customized to a high-throughput format. Notably, we validate a novel role for rIL4 and rIL13 in supporting positive selection of thymocytes.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".