Investigating the effect of invariant natural killer T cell receptor on T cell development and function from human pluripotent stem cells 4127
Bibliographic record
Abstract
Abstract Description Invariant Natural Killer T (iNKT) cells are a subset of unconventional T cells that recognize lipid antigens independent of HLA molecules. This ability to bypass donor-recipient matching, along with their rapid cytokine production, makes them an attractive immunotherapy agent. However, their low frequency in vivo makes investigating their functions difficult. Efforts have been made to scale up “iNKT-like” cell production by manipulating iNKT T cell receptor (iTCR) expression in stem cells. However, their use of xenogeneic feeder cells and non-chemically defined systems make understanding their developmental niche and clinical translation difficult. To investigate if iTCR expression during T cell development affects its developmental trajectory, we turn to combine our lab’s expertise in niche and genetic engineering. Using our feeder-free and chemically defined differentiation protocol, we can robustly generate mature conventional T cells from human pluripotent stem cells (hPSCs). By integrating a functional iTCR into hPSCs through CRISPR/Cas gene editing, we generated different subsets of iTCR expressing mature T cells that express the innate transcription factor PLZF. Further comparison with other engineered hPSC-derived T cells will help elucidate how iTCR expression and signaling regulate iNKT cell development. Ultimately, we aim to harness specific properties of unconventional T cells to develop more effective off-the-shelf T cell therapeutics for various diseases. Topic Categories Hematopoiesis and Immune System Development (HEM)
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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) | 0.002 | 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".