Orderly and Nonstochastic Acquisition of CD94/NKG2 Receptors by Developing NK Cells Derived from Embryonic Stem Cells In Vitro
Bibliographic record
Abstract
In mice there are two families of MHC class I-specific receptors, namely the Ly49 and CD94/NKG2 receptors. The latter receptors recognize the nonclassical MHC class I Qa-1(b) and are thought to be responsible for the recognition of missing-self and the maintenance of self-tolerance of fetal and neonatal NK cells that do not express Ly49. Currently, how NK cells acquire individual CD94/NKG2 receptors during their development is not known. In this study, we have established a multistep culture method to induce differentiation of embryonic stem (ES) cells into the NK cell lineage and examined the acquisition of CD94/NKG2 by NK cells as they differentiate from ES cells in vitro. ES-derived NK (ES-NK) cells express NK cell-associated proteins and they kill certain tumor cell lines as well as MHC class I-deficient lymphoblasts. They express CD94/NKG2 heterodimers, but not Ly49 molecules, and their cytotoxicity is inhibited by Qa-1(b) on target cells. Using RT-PCR analysis, we also report that the acquisition of these individual receptor gene expressions during different stages of differentiation from ES cells to NK cells follows a predetermined order, with their order of acquisition being first CD94; subsequently NKG2D, NKG2A, and NKG2E; and finally, NKG2C. Single-cell RT-PCR showed coexpression of CD94 and NKG2 genes in most ES-NK cells, and flow cytometric analysis also detected CD94/NKG2 on most ES-NK cells, suggesting that the acquisition of these receptors by ES-NK cells in vitro is nonstochastic, orderly, and cumulative.
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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 | 0.001 |
| 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".