Clonal analysis of NK cell development from bone marrow progenitors <i>in vitro</i> : orderly acquisition of receptor gene expression
Bibliographic record
Abstract
In the mouse, two families of MHC class I-specific receptors, namely Ly49 and CD94/NKG2, have been identified on NK cells. Individual NK cells can express several Ly49 molecules as well as members of the CD94/NKG2 family. The expression of multiple receptors with different specificities for MHC class I is thus thought to generate NK cells with diverse recognition patterns. To delineate the mechanism by which NK cells begin to express different patterns of Ly49 and CD94/NKG2 molecules, we developed a clonal assay in which NK1.1(-), IL-2/ IL-15 receptor beta+ NK precursors generated by culture of multipotential Lin(-), c-kit+ progenitors in IL-7, stem cell factor and flt3 ligand are induced to differentiate into NK1.1+ , Ly49+ NK cells. Examination of the clonal populations thus generated revealed heterogeneity in the pattern of Ly49 and CD94/NKG2 gene expression. In addition, a distinct kinetic pattern of expression was observed. CD94, NKG2A, NKG2C and Ly49B were expressed first followed by Ly49G, then Ly49C and I and finally, Ly49A, D, E and F. The data suggest a stochastic but ordered acquisition of class I receptors on NK cells in which developing NK cells become capable of expressing distinct receptors at different times but show no absolute prerequisite to express the receptors that are acquired early in NK development for the expression of those that are acquired later.
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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".