Discovering the determinants of diversity in the human NK cell repertoire by mass cytometry (P3018)
Bibliographic record
Abstract
Abstract NK cells respond to infected and transformed cells using germline-encoded NK receptors (NKRs). Collectively, these NKRs combine to form distinct repertoires that tune the NK cell response. To provide a framework for understanding how perturbations in expression of NKRs influence disease pathogenesis, we defined the phenotypic heterogeneity of NK cells in 22 healthy individuals (ages 21-62; M=10, F=12), including 5 sets of monozygotic twins. Using mass cytometry, surface expression of 38 markers specific for NKRs and lineage markers to identify B cells, T cells and myeloid cells were examined. KIR and HLA class I genotypes were determined by Luminex, and KIR gene content was further assessed using pyrosequencing. NK cell populations, as defined by inhibitory NKRs, were highly concordant between twins (R2 = 0.90) in comparison to unrelated individuals (R2 = 0.66), indicating that the inhibitory repertoire is highly genetically determined. However, when analyzing all twenty-five NKRs, including activating receptors, the concordance in twins (R2=0.34) and in unrelated individuals (R2=0.11) was greatly decreased, indicating significant environmental influence in determining the overall NK cell repertoire. Overall, these analyses reveal tremendous diversity within the NK repertoire that is both genetically and environmentally determined, and offer the ability to examine the functional capacity of NK cells with far greater resolution.
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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) | 0.001 | 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".