Quantifying the unanticipated diversity of the human NK cell repertoire (P1456)
Bibliographic record
Abstract
Abstract Like their T and B lymphocyte counterparts, natural killer (NK) cells exist in small and specialized subpopulations. However, while T and B cell repertoire diversity is generated primarily through DNA rearrangement to produce a single antigen-specific receptor, the diversity of the NK cell repertoire is determined by the expression of a spectrum of activating and inhibitory receptors. To evaluate the human NK cell repertoire on a single-cell basis, we simultaneously measured more than 25 NK cell receptors in the peripheral blood of 22 healthy individuals via mass cytometry. We used Boolean gating to classify cellular phenotypes. Based on the Simpson index, an ecological measure designed to quantify population diversity, we found that total NK cell repertoire diversity was approximately normally distributed across individuals. Using rarefaction curves and non-parametric species estimators, we calculate the expansiveness of the NK cell repertoire at a minimum of 100,000 unique phenotypes. Furthermore, within an individual, the ex vivo addition of the homeostatic cytokine IL-15 biased the repertoire toward highly common and uncommon phenotypes, increasing the total population richness but decreasing the overall diversity. These data show, for the first time, that the receptor-based NK cell repertoire is exquisitely diverse. They further suggest an optimal baseline diversity that is highly sensitive to optimization by exogenous factors.
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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 | 0.001 |
| 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".