Single cell profiling of hematopoietic stem cell transplant recipients reveals TGF-β1 and IL-2 confer immunoregulatory functions to NK cells
Bibliographic record
Abstract
Natural killer (NK) cell activity is influenced by cytokines and microenvironment factors, resulting in remarkably diverse functions, by contributing to inflammatory responses or serving as rheostats of adaptive immunity. Using single cell RNA sequencing (scRNA-seq), we identified a TGFβ1 high CD56 bright NK cell population associated with hematopoietic stem cell transplant recipients protected from acute graft-versus-host disease (GVHD). We further define a role for the combination of interleukin-2 (IL-2) and transforming growth factor β1 (TGF-β1) in promoting a regulatory phenotype in NK cells. "Induced" regulatory NK cells produce high amounts of TGF-β1, inhibited T cells, could promote naive T cells differentiation into regulatory T cells, and exhibited a unique transcriptional program that includes expression of IKZF2 (HELIOS) and ZNF683 (HOBIT). This phenotype was not stable, and "induced" regulatory NK cells lost the ability to secrete TGF-β1 upon exposure to different cytokines. These findings define protective CD56 bright NK cells post-hematopoietic stem cell transplantation, and demonstrate the combination of IL-2 and TGF-β1 promotes regulatory activity in NK cells.
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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".