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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".