Identification of Primary Natural Killer Cell Modulators by Chemical Library Screening with a Luciferase-Based Functional Assay
Bibliographic record
Abstract
Natural killer (NK) cells are essential players of the innate immune response that secrete cytolytic factors and cytokines such as IFN-γ when contacting virus-infected or tumor cells. They represent prime targets in immunotherapy as defects in NK cell functions are hallmarks of many pathological conditions, such as cancer and chronic infections. The functional screening of chemical libraries or biologics would greatly help identify new modulators of NK cell activity, but commonly used methods such as flow cytometry are not easily scalable to high-throughput settings. Here we describe an efficient assay to measure the natural cytotoxicity of primary NK cells where the bioluminescent enzyme NanoLuc is constitutively expressed in the cytoplasm of target cells and is released in co-culture supernatants when lysis occurs. We fully characterized this assay using either purified NK cells or total peripheral blood mononuclear cells (PBMCs), including some patient samples, as effector cells. A pilot screen was also performed on a library of 782 metabolites, xenobiotics, and common drugs, which identified dextrometorphan and diphenhydramine as novel NK cell inhibitors. Finally, this assay was further improved by developing a dual-reporter cell line to simultaneously measure NK cell cytotoxicity and IFN-γ secretion in a single well, extending the potential of this system.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".