Lentiviral Vectors Mediate Stable and Efficient Gene Delivery into Primary Murine Natural Killer Cells
Bibliographic record
Abstract
Natural killer (NK) cells are lymphocytes that provide an important line of defense against many types of microorganisms, viruses and tumors. The development of an efficient gene transfer system for genetically modifying primary murine NK cells will facilitate the studies of NK cell differentiation, acquisition of self-tolerance, and induction of anti-tumor responses. In this study we used an enhanced green fluorescent protein (EGFP)-expressing vector to carry out a systematic evaluation of the efficiency of lentiviral transduction of primary murine NK cells with or without prior interleukin-2 (IL-2) activation. In a single-step transduction protocol, we demonstrated that human immunodeficiency virus type 1-based lentiviral vectors support an average of 40% transduction efficiency on primary NK cells. These genetically modified NK cells are found to maintain stable EGFP transgene expression in vitro, and can be further expanded in IL-2 supplemented culture medium. Lentiviral transduction does not affect NK surface phenotypes or functions (apoptosis, cytokine production and cytotoxicity). We further demonstrated efficient gene transfer into differentiating NK cells derived from the lentiviral-transduced murine hematopoietic progenitor cells in vitro. This study therefore establishes a simple and efficient approach to the genetic engineering of primary murine NK cells, and will prove useful in studying basic NK cell biology and in exploring the therapeutic potential of NK cells in inbred and transgenic mouse models.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".