Serum- and feeder-free culture expansion of human peripheral blood NK cells
Bibliographic record
Abstract
Abstract Natural killer (NK) cells are critical effectors of innate immunity that secrete proinflammatory cytokines and kill tumor cells and virus-infected cells. The ability to expand human NK cells in culture provides a ready source of cells for disease modeling, development of immunotherapies, and basic research. We have developed a culture system that supports the expansion of primary NK cells in the absence of feeder cells and serum. NK cells were enriched from peripheral blood using EasySep™ negative selection and cultured in serum-free medium with interleukin-2 in plates coated with a NK cell ‘activation coating matrix’ for 14 days. Cells were passaged with medium changes on days 7 and 11, and were harvested on day 14 for assessment or functional characterization. The average frequency of CD56+CD3− NK cells was 92% (range 80 – 97%, n=16) with an average expansion of 121-fold (range 10 – 274). On average 79% (48–92%) of expanded NK cells expressed CD16. The ability of expanded NK cells to produce interferon-γ (IFN-γ) and to degranulate were tested after stimulation with phorbol 12-myristate 13-acetate (PMA) + ionomycin or co-culture with K562 cells. As detected by intracellular flow cytometry, the average frequency of IFN-γ+ NK cells was 56% (range 41 – 63%, n = 4) and 40% (range 12 – 55%) when stimulated by PMA/ionomycin or K562 cells, respectively. Similarly, the average frequency of degranulated NK cells as detected by surface expression of CD107a, a lysosomal-associated membrane protein, was 69% (range 35 – 84%, n = 4) and 53% (range 18 – 75%), respectively. These results show that NK cells can be expanded and stimulated under serum- and feeder-free conditions to generate large numbers of functional NK cells for basic and translational research.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.006 | 0.004 |
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".