Differentiation of hematopoietic stem and progenitor cells to NK cells in a stroma-free, serum-free culture system
Bibliographic record
Abstract
Abstract Natural Killer (NK) cells play an important role in innate immunity by secreting proinflammatory cytokines and killing tumor cells and virus-infected cells. Human NK cells can be generated by culturing CD34+ hematopoietic stem and progenitor cells (HSPCs) with stromal cells and cytokines. The use of stroma (and serum) is, however, not desirable in clinical applications. We developed a culture method for generating large numbers of NK cells from purified CD34+ cord blood (CB) HSPCs in the absence of serum and stromal cells. CD34+ CB cells were isolated by EasySep magnetic separation, seeded into culture plates coated with a Notch ligand and cultured for two weeks in StemSpan Serum-Free Expansion Medium (SFEM II) supplemented with SCF, TPO, Flt3L and IL-7. These conditions promote expansion of HSPCs and their differentiation into CD7+CD5+ lymphoid progenitors. Cells were then transferred to non-coated plates and cultured for two more weeks in StemSpan SFEM II supplemented with SCF, Flt3L, IL-7, IL-15 and a small molecule, UM729, to promote differentiation into NK cells. After culture, on average 76% (range: 54–90%, n=12) of cells expressed the NK cell marker CD56 and 58% (range: 25–80%) coexpressed CD56 and NKp46 (a NK cell activating receptor). A more mature CD56+CD16+ NK cell subset also emerged in these cultures (5%, range: 1–11%). The yield of CD56+ NK cells was ~13,000 (600–41,000) per initial CD34+ cell. The NK cells were functional and able to kill K562 target cells. These results show that HSPCs can expand and differentiate into NK cells under stroma- and serum-free culture conditions. This novel culture system will be useful for studies directed toward the development of cancer immunotherapies where large numbers of NK cells are needed.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".