A single‐step enrichment method for generating highly purified mouse NK cell populations by negative selection
Bibliographic record
Abstract
Natural Killer (NK) cells play a critical role in innate immunity and tumor immunology. They are particularly difficult to isolate because they are rare and express several markers found on other cell types. Using column‐free immunomagnetic cell separation technology (EasySep®), we sought to develop a single‐step negative selection method for NK cell isolation, where negative selection enables the isolation of cells with minimal impact on cell function. Unwanted cells were cross‐linked to magnetic particles using a highly optimized cocktail of 10 biotinylated antibodies. This enabled us to achieve NK cell purities of 92.3 ± 2.6% (defined as %CD49b + CD3 neg ; n=6). The isolated cells retained the expression of several markers associated with an NK phenotype, including NKG2D and LY49C/I/F/H. The purified cells could be readily expanded when cultured in the presence of IL‐2 (> 8‐fold expansion after 5 days, n=3). Cr 51 release assays demonstrated the cytotoxic capacity of the isolated cells, as specific lysis was detected at an effector to target ratio of 1:1 and 10:1 for both YAC‐1 cells and Daudi B lymphoma cells, the latter being more resistant to NK‐specific killing as expected (n=3). In conclusion, we present here a novel and unique enrichment method for the isolation of pure NK cells in a single step. Supported by the Manitoba Health Research Council and The Dean of Medicine Strategic Research Fund.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".