Isolation and expansion of pure and functional γδ T cells
Bibliographic record
Abstract
Abstract γδ T cells are important components of the immune system due to their ability to elicit a fast and strong response against infected and transformed cells. Because they can specifically and effectively kill target cells in an MHC independent fashion, there is great interest to utilize these cells in anti-tumor therapies where antigen presentation may be hampered. Since only a small fraction of T cells in the blood or tumor tissue are γδ T cells, they require extensive expansion to allow for fundamental, preclinical and ex vivo research. Although expansion protocols can be successful, most are based on depletion of other cell types rather than γδ T cell specific isolation, resulting in unpredictable purity of the isolated fraction. Moreover, the primary focus only lies with expansion of Vδ2 + T cells, while Vδ1 + T cells likewise have anti-tumor potential. Here, we investigated whether γδ T cells directly isolated from blood could be efficiently expanded while maintaining function. γδ T cell subsets were isolated using MACS separation, followed by FACS sorting, yielding >99% pure γδ T cells. Isolated Vδ1 + and Vδ2 + T cells could effectively expand immediately after isolation or upon freeze/thawing and reached expansion ratios between 200 to 2000-fold starting from varying numbers using cytokine supported feeder stimulations. After expansion, potential effector functions of γδ T cells were demonstrated by IFN-γ, TNF-α and granzyme B production upon PMA/ionomycin stimulation and effective killing capacity of multiple tumor cell lines was confirmed in killing assays. In conclusion, pure γδ T cells can productively be expanded while maintaining their anti-tumor effector functions against tumor cells. Moreover, γδ T cells could be expanded from low starting numbers suggesting that this protocol may even allow for expansion of cells extracted from tumor biopsies.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".