A fast and simple method for the isolation of untouched human gamma-delta T cells from PBMC (45.7)
Bibliographic record
Abstract
Abstract T cells expressing the γδT cell receptor (γδTCR) comprise a minor subset of human circulating T cells (1-10%). γδT cells are distinct from αβT cells in that they exhibit limited combinatorial diversity of the TCR and recognize non-peptide antigens independent of HLA molecules. γδT cells exert innate effector functions including rapid release of cytokines and killing of target cells. Adaptive immune responses such as memory functions have also been attributed to γδT cells. Typically, elaborate purification protocols such as FACS-based cell sorting or expansion in culture are needed to obtain enough γδT cells for subsequent studies. Here, we describe a negative selection method to isolate untouched γδT cells from fresh or previously frozen peripheral blood mononuclear cells (PBMC). This method uses immunomagnetic, column-free cell separation technology (EasySepTM). Briefly, bispecific antibody complexes are used to cross-link non-γδT cells to dextran-coated magnetic particles. The unwanted cells are then removed using an EasySepTM magnet. Starting with 3±0.9% γδTCR+ T cells in PBMC, purities of 90±2% (n=7) are achieved. The isolated γδT cells produce IFN-γ when activated with non-peptide antigens. γδT cells have potential therapeutic applications in cancer and infectious diseases. This rapid method for the isolation of γδT cells will assist in the study of γδT cell biology and the development of γδT cell-based immunotherapies.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.007 |
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".