Optimization of human T cell activation and expansion protocols improves efficiency of genetic modification and overall cell yield
Bibliographic record
Abstract
Abstract Cancer immunotherapy using CAR T-cells is a rapidly progressing field and manufacturing these cells is a complex process that requires multiple optimization steps. We have developed reagents for the isolation, activation and expansion of human T cells that will be available for clinical cell therapy manufacturing. Soluble ImmunoCult™ Human T Cell Activators induce T cell activation via cross-linking CD3 and co-stimulatory molecules on the surface of cells. Activated T cells then can be genetically modified and subsequently expanded in ImmunoCult™-XF, a serum- and xeno-free T cell expansion medium. Here, we present several optimization strategies with ImmunoCult™ products in order to obtain high transfection efficiency and maximum cell yield. By evaluating activation dynamics of T cells and determining the optimal transfection time points, the transfection efficiency can be substantially improved in both CRISPR/Cas9- and lentiviral-mediated gene-modification methods. Our study also suggests that maintaining T cells at lower cell density after the third day following activation greatly improves cell viability and cumulative cell growth, resulting in an >1000-fold expansion of total human T cells with >85% viability over 10–12 days of culture. Expanded T cells co-express CD45RO+CD62L+ with low expression of PD-1. As an example, we applied the workflow described here to generate TCRαβ KO T cells from healthy donors with up to 90% knockout efficiency. The purity of TCRαβ KO T cells can be further increased with the use of an EasySep™ Human TCRαβ depletion kit. Taken together, the processes outlined in this study can be easily and rapidly implemented to improve T cell manufacturing efficacy.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.003 | 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".