Substrate Rigidity Regulates Human T Cell Activation and Proliferation
Bibliographic record
Abstract
Adoptive immunotherapy using cultured T cells holds promise for the treatment of cancer and infectious disease. Ligands immobilized on surfaces fabricated from hard materials such as polystyrene plastic are commonly employed for T cell culture. The mechanical properties of a culture surface can influence the adhesion, proliferation, and differentiation of stem cells and fibroblasts. We therefore explored the impact of culture substrate stiffness on the ex vivo activation and expansion of human T cells. We describe a simple system for the stimulation of the TCR/CD3 complex and the CD28 receptor using substrates with variable rigidity manufactured from poly(dimethylsiloxane), a biocompatible silicone elastomer. We show that softer (Young's Modulus [E] < 100 kPa) substrates stimulate an average 4-fold greater IL-2 production and ex vivo proliferation of human CD4(+) and CD8(+) T cells compared with stiffer substrates (E > 2 MPa). Mixed peripheral blood T cells cultured on the stiffer substrates also demonstrate a trend (nonsignificant) toward a greater proportion of CD62L(neg), effector-differentiated CD4(+) and CD8(+) T cells. Naive CD4(+) T cells expanded on softer substrates yield an average 3-fold greater proportion of IFN-γ-producing Th1-like cells. These results reveal that the rigidity of the substrate used to immobilize T cell stimulatory ligands is an important and previously unrecognized parameter influencing T cell activation, proliferation, and Th differentiation. Substrate rigidity should therefore be a consideration in the development of T cell culture systems as well as when interpreting results of T cell activation based upon solid-phase immobilization of TCR/CD3 and CD28 ligands.
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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 | 0.000 |
| 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".