Matrix alignment and density modulate YAP-mediated T-cell immune suppression
Bibliographic record
Abstract
Abstract T-cells navigate through various mechanical environments within the body, adapting their behavior in response to these cues. An altered extracellular matrix (ECM) characterized by increased density and enhanced fibril alignment, as observed in cancer tissues, can significantly impact essential T-cell functions critical for immune responses. In this study, we used 3D collagen matrices with controlled density and fibril alignment to investigate T-cell migration, activation, and proliferation. Our results revealed that dense and aligned collagen matrices suppress T-cell activation through enhanced YAP signaling. By inhibiting YAP signaling, we demonstrated that T-cell activation within these challenging microenvironments improved, suggesting potential strategies to enhance the efficacy of immunotherapy by modulating T-cell responses in dense and aligned ECMs. Overall, our study deepens our understanding of T-cell mechanobiology within 3D relevant cellular microenvironments and provides insights into countering ECM-induced T-cell immunosuppression in diseases such as cancer. Graphical abstract Dense and aligned extracellular matrices suppress T-cell activation via YAP signaling, affecting immunotherapy efficacy in diseases such as cancer.
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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) | 0.005 | 0.000 |
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".