Macrophage-T Cell Physical Interaction Modulates IFN-γ Secretion
Bibliographic record
Abstract
Macrophages and T cells communicate under homeostatic and pathological conditions. Previous studies elucidated biochemical crosstalk between macrophages and T cells. However, recent technological advances in multiplex tissue imaging reveal that these cells are often located proximally. Notably, recent clinical studies link the proximity of macrophages and T cells to cancer outcomes. These observations suggest that physical contact between the two cell types might influence their phenotype and activity. However, systematic investigations of the potential role of physical contact between these cells are lacking. To address this gap, we developed three-dimensional (3D) coculture assays to study the potential role of physical contact between naïve macrophages and activated CD8 + T cells cultured in 3D Matrigel matrix. Under contact conditions, macrophages and T cells were coencapsulated in the same matrix, whereas under no-contact conditions, they were cultured in matrix, separated by a Transwell membrane. Our findings indicated that T cells changed their migration behavior by stopping and moving over the surface of naïve macrophages during coculture. We quantitatively determined T cells establish durable contact with macrophages, which informed our hypothesis that physical interactions between macrophage-T cells may induce phenotypic changes. Physical contact led to a 3-fold increase in IFN-γ secretion, a key effector molecule of CD8 + T cells. The increase in IFN-γ was mediated by JAK and led to a 2-fold upregulation of ICAM-1 and increased PD-L1 expression. Our findings point to a model whereby activated T cells migrate toward macrophages and are held in proximity by ICAM-1 adhesion molecules, leading to increased production of IFN-γ and PD-L1 expression. These results establish that physical contact is an important determinant of macrophage and T cell states. Broadly, our study establishes a foundation to use biomaterials as a tool to provide mechanistic insights into heterotypic cell interactions to enable rational manipulation of the immune response.
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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 | 0.000 |
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".