Cooperative motility emerges in crowds of T cells but not neutrophils
Bibliographic record
Abstract
Abstract Interacting, self-propelled particles are prone to jamming when crowded. This well-described phenomenon is shared by diverse systems including cars, animal colonies, and pedestrians. T cells, essential effectors of adaptive immunity, seemingly defy this principle: the rapid migration enabling their protective function persists even in tightly packed tissue environments – from the thymus where T cells develop, to lymphoid organs they survey for antigen, to tissues they clear from infection. Here we studied T cell crowds by combining experiments of T cells migrating in microfluidic devices with in silico models. We observed that while single T cells are highly heterogeneous in their motility, in crowds they synchronized their speeds and formed stable, motile trains. Our models showed that the emergence of this flocking-like behavior can be explained by a combination of two interaction mechanisms at the cell-cell interface: adhesion maintains cohesive T cell groups, and force transmission accelerates slower cells. Not all immune cells flock when they are crowded: neutrophils in the same settings slowed down with increasing cell density. Thus, cooperative motion may enable T cells to remain motile in densely packed tissue environments, preventing jams that curtail the motion of other crowded systems.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".