The formation of the ‘footprint of death’ as a mechanism for generating large substrate-bound extracellular vesicles that mark the site of cell death
Bibliographic record
Abstract
Apoptotic cells communicate to phagocytic cells through releasing soluble factors and apoptotic cell-derived extracellular vesicles. However, whether there are additional factors that remain attached at the site of cell death to signal to phagocytic cells is currently unknown. Here we show that apoptotic cell retraction generates a membrane-encased, F-actin-rich 'footprint' tightly anchored to the substrate that marks the site of cell death, coined 'the FOotprint Of Death' or FOOD. Formation of FOOD is observed frequently across many different cell types, apoptotic stimuli and surface composition. Mechanistically, FOOD formation is regulated by the protein kinase ROCK1. 3D time-lapse microscopy studies revealed that FOOD vesicularises into distinct large extracellular vesicles. These extracellular vesicles expose the 'eat-me' signal phosphatidylserine and can function to 'flag' the site of cell death to neighbouring phagocytes for efferocytosis. Under a viral infection setting, FOOD can harbour viral proteins and virions, and propagate infection to healthy cells. Together, this study has revealed another route of apoptotic cell-phagocyte communication.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".