Platelet-derived extracellular vesicles infiltrate and modify the bone marrow during inflammation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
During inflammation, steady-state hematopoiesis switches to emergency hematopoiesis to repopulate myeloid cells, with a bias toward the megakaryocytic lineage. Soluble inflammatory cues are thought to be largely responsible for these alterations. However, how these plasma factors rapidly alter the bone marrow (BM) is not understood. Inflammation also drives platelet activation, causing the release of platelet-derived extracellular vesicles (PEVs), which package diverse cargo and reprogram target cells. We hypothesized that PEVs infiltrate the BM, providing a direct mode of communication between the plasma and BM environments. We transfused fluorescent, wild-type (MPL+) platelets into recipient cMpl-/-mice before triggering systemic inflammation. Twenty hours postinfusion, we observed significant infiltration of donor platelet-derived particles in the BM, which we tracked immunophenotypically (MPL+ immunohistochemistry staining) and quantified by flow cytometry. To determine if this phenomenon relates to humans, we extensively characterized both megakaryocyte-derived and PEVs generated in vitro and in vivo, and found enrichment of extracellular vesicles in bone marrow compared with autologous peripheral blood. Last, BM from cMpl-/- mice was cultured in the presence or absence of wild-type (MPL+) PEVs. After 72 hours, flow cytometry revealed increased megakaryocytes only in cultures with added PEVs. The majority of CD41+ cells were bound to PEVs, suggesting a PEV-mediated rescue of megakaryopoiesis. In conclusion, we report for the first time that plasma-residing PEVs infiltrate the BM. Further, PEVs interact with BM cells in vivo and in vitro, causing functional reprogramming that may represent a novel model of inflammation-induced hematopoiesis.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it