Perivascular niche cells sense thrombocytopenia and activate hematopoietic stem cells in an IL-1 dependent manner
Bibliographic record
Abstract
Abstract Hematopoietic stem cells (HSCs) residing in specialized niches in the bone marrow are responsible for the balanced output of multiple short-lived blood cell lineages in steady-state and in response to different challenges. However, feedback mechanisms by which HSCs, through their niches, sense acute losses of specific blood cell lineages remain to be established. While all HSCs replenish platelets, previous studies have shown that a large fraction of HSCs are molecularly primed for the megakaryocyte-platelet lineage and are rapidly recruited into proliferation upon platelet depletion. Platelets normally turnover in an activation-dependent manner, herein mimicked by antibodies inducing platelet activation and depletion. Antibody-mediated platelet activation upregulates expression of Interleukin-1 (IL-1) in platelets, and in bone marrow extracellular fluid in vivo. Genetic experiments demonstrate that rather than IL-1 directly activating HSCs, activation of bone marrow Lepr+ perivascular niche cells expressing IL-1 receptor is critical for the optimal activation of quiescent HSCs upon platelet activation and depletion. These findings identify a feedback mechanism by which activation-induced depletion of a mature blood cell lineage leads to a niche-dependent activation of HSCs to reinstate its homeostasis.
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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) | 0.002 | 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".