Steel factor coordinately regulates the molecular signature and biologic function of hematopoietic stem cells
Bibliographic record
Abstract
Hematopoietic stem cells (HSCs) regenerated in vivo display sustained differences in their self-renewal and differentiation activities. Variations in Steel factor (SF) signaling are known to affect these functions in vitro, but the cellular and molecular mechanisms involved are not understood. To address these issues, we evaluated highly purified HSCs maintained in single-cell serum-free cultures containing 20 ng/mL IL-11 plus 1, 10, or 300 ng/mL SF. Under all conditions, more than 99% of the cells traversed a first cell cycle with similar kinetics. After 8 hours in the 10 or 300 ng/mL SF conditions, the frequency of HSCs remained unchanged. However, in the next 8 hours (ie, 6 hours before any cell divided), HSC integrity was sustained only in the 300 ng/mL SF cultures. The cells in these cultures also contained significantly higher levels of Bmi1, Lnk, and Ezh2 transcripts but not of several other regulators. Assessment of 21 first division progeny pairs further showed that only those generated in 300 ng/mL SF cultures contained HSCs and pairs of progeny with similar differentiation programs were not observed. Thus, SF signaling intensity can directly and coordinately alter the transcription factor profile and long-term repopulating ability of quiescent HSCs before their first division.
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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) | 0.001 | 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".