Single-cell fate decisions of bipotential hematopoietic progenitors
Bibliographic record
Abstract
PURPOSE OF REVIEW: In hematopoiesis, rapid cell fate decisions are necessary for timely responses to environmental stimuli resulting in the production of diverse types of blood cells. Early studies have led to a hierarchical, tree-like view of hematopoiesis with hematopoietic stem cells residing at the apex and serially branching out to give rise to bipotential progenitors with increasingly restricted lineage potential. Recent single-cell studies have challenged some aspects of the classical model of hematopoiesis. Here, we review the latest articles on cell fate decision in hematopoietic progenitors, highlighting single-cell studies that have questioned previously established concepts and those that have reaffirmed them. RECENT FINDINGS: The hierarchical organization of hematopoiesis and the importance of transcription factors have been largely validated at the single-cell level. In contrast, single-cell studies have shown that lineage commitment is progressive rather than switch-like as originally proposed. Furthermore, the reconstruction of cell fate paths suggested the existence of a gradient of hematopoietic progenitors that are in a continuum of changing fate probabilities rather than in a static bipotential state, leading us to reconsider the notion of bipotential progenitors. SUMMARY: Single-cell transcriptomic and proteomic studies have transformed our view of lineage commitment and offer a drastically different perspective on hematopoiesis.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".