External signals regulate continuous transcriptional states in hematopoietic stem cells
Bibliographic record
Abstract
Hematopoietic stem cells (HSCs) must ensure adequate blood cell production following distinct external stressors. A comprehensive understanding of in vivo heterogeneity and specificity of HSC responses to external stimuli is currently lacking. We performed single-cell RNA sequencing (scRNA-Seq) on functionally validated mouse HSCs and LSK (Lin-, c-Kit+, Sca1+) progenitors after in vivo pharmacological perturbation of niche signals interferon, granulocyte colony-stimulating factor (G-CSF), and prostaglandin. We identified six HSC states that are characterized by enrichment but not exclusive expression of marker genes. External signals induced rapid transitions between HSC states but transcriptional response varied both between external stimulants and within the HSC population for a given perturbation. In contrast to LSK progenitors, HSCs were characterized by a greater link between molecular signatures at baseline and in response to external stressors. Chromatin analysis of unperturbed HSCs and LSKs by scATAC-Seq suggested some HSC-specific, cell intrinsic predispositions to niche signals. We compiled a comprehensive resource of HSC- and LSK progenitor-specific chromatin and transcriptional features that represent determinants of signal receptiveness and regenerative potential during stress 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 | 0.000 |
| 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".