Cytokines and BMP-4 promote hematopoietic differentiation of human embryonic stem cells
Bibliographic record
Abstract
Human embryonic stem cells (hESCs) randomly differentiate into multiple cell types during embryoid body (EB) development. To date, characterization of specific factors capable of influencing hematopoietic cell fate from hESCs remains elusive. Here, we report that the treatment of hESCs during EB development with a combination of cytokines and bone morphogenetic protein-4 (BMP-4), a ventral mesoderm inducer, strongly promotes hematopoietic differentiation. Hematopoietic progenitors of multiple lineages were generated from EBs and were found to be restricted to the population of progeny expressing cell surface CD45. Addition of BMP-4 had no statistically significant effect on hematopoietic differentiation but enabled significant enhancement in progenitor self-renewal, independent of cytokine treatment. Hematopoietic commitment was characterized as the temporal emergence of single CD45+ cells first detectable after day 10 of culture and was accompanied by expression of hematopoietic transcription factors. Despite the removal of cytokines at day 10, hematopoietic differentiation of hESCs continued, suggesting that cytokines act on hematopoietic precursors as opposed to differentiated hematopoietic cells. Our study establishes the first evidence for the role of cytokines and BMP-4 in promoting hematopoietic differentiation of hESC lines and provides an unprecedented system to study early developmental events that govern the initiation of hematopoiesis in the human.
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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".