Deciphering the effect of UM171 on human hematopoietic progenitor cell fate through clonal analysis
Bibliographic record
Abstract
Ex vivo expansion of hematopoietic stem cells (HSC) requires the maintenance of a stemness state while cells are proliferating. This can be achieved via exposure to UM171 which leads to the degradation of chromatin modifiers and prevents the loss of key epigenetic marks. However, the chromatin landscape varies across populations within the hematopoietic system and the effect of UM171 on self-renewal and differentiation potential of different hematopoietic progenitor cells is less characterized. To address this, we use the CellTag barcoding approach to track the fate of individual stem and progenitor cells during in vitro expansion. We show that, in addition to its HSC self-renewing property, UM171 specifically modulates cell fate of a precursor common to erythroid, megakaryocytic, and mast cells in favor of self-renewal and a mast-bias differentiation trajectory. This differentiation bias can be driven by pro-inflammatory signaling pathways that are activated downstream of UM171 and results in an abundant mast cell population that can be transplanted as part of the graft to populate mice tissues in xenotransplantation studies. UM171 is being used to promote self-renewal and expansion of human hematopoietic stem cells in vitro. Here, authors show through clonal analysis that UM171 also biases the differentiation trajectories of hematopoietic progenitor cells in culture.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".