Bibliographic record
Abstract
Umbilical cord blood (UCB) cells fill a clinical need for hematopoietic stem and progenitor cell (HSPC) transplantation, and the ex vivo expansion of these cells provides an approach to increase their clinical relevance. Ex vivo HSPC expansion has been limited by a poor understanding of the interplay between stem cell autonomous and feedback mediated regulators. We hypothesized that developing strategies to modulate the HSPC microenvironment would enable enhanced expansion of these cells and a greater insight into the underlying biologic mechanisms. We developed a system for the optimized delivery of the transcription factor HOXB4 to human hematopoietic culture and revealed new insights into the context-dependent potentials and limitations of HOXB4. We investigated approaches for the global reduction of inhibitory feedback signaling and developed a fed-batch system that minimizes endogenously produced factors to create a more supportive environment for HSPC self-renewal. This strategy led to an 11-fold expansion of long-term repopulating HSCs in a clinically relevant bioreactor, producing a novel system for ex vivo expansion and generating a platform to assess HSPC enhancing factors. By combining the fed-batch system with the Notch Delta-1 ligand (DL1), we identified a mechanism whereby DL1 initiated a conversion from IL-6 cis-signaling to trans-signaling, resulting in the modulation of mature cell population production. This demonstrated the impact of cell lineage skewing on microenvironment regulation and the expansion of HSPCs. This work demonstrates how cell-cell interactions and feedback mediated signaling are critical regulators of HSPCs and the manipulation of these regulators can be used both to engineer HSPC expansion processes and to identify novel mechanisms within the hematopoietic system. This provides an important step towards the development of robust methods for the ex vivo expansion of UCB-derived cells and the ultimate goal of achieving cures in hematologic disease.
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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".