Controlling the Emergence of Hematopoietic Progenitor Cells from Pluripotent Stem Cells
Bibliographic record
Abstract
Embryogenesis occurs within a complex and dynamic cellular environment that influences cell fate decisions. Pluripotent stem cells (PSCs) are a valuable tool for research into disease models as well as a resource for cell therapy due to their capacity to self-renew and differentiate into all cell types. Mimicking aspects of the embryonic microenvironment in vitro impacts the resultant functional cells. The aim of this work was to develop a controlled and scaleable process for the generation of hematopoietic progenitor cells (HPCs) from embryonic stem cells (ESCs). We demonstrated with bioreactor-grown embryoid bodies (EBs) that increased HPC generation can be elicited by decreasing the oxygen tension by a mechanism where vascular endothelial growth factor receptor 2 (VEGFR2) activation is controlled through competition with the ligand decoy VEGFR1. This is important as it demonstrates the inherent responsiveness of the developing hematopoietic system to external forces and influences. We also established a serum-free system that facilitates directed differentiation, determining 5 ng/ml bone morphogenetic protein-4 (BMP4) with 50 ng/ml thrombopoietin (TPO) could generate 292 ± 42 colony forming cells (CFC)/5 x 10^4 cells with early VEGF treatment (25 ng/ml, day 0-5). We also controlled aggregate size influencing relative endogenous and exogenous growth factor signaling and modulating mesodermal differentiation; CFC output was optimal when initialized with 100 cell aggregates. For the first time, we demonstrated efficacy of local growth factor delivery by producing HPCs with gelatin microparticles (MP). Overall, these design components generate HPCs in a controlled and reproducible manner using a serum-free bioprocess that couples size controlled aggregates containing gelatin MPs for localized growth factor release of BMP4 and TPO with hypoxia to induce endogenous VEGF production. These strategies provide a tunable platform for developing cell therapies and high density growth, within a bioreactor system, can be facilitated by hydrogel encapsulation of the aggregates.
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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".