Paracrine mechanisms in early differentiation of human pluripotent stem cells: Insights from a mathematical model
Bibliographic record
Abstract
With their capability to self-renew and differentiate into derivatives of all three germ layers, human pluripotent stem cells (hPSCs) offer a unique model to study aspects of human development in vitro. Directed differentiation towards mesendodermal lineages is a complex process, involving transition through a primitive streak (PS)-like stage. We have recently shown PS-like patterning from hPSCs into definitive endoderm, cardiac as well as presomitic mesoderm by only modulating the bulk cell density and the concentration of the GSK3 inhibitor CHIR99021, a potent activator of the WNT pathway. The patterning process is modulated by a complex paracrine network, whose identity and mechanistic consequences are poorly understood. To study the underlying dynamics, we here applied mathematical modeling based on ordinary differential equations. We compared time-course data of early hPSC differentiation to increasingly complex model structures with incremental numbers of paracrine factors. Model simulations suggest at least three paracrine factors being required to recapitulate the experimentally observed differentiation kinetics. Feedback mechanisms from both undifferentiated and differentiated cells turned out to be crucial. Evidence from double knock-down experiments and secreted protein enrichment allowed us to hypothesize on the identity of two of the three predicted factors. From a practical perspective, the mathematical model predicts optimal settings for directing lineage-specific differentiation. This opens new avenues for rational stem cell bioprocessing in more advanced culture systems, e.g. in perfusion-fed bioreactors enabling cell therapies.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".