Rational bioprocess design for human pluripotent stem cell expansion and endoderm differentiation based on cellular dynamics
Bibliographic record
Abstract
We present a predictive bioprocess design strategy employing cell- and molecular-level analysis of rate-limiting steps in human pluripotent stem cell (hPSC) expansion and differentiation, and apply it to produce definitive endoderm (DE) progenitors using a scalable directed-differentiation technology. We define a bioprocess optimization parameter (L; targeted cell Loss) and, with quantitative cell division tracking and fate monitoring, identify and overcome key suspension bioprocess bottlenecks. Adapting process operating conditions to pivotal parameters (single cell survival and growth rate) in a cell-line-specific manner enabled adherent-equivalent expansion of hPSCs in feeder- and matrix-free defined-medium suspension culture. Predominantly instructive differentiation mechanisms were found to underlie a subsequent 18-fold expansion, during directed differentiation, to high-purity DE competent for further commitment along pancreatic and hepatic lineages. This study demonstrates that iPSC expansion and differentiation conditions can be prospectively specified to guide the enhanced production of target cells in a scale-free directed differentiation system.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".