Modulating cell state to enhance suspension expansion of human pluripotent stem cells
Bibliographic record
Abstract
The development of cell-based therapies to replace missing or damaged tissues within the body or generate cells with a unique biological activity requires a reliable and accessible source of cells. Human pluripotent stem cells (hPSC) have emerged as a strong candidate cell source capable of extended propagation in vitro and differentiation to clinically relevant cell types. However, the application of hPSC in cell-based therapies requires overcoming yield limitations in large-scale hPSC manufacturing. We explored methods to convert hPSC to alternative states of pluripotency with advantageous bioprocessing properties, identifying a suspension-based small-molecule and cytokine combination that supports increased single-cell survival efficiency, faster growth rates, higher densities, and greater expansion than control hPSC cultures. ERK inhibition was found to be essential for conversion to this altered state, but once converted, ERK inhibition led to a loss of pluripotent phenotype in suspension. The resulting suspension medium formulation enabled hPSC suspension yields 5.7 ± 0.2-fold greater than conventional hPSC in 6 d, for at least five passages. Treated cells remained pluripotent, karyotypically normal, and capable of differentiating into all germ layers. Treated cells could also be integrated into directed differentiated strategies as demonstrated by the generation of pancreatic progenitors (NKX6.1+/PDX1+ cells). Enhanced suspension-yield hPSC displayed higher oxidative metabolism and altered expression of adhesion-related genes. The enhanced bioprocess properties of this alternative pluripotent state provide a strategy to overcome cell manufacturing limitations of hPSC.
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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".