Synergistic Effect of Medium, Matrix, and Exogenous Factors on the Adhesion and Growth of Human Pluripotent Stem Cells Under Defined, Xeno-Free Conditions
Bibliographic record
Abstract
Human pluripotent stem cells (hPSCs), including human embryonic stem cells (hESCs) and human induced pluripotent stem cells (hiPSCs), share the properties of unlimited self-renewal and the capacity to become any cell type in the body, making them well suited for regenerative medicine and cell therapy. So far, almost all hPSC lines have been directly or indirectly exposed to animal-derived products, which would hinder their use for clinical purposes. One of the biggest challenges in this area is to remove animal components from the derivation, propagation, and cryopreservation of hPSCs. Moreover, the presence of undefined components of animal or human origin in culture system may interfere with the interpretation of the effect of exogenous agents on the growth and differentiation of hPSCs and are prone to significant variability. To explore hPSC expansion in defined, xeno-free conditions, 2 different groups of culture systems were used to culture different hESC and hiPSC lines. Our results suggested that (1) medium, matrix, and exogenous factors have synergistic effects on the adhesion and growth of hPSCs; (2) cooperation of exogenous factors including basic fibroblast growth factor, Rho-associated kinase inhibitor (ROCK), and other growth factors is critical for hPSC adhesion and proliferation; (3) basal media have different effects on hPSC attachment to the culture surface; and (4) a medium or matrix component can work synergistically in one culture system, and not at all in another. In this study, we found that Vitronectin/TeSR2 and PDL/HEScGRO (Y-27632) systems were optimal for maintaining the long-term culture of 3 hESC lines and 2 hiPSC lines under defined, xeno-free conditions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".