Direct Differentiation of Human Embryonic Stem Cells to 3D Functional Hepatocyte-like Cells in Alginate Microencapsulation Sphere
Bibliographic record
Abstract
BACKGROUND: The lack of a stable source of hepatocytes is one of major limitations in hepatocyte transplantation and clinical applications of a bioartificial liver. Human embryonic stem cells (hESCs) with a high degree of self-renewal and totipotency are a potentially limitless source of a variety of cell lineages, including hepatocytes. Many techniques have been developed for effective differentiation of hESCs into functional hepatocyte-like cells. However, the application of hESC-derived hepatocyte-like cells (hESC-Heps) in the clinic has been constrained by the low yield of fully differentiated cells, small-scale culture, difficulties in harvesting, and immunologic graft rejection. To resolve these shortcomings, we developed a novel 3D differentiation system involving alginate-microencapsulated spheres to improve current hepatic differentiation, providing ready-to-use hESC-Heps. METHODS: In this study, we used alginate microencapsulation technology to differentiate human embryonic stem cells into hepatocyte-like cells (hESC-Heps). Hepatic markers of hESC-Heps were examined by qPCR and Western blotting, and hepatic functions of hESC-Heps were evaluated by indocyanine-green uptake and release, and ammonia removal. RESULTS: The maturity and hepatic functions of the hESC-Heps derived from this 3D system were better than those derived from 2D culture. Hepatocyte-enriched genes, such as HNF4α, AFP, and ALB, were expressed at higher levels in 3D hESC-Heps than in 2D hESC-Heps. 3D hESC-Heps could metabolize indocyanine green and had better capacity to scavenge ammonia. In addition, the 3D sodium alginate hydrogel microspheres could block viral entry into the microspheres, and thus protect hESC-Heps in 3D microspheres from viral infection. CONCLUSION: We developed a novel 3D differentiation system for differentiating hESCs into hepatocyte-like cells by using alginate microcapsules.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".