Leveraging interacting signaling pathways to robustly improve the quality and yield of human pluripotent stem cell-derived hepatoblasts and hepatocytes
Bibliographic record
Abstract
Pluripotent stem cell (PSC)-derived hepatocyte-like cells (HLCs) have shown great potential as an alternative to primary human hepatocytes (PHHs) for in vitro modeling. Several differentiation protocols have been described to direct PSCs toward the hepatic fate. Here, by leveraging recent knowledge of the signaling pathways involved in liver development, we describe a robust, scalable protocol that allowed us to consistently generate high-quality bipotent human hepatoblasts and HLCs from both embryonic stem cells and induced PSC (iPSCs). Although not yet fully mature, such HLCs were more similar to adult PHHs than were cells obtained with previously described protocols, showing good potential as a physiologically representative alternative to PHHs for in vitro modeling. PSC-derived hepatoblasts effectively generated with this protocol could differentiate into mature hepatocytes and cholangiocytes within syngeneic liver organoids, thus opening the way for representative human 3D in vitro modeling of liver development and pathophysiology.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".