Generation of Complex Syngeneic Liver Organoids from Induced Pluripotent Stem Cells to Model Human Liver Pathophysiology
Bibliographic record
Abstract
The study of human liver pathophysiology has been hampered for decades by the lack of easily accessible, robust, and representative in vitro models. The discovery of induced pluripotent stem cells (iPSCs)-which can be generated from patients' somatic cells, engineered to harbor specific mutations, and differentiated into hepatocyte-like cells-opened the way to more meaningful modeling of liver development and disease. Nevertheless, representative modeling of many complex liver conditions requires the recreation of the interplay between hepatocytes and nonparenchymal liver cells. Here we describe protocols we developed to generate and characterize complex human liver organoids composed of iPSC-derived hepatic, endothelial, and mesenchymal cells. With all cell types derived from the same iPSC population, such organoids reproduce the liver niche, allowing for the study of liver development and the modeling of complex inflammatory and fibrotic conditions. © 2022 Wiley Periodicals LLC. Basic Protocol 1: Differentiation of human iPSCs into hepatic progenitor cells (hepatoblasts) Basic Protocol 2: Differentiation of human iPSCs into endothelial progenitor cells Support Protocol 1: Characterization of iPSC-derived endothelial progenitor cells Basic Protocol 3: Differentiation of human iPSCs into mesenchymal progenitor cells Support Protocol 2: Characterization of iPSC-derived mesenchymal progenitor cells Basic Protocol 4: Generation of complex syngeneic liver organoids.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".