Generation of Complex Syngeneic Liver Organoids from Induced Pluripotent Stem Cells to Model Human Liver Pathophysiology
Why this work is in the frame
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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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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) | 0.001 | 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 it