Modeling acute myocardial infarction and cardiac fibrosis using human induced pluripotent stem cell-derived multi-cellular heart organoids
Bibliographic record
Abstract
Heart disease involves irreversible myocardial injury that leads to high morbidity and mortality rates. Numerous cell-based cardiac in vitro models have been proposed as complementary approaches to non-clinical animal research. However, most of these approaches struggle to accurately replicate adult human heart conditions, such as myocardial infarction and ventricular remodeling pathology. The intricate interplay between various cell types within the adult heart, including cardiomyocytes, fibroblasts, and endothelial cells, contributes to the complexity of most heart diseases. Consequently, the mechanisms behind heart disease induction cannot be attributed to a single-cell type. Thus, the use of multi-cellular models becomes essential for creating clinically relevant in vitro cell models. This study focuses on generating self-organizing heart organoids (HOs) using human-induced pluripotent stem cells (hiPSCs). These organoids consist of cardiomyocytes, fibroblasts, and endothelial cells, mimicking the cellular composition of the human heart. The multi-cellular composition of HOs was confirmed through various techniques, including immunohistochemistry, flow cytometry, q-PCR, and single-cell RNA sequencing. Subsequently, HOs were subjected to hypoxia-induced ischemia and ischemia-reperfusion (IR) injuries within controlled culture conditions. The resulting phenotypes resembled those of acute myocardial infarction (AMI), characterized by cardiac cell death, biomarker secretion, functional deficits, alterations in calcium ion handling, and changes in beating properties. Additionally, the HOs subjected to IR efficiently exhibited cardiac fibrosis, displaying collagen deposition, disrupted calcium ion handling, and electrophysiological anomalies that emulate heart disease. These findings hold significant implications for the advancement of in vivo-like 3D heart and disease modeling. These disease models present a promising alternative to animal experimentation for studying cardiac diseases, and they also serve as a platform for drug screening to identify potential therapeutic targets.
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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) | 0.000 | 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 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".