Modeling cardiac fibroblast heterogeneity in fibrotic heart using human pluripotent stem cell derived epicardial cells
Bibliographic record
Abstract
Abstract Introduction Our understanding of human cardiac pathophysiology is limited due to the scarcity of human heart samples. Model organisms do not faithfully recapitulate human physiology and pathophysiology to the extent that is needed. Therefore, human pluripotent stem cell (hPSC)-derived cardiac cells represent promising sources to overcome these limitations to studying heart diseases. Purpose To generate cardiac organoids mimicking the interaction between cardiomyocytes and non-cardiomyocytes in the developing heart and the postnatal pathological conditions. Method It has been well known that epicardium is a main source of non-cardiac cells in the developing heart. We therefore generated cardiac organoids by mixing hPSC-derived cardiomyocytes and hPSC-derived epicardium and test if they could mimic the developing heart and be applicable for modeling heart failure. Results We established a cardiac organoid system generated from human pluripotent stem cells that models the developing heart's early interactions, specifically the epicardial and non-myocyte development, as well as ventricular cardiomyocyte proliferation. Inside the cardiac organoids, epicardium spontaneously differentiated into CD90-positive cardiac fibroblasts expressing fibroblast markers, FN, COL1 and COL3, and CD90-negative smooth muscle cells (SMC) expressing SMC markers, ACTA2 and MYH11. Additionally, we observed the significant increase in cardiomyocyte proliferation in the cardiac organoids compared to in cardiomyocyte only aggregation (p<0.001). We then mature our cardiac organoids using our unique maturation method, the combination of PPARα agonist, dexamethasone, T3 hormone, and palmitate, generating metabolically mature cardiac organoids. These mature organoids recapitulated the phenotypic changes observed in the failing human heart when exposed to pathological stimuli, including fibrosis, metabolic changes, and increase in heart failure markers. We used single-cell transcriptomics to dissect the cellular heterogeneity of our organoids in a model of heart failure with comparisons to primary human data and revealed our organoids recapitulated in vivo human failing heart. This analysis allowed to confirm that our organoids contain a unique subpopulation of cardiac fibroblasts possessing reparative features, suggesting the recapitulation of in vivo heterogeneity in our model. Conclusions We successfully generated cardiac organoids mimicking the interaction between cardiomyocytes and non-cardiac cells. Our system enables the recapitulation of in vivo heterogeneity, providing a platform for the accurate understanding of heart development and disease in a dish. Funding Acknowledgement Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Canadian Institutes of Health Research (CIHR)
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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 | 0.000 | 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 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".