Integrated analysis of high throughput transcriptomic data revealed specific gene expression signature of cardiomyocytes
Bibliographic record
Abstract
Abstract Acquiring a specific transcriptomic signature of the human and mouse cardiomyocyte (CM) will greatly increase our understanding of their biology and associated diseases that remain the most deadly across the world. In this study, using comprehensive transcriptomic mining of 91 cell types over 877 samples from bulk RNA-sequencing, single cell RNA-sequencing, and microarray techniques, we describe a unique 118-gene signature of human and mouse primary CMs. Once we had access to this CM-specific gene signature, we investigated the spatial heterogeneity of CMs throughout the heart tissue. Moreover, we compared the CM-specific gene signature to that of CMs derived from 10 differentiation protocols, and we identified the protocols that generate cells most similar to primary CMs. Finally, we looked at the specific differences between primary and differentiated CMs and found that differentiated cells underexpress genes related to CM development and maturity. The differentiated cells conversely overexpressed cell cycle-related genes, resulting in the progenitor features that remain in differentiated CMs compared to primary adult CMs. The presence of histone post translational modification H3K27ac from ChIP sequencing data sets were used to confirm transcriptomic findings. To the best of our knowledge, this is the most comprehensive study to date that unravels the unique transcriptomic signature of primary and differentiated CMs. This study provides important insights into our understanding of CM biology and the molecular mechanisms that make them such a unique cell type. Moreover, the specific transcriptomic signature of CMs could be used in developmental studies, stem cell therapy, regenerative medicine, and drug screening assays.
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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 | 0.001 | 0.001 |
| 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".