Transcriptional dynamics of the murine heart during perinatal development at single-cell resolution
Bibliographic record
Abstract
Abstract Heart maturation and remodelling during the foetal and early postnatal period are critical for proper survival and growth of the foetus, yet our knowledge of the molecular processes involved are lacking for many cardiac cell types. To gain a deeper understanding of the transcriptional dynamics of the heart during the perinatal period, we performed single-cell RNA-seq on E14.5, E16.5, E18.5, P0, P4 and P7 mouse hearts to establish a catalogue of 49,769 cells. Gene regulatory network and pathway activity analyses underscored that heart maturation is strongly associated with regulation of cell growth and proliferation via pathways such as TGFβ. We additionally identified a common, cell type-independent signature for imprinted genes over time. Surprisingly, bioinformatics analyses and confirmation with RNAscope confirmed that while lncRNA H19 expression decreased over time in multiple cardiac cell types, it remained stably expressed in endocardial cells between E14.5 and P7. This suggests a differential requirement for H19 in the endocardium, and points towards an endocardium-specific maturation process when compared to other cardiac cell types. We envision this dataset to serve as a resource for better understanding perinatal heart maturation at the transcriptomic level, and to help bridge the gap between early developmental and adult heart stages for single-cell transcriptomics.
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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 | 0.000 |
| 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) | 0.002 | 0.001 |
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".