Abstract P2101: Elucidating Heterogeneity Between Left And Right Ventricle-derived Cardiac Fibroblasts
Bibliographic record
Abstract
Cardiac fibrosis is a major risk factor for cardiovascular disease, leading to impaired electrical conduction and reduced ventricular compliance in the heart. Different pathophysiologic conditions can lead to fibrosis in the left ventricle (LV) and/or right ventricle (RV). Despite building evidence to support the transcriptomic heterogeneity of cardiac fibroblasts (CFs) in healthy and diseased states, there have been no direct comparisons of CFs in the LV and RV. Due to the developmental and physiologic differences between the two ventricles, we hypothesized that LV and RV-derived CFs would display transcriptomic differences that influence their proliferation and differentiation following injury. Bulk RNA-seq data from the LVs and RVs of uninjured male mice revealed 442 differentially expressed genes (p<0.05, n=4) with numerous fibrosis-related genes such as Igfbp3 , Col8a1 , Ctgf , Aspn , and Postn being the most significantly different. Single-cell RNA-seq analysis of CFs from uninjured tissue identified nine subpopulations, two of which displayed LV vs RV differences. CFs marked by high expression of Postn , Col8a1 , and Ctgf were more abundant in the LV, whereas CFs marked by high expression of Igfbp3 , Fgl2 , and Sfrp2 were more abundant in the RV. Comparisons with published datasets suggest that Postn -high CFs are primed for differentiation into injury-induced CFs. While the Igfbp3 -high population has not previously been described, top marker genes have mixed pro- and anti-fibrotic roles. We then used surgical models of pressure overload injury (TAC and PAB) to study changes in the transcriptome of LV and RV-derived CFs respectively at 14 days post-surgery. Single cell RNA-seq analysis showed that the LV developed a larger population of pro-fibrotic Thbs4 +/ Cthrc1 + injury-induced CFs while the RV uniquely showed expansion of Igfbp3 - and Inmt -high CFs. Injury experiments were repeated with female mice and showed the same results. These findings demonstrate that LV and RV-derived CFs display subpopulation differences that may cause their diverging responses to pressure overload injury. Further study of these subpopulations will elucidate their role in the development of fibrosis and inform whether LV and RV fibrosis require distinct treatments.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".