Abstract 4142546: Single-Nucleus RNA Sequencing Reveals Region-Specific Cell Composition and Transcriptional Variations in Cardiac Sarcoidosis
Bibliographic record
Abstract
Introduction: Sarcoidosis is a granulomatous disease of unknown cause that can affect any organ. Cardiac involvement, indicated by arrhythmias, conduction abnormalities, heart failure, and sudden cardiac death, occurs in about 5% of cases and accounts for 50% of sarcoid-related deaths. Yet, autopsy studies have found cardiac granulomas in 15-90% of patients. The localized nature of granulomas in the heart makes diagnosis difficult. Treatment usually includes immunosuppression and standard cardiac care, but neither is curative or prevents adverse outcomes. Research Questions: We aimed to enhance the understanding of cardiac sarcoidosis pathobiology by performing single-nucleus RNA sequencing on different regions of diseased hearts, focusing on immune dysregulation and the regional impact of granulomas on the myocardium. Methods: We used 10X Chromium V3 chemistry to investigate granulomatous, seemingly unaffected, and scarred regions within the same patients. We conducted a comparative analysis of samples from sarcoidosis patients, normal donor hearts, and patients with DCM due to their similar clinical phenotype. Results: A total of 608,235 nuclei from 21 sarcoidosis patients (41 samples), 12 control subjects (31 samples) and 29 DCM patients (66 samples) were analyzed in this study. On average, sarcoid samples showed fewer cardiomyocytes and pericytes, with more fibroblasts, myeloid, and lymphoid cells, compared to controls. Those sarcoid tissues with a control-like cardiomyocyte proportion displayed a significant reduction in pericytes (14.3% vs. 3.3%, p = 9.1e-9), suggesting microvascular dysfunction even in regions without granuloma and scarring. All sarcoid samples showed a marked increase in a lipogenic and extracellular matrix remodeling fibroblast state (2.6% vs. 10.7%, p = 2.7e-8). Granulomatous regions contained CHI3L1 and DCSTAMP-expressing granuloma-associated macrophages, as well as pro-inflammatory Th17-like T-cells, which have been implicated in various autoimmune diseases, and IL1R1+ Treg cells resembling exhausted Tregs, which have previously been described in tumor microenvironments. Conclusions: This comprehensive single-nucleus profiling highlights considerable regional heterogeneity in cardiac sarcoidosis. Understanding immune dysregulation and the spatial impact of granulomas on the myocardium may lead to optimized therapies for this understudied and potentially lethal disease, which currently has ineffective treatment options.
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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".