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Abstract 4142546: Single-Nucleus RNA Sequencing Reveals Region-Specific Cell Composition and Transcriptional Variations in Cardiac Sarcoidosis

2024· article· en· W4404359957 on OpenAlexaff
Meraj Neyazi, Yuri Kim, Kemar Brown, Syndi Barish, Joshua Gorham, Olivia Layton, Anissa Viveiros, Daniel M. DeLaughter, Martin Beyer, Viktoria Strohmenger, Daniel Reichart, Dawn E. Bowles, Carolyn Glass, Gavin Y. Oudit, Jonathan G. Seidman, Christine E. Seidman

Bibliographic record

VenueCirculation · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced Biosensing Techniques and Applications
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMedicineCardiac sarcoidosisNucleusRNASarcoidosisComputational biologyGeneticsGenePathologyBiology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.382
Threshold uncertainty score0.470

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.027
GPT teacher head0.252
Teacher spread0.225 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

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Citations0
Published2024
Admission routes1
Has abstractyes

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