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Abstract 12937: Single-cell Transcriptomic Analysis Reveals Developmentally Impaired Endocardial Population in Hypoplastic Left Heart Syndrome

2020· article· en· W3163074191 on OpenAlexaff
Yifei Miao, Lei Tian, Marcy Martin, Sharon L. Paige, Francisco X. Galdos, Soah Lee, Paul Grossfeld, Seema Mital, Joseph C. Wu, Marlene Rabinovitch, Timothy J. Nelson, Shuyi Nie, Sean M. Wu, Mingxia Gu

Bibliographic record

VenueCirculation · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCongenital heart defects research
Canadian institutionsHospital for Sick Children
Fundersnot available
KeywordsHypoplastic left heart syndromeEndocardiumMedicineVentricleHeart developmentInternal medicineEndocardial fibroelastosisPopulationCardiologyCell biologyEmbryonic stem cellHeart diseaseBiologyGeneticsGene

Abstract

fetched live from OpenAlex

Hypoplastic left heart syndrome (HLHS) is one of the most challenging forms of congenital heart diseases. Previous studies were mainly focused on intrinsic defects in myocardium. However, this does not sufficiently explain the abnormal development of the cardiac valve, septum, and vasculature, known to originate from the endocardium. Here, using single-cell transcriptomic profiling, induced pluripotent stem cells (iPSC) derived endocardial cells (iEECs), human fetal heart tissue with underdeveloped left ventricle, as well as a Xenopus model, we identified a developmentally impaired endocardial population in HLHS. The intrinsic endocardial deficits contributed to abnormal endothelial to mesenchymal transition, NOTCH signaling, and extracellular matrix organization, all of which are key factors in valve formation. Consequently, in an endocardium-myocardium co-culture system, we found that endocardial abnormalities conferred reduced proliferation and maturation of iPSC derived cardiomyocyte (iPSC-CMs) judged by Ki67 staining, contractility, sarcomere organization, and related gene expressions through a disrupted fibronectin (FN1)-integrin interaction. Several recently described HLHS de novo mutations such as ETS1 and CHD7 showed reduced binding to FN1 promoter and enhancer in HLHS vs. control iEECs based on ChIP-qPCR analysis. Additionally, we found that suppression of the ETS1 in Xenopus caused reduced endocardial FN1 expression and impaired heart development. Supplementation of FN1 or ETS1 over-expression in HLHS iEECs could rescue dysfunctions in both endocardium and myocardium in HLHS. Our studies reveal a critical role of endocardial abnormality in causing HLHS, and provide a rationale for improving endocardial function in future regenerative strategies. Schematic illustration of the endocardial and myocardial defects in HLHS.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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)0.0020.000

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 head0.022
GPT teacher head0.252
Teacher spread0.230 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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".

Quick stats

Citations3
Published2020
Admission routes1
Has abstractyes

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