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Record W4380769155 · doi:10.1161/circ.146.suppl_1.9501

Abstract 9501: Single-Cell Transcriptomic Analysis and Patient-Specific iPSCs Reveal Dysfunctional Coronary Arterial Endothelial Cells in Hypoplastic Left Heart Syndrome

2022· article· en· W4380769155 on OpenAlexaff
Zhiyun Yu, Xin Zhou, Ziyi Liu, Victor Pastrana-Gomez, Yu Liu, Minzhe Guo, Lei Tian, Timothy J. Nelson, Nian Wang, Seema Mital, David Chitayat, Joseph C. Wu, Marlene Rabinovitch, Sean M. Wu, M Snyder, Yifei Miao, Mingxia Gu

Bibliographic record

VenueCirculation · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCongenital heart defects research
Canadian institutionsHospital for Sick Children
Fundersnot available
KeywordsHypoplastic left heart syndromeMedicineVentricleInduced pluripotent stem cellAngiogenesisInternal medicineCardiologyHeart diseaseEmbryonic stem cellBiologyGeneGenetics

Abstract

fetched live from OpenAlex

Introduction: Hypoplastic left heart syndrome (HLHS) is a severe form of single ventricle congenital heart disease characterized by the underdeveloped left ventricle. Early serial postmortem examinations revealed a high rate of coronary artery abnormalities in HLHS fetal hearts (e.g., thickened wall and kinking arteries). However, the intrinsic defect in HLHS coronary vessels and its genetic basis remain unclear. Methods: We profiled human fetal heart with an underdeveloped left ventricle (ULV) and induced pluripotent stem cells derived endothelial cells (iPSC-ECs) from HLHS patients at single-cell resolution. CD144 + / NPR3 - vascular ECs were selected and classified as venous, arterial, and late arterial populations. To study the arterial EC phenotypes, we generated iPSC-arterial ECs (AECs, CDH5 + CXCR4 + NT5E -/low ) derived from 3 HLHS patients and 3 age-matched controls, and evaluated their functionalities including cell cycle regulation, angiogenesis, and inflammatory response. Results: Revealed by single cell RNA-seq and subsequent gene ontology analysis, ULV late arterial EC population showed significant defects in EC development, proliferation, angiogenesis, and Notch signaling compared to the control. Consistently, HLHS iPSCs exhibited impaired AEC differentiation judged by the reduced CXCR4 + NT5E -/low AEC progenitors. Mature HLHS iPSC-AECs showed reduced angiogenesis and enhanced G0/G1 cell cycle arrest with downregulated cell cycle-related genes (e.g., Ki67, CCND1/2 ). Healthy human aortic smooth muscle cells exhibited abnormal proliferation and synthetic phenotypes when co-cultured with HLHS iPSC-AECs. Additionally, NOTCH pathway genes (e.g., DLL4, HEY1, GJA5 ) were suppressed in both ULV AECs and HLHS iPSC-AECs. HLHS de novo variant KMT2D directly regulated the transcription of NOTCH targeted genes involved in arterial development and proliferation via H3K4me2. Intriguingly, the treatment of NOTCH ligands (Jag1, Dll1) significantly improved the proliferation of HLHS AECs. Conclusions: Our study revealed that HLHS coronary AECs were dysfunctional in angiogenesis, proliferation, and EC-SMC interaction. KMT2D-NOTCH signaling may contribute to the impaired development and proliferation of HLHS AECs.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

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)0.0030.001

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.

Opus teacher head0.013
GPT teacher head0.218
Teacher spread0.205 · 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 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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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