Decreased left heart flow in fetal lambs causes left heart hypoplasia and pro-fibrotic tissue remodeling

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Abstract

Low blood flow through the fetal left heart is often conjectured as an etiology for hypoplastic left heart syndrome (HLHS). To investigate if a decrease in left heart flow results in growth failure, we generate left ventricular inflow obstruction (LVIO) in mid-gestation fetal lambs by implanting coils in their left atrium using an ultrasound-guided percutaneous technique. Significant LVIO recapitulates important clinical features of HLHS: decreased antegrade aortic valve flow, compensatory retrograde perfusion of the brain and ascending aorta (AAo) from the arterial duct, severe left heart hypoplasia, a non-apex forming LV, and a thickened endocardial layer. The hypoplastic AAo have miRNA-gene pairs annotating to cell proliferation that are inversely differentially expressed by bulk RNA-seq. Single-nucleus RNA-seq of the hypoplastic LV myocardium shows an increase in fibroblasts with a reciprocal decrease in cardiomyocyte nuclei proportions. Fibroblasts, cardiomyocytes and endothelial cells from hypoplastic myocardium have increased expression of extracellular matrix component or fibrosis genes with dysregulated fibroblast growth factor signaling. Hence, a severe sustained ( ~ 1/3 gestation) reduction in fetal left heart flow is sufficient to cause left heart hypoplasia. This is accompanied by changes in cellular composition and gene expression consistent with a pro-fibrotic environment and aberrant induction of mesenchymal programs.

Abstract

A fetal lamb model of mitral stenosis shows that sustained reduction in fetal left heart flow causes left heart hypoplasia and sequencing analysis identifies changes associated with pro-fibrotic environment and aberrant induction of mesenchymal programs.

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Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

Michael Love, Wolfgang Huber, Simon Anders (2014)

In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.

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GSVA: gene set variation analysis for microarray and RNA-Seq data

Sonja Hänzelmann, Robert Castelo, Justin Guinney (2013)

Background Gene set enrichment (GSE) analysis is a popular framework for condensing information from gene expression profiles into a pathway or signature summary. The strengths of this approach over single gene analysis include noise and dimension reduction, as well as greater biological interpretability. As molecular profiling experiments move beyond simple case-control studies, robust and flexible GSE methodologies are needed that can model pathway activity within highly heterogeneous data sets. Results To address this challenge, we introduce Gene Set Variation Analysis (GSVA), a GSE method that estimates variation of pathway activity over a sample population in an unsupervised manner. We demonstrate the robustness of GSVA in a comparison with current state of the art sample-wise enrichment methods. Further, we provide examples of its utility in differential pathway activity and survival analysis. Lastly, we show how GSVA works analogously with data from both microarray and RNA-seq experiments. Conclusions GSVA provides increased power to detect subtle pathway activity changes over a sample population in comparison to corresponding methods. While GSE methods are generally regarded as end points of a bioinformatic analysis, GSVA constitutes a starting point to build pathway-centric models of biology. Moreover, GSVA contributes to the current need of GSE methods for RNA-seq data. GSVA is an open source software package for R which forms part of the Bioconductor project and can be downloaded at http://www.bioconductor.org.

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Integrating single-cell transcriptomic data across different conditions, technologies, and species

Rahul Satija, Efthymia Papalexi, Peter Smibert … (2018)

Computational single-cell RNA-seq (scRNA-seq) methods have been successfully applied to experiments representing a single condition, technology, or species to discover and define cellular phenotypes. However, identifying subpopulations of cells that are present across multiple data sets remains challenging. Here, we introduce an analytical strategy for integrating scRNA-seq data sets based on common sources of variation, enabling the identification of shared populations across data sets and downstream comparative analysis. We apply this approach, implemented in our R toolkit Seurat (http://satijalab.org/seurat/), to align scRNA-seq data sets of peripheral blood mononuclear cells under resting and stimulated conditions, hematopoietic progenitors sequenced using two profiling technologies, and pancreatic cell 'atlases' generated from human and mouse islets. In each case, we learn distinct or transitional cell states jointly across data sets, while boosting statistical power through integrated analysis. Our approach facilitates general comparisons of scRNA-seq data sets, potentially deepening our understanding of how distinct cell states respond to perturbation, disease, and evolution.

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Author and article information

Contributors

Rajiv R. Chaturvedi:

ORCID: http://orcid.org/0000-0002-4474-7304

rajiv.chaturvedi@sickkids.ca

Journal

Journal ID (nlm-ta): Commun Biol

Journal ID (iso-abbrev): Commun Biol

Title: Communications Biology

Publisher: Nature Publishing Group UK (London )

ISSN (Electronic): 2399-3642

Publication date (Electronic): 22 July 2023

Publication date PMC-release: 22 July 2023

Publication date Collection: 2023

Volume: 6

Electronic Location Identifier: 770

Affiliations

[1 ]GRID grid.42327.30, ISNI 0000 0004 0473 9646, CGEn, The Hospital for Sick Children, ; Toronto, ON Canada

[2 ]GRID grid.42327.30, ISNI 0000 0004 0473 9646, The Centre for Applied Genomics, , The Hospital for Sick Children, ; Toronto, ON Canada

[3 ]GRID grid.42327.30, ISNI 0000 0004 0473 9646, Genetics and Genome Biology, SickKids Research Institute, ; Toronto, ON Canada

[4 ]GRID grid.17063.33, ISNI 0000 0001 2157 2938, Department of Molecular Genetics, , University of Toronto, ; Toronto, ON Canada

[5 ]GRID grid.231844.8, ISNI 0000 0004 0474 0428, Princess Margaret Cancer Centre, , University Health Network, ; Toronto, ON Canada

[6 ]GRID grid.416166.2, ISNI 0000 0004 0473 9881, Ontario Fetal Centre, Department of Obstetrics & Gynaecology, Mount Sinai Hospital, ; Toronto, ON Canada

[7 ]GRID grid.17063.33, ISNI 0000 0001 2157 2938, Department of Obstetrics & Gynaecology, , University of Toronto, ; Toronto, ON Canada

[8 ]GRID grid.39381.30, ISNI 0000 0004 1936 8884, Department of Obstetrics & Gynaecology, , Western University, ; London, ON Canada

[9 ]GRID grid.413953.9, ISNI 0000 0004 5906 3102, Children’s Health Research Institute, ; London, ON Canada

[10 ]GRID grid.416847.8, ISNI 0000 0004 0626 7267, London Health Sciences Centre, , Victoria Hospital, ; London, ON Canada

[11 ]GRID grid.42327.30, ISNI 0000 0004 0473 9646, Department of Paediatric Laboratory Medicine, , The Hospital for Sick Children, ; Toronto, ON Canada

[12 ]GRID grid.17063.33, ISNI 0000 0001 2157 2938, Department of Laboratory Medicine & Pathobiology, , University of Toronto, ; Toronto, ON Canada

[13 ]GRID grid.17063.33, ISNI 0000 0001 2157 2938, McLaughlin Centre, , University of Toronto, ; Toronto, ON Canada

[14 ]GRID grid.42327.30, ISNI 0000 0004 0473 9646, Labatt Family Heart Centre, , Division of Cardiology, The Hospital for Sick Children, ; Toronto, ON Canada

[15 ]GRID grid.17063.33, ISNI 0000 0001 2157 2938, Department of Paediatrics, , University of Toronto, ; Toronto, ON Canada

Author information

Miriam S. Reuter http://orcid.org/0000-0001-8662-6685

J. Javier Diaz-Mejia http://orcid.org/0000-0003-2475-3077

Cadia Chan http://orcid.org/0000-0002-4810-8366

Stephen W. Scherer http://orcid.org/0000-0002-8326-1999

Michael D. Wilson http://orcid.org/0000-0002-4015-3066

Rajiv R. Chaturvedi http://orcid.org/0000-0002-4474-7304

Article

Publisher ID: 5132

DOI: 10.1038/s42003-023-05132-2

PMC ID: 10363152

PubMed ID: 37481629

SO-VID: d7496ac7-5d39-4de2-8414-df3c38cc5500

License:

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

History

Date received : 4 April 2022

Date accepted : 11 July 2023

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Keywords: heart development,congenital heart defects

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Keywords: heart development, congenital heart defects

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Decreased left heart flow in fetal lambs causes left heart hypoplasia and pro-fibrotic tissue remodeling

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Most cited references 75

Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

GSVA: gene set variation analysis for microarray and RNA-Seq data

Integrating single-cell transcriptomic data across different conditions, technologies, and species

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