The Endocardium and Heart Valves

Dye, Bailey K; Lincoln, Joy

doi:10.1101/cshperspect.a036723

Cited by 33 publications

(27 citation statements)

References 137 publications

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“…An obvious function of the endocardium is to act as a physical barrier which protects the myocardium from the vigorous blood flow. Another important function is to dynamically sense the signals coming from the flow (mainly pressure and shear stresses) to stimulate and produce lineages of different cardiovascular cell types like valves, endothelial cells of atria and ventricles or the lining of coronary vasculature (Dye & Lincoln 2020).…”

Section: The Tissuesmentioning

confidence: 99%

Electro-fluid-mechanics of the heart

Verzicco

2022

J. Fluid Mech.

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This article presents an overview of the dynamics of the human heart and the main goal is the discussion of its fluid mechanic features. We will see, however, that the flow in the heart can not be fully described without considering its electrophysiology and elastomechanics as well as the interaction with the systemic and pulmonary circulations with which it is strongly connected. Biologically, the human heart is similar to that of all warm-blooded mammals and it satisfies the same allometric laws. Since the Paleolithic Age, however, humans have improved their living conditions, have modified the environment to satisfy their needs and, more recently, have developed advanced medical knowledge which has allowed triple the number of heartbeats with respect to other mammals. In the last century, effective diagnostic tools, reliable surgical procedures and prosthetic devices have been developed and refined leading to substantial progress in cardiology and heart surgery with routine clinical practice which nowadays cures many disorders, once lethal. Pulse duplicators have been built to reproduce the pulsatile flow and ‘blood analogues’, have been realized. Heart phantoms, can attain deformations similar to the real heart although the active contraction and the tissue anisotropy still can not be replicated. Numerical models have also become a viable alternative for cardiovascular research: they do not suffer from limitations of material properties and device technologies, thus making possible the realization of truly digital twins. Unfortunately, a high-fidelity model for the whole heart consists of a system of coupled, nonlinear partial differential equations with a number of degrees of freedom of the order of a billion and computational costs become the bottleneck. An additional challenge comes from the inherent human variability and the uncertainty of the heart parameters whose statistical assessment requires a campaign of simulations rather than a single deterministic calculation; reduced and surrogate models can be employed to alleviate the huge computational burden and all possibilities are currently being pursued. In the era of big data and artificial intelligence, cardiovascular research is also advancing by exploiting the latest technologies: equation-based augmented reality, virtual surgery and computational prediction of disease progression are just a few examples among many that will become standard practice in the forthcoming years.

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Section: The Tissuesmentioning

confidence: 99%

Electro-fluid-mechanics of the heart

Verzicco

2022

J. Fluid Mech.

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“…Interestingly, we observed variation in mapping confidence among endothelial cells, with higher confidence for endothelial cells predicted to be closer to the developing heart tube (Figure 2G-I). The inner wall of the developing heart tube is lined with a specialised subset of endothelial cells, termed the endocardium (Dye and Lincoln 2020). The higher confidence of endothelial cells predicted closer to the heart, supports the hypothesis that these endothelial cells represent the endocardium, as evidenced by their distinct expression of genes such as Gata4, Gata5, Gata6, Hand2, compared to endothelial cells mapped to other regions of the embryo that exhibit a less distinct transcriptional profile (Figure 2J, (Dittrich et al 2021; Duan et al 2019)).…”

Section: Resultsmentioning

confidence: 99%

Supervised spatial inference of dissociated single-cell data with SageNet

Heidari

Lohoff

Tyser

et al. 2022

Preprint

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Spatially-resolved transcriptomics uncovers patterns of gene expression at supercellular, cellular, or subcellular resolution, providing insights into spatially variable cellular functions, diffusible morphogens, and cell-cell interactions. However, for practical reasons, multiplexed single cell RNA-sequencing remains the most widely used technology for profiling transcriptomes of single cells, especially in the context of large-scale anatomical atlassing. Devising techniques to accurately predict the latent physical positions as well as the latent cell-cell proximities of such dissociated cells, represents an exciting and new challenge. Most of the current approaches rely on an ‘autocorrelation’ assumption, i.e., cells with similar transcriptomic profiles are located close to each other in physical space and vice versa. However, this is not always the case in native biological contexts due to complex morphological and functional patterning. To address this challenge, we developed SageNet, a graph neural network approach that spatially reconstructs dissociated single cell data using one or more spatial references. SageNet first estimates a gene-gene interaction network from a reference spatial dataset. This informs the structure of the graph on which the graph neural network is trained to predict the region of dissociated cells. Finally, SageNet produces a low-dimensional embedding of the query dataset, corresponding to the reconstructed spatial coordinates of the dissociated tissue. Furthermore, SageNet reveals spatially informative genes by extracting the most important features from the neural network model. We demonstrate the utility and robust performance of SageNet using molecule-resolved seqFISH and spot-based Spatial Transcriptomics reference datasets as well as dissociated single-cell data, across multiple biological contexts. SageNet is provided as an open-source python software package at https://github.com/MarioniLab/SageNet.

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“…Endothelial cells can revert to a mesenchymal phenotype, a process in which the endothelial cells lose their tight junctions, develop increased motility and increase production of extracellular matrix proteins in a process termed endothelium-to-mesenchymal transition (EndMT) [ 18 ]. EndMT is critical for heart valve development; endothelial cells undergo EndMT and migrate into the cardiac jelly, thereby forming endocardial cushions that will lead to formation of the valves and septa of the mature heart [ 19 ]. EndMT is driven by multiple signaling pathways, including BMP, TGF-β and Notch ( Figure 1 B) [ 20 ].…”

Section: Endothelial Plasticity and Heterogeneitymentioning

confidence: 99%

Endothelial Extracellular Vesicles: From Keepers of Health to Messengers of Disease

Mathiesen

Hamilton

Carter

et al. 2021

IJMS

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Endothelium has a rich vesicular network that allows the exchange of macromolecules between blood and parenchymal cells. This feature of endothelial cells, along with their polarized secretory machinery, makes them the second major contributor, after platelets, to the particulate secretome in circulation. Extracellular vesicles (EVs) produced by the endothelial cells mirror the remarkable molecular heterogeneity of their parent cells. Cargo molecules carried by EVs were shown to contribute to the physiological functions of endothelium and may support the plasticity and adaptation of endothelial cells in a paracrine manner. Endothelium-derived vesicles can also contribute to the pathogenesis of cardiovascular disease or can serve as prognostic or diagnostic biomarkers. Finally, endothelium-derived EVs can be used as therapeutic tools to target endothelium for drug delivery or target stromal cells via the endothelial cells. In this review we revisit the recent evidence on the heterogeneity and plasticity of endothelial cells and their EVs. We discuss the role of endothelial EVs in the maintenance of vascular homeostasis along with their contributions to endothelial adaptation and dysfunction. Finally, we evaluate the potential of endothelial EVs as disease biomarkers and their leverage as therapeutic tools.

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The Endocardium and Heart Valves

Cited by 33 publications

References 137 publications

Electro-fluid-mechanics of the heart

Electro-fluid-mechanics of the heart

Supervised spatial inference of dissociated single-cell data with SageNet

Endothelial Extracellular Vesicles: From Keepers of Health to Messengers of Disease

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