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De novo spatiotemporal modelling of cell-type signatures in the developmental human heart using graph convolutional neural networks

With the emergence of high throughput single cell techniques, the understanding of the molecular and cellular diversity of mammalian organs have rapidly increased. In order to understand the spatial organization of this diversity, single cell data is often integrated with spatial data to create prob...

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Autores principales: Marco Salas, Sergio, Yuan, Xiao, Sylven, Christer, Nilsson, Mats, Wählby, Carolina, Partel, Gabriele
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9401155/
https://www.ncbi.nlm.nih.gov/pubmed/35960757
http://dx.doi.org/10.1371/journal.pcbi.1010366
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author Marco Salas, Sergio
Yuan, Xiao
Sylven, Christer
Nilsson, Mats
Wählby, Carolina
Partel, Gabriele
author_facet Marco Salas, Sergio
Yuan, Xiao
Sylven, Christer
Nilsson, Mats
Wählby, Carolina
Partel, Gabriele
author_sort Marco Salas, Sergio
collection PubMed
description With the emergence of high throughput single cell techniques, the understanding of the molecular and cellular diversity of mammalian organs have rapidly increased. In order to understand the spatial organization of this diversity, single cell data is often integrated with spatial data to create probabilistic cell maps. However, targeted cell typing approaches relying on existing single cell data achieve incomplete and biased maps that could mask the true diversity present in a tissue slide. Here we applied a de novo technique to spatially resolve and characterize cellular diversity of in situ sequencing data during human heart development. We obtained and made accessible well defined spatial cell-type maps of fetal hearts from 4.5 to 9 post conception weeks, not biased by probabilistic cell typing approaches. With our analysis, we could characterize previously unreported molecular diversity within cardiomyocytes and epicardial cells and identified their characteristic expression signatures, comparing them with specific subpopulations found in single cell RNA sequencing datasets. We further characterized the differentiation trajectories of epicardial cells, identifying a clear spatial component on it. All in all, our study provides a novel technique for conducting de novo spatial-temporal analyses in developmental tissue samples and a useful resource for online exploration of cell-type differentiation during heart development at sub-cellular image resolution.
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spelling pubmed-94011552022-08-25 De novo spatiotemporal modelling of cell-type signatures in the developmental human heart using graph convolutional neural networks Marco Salas, Sergio Yuan, Xiao Sylven, Christer Nilsson, Mats Wählby, Carolina Partel, Gabriele PLoS Comput Biol Research Article With the emergence of high throughput single cell techniques, the understanding of the molecular and cellular diversity of mammalian organs have rapidly increased. In order to understand the spatial organization of this diversity, single cell data is often integrated with spatial data to create probabilistic cell maps. However, targeted cell typing approaches relying on existing single cell data achieve incomplete and biased maps that could mask the true diversity present in a tissue slide. Here we applied a de novo technique to spatially resolve and characterize cellular diversity of in situ sequencing data during human heart development. We obtained and made accessible well defined spatial cell-type maps of fetal hearts from 4.5 to 9 post conception weeks, not biased by probabilistic cell typing approaches. With our analysis, we could characterize previously unreported molecular diversity within cardiomyocytes and epicardial cells and identified their characteristic expression signatures, comparing them with specific subpopulations found in single cell RNA sequencing datasets. We further characterized the differentiation trajectories of epicardial cells, identifying a clear spatial component on it. All in all, our study provides a novel technique for conducting de novo spatial-temporal analyses in developmental tissue samples and a useful resource for online exploration of cell-type differentiation during heart development at sub-cellular image resolution. Public Library of Science 2022-08-12 /pmc/articles/PMC9401155/ /pubmed/35960757 http://dx.doi.org/10.1371/journal.pcbi.1010366 Text en © 2022 Marco Salas et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Marco Salas, Sergio
Yuan, Xiao
Sylven, Christer
Nilsson, Mats
Wählby, Carolina
Partel, Gabriele
De novo spatiotemporal modelling of cell-type signatures in the developmental human heart using graph convolutional neural networks
title De novo spatiotemporal modelling of cell-type signatures in the developmental human heart using graph convolutional neural networks
title_full De novo spatiotemporal modelling of cell-type signatures in the developmental human heart using graph convolutional neural networks
title_fullStr De novo spatiotemporal modelling of cell-type signatures in the developmental human heart using graph convolutional neural networks
title_full_unstemmed De novo spatiotemporal modelling of cell-type signatures in the developmental human heart using graph convolutional neural networks
title_short De novo spatiotemporal modelling of cell-type signatures in the developmental human heart using graph convolutional neural networks
title_sort de novo spatiotemporal modelling of cell-type signatures in the developmental human heart using graph convolutional neural networks
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9401155/
https://www.ncbi.nlm.nih.gov/pubmed/35960757
http://dx.doi.org/10.1371/journal.pcbi.1010366
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