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...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2022-08-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1010366 |
| _version_ | 1798032766709792768 |
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| author | Sergio Marco Salas Xiao Yuan Christer Sylven Mats Nilsson Carolina Wählby Gabriele Partel |
| author_facet | Sergio Marco Salas Xiao Yuan Christer Sylven Mats Nilsson Carolina Wählby Gabriele Partel |
| author_sort | Sergio Marco Salas |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-04-11T20:19:23Z |
| format | Article |
| id | doaj.art-6dcd79603c5c473eb053649c29d706ec |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-04-11T20:19:23Z |
| publishDate | 2022-08-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-6dcd79603c5c473eb053649c29d706ec2022-12-22T04:04:52ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582022-08-01188e101036610.1371/journal.pcbi.1010366De novo spatiotemporal modelling of cell-type signatures in the developmental human heart using graph convolutional neural networks.Sergio Marco SalasXiao YuanChrister SylvenMats NilssonCarolina WählbyGabriele PartelWith 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.https://doi.org/10.1371/journal.pcbi.1010366 |
| spellingShingle | Sergio Marco Salas Xiao Yuan Christer Sylven Mats Nilsson Carolina Wählby Gabriele Partel De novo spatiotemporal modelling of cell-type signatures in the developmental human heart using graph convolutional neural networks. PLoS Computational Biology |
| 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 |
| url | https://doi.org/10.1371/journal.pcbi.1010366 |
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