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


Journal article


Sergio Marco Salas, Xiao Yuan, Christer Sylven, M. Nilsson, C. Wählby, Gabriele Partel
PLoS Comput. Biol., 2022

Semantic Scholar DBLP DOI PubMedCentral PubMed
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APA   Click to copy
Salas, S. M., Yuan, X., Sylven, C., Nilsson, M., Wählby, C., & Partel, G. (2022). De novo spatiotemporal modelling of cell-type signatures in the developmental human heart using graph convolutional neural networks. PLoS Comput. Biol.


Chicago/Turabian   Click to copy
Salas, Sergio Marco, Xiao Yuan, Christer Sylven, M. Nilsson, C. Wählby, and Gabriele Partel. “De Novo Spatiotemporal Modelling of Cell-Type Signatures in the Developmental Human Heart Using Graph Convolutional Neural Networks.” PLoS Comput. Biol. (2022).


MLA   Click to copy
Salas, Sergio Marco, et al. “De Novo Spatiotemporal Modelling of Cell-Type Signatures in the Developmental Human Heart Using Graph Convolutional Neural Networks.” PLoS Comput. Biol., 2022.


BibTeX   Click to copy

@article{sergio2022a,
  title = {De novo spatiotemporal modelling of cell-type signatures in the developmental human heart using graph convolutional neural networks},
  year = {2022},
  journal = {PLoS Comput. Biol.},
  author = {Salas, Sergio Marco and Yuan, Xiao and Sylven, Christer and Nilsson, M. and Wählby, C. and Partel, Gabriele}
}

Abstract

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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