Journal article
Sergio Marco Salas, Xiao-ying Yuan, C. Sylvén, M. Nilsson, Carolina Wählby, Gabriele Partel
bioRxiv, 2021
bioRxiv, 2021
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DOI
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APA
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Salas, S. M., Yuan, X.-ying, Sylvén, C., Nilsson, M., Wählby, C., & Partel, G. (2021). De novo spatiotemporal modelling of cell-type signatures identifies novel cell populations in the developmental human heart. BioRxiv.
Chicago/Turabian
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Salas, Sergio Marco, Xiao-ying Yuan, C. Sylvén, M. Nilsson, Carolina Wählby, and Gabriele Partel. “De Novo Spatiotemporal Modelling of Cell-Type Signatures Identifies Novel Cell Populations in the Developmental Human Heart.” bioRxiv (2021).
MLA
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Salas, Sergio Marco, et al. “De Novo Spatiotemporal Modelling of Cell-Type Signatures Identifies Novel Cell Populations in the Developmental Human Heart.” BioRxiv, 2021.
BibTeX Click to copy
@article{sergio2021a,
title = {De novo spatiotemporal modelling of cell-type signatures identifies novel cell populations in the developmental human heart},
year = {2021},
journal = {bioRxiv},
author = {Salas, Sergio Marco and Yuan, Xiao-ying and Sylvén, C. and Nilsson, M. and Wählby, Carolina and Partel, Gabriele}
}
Abstract
With the emergence of high throughput single cell techniques, the understanding of cellular diversity in biologically complex processes has rapidly increased. The next step towards comprehension of e.g. key organs in the mammal development is to obtain spatiotemporal atlases of the cellular diversity. However, targeted cell typing approaches relying on existing single cell data achieve incomplete and biased maps that could mask the molecular and cellular heterogeneity present in a tissue slide. Here we applied spage2vec, a de novo approach to spatially resolve and characterize cellular diversity during human heart development. We obtained well defined spatial maps of tissue samples from 4.5 to 9 post conception weeks, not biased by probabilistic cell typing approaches. We found previously unreported molecular diversity within cardiomyocytes and epicardial cells and identified their characteristic expression signatures by matching them with specific subpopulations found in single cell RNA sequencing datasets.
