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

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 cel

SUBMITTER: Marco Salas S 

PROVIDER: S-EPMC9401155 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature