The emergent landscape of the mouse gut endoderm at single-cell resolution
By Sonja Nowotschin, Manu N. Setty, Ying-Yi Kuo, V. Liu, Vidur Garg, Roshan Sharma, Claire S. Simon, Néstor Saiz, R. Gardner, S. Boutet and 4 more. Nature, 2019
346 citations recorded by the source corpus.
Here we delineate the ontogeny of the mammalian endoderm by generating 112,217 single-cell transcriptomes, which represent all endoderm populations within the mouse embryo until midgestation. We use graph-based approaches to model differentiating cells, which provides a spatio-temporal characterization of developmental trajectories and defines the transcriptional architecture that accompanies the emergence of the first (primitive or extra-embryonic) endodermal population and its sister pluripotent (embryonic) epiblast lineage. We uncover a relationship between descendants of these two lineages, in which epiblast cells differentiate into endoderm at two distinct time points—before and during gastrulation. Trajectories of endoderm cells were mapped as they acquired embryonic versus extra-embryonic fates and as they spatially converged within the nascent gut endoderm, which revealed these cells to be globally similar but retain aspects of their lineage history. We observed the regionalized identity of cells along the anterior–posterior axis of the emergent gut tube, which reflects their embryonic or extra-embryonic origin, and the coordinated patterning of these cells into organ-specific territories. The developing mouse gut endoderm, mapped at single-cell resolution, reveals trajectories of cell differentiation before and during gastrulation and the emergence of regionalized cell identities along the anterior–posterior axis of the gut tube.
Subfields: Molecular Biology
View Paper: https://www.semanticscholar.org/paper/2f30e22513e11eba83cb3a25df4eff23917a4b3f
Citations in the corpus
- CellRank for directed single-cell fate mapping (2020, neutral): “CellRank could also be extended by using temporal information, such as timepoints in the lung dataset 52 , to regularize the model, by only allowing transitions consistent with experimental time 64 .”