Deep learning-based models for preimplantation mouse and human embryos based on single-cell RNA sequencing.

ABSTRACT: The rapid growth of single-cell transcriptomic technology has produced an increasing number of datasets for both embryonic development and in vitro pluripotent stem cell-derived models. This avalanche of data surrounding pluripotency and the process of lineage specification has meant it has become increasingly difficult to define specific cell types or states in vivo, and compare these with in vitro differentiation. Here we utilize a set of deep learning tools to integrate and classify multiple datasets. This allows the definition of both mouse and human embryo cell types, lineages and states, thereby maximizing the information one can garner from these precious experimental resources. Our approaches are built on recent initiatives for large-scale human organ atlases, but here we focus on

SUBMITTER: Proks M 

PROVIDER: S-EPMC11725497 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature