Barcode-free prediction of cell lineages from scRNA-seq datasets

ABSTRACT: The integration of lineage tracing with scRNA-seq has transformed our understanding of gene expression dynamics during development, regeneration, and disease. However, lineage tracing is technically demanding and most existing scRNA-seq datasets are devoid of lineage information. By analyzing our own (mouse embryonic stem cells;mESCs) and public lineage-annotated scRNA-seq datastes, we could identify and characterize genes displaying conserved expression levels over cell divisions in multiple cell types. This resulted in the development of Gene Expression Memory-based Lineage Inference (GEMLI), a computational pipeline allowing to predict cell lineages over several cell divisions solely from scRNA-seq datasets.

ORGANISM(S): Mus musculus

PROVIDER: GSE226169 | GEO | 2024/01/28

REPOSITORIES: GEO