Barcode-free prediction of cell lineages from scRNA-seq datasets
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. Overall design: CGR8 mESCs were barcoded using the LARRY cellular barcoding library and grown for 48h in N2B27 medium supplemented with 2i and LIF before analysis by scRNA-seq. Extraction of LARRY lineage barcodes in the scRNA-seq data allows to link single cell transcriptomes to lineage information.