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

Abstract

AbstractThe integration of lineage tracing with scRNA-seq has transformed our understanding of gene expression heritability during development, regeneration, and disease. However, lineage tracing is technically demanding and most existing scRNA-seq datasets are devoid of lineage information. Here we introduce Gene Expression Memory-based Lineage Inference (GEMLI), a computational pipeline allowing to predict cell lineages over several cell divisions solely from scRNA-seq datasets. GEMLI leverages genes displaying conserved expression levels over cell divisions, and allows i.a. identifying cell lineages in a broad range of cultured cell types, in intestinal organoids, and in crypts from adult mice. GEMLI recovers GO-terms enriched for heritable gene expression, allows to discriminate symmetric and asymmetric cell fate decisions and to reconstruct individual cellular structures from pooled scRNA-seq datasets. GEMLI considerably extends the pool of datasets from which lineage information can be obtained, thereby facilitating the study of gene expression heritability in a broad range of contexts. GEMLI is available at (https://github.com/UPSUTER/GEMLI).