GSE201339
Tethering distinct molecular profiles of single cells by their lineage histories to investigate sources of cell state heterogeneity
Gene expression heterogeneity is ubiquitous within single cell datasets, even among cells of the same type. Heritable expression differences, defined here as those which persist over multiple cell divisions, are of particular interest, as they can underlie processes including cell differentiation during development as well as the clonal selection of drug-resistant cancer cells. However, heritable sources of variation are difficult to disentangle from non-heritable ones, such as cell cycle stage, asynchronous transcription, and measurement noise. Since heritable states should be shared by lineally related cells, we sought to leverage CRISPR-based lineage tracing, together with single cell molecular profiling, to discriminate between heritable and non-heritable variation in gene expression. We show that high efficiency capture of lineage profiles alongside single cell gene expression enables accurate lineage tree reconstruction and reveals an abundance of progressive, heritable gene expression changes. We find that a subset of these are likely mediated by structural genetic variation (copy number alterations, translocations), but that the stable attributes of others cannot be understood with expression data alone. Towards addressing this, we develop a method to capture cell lineage histories alongside single cell chromatin accessibility profiles, such that expression and chromatin accessibility of closely related cells can be linked via their lineage histories. We call this then leverage this indirect “coassay” approach "THE LORAX" and leverage it to explore the genetic and epigenetic mechanisms underlying heritable gene expression changes. Using this approach, we show that we can discern between heritable gene expression differences mediated by large and small copy number changes, trans effects, and possible epigenetic variation. Overall design: Single cell RNA-seq (sci-RNA-seq), single cell ATAC-seq (sci-ATAC-seq), and targeted transcript capture encoding lineage relationships alongside both single cell methods. Cell line: HEK293.