SLICE: determining cell differentiation and lineage based on single cell entropy

Abstract

Abstract A complex organ contains a variety of cell types, each with its own distinct lineage and function. Understanding the lineage and differentiation state of each cell is fundamentally important for the ultimate delineation of organ formation and function. We developed SLICE, a novel algorithm that utilizes single-cell RNA-seq (scRNA-seq) to quantitatively measure cellular differentiation states based on single cell entropy and predict cell differentiation lineages via the construction of entropy directed cell trajectories. We validated our approach using three independent data sets with known lineage and developmental time information from both Homo sapiens and Mus musculus. SLICE successfully measured the differentiation states of single cells and reconstructed cell differentiation trajectories that have been previously experimentally validated. We then applied SLICE to scRNA-seq of embryonic mouse lung at E16.5 to identify lung mesenchymal cell lineage relationships that currently remain poorly defined. A two-branched differentiation pathway of five fibroblastic subtypes was predicted using SLICE. The present study demonstrated the general applicability and high predictive accuracy of SLICE in determining cellular differentiation states and reconstructing cell differentiation lineages in scRNA-seq analysis.