Elucidating the lineages and molecular states of single cells is fundamentally important for understanding the formation and functions of complex organs such as the lung. 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 four independent scRNA-seq data sets. SLICE successfully reconstructed entropy-directed cell differentiation trajectories that have been previously experimentally validated, supporting the general applicability and high predictive accuracy of SLICE in determining cellular differentiation states and reconstructing cell differentiation lineages. In addition, we 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 transitional trajectory consisting of five lung fibroblastic subtypes was identified using SLICE.