Characterizing the temporal dynamics of gene expression in single cells with sci-fate

Gene expression programs are dynamic, e.g. the cell cycle, response to stimuli, normal differentiation and development, etc. However, nearly all techniques for profiling gene expression in single cells fail to directly capture the dynamics of transcriptional programs, which limits the scope of biology that can be effectively investigated. Towards addressing this, we developed sci-fate, a new technique that combines S4U labeling of newly synthesized mRNA with single cell combinatorial indexing (sci-), in order to concurrently profile the whole and newly synthesized transcriptome in each of many single cells. As a proof-of-concept, we applied sci-fate to a model system of cortisol response and characterized expression dynamics in over 6,000 single cells. From these data, we quantify the dynamics of the cell cycle and glucocorticoid receptor activation, while also exploring their intersection. We furthermore use these data to develop a framework for inferring the distribution of cell state transitions. We anticipate sci-fate will be broadly applicable to quantitatively characterize transcriptional dynamics in diverse systems.