GSE131351
Characterizing the temporal dynamics of gene expression in single cells with sci-fate
Gene expression is a dynamic process on multiple scales, 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 these temporal dynamics, 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 of glucocorticoid receptor activation, while also exploring their intersection. We furthermore use these data to develop a framework for estimating cell state transition probabilities, and to identify factors whose dynamic expression potentially regulates these transitions. The experimental and computational methods described here may be broadly applicable to quantitatively characterize cell state dynamics in in vitro systems. Overall design: sci-fate profiling for HEK293T cells, NIH/3T3 cells, A549 cells across different treatment conditions (DEX 0 hour, 2 hour, 4 hour, 6 hour, 8 hour and 10 hour treatment). Please note that [1] the fastq files are generated from combined samples of different treatment samples [2] the processed cell information and transcriptome barcode are listed in the cell annotation file of processed data [3] the *gene_annotate.txt and *gene_annotate_newly_synthesised.txt files are identical, yet provided in duplicate as they are reference data for two different data sets.