Sci-fate characterizes the dynamics of gene expression in single cells

Gene expression programs change over time, differentiation and development, and in response to stimuli. However, nearly all techniques for profiling gene expression in single cells do not directly capture transcriptional dynamics. In the present study, University of Washington researchers present a method for combined single-cell combinatorial indexing and messenger RNA labeling (sci-fate), which uses combinatorial cell indexing and 4-thiouridine labeling of newly synthesized mRNA to concurrently profile the whole and newly synthesized transcriptome in each of many single cells. The researchers used sci-fate to study the cortisol response in >6,000 single cultured cells. From these data, they quantified the dynamics of the cell cycle and glucocorticoid receptor activation, and explored their intersection. Finally, they developed software to infer and analyze cell-state transitions. The researchers anticipate that sci-fate will be broadly applicable to quantitatively characterize transcriptional dynamics in diverse systems.

Sci-fate enables joint profiling of whole and newly synthesized transcriptomes

Fig. 1

a, The sci-fate workflow. Key steps are outlined in the text. IAA, iodoacetamide. Asterisk, chemically modified 4sU. b, Experimental scheme. A549 cells were treated with dexamethasone for varying amounts of time ranging from 0 h to 10 h. Cells from all treatment conditions were labeled with 4sU 2 h before harvest for sci-fate. c, Violin plot showing the fraction of 4sU-labeled reads per cell for each of the six treatment conditions. Cell number, n = 1,054 (0 h), 1,049 (2 h), 949 (4 h), 1,262 (6 h), 1,041 (8 h) and 1,325 (10 h). For all violin plots in this figure: thick lines in the middle are the medians; upper and lower box edges are the first and third quartiles, respectively; whiskers are 1.5 times the interquartile range; and circles are outliers. d, Violin plot showing the fraction of 4sU-labeled reads per cell (n = 6,680), split out by the subsets that map to exons versus introns. e, UMAP visualization of A549 cells (n = 6,680) based on their whole transcriptomes (left), newly synthesized transcriptomes (middle) or joint analysis, that is, combining the top PCs from each (right). f, Same as left and right of e, respectively, but colored by cluster ID from UMAP based on whole transcriptomes. g, Same as right of e, but colored by normalized expression of G2/M-marker genes by their overall expression levels (left) or their levels of newly synthesized transcripts (right). UMI counts for these genes are scaled by library size, log(transformed), aggregated and then mapped to Z-scores.

Availability – Scripts for processing sci-fate sequencing were written in Python and R with code available at https://github.com/JunyueC/sci-fate_analysis.

Cao J, Zhou W, Steemers F, Trapnell C, Shendure J. (2020) Sci-fate characterizes the dynamics of gene expression in single cells. Nature Biotechnology ([Epub ahead of print]. [abstract]

Leave a Reply

Your email address will not be published. Required fields are marked *

*

Time limit is exhausted. Please reload CAPTCHA.