Well-TEMP-seq as a microwell-based strategy for massively parallel profiling of single-cell temporal RNA dynamics

Single-cell RNA sequencing (scRNA-seq) reveals the transcriptional heterogeneity of cells, but the static snapshots fail to reveal the time-resolved dynamics of transcription. Herein, we develop Well-TEMP-seq, a high-throughput, cost-effective, accurate, and efficient method for massively parallel profiling the temporal dynamics of single-cell gene expression. Well-TEMP-seq combines metabolic RNA labeling with scRNA-seq method Well-paired-seq to distinguish newly transcribed RNAs marked by T-to-C substitutions from pre-existing RNAs in each of thousands of single cells. The Well-paired-seq chip ensures a high single cell/barcoded bead pairing rate (similar to 80%) and the improved alkylation chemistry on beads greatly alleviates chemical conversion-induced cell loss (similar to 67.5% recovery). We further apply Well-TEMP-seq to profile the transcriptional dynamics of colorectal cancer cells exposed to 5-AZA-CdR, a DNA-demethylating drug. Well-TEMP-seq unbiasedly captures the RNA dynamics and outperforms the splicing-based RNA velocity method. We anticipate that Well-TEMP-seq will be broadly applicable to unveil the dynamics of single-cell gene expression in diverse biological processes.

Automated summary

In this study, a method called Well-TEMP-seq was developed, which is a high-throughput, cost-effective, accurate, and efficient approach for profiling the temporal dynamics of single-cell gene expression. By combining metabolic RNA labeling with single-cell RNA sequencing, Well-TEMP-seq can distinguish newly transcribed RNAs from pre-existing RNAs in thousands of single cells. The application of this method to colorectal cancer cells exposed to the DNA demethylating drug 5-AZA-CdR revealed its unbiased capturing of RNA dynamics and superior performance compared to splicing-based RNA velocity method.