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

Shichao Lin, Kun Yin, Yingkun Zhang, Fanghe Lin, Xiaoyong Chen, Xi Zeng, Xiaoxu Guo, Huimin Zhang, Jia Song () and Chaoyong Yang ()
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Shichao Lin: Xiamen University
Kun Yin: Xiamen University
Yingkun Zhang: Xiamen University
Fanghe Lin: Xiamen University
Xiaoyong Chen: Xiamen University
Xi Zeng: Xiamen University
Xiaoxu Guo: Xiamen University
Huimin Zhang: Xiamen University
Jia Song: Shanghai Jiao Tong University
Chaoyong Yang: Xiamen University

Nature Communications, 2023, vol. 14, issue 1, 1-11

Abstract: Abstract 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 (~80%) and the improved alkylation chemistry on beads greatly alleviates chemical conversion-induced cell loss (~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.

Date: 2023
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DOI: 10.1038/s41467-023-36902-5

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