Benchmarking metabolic RNA labeling techniques for high-throughput single-cell RNA sequencing

Xiaowen Zhang, Mingjian Peng, Jianghao Zhu, Xue Zhai, Chaoguang Wei, He Jiao, Zhichao Wu, Songqian Huang, Mingli Liu, Wenhao Li, Wenyi Yang, Kai Miao, Qiongqiong Xu, Liangbiao Chen and Peng Hu ()
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Xiaowen Zhang: Shanghai Ocean University
Mingjian Peng: Shanghai Ocean University
Jianghao Zhu: Shanghai Ocean University
Xue Zhai: Shanghai Ocean University
Chaoguang Wei: Shanghai Ocean University
He Jiao: Shanghai Ocean University
Zhichao Wu: Shanghai Ocean University
Songqian Huang: Shanghai Ocean University
Mingli Liu: Shanghai Ocean University
Wenhao Li: Shanghai Ocean University
Wenyi Yang: Shanghai Ocean University
Kai Miao: University of Macau
Qiongqiong Xu: Shanghai Ocean University
Liangbiao Chen: Shanghai Ocean University
Peng Hu: Shanghai Ocean University

Nature Communications, 2025, vol. 16, issue 1, 1-17

Abstract: Abstract Metabolic RNA labeling with high-throughput single-cell RNA sequencing (scRNA-seq) enables precise measurement of gene expression dynamics in complex biological processes, such as cell state transitions and embryogenesis. This technique, which tags newly synthesized RNA for detection through induced base conversions, relies on conversion efficiency, RNA integrity, and transcript recovery. These factors are influenced by the chosen chemical conversion method and platform compatibility. Despite its potential, a comprehensive comparison of chemical methods and platform compatibility has been lacking. Here, we benchmark ten chemical conversion methods using the Drop-seq platform, analyzing 52,529 cells. We find that on-beads methods, particularly the meta-chloroperoxy-benzoic acid/2,2,2-trifluoroethylamine combination, outperform in-situ approaches. To assess in vivo applications, we apply these optimized methods to 9883 zebrafish embryonic cells during the maternal-to-zygotic transition, identifying and experimentally validating zygotically activated transcripts, which enhanced zygotic gene detection capabilities. Additionally, we evaluate two commercial platforms with higher capture efficiency and find that on-beads iodoacetamide chemistry is the most effective. Our results provide critical guidance for selecting optimal chemical methods and scRNA-seq platforms, advancing the study of RNA dynamics in complex biological systems.

Date: 2025
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DOI: 10.1038/s41467-025-61375-z

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