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

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.