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项目编号 PRJCA037071
项目标题 Benchmarking Metabolic RNA Labeling Techniques for High-Throughput Single-Cell RNA Sequencing
涉及领域 Model organism
数据类型 Single cell sequencing
物种名称 Danio rerio
描述信息 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 benchmarked ten chemical conversion methods using the Drop-seq platform, analyzing xxx cells. We found that on-beads methods, particularly the meta-chloroperoxy-benzoic acid/2,2,2-trifluoroethylamine combination, outperformed in-situ approaches. To assess in vivo applications, we applied these optimized methods to 9,883 zebrafish embryonic cells during the maternal-to-zygotic transition, identifying and experimentally validating novel zygotically activated transcripts, which enhanced zygotic gene detection capabilities. Additionally, we evaluated a commercial platform with higher capture efficiency and found that on-beads iodoacetamide chemistry was 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.
样品范围 Single cell
发布日期 2025-05-26
项目资金来源
机构 项目类型 授权项目ID 授权项目名称
National Natural Science Foundation of China (NSFC) 32200414
提交者 Peng Hu (phu@shou.edu.cn)
提交单位 Shanghai Ocean University
提交日期 2025-03-08

项目包含数据信息

资源名称 描述
BioSample (30)  show -
GSA (1) -
CRA023644 Benchmarking Metabolic RNA Labeling Techniques for High-Throughput Single-Cell RNA Sequencing