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Single-cell full-length total RNA sequencing uncovers dynamics of recursive splicing and enhancer RNAs

Author

Listed:
  • Tetsutaro Hayashi

    (RIKEN)

  • Haruka Ozaki

    (RIKEN)

  • Yohei Sasagawa

    (RIKEN)

  • Mana Umeda

    (RIKEN)

  • Hiroki Danno

    (RIKEN)

  • Itoshi Nikaido

    (RIKEN
    RIKEN)

Abstract

Total RNA sequencing has been used to reveal poly(A) and non-poly(A) RNA expression, RNA processing and enhancer activity. To date, no method for full-length total RNA sequencing of single cells has been developed despite the potential of this technology for single-cell biology. Here we describe random displacement amplification sequencing (RamDA-seq), the first full-length total RNA-sequencing method for single cells. Compared with other methods, RamDA-seq shows high sensitivity to non-poly(A) RNA and near-complete full-length transcript coverage. Using RamDA-seq with differentiation time course samples of mouse embryonic stem cells, we reveal hundreds of dynamically regulated non-poly(A) transcripts, including histone transcripts and long noncoding RNA Neat1. Moreover, RamDA-seq profiles recursive splicing in >300-kb introns. RamDA-seq also detects enhancer RNAs and their cell type-specific activity in single cells. Taken together, we demonstrate that RamDA-seq could help investigate the dynamics of gene expression, RNA-processing events and transcriptional regulation in single cells.

Suggested Citation

  • Tetsutaro Hayashi & Haruka Ozaki & Yohei Sasagawa & Mana Umeda & Hiroki Danno & Itoshi Nikaido, 2018. "Single-cell full-length total RNA sequencing uncovers dynamics of recursive splicing and enhancer RNAs," Nature Communications, Nature, vol. 9(1), pages 1-16, December.
    • Handle: RePEc:nat:natcom:v:9:y:2018:i:1:d:10.1038_s41467-018-02866-0
    DOI: 10.1038/s41467-018-02866-0
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    Cited by:

    1. Rong Ma & Eric D. Sun & James Zou, 2023. "A spectral method for assessing and combining multiple data visualizations," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    2. Peizhuo Wang & Xiao Wen & Han Li & Peng Lang & Shuya Li & Yipin Lei & Hantao Shu & Lin Gao & Dan Zhao & Jianyang Zeng, 2023. "Deciphering driver regulators of cell fate decisions from single-cell transcriptomics data with CEFCON," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    3. Kai Battenberg & S. Thomas Kelly & Radu Abu Ras & Nicola A. Hetherington & Makoto Hayashi & Aki Minoda, 2022. "A flexible cross-platform single-cell data processing pipeline," Nature Communications, Nature, vol. 13(1), pages 1-7, December.
    4. Wanying Wu & Jinyang Zhang & Xiaofei Cao & Zhengyi Cai & Fangqing Zhao, 2022. "Exploring the cellular landscape of circular RNAs using full-length single-cell RNA sequencing," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    5. Yuki Takakura & Moeka Machida & Natsumi Terada & Yuka Katsumi & Seika Kawamura & Kenta Horie & Maki Miyauchi & Tatsuya Ishikawa & Nobuko Akiyama & Takao Seki & Takahisa Miyao & Mio Hayama & Rin Endo &, 2024. "Mitochondrial protein C15ORF48 is a stress-independent inducer of autophagy that regulates oxidative stress and autoimmunity," Nature Communications, Nature, vol. 15(1), pages 1-19, December.

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