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Single-cell Ribo-seq reveals cell cycle-dependent translational pausing

Author

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  • Michael VanInsberghe

    (Oncode Institute, Hubrecht Institute-KNAW (Royal Netherlands Academy of Arts and Sciences) and University Medical Center Utrecht)

  • Jeroen Berg

    (Oncode Institute, Hubrecht Institute-KNAW (Royal Netherlands Academy of Arts and Sciences) and University Medical Center Utrecht)

  • Amanda Andersson-Rolf

    (Oncode Institute, Hubrecht Institute-KNAW (Royal Netherlands Academy of Arts and Sciences) and University Medical Center Utrecht)

  • Hans Clevers

    (Oncode Institute, Hubrecht Institute-KNAW (Royal Netherlands Academy of Arts and Sciences) and University Medical Center Utrecht)

  • Alexander Oudenaarden

    (Oncode Institute, Hubrecht Institute-KNAW (Royal Netherlands Academy of Arts and Sciences) and University Medical Center Utrecht)

Abstract

Single-cell sequencing methods have enabled in-depth analysis of the diversity of cell types and cell states in a wide range of organisms. These tools focus predominantly on sequencing the genomes1, epigenomes2 and transcriptomes3 of single cells. However, despite recent progress in detecting proteins by mass spectrometry with single-cell resolution4, it remains a major challenge to measure translation in individual cells. Here, building on existing protocols5–7, we have substantially increased the sensitivity of these assays to enable ribosome profiling in single cells. Integrated with a machine learning approach, this technology achieves single-codon resolution. We validate this method by demonstrating that limitation for a particular amino acid causes ribosome pausing at a subset of the codons encoding the amino acid. Of note, this pausing is only observed in a sub-population of cells correlating to its cell cycle state. We further expand on this phenomenon in non-limiting conditions and detect pronounced GAA pausing during mitosis. Finally, we demonstrate the applicability of this technique to rare primary enteroendocrine cells. This technology provides a first step towards determining the contribution of the translational process to the remarkable diversity between seemingly identical cells.

Suggested Citation

  • Michael VanInsberghe & Jeroen Berg & Amanda Andersson-Rolf & Hans Clevers & Alexander Oudenaarden, 2021. "Single-cell Ribo-seq reveals cell cycle-dependent translational pausing," Nature, Nature, vol. 597(7877), pages 561-565, September.
  • Handle: RePEc:nat:nature:v:597:y:2021:i:7877:d:10.1038_s41586-021-03887-4
    DOI: 10.1038/s41586-021-03887-4
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    Cited by:

    1. T.V., Binil Shyam & Sharma, Rati, 2024. "mRNA translation from a unidirectional traffic perspective," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 637(C).
    2. Wenqi Hu & Haitao Zeng & Yanan Shi & Chuanchuan Zhou & Jiana Huang & Lei Jia & Siqi Xu & Xiaoyu Feng & Yanyan Zeng & Tuanlin Xiong & Wenze Huang & Peng Sun & Yajie Chang & Tingting Li & Cong Fang & Ke, 2022. "Single-cell transcriptome and translatome dual-omics reveals potential mechanisms of human oocyte maturation," Nature Communications, Nature, vol. 13(1), pages 1-17, December.
    3. Azelle Hawdon & Niall D. Geoghegan & Monika Mohenska & Anja Elsenhans & Charles Ferguson & Jose M. Polo & Robert G. Parton & Jennifer Zenker, 2023. "Apicobasal RNA asymmetries regulate cell fate in the early mouse embryo," Nature Communications, Nature, vol. 14(1), pages 1-18, December.
    4. Bin Shao & Jiawei Yan & Jing Zhang & Lili Liu & Ye Chen & Allen R. Buskirk, 2024. "Riboformer: a deep learning framework for predicting context-dependent translation dynamics," Nature Communications, Nature, vol. 15(1), pages 1-10, December.

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