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Single-cell messenger RNA sequencing reveals rare intestinal cell types

Author

Listed:
  • Dominic Grün

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

  • Anna Lyubimova

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

  • Lennart Kester

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

  • Kay Wiebrands

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

  • Onur Basak

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

  • Nobuo Sasaki

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

  • Hans Clevers

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

  • Alexander van Oudenaarden

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

Abstract

An algorithm that allows rare cell type identification in a complex population of single cells, based on single-cell mRNA-sequencing, is applied to mouse intestinal cells, revealing novel subtypes of enteroendocrine cells and showing that the Lgr5-expressing population consists of a homogenous stem cell population with a few rare secretory cells, including Paneth cells.

Suggested Citation

  • Dominic Grün & Anna Lyubimova & Lennart Kester & Kay Wiebrands & Onur Basak & Nobuo Sasaki & Hans Clevers & Alexander van Oudenaarden, 2015. "Single-cell messenger RNA sequencing reveals rare intestinal cell types," Nature, Nature, vol. 525(7568), pages 251-255, September.
    • Handle: RePEc:nat:nature:v:525:y:2015:i:7568:d:10.1038_nature14966
    DOI: 10.1038/nature14966
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    Citations

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    Cited by:

    1. Suner Aslı, 2019. "Clustering methods for single-cell RNA-sequencing expression data: performance evaluation with varying sample sizes and cell compositions," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 18(5), pages 1-14, October.
    2. Weimiao Wu & Yunqing Liu & Qile Dai & Xiting Yan & Zuoheng Wang, 2021. "G2S3: A gene graph-based imputation method for single-cell RNA sequencing data," PLOS Computational Biology, Public Library of Science, vol. 17(5), pages 1-24, May.
    3. Zhiyuan Yuan & Yisi Li & Minglei Shi & Fan Yang & Juntao Gao & Jianhua Yao & Michael Q. Zhang, 2022. "SOTIP is a versatile method for microenvironment modeling with spatial omics data," Nature Communications, Nature, vol. 13(1), pages 1-19, December.
    4. Ihab Ansari & Llorenç Solé-Boldo & Meshi Ridnik & Julian Gutekunst & Oliver Gilliam & Maria Korshko & Timur Liwinski & Birgit Jickeli & Noa Weinberg-Corem & Michal Shoshkes-Carmel & Eli Pikarsky & Era, 2023. "TET2 and TET3 loss disrupts small intestine differentiation and homeostasis," Nature Communications, Nature, vol. 14(1), pages 1-18, December.
    5. Davide Risso & Liam Purvis & Russell B Fletcher & Diya Das & John Ngai & Sandrine Dudoit & Elizabeth Purdom, 2018. "clusterExperiment and RSEC: A Bioconductor package and framework for clustering of single-cell and other large gene expression datasets," PLOS Computational Biology, Public Library of Science, vol. 14(9), pages 1-16, September.
    6. Ming-Wen Hu & Dong Won Kim & Sheng Liu & Donald J Zack & Seth Blackshaw & Jiang Qian, 2019. "PanoView: An iterative clustering method for single-cell RNA sequencing data," PLOS Computational Biology, Public Library of Science, vol. 15(8), pages 1-17, August.

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