Letter | Published:

Single-cell messenger RNA sequencing reveals rare intestinal cell types

Nature volume 525, pages 251255 (10 September 2015) | Download Citation

Abstract

Understanding the development and function of an organ requires the characterization of all of its cell types. Traditional methods for visualizing and isolating subpopulations of cells are based on messenger RNA or protein expression of only a few known marker genes. The unequivocal identification of a specific marker gene, however, poses a major challenge, particularly if this cell type is rare. Identifying rare cell types, such as stem cells, short-lived progenitors, cancer stem cells, or circulating tumour cells, is crucial to acquire a better understanding of normal or diseased tissue biology. To address this challenge we first sequenced the transcriptome of hundreds of randomly selected cells from mouse intestinal organoids1, cultured self-organizing epithelial structures that contain all cell lineages of the mammalian intestine. Organoid buds, like intestinal crypts, harbour stem cells that continuously differentiate into a variety of cell types, occurring at widely different abundances2. Since available computational methods can only resolve more abundant cell types, we developed RaceID, an algorithm for rare cell type identification in complex populations of single cells. We demonstrate that this algorithm can resolve cell types represented by only a single cell in a population of randomly sampled organoid cells. We use this algorithm to identify Reg4 as a novel marker for enteroendocrine cells, a rare population of hormone-producing intestinal cells3. Next, we use Reg4 expression to enrich for these rare cells and investigate the heterogeneity within this population. RaceID confirmed the existence of known enteroendocrine lineages, and moreover discovered novel subtypes, which we subsequently validated in vivo. Having validated RaceID we then applied the algorithm to ex vivo-isolated Lgr5-positive stem cells and their direct progeny. We find that Lgr5-positive cells represent a homogenous abundant population of stem cells mixed with a rare population of Lgr5-positive secretory cells. We envision broad applicability of our method for discovering rare cell types and the corresponding marker genes in healthy and diseased organs.

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Accessions

Primary accessions

Gene Expression Omnibus

Data deposits

RNA-seq data are deposited in Gene Expression Omnibus, accession number GSE62270.

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Acknowledgements

This work was supported by an European Research Council Advanced grant (ERC-AdG 294325-GeneNoiseControl) and a Nederlandse Organisatie voor Wetenschappelijk Onderzoek (NWO) Vici award.

Author information

Author notes

    • Dominic Grün
    •  & Anna Lyubimova

    These authors contributed equally to this work.

Affiliations

  1. Hubrecht Institute-KNAW (Royal Netherlands Academy of Arts and Sciences), 3584 CT Utrecht, The Netherlands

    • Dominic Grün
    • , Anna Lyubimova
    • , Lennart Kester
    • , Kay Wiebrands
    • , Onur Basak
    • , Nobuo Sasaki
    • , Hans Clevers
    •  & Alexander van Oudenaarden
  2. University Medical Center Utrecht, Cancer Genomics Netherlands, 3584 CG Utrecht, The Netherlands

    • Dominic Grün
    • , Anna Lyubimova
    • , Lennart Kester
    • , Kay Wiebrands
    • , Onur Basak
    • , Nobuo Sasaki
    • , Hans Clevers
    •  & Alexander van Oudenaarden

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Contributions

D.G., A.L. and A.v.O. conceived the project. D.G. developed the algorithm, performed all computations and wrote the manuscript. A.L., L.K. and K.W. performed all sequencing experiments. A.L. performed the lineage tracing experiment and all imaging experiments. N.S. made the Reg4–dsRed mouse and was supervised by H.C.; O.B. contributed the Lgr5–GFP organoids. A.L., L.K., N.S. and H.C. edited the manuscript. A.v.O. guided experiments, data analysis and writing of the manuscript, and edited the manuscript.

Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to Alexander van Oudenaarden.

Extended data

Supplementary information

Excel files

  1. 1.

    Supplementary Table 1

    This file contains Supplementary Table 1. Differentially regulated genes within cell clusters derived for random organoid cells. For each cluster, the first column contains the gene identifier, composed of the official gene symbol and the chromosome separated by a double underscore. The second and third columns contain the average expression across all cells and across cells within the cluster, respectively, normalized to the median expression in the cluster. The third column indicates the fold change and the last column shows the p-value for the observed fold change (see Methods).

  2. 2.

    Supplementary Table 2

    This file contains Supplementary Table 2. Differentially regulated genes within cell clusters derived from random Reg4-dsRed positive mouse intestinal cells. For each cluster, the first column contains the gene identifier, composed of the official gene symbol and the chromosome separated by a double underscore. The second and third columns contain the average expression across all cells and across cells within the cluster, respectively, normalized to the median expression in the cluster. The third column indicates the fold change and the last column shows the p-value for the observed fold change (see Methods).

  3. 3.

    Supplementary Table 3

    This file contains Supplementary Table 3, a list of 96 primers used for single cell sequencing and list of smFISH probes.

  4. 4.

    Supplementary Table 4

    This file contains Supplementary Table 4, mRNA molecule count in intestinal cells measured by smFISH.

  5. 5.

    Supplementary Table 5

    This file contains Supplementary Table 5, read statistics for Lgr5-EGFP+ and YFP+ cells isolated from life intestine. See Extended Data Figure 1 and 10 for details of the organoid data.

Zip files

  1. 1.

    Supplementary Table 6 (transcript_counts_intestine)

    This file contains Supplementary Table 6, sample data for applying the RaceID algorithm. The table contains the transcript count data for the 288 sequenced organoid cells presented in Figure 1 and 2. Primary and processed data for all experiments can be retrieved from Gene Expression Omnibus, accession no. GSE62270. This file was updated on 10 September 2015 to correct a file name.

  2. 2.

    Supplementary Data 1 (RaceID_class.R)

    This file contains the R code of the RaceID algorithm. This file was updated on 10 September 2015 to correct a file name.

  3. 3.

    Supplementary Data 2 (RaceID_sample.R)

    This file contains the R code of the RaceID sample commands. This file was updated on 10 September 2015 to correct a file name.

PDF files

  1. 1.

    Supplementary Information

    This file contains a Supplementary Note, including benchmarking and experimental validation of the RaceID algorithm.

  2. 2.

    Supplementary Data 3

    This file contains a manual of the R code for the RaceID algorithm.

About this article

Publication history

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DOI

https://doi.org/10.1038/nature14966

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