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A pooled single-cell genetic screen identifies regulatory checkpoints in the continuum of the epithelial-to-mesenchymal transition

Nature Genetics volume 51, pages 1389–1398 (2019)Cite this article

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Abstract

Integrating single-cell trajectory analysis with pooled genetic screening could reveal the genetic architecture that guides cellular decisions in development and disease. We applied this paradigm to probe the genetic circuitry that controls epithelial-to-mesenchymal transition (EMT). We used single-cell RNA sequencing to profile epithelial cells undergoing a spontaneous spatially determined EMT in the presence or absence of transforming growth factor-β. Pseudospatial trajectory analysis identified continuous waves of gene regulation as opposed to discrete ‘partial’ stages of EMT. KRAS was connected to the exit from the epithelial state and the acquisition of a fully mesenchymal phenotype. A pooled single-cell CRISPR-Cas9 screen identified EMT-associated receptors and transcription factors, including regulators of KRAS, whose loss impeded progress along the EMT. Inhibiting the KRAS effector MEK and its upstream activators EGFR and MET demonstrates that interruption of key signaling events reveals regulatory ‘checkpoints’ in the EMT continuum that mimic discrete stages, and reconciles opposing views of the program that controls EMT.

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Fig. 1: Pseudospatial trajectory reconstruction of spontaneous EMT reveals the transition as a continuum of epithelial–mesenchymal states.
Fig. 2: Alignment of spontaneous and TGF-β-driven EMT pseudospatial trajectories identifies discrete waves along the EMT continuum.
Fig. 3: Multiplexed loss-of-function screening of EMT-associated genes recovers deficiencies in TGF-β-induced EMT.
Fig. 4: Accumulation of knockout cells across spontaneous and TGF-β-driven EMT trajectories identifies regulators of discrete checkpoints across the EMT continuum.

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Data availability

Data are available on GEO under accession number GSE114687. Data will also be provided via the Github repository described in ‘Code availability’.

Code availability

Code can be found on Github at https://github.com/cole-trapnell-lab/pseudospace.

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Acknowledgements

We thank all members of the Trapnell and Shendure laboratories for helpful discussions during the course of this study and feedback on our manuscript, particularly S. Srivatsan, L. Saunders, H. Pliner and J. Packer. We thank N.M. Cruz for feedback on our manuscript. J.L.M.F. thanks S.V. McFaline-Cruz for support. J.L.M.F. was supported by NIH grants T32HL007828 and T32HG000035. A.J.H. was supported by an NSF Graduate Research Fellowship. J.S. and C.T. are supported by NIH grant no. U54DK107979 and the Paul G. Allen Frontiers Group. C.T. is supported by NIH grant nos. DP2HD088158, RC2DK114777 and R01HL118342 and is partly supported by an Alfred P. Sloan Foundation Research Fellowship. J.S. is supported by NIH grant nos. DP1HG007811 and R01HG006283 and is an investigator of the Howard Hughes Medical Institute.

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Authors and Affiliations

  1. Department of Genome Sciences, University of Washington, Seattle, WA, USA

    José L. McFaline-Figueroa, Andrew J. Hill, Xiaojie Qiu, Dana Jackson, Jay Shendure & Cole Trapnell

  2. Department of Cellular and Molecular Pharmacology, University of California, San Francisco, CA, USA

    Xiaojie Qiu

  3. Brotman Baty Institute for Precision Medicine, Seattle, WA, USA

    Jay Shendure & Cole Trapnell

  4. Howard Hughes Medical Institute, Seattle, WA, USA

    Jay Shendure

  5. Allen Discovery Center for Cell Lineage Tracing, Seattle, WA, USA

    Jay Shendure & Cole Trapnell

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  1. José L. McFaline-Figueroa
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Contributions

J.L.M.F., J.S. and C.T. devised the project. J.L.M.F., A.J.H., J.S. and C.T. designed experiments. J.L.M.F., A.J.H. and D.J. performed experiments. D.J. and X.Q. provided substantial technical and computational support, respectively. J.L.M.F and A.J.H. performed analyses. J.L.M.F. and C.T. wrote the manuscript with the support of the other authors.

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Correspondence to Cole Trapnell.

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Supplementary information

Supplementary Information

Supplementary Figs. 1–32 and Table 1

Reporting Summary

Supplementary Table 2

Differential expression analysis between cell fractions of MCF10A cells undergoing spontaneous EMT.

Supplementary Table 3

Pseudospatial differential expression analysis of spontaneous EMT.

Supplementary Table 4

Geneset analysis of genes differentially expressed across pseudospace in cells undergoing spontaneous EMT.

Supplementary Table 5

Differential expression analysis between cell fractions of HuMEC cells undergoing spontaneous EMT.

Supplementary Table 6

Differential expression analysis between aligned spontaneous and TGF-β-driven MCF10A EMT trajectories.

Supplementary Table 7

Geneset analysis of genes differentially expressed between aligned spontaneous and TGF-β-driven MCF10A EMT trajectories.

Supplementary Table 8

Pseudospatial differential expression analysis between knockout and non-targeting control MCF10A cells undergoing spontaneous EMT.

Supplementary Table 9

Pseudospatial differential expression analysis between knockout and non-targeting control MCF10A cells undergoing TGF-β-driven EMT.

Supplementary Table 10

Sequences of oligonucleotides used in this study.

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McFaline-Figueroa, J.L., Hill, A.J., Qiu, X. et al. A pooled single-cell genetic screen identifies regulatory checkpoints in the continuum of the epithelial-to-mesenchymal transition. Nat Genet 51, 1389–1398 (2019). https://doi.org/10.1038/s41588-019-0489-5

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