Letter | Published:

Dissecting direct reprogramming from fibroblast to neuron using single-cell RNA-seq

Nature volume 534, pages 391395 (16 June 2016) | Download Citation

Abstract

Direct lineage reprogramming represents a remarkable conversion of cellular and transcriptome states1,2,3. However, the intermediate stages through which individual cells progress during reprogramming are largely undefined. Here we use single-cell RNA sequencing4,5,6,7 at multiple time points to dissect direct reprogramming from mouse embryonic fibroblasts to induced neuronal cells. By deconstructing heterogeneity at each time point and ordering cells by transcriptome similarity, we find that the molecular reprogramming path is remarkably continuous. Overexpression of the proneural pioneer factor Ascl1 results in a well-defined initialization, causing cells to exit the cell cycle and re-focus gene expression through distinct neural transcription factors. The initial transcriptional response is relatively homogeneous among fibroblasts, suggesting that the early steps are not limiting for productive reprogramming. Instead, the later emergence of a competing myogenic program and variable transgene dynamics over time appear to be the major efficiency limits of direct reprogramming. Moreover, a transcriptional state, distinct from donor and target cell programs, is transiently induced in cells undergoing productive reprogramming. Our data provide a high-resolution approach for understanding transcriptome states during lineage differentiation.

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Primary accessions

Gene Expression Omnibus

Data deposits

The single-cell RNA-seq data were deposited on NCBI GEO with the accession number GSE67310.

References

  1. 1.

    , & Direct lineage reprogramming: strategies, mechanisms, and applications. Cell Stem Cell 16, 119–134 (2015).

  2. 2.

    & Brains in metamorphosis: reprogramming cell identity within the central nervous system. Curr. Opin. Neurobiol. 27, 208–214 (2014).

  3. 3.

    Historical origins of transdifferentiation and reprogramming. Cell Stem Cell 9, 504–516 (2011).

  4. 4.

    et al. Reconstructing lineage hierarchies of the distal lung epithelium using single-cell RNA-seq. Nature 509, 371–375 (2014).

  5. 5.

    et al. Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells. Nature 498, 236–240 (2013).

  6. 6.

    et al. Brain structure. Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq. Science 347, 1138–1142 (2015).

  7. 7.

    et al. Full-length mRNA-seq from single-cell levels of RNA and individual circulating tumor cells. Nat. Biotechnol. 30, 777–782 (2012).

  8. 8.

    et al. Direct conversion of fibroblasts to functional neurons by defined factors. Nature 463, 1035–1041 (2010).

  9. 9.

    et al. Direct conversion of human fibroblasts to dopaminergic neurons. Proc. Natl Acad. Sci. USA 108, 10343–10348 (2011).

  10. 10.

    et al. MicroRNA-mediated conversion of human fibroblasts to neurons. Nature 476, 228–231 (2011).

  11. 11.

    et al. Direct reprogramming of adult human fibroblasts to functional neurons under defined conditions. Cell Stem Cell 9, 113–118 (2011).

  12. 12.

    et al. Direct generation of functional dopaminergic neurons from mouse and human fibroblasts. Nature 476, 224–227 (2011).

  13. 13.

    et al. Hierarchical mechanisms for direct reprogramming of fibroblasts to neurons. Cell 155, 621–635 (2013).

  14. 14.

    et al. Generation of induced neuronal cells by the single reprogramming factor ASCL1. Stem Cell Rep. 3, 282–296 (2014).

  15. 15.

    et al. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat. Biotechnol. 32, 381–386 (2014).

  16. 16.

    et al. Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells. Nat. Biotechnol. 33, 155–160 (2015).

  17. 17.

    & Modeling human disease with pluripotent stem cells: from genome association to function. Cell Stem Cell 12, 656–668 (2013).

  18. 18.

    et al. Derivation of midbrain dopamine neurons from human embryonic stem cells. Proc. Natl Acad. Sci. USA 101, 12543–12548 (2004).

  19. 19.

    et al. Specification of motoneurons from human embryonic stem cells. Nat. Biotechnol. 23, 215–221 (2005).

  20. 20.

    et al. Human cerebral organoids recapitulate gene expression programs of fetal neocortex development. Proc. Natl Acad. Sci. USA 112, 15672–15677 (2015).

  21. 21.

    et al. C/EBPα poises B cells for rapid reprogramming into induced pluripotent stem cells. Nature 506, 235–239 (2014).

  22. 22.

    et al. Early reprogramming regulators identified by prospective isolation and mass cytometry. Nature 521, 352–356 (2015).

  23. 23.

    et al. Induction of pluripotency in human somatic cells via a transient state resembling primitive streak-like mesendoderm. Nat. Commun. 5, 3678 (2014).

  24. 24.

    et al. An RNA-sequencing transcriptome and splicing database of glia, neurons, and vascular cells of the cerebral cortex. J. Neurosci. 34, 11929–11947 (2014).

  25. 25.

    et al. Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat. Biotechnol. 28, 511–515 (2010).

  26. 26.

    et al. The External RNA Controls Consortium: a progress report. Nat. Methods 2, 731–734 (2005).

  27. 27.

    et al. Synthetic spike-in standards for RNA-seq experiments. Genome Res. 21, 1543–1551 (2011).

  28. 28.

    et al. CellProfiler: image analysis software for identifying and quantifying cell phenotypes. Genome Biol. 7, R100 (2006).

  29. 29.

    et al. Quantitative assessment of single-cell RNA-sequencing methods. Nat. Methods 11, 41–46 (2014).

  30. 30.

    Babraham Institute. Babraham Bioinformatics. FASTQC.

  31. 31.

    Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet.journal 17, 10–12 (2011).

  32. 32.

    & Quality control and preprocessing of metagenomic datasets. Bioinformatics 27, 863–864 (2011).

  33. 33.

    , , & Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 10, R25 (2009).

  34. 34.

    & Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).

  35. 35.

    , & TopHat: discovering splice junctions with RNA-seq. Bioinformatics 25, 1105–1111 (2009).

  36. 36.

    et al. The sequence alignment/map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).

  37. 37.

    RStudio. Integrated Development for R. RStudio, Inc., Boston, MA URL (2015).

  38. 38.

    R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing.

  39. 39.

    et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161, 1202–1214 (2015).

  40. 40.

    , , , & Spatial reconstruction of single-cell gene expression data. Nat. Biotechnol. 33, 495–502 (2015).

  41. 41.

    & The igraph software package for complex network research. InterJournal 1695 (2006).

  42. 42.

    , & Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat. Protocols 4, 44–57 (2009).

  43. 43.

    et al. AnimalTFDB: a comprehensive animal transcription factor database. Nucleic Acids Res. 40, D144–D149 (2012).

  44. 44.

    et al. Deconstructing transcriptional heterogeneity in pluripotent stem cells. Nature 516, 56–61 (2014).

  45. 45.

    et al. Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma. Science 344, 1396–1401 (2014).

Download references

Acknowledgements

The authors would like to acknowledge B. Passarelli and B. Vernot for discussions regarding bioinformatic pipelines, P. Lovelace for support with FACS and other Quake and Wernig laboratory members for discussions and support. This work was supported by NIH grant RC4NS073015-01 (M.W., S.Q.R., B.T.), the Stinehart-Reed Foundation, the Ellison Medical Foundation, the New York Stem Cell Foundation, CIRM grant RB5-07466 (all to M.W.), a National Science Scholarship from the Agency for Science, Technology and Research (Q.Y.L.), NIH grant GM092925 (S.A.M.S., J.S.), the German Research Foundation (M.M.) and a PhRMA foundation Informatics fellowship (J.G.C.). S.R.Q. is an investigator of the Howard Hughes Medical Institute. M.W. is a New York Stem Cell Foundation (NYSCF) Robertson Investigator and a Tashia and John Morgridge Faculty Scholar at the Child Health Research Institute at Stanford.

Author information

Author notes

    • Barbara Treutlein
    •  & Qian Yi Lee

    These authors contributed equally to this work.

    • Marius Wernig
    •  & Stephen R. Quake

    These authors jointly supervised this work.

Affiliations

  1. Department of Bioengineering, Stanford University, Stanford, California 94305, USA

    • Barbara Treutlein
    • , Qian Yi Lee
    • , Winston Koh
    • , Norma F. Neff
    •  & Stephen R. Quake
  2. Department of Evolutionary Genetics, Max Planck Institute for Evolutionary Anthropology, Leipzig 04103, Germany

    • Barbara Treutlein
  3. Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, California 94305, USA

    • Qian Yi Lee
    • , Moritz Mall
    • , Sopheak Sim
    •  & Marius Wernig
  4. Department of Pathology, Stanford University School of Medicine, Stanford, California 94305, USA

    • Qian Yi Lee
    • , Moritz Mall
    •  & Marius Wernig
  5. Department of Developmental Biology, Stanford University School of Medicine, Stanford, California 94305, USA

    • J. Gray Camp
  6. Department of Biology, Stanford University, Stanford, California 94305, USA

    • Seyed Ali Mohammad Shariati
    •  & Jan M. Skotheim
  7. Howard Hughes Medical Institute, Stanford, California 94305, USA

    • Stephen R. Quake
  8. Department of Applied Physics, Stanford University, Stanford, California 94305, USA

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Contributions

B.T., Q.Y.L., M.W. and S.R.Q. conceived the study and designed the experiments. Q.Y.L. performed direct reprogramming, qRT–PCR and western blot experiments; B.T., Q.Y.L., and S.S. performed single-cell RNA-seq experiments; N.F.N. assisted with single-cell RNA-seq experiments and sequenced the libraries; Q.Y.L., S.A.M.S. and M.M. performed time-lapse imaging experiments. B.T., J.G.C. and W.K. analysed single-cell RNA-seq data, Q.Y.L. analysed qRT–PCR and time-lapse imaging data, J.M.S., M.W. and S.R.Q. provided intellectual guidance in the interpretation of the data. B.T., Q.Y.L., J.G.C., M.W., and S.R.Q. wrote the paper.

Competing interests

S.R.Q. is a founder and consultant for Fluidigm Corporation.

Corresponding authors

Correspondence to Marius Wernig or Stephen R. Quake.

Reviewer Information Nature thanks F. Tang and the other anonymous reviewer(s) for their contribution to the peer review of this work.

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DOI

https://doi.org/10.1038/nature18323

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