Letter | Published:

Single-cell transcriptomics reconstructs fate conversion from fibroblast to cardiomyocyte

Nature volume 551, pages 100104 (02 November 2017) | Download Citation


Direct lineage conversion offers a new strategy for tissue regeneration and disease modelling. Despite recent success in directly reprogramming fibroblasts into various cell types, the precise changes that occur as fibroblasts progressively convert to the target cell fates remain unclear. The inherent heterogeneity and asynchronous nature of the reprogramming process renders it difficult to study this process using bulk genomic techniques. Here we used single-cell RNA sequencing to overcome this limitation and analysed global transcriptome changes at early stages during the reprogramming of mouse fibroblasts into induced cardiomyocytes (iCMs)1,2,3,4. Using unsupervised dimensionality reduction and clustering algorithms, we identified molecularly distinct subpopulations of cells during reprogramming. We also constructed routes of iCM formation, and delineated the relationship between cell proliferation and iCM induction. Further analysis of global gene expression changes during reprogramming revealed unexpected downregulation of factors involved in mRNA processing and splicing. Detailed functional analysis of the top candidate splicing factor, Ptbp1, revealed that it is a critical barrier for the acquisition of cardiomyocyte-specific splicing patterns in fibroblasts. Concomitantly, Ptbp1 depletion promoted cardiac transcriptome acquisition and increased iCM reprogramming efficiency. Additional quantitative analysis of our dataset revealed a strong correlation between the expression of each reprogramming factor and the progress of individual cells through the reprogramming process, and led to the discovery of new surface markers for the enrichment of iCMs. In summary, our single-cell transcriptomics approaches enabled us to reconstruct the reprogramming trajectory and to uncover intermediate cell populations, gene pathways and regulators involved in iCM induction.

  • Subscribe to Nature for full access:



Additional access options:

Already a subscriber?  Log in  now or  Register  for online access.


Primary accessions

Gene Expression Omnibus


  1. 1.

    et al. Direct reprogramming of fibroblasts into functional cardiomyocytes by defined factors. Cell 142, 375–386 (2010)

  2. 2.

    et al. microRNA-mediated in vitro and in vivo direct reprogramming of cardiac fibroblasts to cardiomyocytes. Circ. Res. 110, 1465–1473 (2012)

  3. 3.

    et al. In vivo reprogramming of murine cardiac fibroblasts into induced cardiomyocytes. Nature 485, 593–598 (2012)

  4. 4.

    et al. Heart repair by reprogramming non-myocytes with cardiac transcription factors. Nature 485, 599–604 (2012)

  5. 5.

    , , , & Demethylation of H3K27 is essential for the induction of direct cardiac reprogramming by miR combo. Circ. Res. 120, 1403–1413 (2017)

  6. 6.

    , , , & In vivo cardiac reprogramming using an optimal single polycistronic construct. Cardiovasc. Res. 108, 217–219 (2015)

  7. 7.

    et al. In vivo cardiac cellular reprogramming efficacy is enhanced by angiogenic preconditioning of the infarcted myocardium with vascular endothelial growth factor. J. Am. Heart Assoc. 1, e005652 (2012)

  8. 8.

    et al. Chemical enhancement of in vitro and in vivo direct cardiac reprogramming. Circulation 135, 978–995 (2017)

  9. 9.

    et al. High-efficiency reprogramming of fibroblasts into cardiomyocytes requires suppression of pro-fibrotic signalling. Nat. Commun. 6, 8243 (2015)

  10. 10.

    , , , & Akt1/protein kinase B enhances transcriptional reprogramming of fibroblasts to functional cardiomyocytes. Proc. Natl Acad. Sci. USA 112, 11864–11869 (2015)

  11. 11.

    , , & Inhibition of TGFβ signaling increases direct conversion of fibroblasts to induced cardiomyocytes. PLoS ONE 9, e89678 (2014)

  12. 12.

    et al. Re-patterning of H3K27me3, H3K4me3 and DNA methylation during fibroblast conversion into induced cardiomyocytes. Stem Cell Res. 16, 507–518 (2016)

  13. 13.

    et al. MiR-133 promotes cardiac reprogramming by directly repressing Snai1 and silencing fibroblast signatures. EMBO J. 33, 1565–1581 (2014)

  14. 14.

    et al. Stoichiometry of Gata4, Mef2c, and Tbx5 influences the efficiency and quality of induced cardiac myocyte reprogramming. Circ. Res. 116, 237–244 (2015)

  15. 15.

    et al. Bmi1 is a key epigenetic barrier to direct cardiac reprogramming. Cell Stem Cell 18, 382–395 (2016)

  16. 16.

    et al. Single-cell RNA-Seq with waterfall reveals molecular cascades underlying adult neurogenesis. Cell Stem Cell 17, 360–372 (2015)

  17. 17.

    , & SLICER: inferring branched, nonlinear cellular trajectories from single cell RNA-seq data. Genome Biol. 17, 106 (2016)

  18. 18.

    et al. A molecular roadmap of reprogramming somatic cells into iPS cells. Cell 151, 1617–1632 (2012)

  19. 19.

    et al. Generation of an inducible fibroblast cell line for studying direct cardiac reprogramming. Genesis 54, 398–406 (2016)

  20. 20.

    & Context-dependent control of alternative splicing by RNA-binding proteins. Nat. Rev. Genet. 15, 689–701 (2014)

  21. 21.

    , , , & rMAPS: RNA map analysis and plotting server for alternative exon regulation. Nucleic Acids Res. 44, W333–W338 (2016)

  22. 22.

    , & Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014)

  23. 23.

    et al. Improved generation of induced cardiomyocytes using a polycistronic construct expressing optimal ratio of Gata4, Mef2c and Tbx5. J. Vis. Exp. 105, e53426 (2015)

  24. 24.

    , , & Co-occupancy by multiple cardiac transcription factors identifies transcriptional enhancers active in heart. Proc. Natl Acad. Sci. USA 108, 5632–5637 (2011)

  25. 25.

    et al. The cardiac TBX5 interactome reveals a chromatin remodeling network essential for cardiac septation. Dev. Cell 36, 262–275 (2016)

  26. 26.

    et al. Prolyl hydroxylation by EglN2 destabilizes FOXO3a by blocking its interaction with the USP9x deubiquitinase. Genes Dev. 28, 1429–1444 (2014)

  27. 27.

    , , , & Reprogramming of mouse fibroblasts into cardiomyocyte-like cells in vitro. Nat. Protoc. 8, 1204–1215 (2013)

  28. 28.

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

  29. 29.

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

  30. 30.

    , & HTSeq—a Python framework to work with high-throughput sequencing data. Bioinformatics 31, 166–169 (2015)

  31. 31.

    et al. Accounting for technical noise in single-cell RNA-seq experiments. Nat. Methods 10, 1093–1095 (2013)

  32. 32.

    , & Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics 8, 118–127 (2007)

  33. 33.

    et al. Removing batch effects in analysis of expression microarray data: an evaluation of six batch adjustment methods. PLoS ONE 6, e17238 (2011)

  34. 34.

    et al. MBNL proteins repress ES-cell-specific alternative splicing and reprogramming. Nature 498, 241–245 (2013)

  35. 35.

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

  36. 36.

    et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015)

  37. 37.

    et al. rMATS: robust and flexible detection of differential alternative splicing from replicate RNA-seq data. Proc. Natl Acad. Sci. USA 111, E5593–E5601 (2014)

  38. 38.

    et al. An abundant tissue macrophage population in the adult murine heart with a distinct alternatively-activated macrophage profile. PLoS ONE 7, e36814 (2012)

  39. 39.

    et al. Identification of genes periodically expressed in the human cell cycle and their expression in tumors. Mol. Biol. Cell 13, 1977–2000 (2002)

  40. 40.

    & Tropomyosin exons as models for alternative splicing. Adv. Exp. Med. Biol. 644, 27–42 (2008)

  41. 41.

    & Conserved developmental alternative splicing of muscleblind-like (MBNL) transcripts regulates MBNL localization and activity. RNA Biol. 7, 43–55 (2010)

  42. 42.

    , , & Translational control of tropomyosin expression in vertebrate hearts. Anat. Rec. (Hoboken) 297, 1585–1595 (2014)

Download references


We thank UNC AAC Core, HTSF Core, Flow Core for technical support. This study was supported by NIH HG06272 to J.F.P., NIH BD2K Fellowship (T32 CA201159) and NIH F31 Fellowship (HG008912) to J.D.W., NIH/NHLBI R00 HL109079 and American Heart Association (AHA) 15GRNT25530005 to J.L., AHA 13SDG17060010, Ellison Medical Foundation (EMF) AG-NS-1064-13, and NIH/NHLBI R01HL128331 to L.Q., and gifts from H. McAllister and C. Sewell.

Author information

Author notes

    • Ziqing Liu
    • , Li Wang
    •  & Joshua D. Welch

    These authors contributed equally to this work.


  1. McAllister Heart Institute, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, USA.

    • Ziqing Liu
    • , Li Wang
    • , Hong Ma
    • , Yang Zhou
    • , Haley Ruth Vaseghi
    • , Shuo Yu
    • , Joseph Blake Wall
    • , Sahar Alimohamadi
    • , Michael Zheng
    • , Chaoying Yin
    • , Jiandong Liu
    •  & Li Qian
  2. Department of Pathology and Laboratory Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, USA

    • Ziqing Liu
    • , Li Wang
    • , Hong Ma
    • , Yang Zhou
    • , Haley Ruth Vaseghi
    • , Shuo Yu
    • , Joseph Blake Wall
    • , Sahar Alimohamadi
    • , Michael Zheng
    • , Chaoying Yin
    • , Jiandong Liu
    •  & Li Qian
  3. Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, USA

    • Joshua D. Welch
    •  & Jan F. Prins
  4. Department of Statistics, University of California at Irvine, Irvine, California 92697, USA

    • Weining Shen


  1. Search for Ziqing Liu in:

  2. Search for Li Wang in:

  3. Search for Joshua D. Welch in:

  4. Search for Hong Ma in:

  5. Search for Yang Zhou in:

  6. Search for Haley Ruth Vaseghi in:

  7. Search for Shuo Yu in:

  8. Search for Joseph Blake Wall in:

  9. Search for Sahar Alimohamadi in:

  10. Search for Michael Zheng in:

  11. Search for Chaoying Yin in:

  12. Search for Weining Shen in:

  13. Search for Jan F. Prins in:

  14. Search for Jiandong Liu in:

  15. Search for Li Qian in:


L.Q., Z.L. and L.W. conceived and designed the study. Z.L. and L.W. designed and performed single-cell RNA-seq. Z.L., L.W., Y.Z. and C.Y. prepared samples for microarray and bulk RNA-seq. L.W., Y.Z., H.M., H.R.V., C.Y., S.Y., J.B.W., S.A. and M.Z. performed other experiments. Z.L., J.D.W. and J.F.P. performed data analysis and modelling. W.S. helped with statistical analysis. Z.L, J.D.W., J.L. and L.Q. wrote the manuscript, with extensive input from all authors. J.L. and L.Q. provided funding and overall supervision.

Competing interests

The authors declare no competing financial interests.

Corresponding authors

Correspondence to Jiandong Liu or Li Qian.

Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Supplementary information

PDF files

  1. 1.

    Supplementary Information

    This file contains Supplementary Discussions 1-3 and Supplementary Figure 1, the raw images of the western blots.

  2. 2.

    Life Sciences Reporting Summary

Excel files

  1. 1.

    Supplementary Tables

    This file contains Supplementary Tables 1-8.

About this article

Publication history






Rights and permissions

To obtain permission to re-use content from this article visit RightsLink.


By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.