Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Wishbone identifies bifurcating developmental trajectories from single-cell data

Abstract

Recent single-cell analysis technologies offer an unprecedented opportunity to elucidate developmental pathways. Here we present Wishbone, an algorithm for positioning single cells along bifurcating developmental trajectories with high resolution. Wishbone uses multi-dimensional single-cell data, such as mass cytometry or RNA-Seq data, as input and orders cells according to their developmental progression, and it pinpoints bifurcation points by labeling each cell as pre-bifurcation or as one of two post-bifurcation cell fates. Using 30-channel mass cytometry data, we show that Wishbone accurately recovers the known stages of T-cell development in the mouse thymus, including the bifurcation point. We also apply the algorithm to mouse myeloid differentiation and demonstrate its generalization to additional lineages. A comparison of Wishbone to diffusion maps, SCUBA and Monocle shows that it outperforms these methods both in the accuracy of ordering cells and in the correct identification of branch points.

This is a preview of subscription content, access via your institution

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Alignment of cells along bifurcating trajectories.
Figure 2: Wishbone robustly recovers hallmarks of T-cell differentiation.
Figure 3: Heterogeneity in gated populations is explained in part by variance along a trajectory.
Figure 4: Transcription factors show distinct dynamics in SP populations.
Figure 5: Generalization of Wishbone to branches in human and mouse myeloid development spanning mass cytometry and single-cell RNA-Seq.
Figure 6: Wishbone outperformed competing methods in both ordering of cells and branch associations.

Similar content being viewed by others

Accession codes

Accessions

Gene Expression Omnibus

References

  1. Tang, F. et al. mRNA-Seq whole-transcriptome analysis of a single cell. Nat. Methods 6, 377–382 (2009).

    Article  CAS  Google Scholar 

  2. Bendall, S.C. et al. Single-cell mass cytometry of differential immune and drug responses across a human hematopoietic continuum. Science 332, 687–696 (2011).

    Article  CAS  Google Scholar 

  3. Bendall, S.C. et al. Single-cell trajectory detection uncovers progression and regulatory coordination in human B cell development. Cell 157, 714–725 (2014).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  5. Marco, E. et al. Bifurcation analysis of single-cell gene expression data reveals epigenetic landscape. Proc. Natl. Acad. Sci. USA 111, E5643–E5650 (2014).

    Article  CAS  Google Scholar 

  6. Paul, F. et al. Transcriptional heterogeneity and lineage commitment in myeloid progenitors. Cell 163, 1663–1677 (2015).

    Article  CAS  Google Scholar 

  7. Coifman, R.R. et al. Geometric diffusions as a tool for harmonic analysis and structure definition of data: diffusion maps. Proc. Natl. Acad. Sci. USA 102, 7426–7431 (2005).

    Article  CAS  Google Scholar 

  8. Koch, U. & Radtke, F. Mechanisms of T cell development and transformation. Annu. Rev. Cell Dev. Biol. 27, 539–562 (2011).

    Article  CAS  Google Scholar 

  9. Yui, M.A. & Rothenberg, E.V. Developmental gene networks: a triathlon on the course to T cell identity. Nat. Rev. Immunol. 14, 529–545 (2014).

    Article  CAS  Google Scholar 

  10. Egawa, T. Regulation of CD4 and CD8 coreceptor expression and CD4 versus CD8 lineage decisions. Adv. Immunol. 125, 1–40 (2015).

    Article  CAS  Google Scholar 

  11. Wang, L. et al. Distinct functions for the transcription factors GATA-3 and ThPOK during intrathymic differentiation of CD4(+) T cells. Nat. Immunol. 9, 1122–1130 (2008).

    Article  CAS  Google Scholar 

  12. Love, P.E. & Bhandoola, A. Signal integration and crosstalk during thymocyte migration and emigration. Nat. Rev. Immunol. 11, 469–477 (2011).

    Article  CAS  Google Scholar 

  13. Mingueneau, M. et al. The transcriptional landscape of αβ T cell differentiation. Nat. Immunol. 14, 619–632 (2013).

    Article  CAS  Google Scholar 

  14. Yamashita, I., Nagata, T., Tada, T. & Nakayama, T. CD69 cell surface expression identifies developing thymocytes which audition for T cell antigen receptor-mediated positive selection. Int. Immunol. 5, 1139–1150 (1993).

    Article  CAS  Google Scholar 

  15. Singer, A., Adoro, S. & Park, J.H. Lineage fate and intense debate: myths, models and mechanisms of CD4- versus CD8-lineage choice. Nat. Rev. Immunol. 8, 788–801 (2008).

    Article  CAS  Google Scholar 

  16. Heng, T.S. & Painter, M.W. The Immunological Genome Project: networks of gene expression in immune cells. Nat. Immunol. 9, 1091–1094 (2008).

    Article  CAS  Google Scholar 

  17. Rosenbauer, F. & Tenen, D.G. Transcription factors in myeloid development: balancing differentiation with transformation. Nat. Rev. Immunol. 7, 105–117 (2007).

    Article  CAS  Google Scholar 

  18. Doulatov, S. et al. Revised map of the human progenitor hierarchy shows the origin of macrophages and dendritic cells in early lymphoid development. Nat. Immunol. 11, 585–593 (2010).

    Article  CAS  Google Scholar 

  19. Klein, A.M. et al. Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells. Cell 161, 1187–1201 (2015).

    Article  CAS  Google Scholar 

  20. Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci. USA 102, 15545–15550 (2005).

    Article  CAS  Google Scholar 

  21. Buettner, F. 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).

    Article  CAS  Google Scholar 

  22. Pinkus, G.S. & Pinkus, J.L. Myeloperoxidase: a specific marker for myeloid cells in paraffin sections. Mod. Pathol. 4, 733–741 (1991).

    CAS  PubMed  Google Scholar 

  23. Kaneko, H., Shimizu, R. & Yamamoto, M. GATA factor switching during erythroid differentiation. Curr. Opin. Hematol. 17, 163–168 (2010).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  25. Haghverdi, L., Buettner, F. & Theis, F.J. Diffusion maps for high-dimensional single-cell analysis of differentiation data. Bioinformatics 31, 2989–2998 (2015).

    Article  CAS  Google Scholar 

  26. Moignard, V. et al. Decoding the regulatory network of early blood development from single-cell gene expression measurements. Nat. Biotechnol. 33, 269–276 (2015).

    Article  CAS  Google Scholar 

  27. Stegle, O., Teichmann, S.A. & Marioni, J.C. Computational and analytical challenges in single-cell transcriptomics. Nat. Rev. Genet. 16, 133–145 (2015).

    Article  CAS  Google Scholar 

  28. Levine, J.H. et al. Data-driven phenotypic dissection of AML reveals progenitor-like cells that correlate with prognosis. Cell 162, 184–197 (2015).

    Article  CAS  Google Scholar 

  29. Waddington, C.H. An Introduction to Modern Genetics (George Allen & Unwin, 1939).

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

    Article  CAS  Google Scholar 

  31. de Silva, V. & Tenenbaum, J.B. Global versus local methods in nonlinear dimensionality reduction. Advances in Neural Information Processing Systems 15, 721–728 (2003).

    Google Scholar 

  32. Amir, A.D. et al. viSNE enables visualization of high dimensional single-cell data and reveals phenotypic heterogeneity of leukemia. Nat. Biotechnol. 31, 545–552 (2013).

    Article  CAS  Google Scholar 

  33. Gut, G., Tadmor, M.D., Pe'er, D., Pelkmans, L. & Liberali, P. Trajectories of cell-cycle progression from fixed cell populations. Nat. Methods 12, 951–954 (2015).

    Article  CAS  Google Scholar 

  34. von Luxburg, U. A tutorial on spectral clustering. Stat. Comput. 17, 395–416 (2007).

    Article  Google Scholar 

  35. Gautier, L., Cope, L., Bolstad, B.M. & Irizarry, R.A. affy--analysis of Affymetrix GeneChip data at the probe level. Bioinformatics 20, 307–315 (2004).

    Article  CAS  Google Scholar 

  36. Grün, D. et al. Single-cell messenger RNA sequencing reveals rare intestinal cell types. Nature 525, 251–255 (2015).

    Article  Google Scholar 

  37. Huang, W., Sherman, B.T. & Lempicki, R.A. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat. Protoc. 4, 44–57 (2009).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

We would like to thank A. Bloemendal, Z. Good, N. Hacohen, S. Krishnaswamy, J. Levine and A.J. Carr for their helpful comments. M.D.T. is supported by an NSF graduate fellowship. This work was supported by NSF MCB-1149728, NIH DP1- HD084071, NIH R01CA164729 to D.P. D.P. holds a Packard Fellowship for Science and Engineering. This work was also supported by David and Fela Shapell Family Foundation INCPM Fund, the WIS staff scientists grant from the Nissim Center, for the Development of Scientific Resources, and ISF 1184/15 to N.F.

Author information

Authors and Affiliations

Authors

Contributions

S.B. and D.P. conceived the study. M.S., M.D.T., O.A., and D.P. designed and developed Wishbone. M.S. and D.P. performed statistical analysis and comparison of Wishbone. S.R.-Z., T.M.S., and N.F. performed all bench experiments and data acquisition. M.S., S.R.-Z., N.F., and D.P. performed the biological analysis and interpretation. M.D.T., M.S., P.K., and K.C. programmed the software tools. M.S., S.R.-Z., N.F., and D.P. wrote the manuscript.

Corresponding author

Correspondence to Dana Pe'er.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Figure 1–25 and Supplementary Notes 1–4 (PDF 4787 kb)

Supplementary Table 1 (XLSX 13 kb)

Supplementary Software (ZIP 20989 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Setty, M., Tadmor, M., Reich-Zeliger, S. et al. Wishbone identifies bifurcating developmental trajectories from single-cell data. Nat Biotechnol 34, 637–645 (2016). https://doi.org/10.1038/nbt.3569

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nbt.3569

This article is cited by

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing