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.

  • Analysis
  • Published:

Extracting a cellular hierarchy from high-dimensional cytometry data with SPADE

Abstract

The ability to analyze multiple single-cell parameters is critical for understanding cellular heterogeneity. Despite recent advances in measurement technology, methods for analyzing high-dimensional single-cell data are often subjective, labor intensive and require prior knowledge of the biological system. To objectively uncover cellular heterogeneity from single-cell measurements, we present a versatile computational approach, spanning-tree progression analysis of density-normalized events (SPADE). We applied SPADE to flow cytometry data of mouse bone marrow and to mass cytometry data of human bone marrow. In both cases, SPADE organized cells in a hierarchy of related phenotypes that partially recapitulated well-described patterns of hematopoiesis. We demonstrate that SPADE is robust to measurement noise and to the choice of cellular markers. SPADE facilitates the analysis of cellular heterogeneity, the identification of cell types and comparison of functional markers in response to perturbations.

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: Flowchart of SPADE and SPADE analysis of a simulated data set.
Figure 2: SPADE applied to mouse bone marrow flow cytometry data.
Figure 3: SPADE applied to human bone marrow data of 30 experiments with two overlapping staining panels and multiple experimental conditions.
Figure 4: SPADE tree colored by two NK-specific markers CD7 and CD16, which were not used to derive the SPADE tree.
Figure 5: SPADE trees that describe the cell type–dependent behavior of functional markers in response to perturbations.

Similar content being viewed by others

References

  1. Chattopadhyay, P. et al. Quantum dot semiconductor nanocrystals for immunophenotyping by polychromatic flow cytometry. Nat. Med. 12, 972–977 (2006).

    Article  CAS  Google Scholar 

  2. Bandura, D.R. et al. Mass cytometry: Technique for real time single cell multitarget immunoassay based on inductively coupled plasma time-of-flight mass spectrometry. Anal. Chem. 81, 6813–6822 (2009).

    Article  CAS  Google Scholar 

  3. Herzenberg, L., Tung, J., Moore, W., Herzenberg, L. & Parks, D. Interpreting flow cytometry data: a guide for the perplexed. Nat. Immunol. 7, 681–685 (2006).

    Article  CAS  Google Scholar 

  4. Ellis, B., Haaland, P., Hahne, F., Le Meur, N. & Gopalakrishnan, N. Flowcore: basic structures for flow cytometry data. R package version 1.10.0. (2009).

  5. Murphy, R.F. Automated identification of subpopulations in flow cytometric list mode data using cluster analysis. Cytometry 6, 302–309 (1985).

    Article  CAS  Google Scholar 

  6. Lo, K., Brinkman, R. & Gottardo, R. Automated gating of flow cytometry data via robust model-based clustering. Cytometry A 73, 321–332 (2008).

    Article  Google Scholar 

  7. Boedigheimer, M. & Ferbas, J. Mixture modeling approach to flow cytometry data. Cytometry A 73, 421–429 (2008).

    Article  Google Scholar 

  8. Chan, C. et al. Statistical mixture modeling for cell subtype identification in flow cytometry. Cytometry A 73, 693–701 (2008).

    Article  Google Scholar 

  9. Walther, G. et al. Automatic clustering of flow cytometry data with density-based merging. Adv. Bioinformatics, published online, doi:10.1155/2009/686759 (19 November 2009).

  10. Pyne, S. et al. Automated high-dimensional flow cytometric data anlysis. Proc. Natl. Acad. Sci. USA 106, 8519–8524 (2009).

    Article  CAS  Google Scholar 

  11. van Lochem, E.G. et al. Immunophenotypic differentiation patterns of normal hematopoiesis in human bone marrow: Reference patterns for age-related changes and disease-induced shifts. Cytometry B Clin. Cytom. 60, 1–13 (2004).

    Article  CAS  Google Scholar 

  12. Zare, H., Shooshtari, P., Gupta, A. & Brinkman, R. Data reduction for spectral clustering to analyze high throughput flow cytometry data. BMC Bioinformatics 11, 403 (2010).

    Article  Google Scholar 

  13. Bagwell, B.C. Probability state models. US patent 7,653,509 (2010).

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

    Article  CAS  Google Scholar 

  15. Fruchterman, T. & Reingold, E. Graph drawing by force-directed placement. Softw. Pract. Exp. 21, 1129–1164 (1991).

    Article  Google Scholar 

  16. Bryder, D., Rossi, D. & Weissman, I.L. Hematopoietic stem cells: the paradigmatic tissue specific stem cell. Am. J. Pathol. 169, 338–346 (2006).

    Article  CAS  Google Scholar 

  17. Chao, M.P., Seita, J. & Weissman, I.L. Establishment of a normal hematopoietic and leukemia stem cell hierarchy. Cold Spring Harb. Symp. Quant. Biol. 73, 439–449 (2008).

    Article  CAS  Google Scholar 

  18. Ashwell, J.D. The many paths to p38 mitogen-activated protein kinase activation in the immune system. Nat. Rev. Immunol. 6, 532–540 (2006).

    Article  CAS  Google Scholar 

  19. Guha, M. & Mackman, N. Lps induction of gene expression in human monocytes. Cell. Signal. 13, 85–94 (2001).

    Article  CAS  Google Scholar 

  20. Chen, W. et al. Thrombopoietin cooperates with flt3-ligand in the generation of plasmacytoid dendritic cell precursors from human hematopoietic progenitors. Blood 103, 2547–2553 (2004).

    Article  CAS  Google Scholar 

  21. Qiu, P., Gentles, A.J. & Plevritis, S.K. Discovering biological progression underlying microarray samples. PLoS Comput. Biol. 7, e1001123 (2011).

    Article  CAS  Google Scholar 

  22. Kotecha, N., Krutzik, P.O. & Irish, J.M. Web-based analysis and publication of flow cytometry experiments. Curr. Prot. Cytom. 53, 10.17.1–10.17.24 (2010).

    Google Scholar 

  23. Pettie, S. & Ramach, V. An optimal minimum spanning tree algorithm. JACM 49, 49–60 (1999).

    Google Scholar 

Download references

Acknowledgements

The authors gratefully acknowledge funding from National Cancer Institute Integrative Cancer Biology Program (ICBP), grants U56CA112973 and U54CA149145 to S.K.P. A Damon Runyon Cancer Research Foundation Fellowship supports S.C.B. National Science Foundation Graduate Research Fellowship and Stanford DARE Fellowship support K.D.G. This work is also supported by US National Institutes of Health grants U19 AI057229, P01 CA034233, HHSN272200700038C, 1R01CA130826, 5U54 CA143907, RB2-01592, PN2EY018228, N01-HV-00242, HEALTH.2010.1.2-1 (European Commission), as well as the California Institute for Regenerative Medicine (DR1-01477) to G.P.N.

Author information

Authors and Affiliations

Authors

Contributions

P.Q., G.P.N. and S.K.P. conceived the study and developed the method. E.F.S., S.C.B. and K.D.G.Jr. performed mass and flow cytometry experiments, and participated in the biological interpretation. P.Q., R.V.B., M.D.L. and K.S. performed robustness analysis of the method. P.Q., E.F.S., S.C.B., K.D.G.Jr., G.P.N. and S.K.P. wrote the manuscript and developed the figures.

Corresponding author

Correspondence to Peng Qiu.

Ethics declarations

Competing interests

A patent for the SPADE algorithm has been applied for on behalf of Stanford University.

Supplementary information

Supplementary Text and Figures

Supplementary Sections 1–8 (PDF 10072 kb)

Supplementary Data 1

simulated fcs file (ZIP 358 kb)

Supplementary Data 2

Qiu_SPADE_MouseBM.fcs (ZIP 25436 kb)

Supplementary Data 3 (ZIP 516 kb)

Rights and permissions

Reprints and permissions

About this article

Cite this article

Qiu, P., Simonds, E., Bendall, S. et al. Extracting a cellular hierarchy from high-dimensional cytometry data with SPADE. Nat Biotechnol 29, 886–891 (2011). https://doi.org/10.1038/nbt.1991

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nbt.1991

This article is cited by

Search

Quick links

Nature Briefing: Translational Research

Sign up for the Nature Briefing: Translational Research newsletter — top stories in biotechnology, drug discovery and pharma.

Get what matters in translational research, free to your inbox weekly. Sign up for Nature Briefing: Translational Research