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.

  • Review Article
  • Published:

Computational flow cytometry: helping to make sense of high-dimensional immunology data

Nature Reviews Immunology volume 16, pages 449–462 (2016)Cite this article

Subjects

Key Points

  • Recent advances in flow and mass cytometry have spurred the development of novel computational tools to assist in data analysis and visualization. These techniques should be adopted, evaluated and improved upon by the broad immunological community.

  • Standardization is key to making computational flow cytometry work, and researchers should use standard procedures for data generation, analysis, interpretation and deposition. Standardized marker panels should be used as much as possible.

  • Computational flow cytometry allows the automation of population identification, biomarker discovery and predictive modelling to highlight potentially new and interesting cell types that correlate with clinical outcomes.

  • New algorithms allow the modelling of gradual changes that can shed new light on cell developmental processes.

  • Computational flow cytometry offers an additional toolbox, and young immunologists should be trained in basic programming and modelling skills to be able to adequately use these tools and interpret their outcome.

Abstract

Recent advances in flow cytometry allow scientists to measure an increasing number of parameters per cell, generating huge and high-dimensional datasets. To analyse, visualize and interpret these data, newly available computational techniques should be adopted, evaluated and improved upon by the immunological community. Computational flow cytometry is emerging as an important new field at the intersection of immunology and computational biology; it allows new biological knowledge to be extracted from high-throughput single-cell data. This Review provides non-experts with a broad and practical overview of the many recent developments in computational flow cytometry.

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

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

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

Figure 1: Evaluation of three alternative visualization techniques using a manually gated dataset.
Figure 2: Marker visualization of mouse splenocytes.
Figure 3: Cell development modelling.

Similar content being viewed by others

References

  1. Fulwyler, M. J. Electronic separation of biological cells by volume. Science 150, 910–911 (1965).

    CAS  PubMed  Google Scholar 

  2. Gray, J. W. et al. Chromosome measurement and sorting by flow systems. Proc. Natl Acad. Sci. USA 72, 1231–1234 (1975).

    CAS  PubMed  Google Scholar 

  3. Robinson, J. P. & Roederer, M. Flow cytometry strikes gold. Science 350, 739–740 (2015).

    CAS  PubMed  Google Scholar 

  4. Perfetto, S. P., Chattopadhyay, P. K. & Roederer, M. Seventeen-colour flow cytometry: unravelling the immune system. Nat. Rev. Immunol. 4, 648–655 (2004).

    CAS  PubMed  Google Scholar 

  5. Chattopadhyay, P. et al. Toward 40+ parameter flow cytometry. Proc. Congress Int. Soc. Advancement Cytom. Abstr. 2014, 215 (2014).

    Google Scholar 

  6. 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).

    CAS  PubMed  Google Scholar 

  7. Nolan, J. P. & Condello, D. Spectral flow cytometry. Curr. Protoc. Cytom. http://www.dx.doi.org/10.1002/0471142956.cy0127s63 (2013).

  8. McGrath, K. E., Bushnell, T. P. & Palis, J. Multispectral imaging of hematopoietic cells: where flow meets morphology. J. Immunol. Methods 336, 91–97 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  9. Giesen, C. et al. Highly multiplexed imaging of tumor tissues with subcellular resolution by mass cytometry. Nat. Methods 11, 417–422 (2014).

    CAS  PubMed  Google Scholar 

  10. Nomura, L., Maino, V. C. & Maecker, H. T. Standardization and optimization of multiparameter intracellular cytokine staining. Cytometry A 73, 984–991 (2008).

    PubMed  Google Scholar 

  11. Maecker, H. T., McCoy, J. P. & Nussenblatt, R. Standardizing immunophenotyping for the Human Immunology Project. Nat. Rev. Immunol. 12, 191–200 (2012). This work describes a large-scale effort to standardize flow cytometry data generation and analysis within the Human Immunology Project.

    CAS  PubMed  PubMed Central  Google Scholar 

  12. Pachon, G., Caragol, I. & Petriz, J. Subjectivity and flow cytometric variability. Nat. Rev. Immunol. 12, 396–396 (2012).

    CAS  PubMed  Google Scholar 

  13. Gouttefangeas, C. et al. Data analysis as a source of variability of the HLA-peptide multimer assay: from manual gating to automated recognition of cell clusters. Cancer Immunol. Immunother. 64, 585–598 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  14. Irish, J. Beyond the age of cellular discovery. Nat. Immunol. 15, 1095–1097 (2014).

    CAS  PubMed  Google Scholar 

  15. Spidlen, J. et al. Data file standard for flow cytometry version FCS 3.1. Cytometry A 77, 97–100 (2010).

    PubMed  PubMed Central  Google Scholar 

  16. Spidlen, J., Shooshtari, P. T., Kollmann, R. & Brinkman, R. R. Flow cytometry data standards. BMC Res. Notes 4, 50 (2011).

    PubMed  PubMed Central  Google Scholar 

  17. Spidlen, J., Moore, W. & Brinkman, R. R. ISAC's Gating-ML 2.0 data exchange standard for gating description. Cytometry A 87, 683–687 (2015).

    PubMed  PubMed Central  Google Scholar 

  18. Spidlen, J., Bray, C. & Brinkman, R. R. ISAC's classification results file format. Cytometry A 87, 86–88 (2014).

    PubMed  PubMed Central  Google Scholar 

  19. Spidlen, J. & Novo, D. ICEFormat — the image cytometry experiment format. Cytometry A 81, 1015–1018 (2012).

    PubMed  Google Scholar 

  20. Schlickeiser, S., Streitz, M. & Sawitzki, B. Standardized multi-color flow cytometry and computational biomarker discovery. Methods Mol. Biol. 1371, 225–238 (2016).

    CAS  PubMed  Google Scholar 

  21. Roederer, M. A proposal for unified flow cytometer parameter naming. Cytometry A 87, 689–691 (2015).

    PubMed  Google Scholar 

  22. Tung, J. W., Parks, D. R., Moore, W. A., Herzenberg, L. A. & Herzenberg, L. A. New approaches to fluorescence compensation and visualization of FACS data. Clin. Immunol. 110, 277–283 (2004).

    CAS  PubMed  Google Scholar 

  23. Lee, J. A. et al. MIFlowCyt: the minimum information about a flow cytometry experiment. Cytometry A 73, 926–930 (2008).

    PubMed  PubMed Central  Google Scholar 

  24. van Dongen, J. J. M. et al. EuroFlow antibody panels for standardized n-dimensional flow cytometric immunophenotyping of normal reactive and malignant leukocytes. Leukemia 26, 1908–1975 (2012). This work describes a community-wide effort to standardize flow cytometry marker panel design for leukaemic disorders.

    CAS  PubMed  PubMed Central  Google Scholar 

  25. Finak, G. et al. Standardizing flow cytometry immunophenotyping analysis from the Human ImmunoPhenotyping Consortium. Sci. Rep. 6, 20686 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  26. Hasan, M. et al. Semiautomated and standardized cytometric procedures for multi-panel and multi-parametric whole blood immunophenotyping. Clin. Immunol. 157, 261–276 (2015).

    CAS  PubMed  Google Scholar 

  27. Mahnke, Y., Chattopadhyay, P. & Roederer, M. Publication of optimized multicolor immunofluorescence panels. Cytometry A 77, 814–818 (2010).

    PubMed  Google Scholar 

  28. Aghaeepour, N. et al. Critical assessment of automated flow cytometry data analysis techniques. Nat. Methods 10, 445–445 (2013). This work highlights the results of the FlowCAP I and II challenges, which are benchmarks for comparing automated methods for computational flow cytometry.

    Google Scholar 

  29. Aghaeepour, N. et al. A benchmark for evaluation of algorithms for identification of cellular correlates of clinical outcomes. Cytometry A 89, 16–21 (2015).

    PubMed  PubMed Central  Google Scholar 

  30. Gentleman, R. C. et al. Bioconductor: open software development for computational biology and bioinformatics. Genome Biol. 5, R80 (2004).

    PubMed  PubMed Central  Google Scholar 

  31. Reich, M. et al. GenePattern 2.0. Nat. Genet. 38, 500–501 (2006).

    CAS  PubMed  Google Scholar 

  32. Perfetto, S. P., Ambrozak, D., Nguyen, R., Chattopadhyay, P. & Roederer, M. Quality assurance for polychromatic flow cytometry. Nat. Protoc. 1, 1522–1530 (2006).

    CAS  PubMed  Google Scholar 

  33. Hahne, F. et al. flowCore: a Bioconductor package for high throughput flow cytometry. BMC Bioinformatics 10, 106 (2009).

    PubMed  PubMed Central  Google Scholar 

  34. Le Meur, N. et al. Data quality assessment of ungated flow cytometry data in high throughput experiments. Cytometry A 71, 393–403 (2007).

    PubMed  PubMed Central  Google Scholar 

  35. Fletez-Brant, K. et al. flowClean: automated identification and removal of fluorescence anomalies in flow cytometry data. Cytometry A 89, 461–471 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. Hahne, F. et al. Per-channel basis normalization methods for flow cytometry data. Cytometry A 77, 121–131 (2010).

    PubMed  PubMed Central  Google Scholar 

  37. Finak, G. et al. OpenCyto: an open source infrastructure for scalable, robust, reproducible, and automated, end-to-end flow cytometry data analysis. PLoS Comput. Biol. 10, e1003806 (2014).

    PubMed  PubMed Central  Google Scholar 

  38. Malek, M. et al. flowDensity: reproducing manual gating of flow cytometry data by automated density-based cell population identification. Bioinformatics 31, 606–607 (2015).

    CAS  PubMed  Google Scholar 

  39. Kvistborg, P. et al. Thinking outside the gate: single-cell assessments in multiple dimensions. Immunity 42, 591–592 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. Chester, C. & Maecker, H. T. Algorithmic tools for mining high-dimensional cytometry data. J. Immunol. 195, 773–779 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. Mair, F. et al. The end of gating? An introduction to automated analysis of high dimensional cytometry data. Eur. J. Immunol. 46, 34–43 (2016).

    CAS  PubMed  Google Scholar 

  42. Diggins, K. E., Ferrell, P. B., Irish, J. M. Methods for discovery and characterization of cell subsets in high dimensional mass cytometry data. Methods. 82, 55–63 (2015). This work describes an in-depth overview of visualization methods for high-dimensional cytometry data analysis.

    CAS  PubMed  PubMed Central  Google Scholar 

  43. Krishnaswamy, S. et al. Conditional density-based analysis of T cell signaling in single-cell data. Science 346, 1250689 (2014).

    PubMed  PubMed Central  Google Scholar 

  44. Bendall, S. C. et al. Single-cell trajectory detection uncovers progression and regulatory coordination in human B cell development. Cell 157, 714–725 (2014). This work introduces the concept of cell development modelling, showcasing the Wanderlust algorithm applied to B cell development.

    CAS  PubMed  PubMed Central  Google Scholar 

  45. Lugli, E. et al. Subject classification obtained by cluster analysis and principal component analysis applied to flow cytometric data. Cytometry A 71, 334–344 (2007).

    PubMed  Google Scholar 

  46. Van der Maaten, L. & Hinton, G. Visualizing data using t-SNE. J. Machine Learn. Res. 9, 2579–2605 (2008).

    Google Scholar 

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

    CAS  PubMed Central  Google Scholar 

  48. Cheng, Y., Wong, M. T., van der Maaten, L. & Newell, E. W. Categorical analysis of human T cell heterogeneity with one-dimensional soli-expression by nonlinear stochastic embedding. J. Immunol. 196, 924–932 (2016).

    CAS  PubMed  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  50. Qiu, P. et al. Extracting a cellular hierarchy from high-dimensional cytometry data with SPADE. Nat. Biotechnol. 29, 886–891 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  51. Zunder, E. R., Lujan, E., Goltsev, Y., Wernig, M. & Nolan, G. P. A continuous molecular roadmap to iPSC reprogramming through progression analysis of single-cell mass cytometry. Cell Stem Cell 16, 323–337 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  52. Van Gassen, S. et al. FlowSOM: using self-organizing maps for visualization and interpretation of cytometry data. Cytometry A 87, 636–645 (2015).

    PubMed  Google Scholar 

  53. Spitzer, M. H. et al. An interactive reference framework for modeling a dynamic immune system. Science 349, 1259425 (2015).

    PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  Google Scholar 

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

    PubMed  Google Scholar 

  56. Finak, G., Bashashati, A., Brinkman, R. & Gottardo, R. Merging mixture components for cell population identification in flow cytometry. Adv. Bioinformatics 2009, 247646 (2009).

    PubMed Central  Google Scholar 

  57. Chen, X. et al. Automated flow cytometric analysis across large numbers of samples and cell types. Clin. Immunol. 157, 249–260 (2015).

    CAS  PubMed  Google Scholar 

  58. Sorensen, T., Baumgart, S., Durek, P., Grutzkau, A. & Haupl, T. immunoClust — an automated analysis pipeline for the identification of immunophenotypic signatures in high-dimensional cytometric datasets. Cytometry A 87, 603–615 (2015).

    PubMed  Google Scholar 

  59. Naim, I. et al. SWIFT-scalable clustering for automated identification of rare cell populations in large high-dimensional flow cytometry datasets, part 1: algorithm design. Cytometry A 85, 408–421 (2014).

    PubMed  PubMed Central  Google Scholar 

  60. Aghaeepour, N., Nikolic, R., Hoos, H. H. & Brinkman, R. R. Rapid cell population identification in flow cytometry data. Cytometry A 79, 6–13 (2010).

    Google Scholar 

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

    PubMed  PubMed Central  Google Scholar 

  62. Qian, Y. et al. Elucidation of seventeen human peripheral blood B-cell subsets and quantification of the tetanus response using a density-based method for the automated identification of cell populations in multidimensional flow cytometry data. Cytometry B. Clin. Cytom 78, S69–S82 (2010).

    PubMed  PubMed Central  Google Scholar 

  63. Ge, Y. & Sealfon, S. C. flowPeaks: a fast unsupervised clustering for flow cytometry data via K-means and density peak finding. Bioinformatics. 28, 2052–2058 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  64. Johnsson, K., Wallin, J. & Fontes, M. BayesFlow: latent modeling of flow cytometry cell populations. BMC Bioinformatics 17, 25 (2016).

    PubMed  PubMed Central  Google Scholar 

  65. Shekhar, K., Brodin, P., Davis, M. M. & Chakraborty, A. K. Automatic classification of cellular expression by nonlinear stochastic embedding (ACCENSE). Proc. Natl Acad. Sci. USA 111, 202–207 (2013).

    PubMed  Google Scholar 

  66. Becher, B. et al. High-dimensional analysis of the murine myeloid cell system. Nat. Immunol. 15, 1181–1189 (2014).

    CAS  PubMed  Google Scholar 

  67. Cron, A. et al. Hierarchical modeling for rare event detection and cell subset alignment across flow cytometry samples. PLoS Comput. Biol. 9, e1003130 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  68. Dundar, M., Akova, F., Yerebakan, H. Z. & Rajwa, B. A non-parametric Bayesian model for joint cell clustering and cluster matching: identification of anomalous sample phenotypes with random effects. BMC Bioinformatics. 15, 314 (2014).

    PubMed  PubMed Central  Google Scholar 

  69. Hsiao, C. et al. Mapping cell populations in flow cytometry data for cross-sample comparison using the Friedman-Rafsky test statistic as a distance measure. Cytometry A 89, 71–88 (2016).

    PubMed  Google Scholar 

  70. Feher, K., Kirsch, J., Radbruch, A., Chang, H. D. & Kaiser, T. Cell population identification using fluorescence-minus-one controls with a one-class classifying algorithm. Bioinformatics. 30, 3372–3378 (2014).

    CAS  PubMed  Google Scholar 

  71. Zare, H. et al. Automated analysis of multidimensional flow cytometry data improves diagnostic accuracy between mantle cell lymphoma and small lymphocytic lymphoma. Am. J. Clin. Pathol. 137, 75–85 (2012).

    PubMed  PubMed Central  Google Scholar 

  72. Bashashati, A. et al. B cells with high side scatter parameter by flow cytometry correlate with inferior survival in diffuse large B-cell lymphoma. Am. J. Clin. Pathol. 137, 805–814 (2012).

    PubMed  PubMed Central  Google Scholar 

  73. O'Neill, K., Jalali, A., Aghaeepour, N., Hoos, H. & Brinkman, R. R. Enhanced flowType/RchyOptimyx: a Bioconductor pipeline for discovery in high-dimensional cytometry data. Bioinformatics 30, 1329–1330 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  74. Bruggner, R. V., Bodenmiller, B., Dill, D. L., Tibshirani, R. J. & Nolan, G. P. Automated identification of stratifying signatures in cellular subpopulations. Proc. Natl Acad. Sci. USA 111, E2770–E2777 (2014).

    CAS  PubMed  Google Scholar 

  75. Lin, L. et al. COMPASS identifies T-cell subsets correlated with clinical outcomes. Nat. Biotechnol. 33, 610–616 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  76. Rebhahn, J. A. et al. Competitive SWIFT cluster templates enhance detection of aging changes. Cytometry A 89, 59–70 (2016).

    PubMed  Google Scholar 

  77. Van Gassen, S., Vens, C., Dhaene, T., Lambrecht, B. N. & Saeys, Y. FloReMi: flow density survival regression using minimal feature redundancy. Cytometry A 89, 22–29 (2016).

    PubMed  Google Scholar 

  78. Bagwell, C. B. et al. Probability state modeling theory. Cytometry A 87, 646–660 (2015).

    PubMed  Google Scholar 

  79. Inokuma, M. S., Maino, V. C. & Bagwell, C. B. Probability state modeling of memory CD8+ T-cell differentiation. J. Immunol. Methods 397, 8–17 (2013).

    CAS  PubMed  Google Scholar 

  80. Bagwell, C. B. et al. Human B-cell and progenitor stages as determined by probability state modeling of multidimensional cytometry data. Cytometry B Clin. Cytom. 88, 214–226 (2015).

    PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  Google Scholar 

  82. Setty, M. et al. Wishbone identifies bifurcating developmental trajectories from single cell data. Nat. Biotechnol. http://dx.doi.org/10.1038/nbt.3569 (2016).

  83. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  Google Scholar 

  85. Macaulay, I. C. et al. Single-cell RNA-sequencing reveals a continuous spectrum of differentiation in hematopoietic cells. Cell Rep. 14, 966–977 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

S.V.G. is funded by the Flanders Agency for Innovation by Science and Technology (IWT). Y.S. is an ISAC Marylou Ingram Scholar. B.N.L. is funded by a European Research Council (ERC) Consolidator grant and several FWO (Research Foundation Flanders) grants.

Author information

Authors and Affiliations

  1. VIB Inflammation Research Center, Technologiepark 927, Ghent, B-9052, Belgium

    Yvan Saeys, Sofie Van Gassen & Bart N. Lambrecht

  2. Department of Internal Medicine, Ghent University, De Pintelaan 185, Ghent, B-9000, Belgium

    Yvan Saeys & Bart N. Lambrecht

  3. Department of Information Technology, Technologiepark 15, Ghent, B-9052, Belgium

    Sofie Van Gassen

  4. Department of Pulmonary Medicine, Erasmus MC Rotterdam, Dr Molewaterplein 50, Rotterdam, 3015 GE, The Netherlands

    Bart N. Lambrecht

Authors
  1. Yvan Saeys
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sofie Van Gassen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Bart N. Lambrecht
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yvan Saeys.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Related links

PowerPoint slides

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Saeys, Y., Van Gassen, S. & Lambrecht, B. Computational flow cytometry: helping to make sense of high-dimensional immunology data. Nat Rev Immunol 16, 449–462 (2016). https://doi.org/10.1038/nri.2016.56

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nri.2016.56

This article is cited by

Search

Advanced 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