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.

  • Protocol
  • Published:

Development, application and computational analysis of high-dimensional fluorescent antibody panels for single-cell flow cytometry

Abstract

The interrogation of single cells is revolutionizing biology, especially our understanding of the immune system. Flow cytometry is still one of the most versatile and high-throughput approaches for single-cell analysis, and its capability has been recently extended to detect up to 28 colors, thus approaching the utility of cytometry by time of flight (CyTOF). However, flow cytometry suffers from autofluorescence and spreading error (SE) generated by errors in the measurement of photons mainly at red and far-red wavelengths, which limit barcoding and the detection of dim markers. Consequently, development of 28-color fluorescent antibody panels for flow cytometry is laborious and time consuming. Here, we describe the steps that are required to successfully achieve 28-color measurement capability. To do this, we provide a reference map of the fluorescence spreading errors in the 28-color space to simplify panel design and predict the success of fluorescent antibody combinations. Finally, we provide detailed instructions for the computational analysis of such complex data by existing, popular algorithms (PhenoGraph and FlowSOM). We exemplify our approach by designing a high-dimensional panel to characterize the immune system, but we anticipate that our approach can be used to design any high-dimensional flow cytometry panel of choice. The full protocol takes a few days to complete, depending on the time spent on panel design and data analysis.

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

Fig. 1: Considerations during panel development.
Fig. 2: Spreading error, and not compensation, is the major determinant of panel success.
Fig. 3: Panel development and troubleshooting.
Fig. 4: Data preprocessing before computational analysis.
Fig. 5: Compensation quality check before computational analysis.
Fig. 6: Fluorescence and antigen expression quality check before computational analysis.
Fig. 7: PhenoGraph clustering workflow and output.
Fig. 8: FlowSOM clustering workflow and output.

Similar content being viewed by others

Data availability

Flow cytometry data used in Figs. 2 and 4–8 are available at https://flowrepository.org/, ID: FR-FCM-ZYV3.

References

  1. Bendall, S. C., Nolan, G. P., Roederer, M. & Chattopadhyay, P. K. A deep profiler’s guide to cytometry. Trends Immunol. 33, 323–332 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Lugli, E., Roederer, M. & Cossarizza, A. Data analysis in flow cytometry: the future just started. Cytometry A 77, 705–713 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  3. Lugli, E. et al. Identification, isolation and in vitro expansion of human and nonhuman primate T stem cell memory cells. Nat. Protoc. 8, 33–42 (2013).

    Article  CAS  PubMed  Google Scholar 

  4. Mazza, E. M. C. et al. Background fluorescence and spreading error are major contributors of variability in high-dimensional flow cytometry data visualization by t-distributed stochastic neighboring embedding. Cytometry A 93, 785–792 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Brummelman, J. et al. High-dimensional single cell analysis identifies stem-like cytotoxic CD8(+) T cells infiltrating human tumors. J. Exp. Med. 215, 2520–2535 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Mair, F. & Prlic, M. OMIP-044: 28-color immunophenotyping of the human dendritic cell compartment. Cytometry A 93, 402–405 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  7. Nettey, L., Giles, A. J. & Chattopadhyay, P. K. OMIP-050: a 28-color/30-parameter fluorescence flow cytometry panel to enumerate and characterize cells expressing a wide array of immune checkpoint molecules. Cytometry A 93, 1094–1096 (2018).

    Article  PubMed  Google Scholar 

  8. Mrdjen, D. et al. High-dimensional single-cell mapping of central nervous system immune cells reveals distinct myeloid subsets in health, aging, and disease. Immunity 48, 380–395 e386 (2018).

    Article  CAS  PubMed  Google Scholar 

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

    Article  PubMed  Google Scholar 

  10. 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).

    Article  CAS  PubMed  Google Scholar 

  11. 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  PubMed  PubMed Central  Google Scholar 

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

    Article  PubMed  Google Scholar 

  13. Maecker, H. T. & Trotter, J. Flow cytometry controls, instrument setup, and the determination of positivity. Cytometry A 69, 1037–1042 (2006).

    Article  PubMed  Google Scholar 

  14. Mahnke, Y. D. & Roederer, M. Optimizing a multicolor immunophenotyping assay. Clin. Lab. Med. 27, 469–485 (2007).

    Article  PubMed  PubMed Central  Google Scholar 

  15. Cossarizza, A. et al. Guidelines for the use of flow cytometry and cell sorting in immunological studies. Eur. J. Immunol. 47, 1584–1797 (2017).

    Article  CAS  PubMed  Google Scholar 

  16. Lugli, E., Zanon, V., Mavilio, D. & Roberto, A. FACS analysis of memory T lymphocytes. Methods Mol. Biol. 1514, 31–47 (2017).

    Article  CAS  PubMed  Google Scholar 

  17. Roederer, M. Spectral compensation for flow cytometry: visualization artifacts, limitations, and caveats. Cytometry 45, 194–205 (2001).

    Article  CAS  PubMed  Google Scholar 

  18. Nguyen, R., Perfetto, S., Mahnke, Y. D., Chattopadhyay, P. & Roederer, M. Quantifying spillover spreading for comparing instrument performance and aiding in multicolor panel design. Cytometry A 83, 306–315 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  19. Fontenot, J. D., Gavin, M. A. & Rudensky, A. Y. Foxp3 programs the development and function of CD4+CD25+ regulatory T cells. Nat. Immunol. 4, 330–336 (2003).

    Article  CAS  PubMed  Google Scholar 

  20. Liu, W. et al. CD127 expression inversely correlates with FoxP3 and suppressive function of human CD4+ T reg cells. J. Exp. Med. 203, 1701–1711 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. De Simone, M. et al. Transcriptional landscape of human tissue lymphocytes unveils uniqueness of tumor-infiltrating T regulatory cells. Immunity 45, 1135–1147 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  22. Raziorrouh, B. et al. The immunoregulatory role of CD244 in chronic hepatitis B infection and its inhibitory potential on virus-specific CD8+ T-cell function. Hepatology 52, 1934–1947 (2010).

    Article  CAS  PubMed  Google Scholar 

  23. Saeys, Y., Gassen, S. V. & Lambrecht, B. N. Computational flow cytometry: helping to make sense of high-dimensional immunology data. Nat. Rev. Immunol. 16, 449–462 (2016).

    Article  CAS  PubMed  Google Scholar 

  24. Todorov, H. & Saeys, Y. Computational approaches for high-throughput single-cell data analysis. FEBS J. https://doi.org/10.1111/febs.14613 (2018).

    Article  PubMed  Google Scholar 

  25. Perfetto, S. P., Ambrozak, D., Nguyen, R., Chattopadhyay, P. K. & Roederer, M. Quality assurance for polychromatic flow cytometry using a suite of calibration beads. Nat. Protoc. 7, 2067–2079 (2012).

    Article  CAS  PubMed  Google Scholar 

  26. Finak, G., Perez, J. M., Weng, A. & Gottardo, R. Optimizing transformations for automated, high throughput analysis of flow cytometry data. BMC Bioinformatics 11, 546 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  27. Bagwell, C. B. Hyperlog-a flexible log-like transform for negative, zero, and positive valued data. Cytometry A 64, 34–42 (2005).

    Article  PubMed  Google Scholar 

  28. Guo, X. et al. Global characterization of T cells in non-small-cell lung cancer by single-cell sequencing. Nat. Med. 24, 978–985 (2018).

    Article  CAS  PubMed  Google Scholar 

  29. Weber, L. M. & Robinson, M. D. Comparison of clustering methods for high-dimensional single-cell flow and mass cytometry data. Cytometry A 89, 1084–1096 (2016).

    Article  CAS  PubMed  Google Scholar 

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

    Article  PubMed  PubMed Central  Google Scholar 

  31. Fletez-Brant, K., Spidlen, J., Brinkman, R. R., Roederer, M. & Chattopadhyay, P. K. flowClean: automated identification and removal of fluorescence anomalies in flow cytometry data. Cytometry A 89, 461–471 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Roederer, M. How many events is enough? Are you positive? Cytometry A 73, 384–385 (2008).

    Article  PubMed  Google Scholar 

  33. Chen, H. et al. Cytofkit: a bioconductor package for an integrated mass cytometry data analysis pipeline. PLoS Comput. Biol. 12, e1005112 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

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

  35. Nowicka, M. et al. CyTOF workflow: differential discovery in high-throughput high-dimensional cytometry datasets. F1000Res 6, 748 (2017).

    Article  PubMed  Google Scholar 

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

    Google Scholar 

  37. Monti, S., Tamayo, P., Mesirov, J. & Golub, T. Consensus clustering: a resampling-based method for class discovery and visualization of gene expression microarray data. Mach. Learn. 52, 91–118 (2003).

    Article  Google Scholar 

  38. Mahnke, Y. D., Brodie, T. M., Sallusto, F., Roederer, M. & Lugli, E. The who’s who of T-cell differentiation: human memory T-cell subsets. Eur. J. Immunol. 43, 2797–2809 (2013).

    Article  CAS  PubMed  Google Scholar 

  39. Farber, D. L., Yudanin, N. A. & Restifo, N. P. Human memory T cells: generation, compartmentalization and homeostasis. Nat. Rev. Immunol. 14, 24–35 (2014).

    Article  CAS  PubMed  Google Scholar 

  40. Brummelman, J., Pilipow, K. & Lugli, E. The single-cell phenotypic identity of human CD8(+) and CD4(+) T cells. Int. Rev. Cell Mol. Biol. 341, 63–124 (2018).

    Article  PubMed  Google Scholar 

  41. Collin, M. & Bigley, V. Human dendritic cell subsets: an update. Immunology 154, 3–20 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. See, P. et al. Mapping the human DC lineage through the integration of high-dimensional techniques. Science 356, eaag3009 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  43. Robbins, S. H. et al. Novel insights into the relationships between dendritic cell subsets in human and mouse revealed by genome-wide expression profiling. Genome Biol. 9, R17 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  44. Guilliams, M. et al. Unsupervised high-dimensional analysis aligns dendritic cells across tissues and species. Immunity 45, 669–684 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Villani, A. C. et al. Single-cell RNA-seq reveals new types of human blood dendritic cells, monocytes, and progenitors. Science 356 (2017).

Download references

Acknowledgements

We thank G. Veronesi and P. Novellis (Thoracic Surgery, Humanitas) for providing the human lung cancer samples (under IRB Approval No. 1501), and F. Colombo and A. Anselmo (Humanitas Flow Cytometry Core) for FACSymphony A5 quality control. We also thank E. Friebel (Laboratory of Inflammation Research, University of Zurich) for discussion and technical assistance. E.L. was supported by the Associazione Italiana per la Ricerca sul Cancro (AIRC; Investigator Grant No. 20607) and the Humanitas Clinical and Research Center. B.B. was supported by the Swiss National Science Foundation (nos. 316030_150768, 310030_146130 and CRSII3_136203), the University Research Priority Program (URPP) for Translational Cancer Research and the European Community FP7 (grant no. 602239 (ATECT). J.B. is a recipient of the ‘Fondo di beneficenza Intesa San Paolo’ fellowship from AIRC. C.H. is a recipient of Forschungskredit of the University of Zurich and a recipient of the German Research Council (DFG) Postdoc stipend. N.G.N. is a recipient of a University Research Priority Program (URPP) postdoctoral fellowship. E.M.C.M. is a recipient of a 2017 Fondazione Umberto Veronesi postdoctoral fellowship. Purchase of the BD FACSymphony A5 was in part defrayed by a grant from the Italian Ministry of Health (agreement no. 82/2015).

Author information

Authors and Affiliations

Authors

Contributions

J.B., C.H., N.G.N., E.M.C.M., B.B. and E.L. participated in the design of the protocol. J.B. and E.L. designed the panel. J.B. and G.A. performed the experiments. J.B., C.H and N.G.N. wrote the manuscript. J.B., C.H., N.G.N. and E.M.C.M. performed the data analysis. E.L. and B.B. supervised the research. All authors commented on the manuscript.

Corresponding author

Correspondence to Enrico Lugli.

Ethics declarations

Competing interests

The Laboratory of Translational Immunology receives reagents in kind from BD Biosciences Italy as part of a collaborative research agreement. The authors declare no other competing interests.

Additional information

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

Related links

Key references using this protocol

Mrdjen, D. et al. Immunity 48, P380–395.E6 (2018): https://www.cell.com/immunity/fulltext/S1074-7613(18)30032-3

Mazza, E. M. C. et al. Cytometry A 93, 785–792 (2018): https://onlinelibrary.wiley.com/doi/full/10.1002/cyto.a.23566

Brummelman, J. et al. J. Exp. Med. 215, 2520-2535 (2018): http://jem.rupress.org/content/215/10/2520

Supplementary information

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Brummelman, J., Haftmann, C., Núñez, N.G. et al. Development, application and computational analysis of high-dimensional fluorescent antibody panels for single-cell flow cytometry. Nat Protoc 14, 1946–1969 (2019). https://doi.org/10.1038/s41596-019-0166-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41596-019-0166-2

This article is cited by

Comments

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.

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