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An updated guide for the perplexed: cytometry in the high-dimensional era

Nature Immunology volume 22, pages 1190–1197 (2021)Cite this article

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High-dimensional cytometry experiments measuring 20–50 cellular markers have become routine in many laboratories. The increased complexity of these datasets requires added rigor during the experimental planning and the subsequent manual and computational data analysis to avoid artefacts and misinterpretation of results. Here we discuss pitfalls frequently encountered during high-dimensional cytometry data analysis and aim to provide a basic framework and recommendations for reporting and analyzing these datasets.

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Fig. 1: Overview of experiment and data analysis workflow with associated issues.
Fig. 2: Influence of SE and transformation settings on data visualization and computational analysis.
Fig. 3: Illustration of common pitfalls encountered during computational analysis and data visualization.

Data availability

Data used to generate Figs. 2c and 3 are sourced from OMIP-044 (ref. 25). Data used to generate Fig. 2d–f were derived from ref. 16.

Code availability

The code used to generate plots in Fig. 2d–f can be found at https://github.com/immunedynamics/Liechti-2021. Code to reproduce Fig. 3 is available from https://github.com/lmweber/cytometry-perplexed.

References

  1. Saeys, Y., Van Gassen, S. & Lambrecht, B. N. Nat. Rev. Immunol. 16, 449–462 (2016).

    Article  CAS  Google Scholar 

  2. Nguyen, R., Perfetto, S., Mahnke, Y. D., Chattopadhyay, P. & Roederer, M. Cytometry A 83, 306–315 (2013).

    Article  Google Scholar 

  3. Mair, F. & Tyznik, A. J. Methods Mol. Biol. 2032, 1–29 (2019).

    Article  CAS  Google Scholar 

  4. Ferrer-Font, L. et al. Curr. Protoc. Cytom. 92, e70 (2020).

    CAS  PubMed  Google Scholar 

  5. Ashhurst, T. M., Smith, A. L. & King, N. J. C. Curr. Protoc. Immunol. 119, 5.8.1–5.8.38 (2017).

    Article  Google Scholar 

  6. Roederer, M. Cytometry 45, 194–205 (2001).

    Article  CAS  Google Scholar 

  7. Liechti, T. & Roederer, M. Cytometry A 95, 1129–1134 (2019).

    Article  CAS  Google Scholar 

  8. Herzenberg, L. A., Tung, J., Moore, W. A., Herzenberg, L. A. & Parks, D. R. Nat. Immunol. 7, 681–685 (2006).

    Article  CAS  Google Scholar 

  9. Parks, D. R., Roederer, M. & Moore, W. A. Cytometry A 69, 541–551 (2006).

    Article  Google Scholar 

  10. Maecker, H. T., McCoy, J. P. & Nussenblatt, R. Nat. Rev. Immunol. 12, 191–200 (2012).

    Article  CAS  Google Scholar 

  11. Van Gassen, S., Gaudillière, B., Angst, M. S., Saeys, Y. & Aghaeepour, N. Cytometry A 97, 268–278 (2020).

    Article  Google Scholar 

  12. Schuyler, R. P. et al. Front. Immunol. 10, 2367 (2019).

    Article  CAS  Google Scholar 

  13. Weber, L. M. & Robinson, M. D. Cytometry A 89, 1084–1096 (2016).

    Article  CAS  Google Scholar 

  14. Aghaeepour, N. et al. Nat. Meth. 10, 228–238 (2013).

    Article  CAS  Google Scholar 

  15. Brinkman, R. R. et al. Cytometry A 89, 13–15 (2016).

    Article  Google Scholar 

  16. Ashhurst, T. M. et al. Cytometry A https://doi.org/10.1002/cyto.a.24350 (2021).

  17. Nowicka, M. et al. F1000Res. 6, 748 (2017).

    Article  Google Scholar 

  18. Van Gassen, S. et al. Cytometry A 87, 636–645 (2015).

    Article  Google Scholar 

  19. Levine, J. H. et al. Cell 162, 184–197 (2015).

    Article  CAS  Google Scholar 

  20. Kobak, D. & Linderman, G. C. Nat. Biotechnol. 39, 156–157 (2021).

    Article  CAS  Google Scholar 

  21. Amezquita, R. A. et al. Nat. Meth. 17, 137–145 (2020).

    Article  CAS  Google Scholar 

  22. Lee, J. A. et al. Cytometry A 73, 926–930 (2008).

    Article  Google Scholar 

  23. Lucas, F. et al. Cytometry A 97, 148–155 (2020).

    Article  Google Scholar 

  24. O’Neill, K. & Brinkman, R. R. Cytometry A 89, 10–11 (2016).

    Article  Google Scholar 

  25. Mair, F. & Prlic, M. Cytometry A 93, 402–405 (2018).

    Article  Google Scholar 

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Acknowledgements

We thank M. Roederer (Vaccine Research Center, NIH) for critical reading and feedback. We thank D. Shinko and N. J. C. King for assistance in generating flow, spectral and mass cytometry data. T.L was supported by the Intramural Research Program of the Vaccine Research Center, NIAID, NIH. L.M.W. was supported by the National Institutes of Health (NIH) grant R01CA237170 from the National Cancer Institute; NIH grant U01MH122849 from the National Institute of Mental Health to the Lieber Institute for Brain Development; and CZF2019-002443 from the Chan Zuckerberg Initiative DAF, an advised fund of the Silicon Valley Community Foundation. M.P was supported by NIH grants R01AI123323 and R21AI144677. F.M was supported by an AAI Intersect Fellowship. S.V.G. was supported by an FWO postdoctoral research grant (Research Foundation, Flanders). T.L., T.M.A., S.V.G. and F.M. are ISAC Marylou Ingram Scholars.

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Authors and Affiliations

  1. ImmunoTechnology Section, Vaccine Research Center, NIAID, NIH, Bethesda, MD, USA

    Thomas Liechti

  2. Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA

    Lukas M. Weber

  3. Sydney Cytometry Core Research Facility, Charles Perkins Centre, The University of Sydney and Centenary Institute, Sydney, New South Wales, Australia

    Thomas M. Ashhurst

  4. School of Medical Sciences, Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia

    Thomas M. Ashhurst

  5. Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Natalie Stanley

  6. Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA

    Martin Prlic & Florian Mair

  7. Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium

    Sofie Van Gassen

  8. Data Mining and Modeling for Biomedicine, VIB Center for Inflammation Research, Ghent, Belgium

    Sofie Van Gassen

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  1. Thomas Liechti
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  2. Lukas M. Weber
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  3. Thomas M. Ashhurst
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  4. Natalie Stanley
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  6. Sofie Van Gassen
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  7. Florian Mair
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Correspondence to Florian Mair.

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Liechti, T., Weber, L.M., Ashhurst, T.M. et al. An updated guide for the perplexed: cytometry in the high-dimensional era. Nat Immunol 22, 1190–1197 (2021). https://doi.org/10.1038/s41590-021-01006-z

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