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.
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Data availability
Flow cytometry data used in Figs. 2 and 4–8 are available at https://flowrepository.org/, ID: FR-FCM-ZYV3.
References
Bendall, S. C., Nolan, G. P., Roederer, M. & Chattopadhyay, P. K. A deep profiler’s guide to cytometry. Trends Immunol. 33, 323–332 (2012).
Lugli, E., Roederer, M. & Cossarizza, A. Data analysis in flow cytometry: the future just started. Cytometry A 77, 705–713 (2010).
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).
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).
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).
Mair, F. & Prlic, M. OMIP-044: 28-color immunophenotyping of the human dendritic cell compartment. Cytometry A 93, 402–405 (2018).
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).
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).
Mahnke, Y., Chattopadhyay, P. & Roederer, M. Publication of optimized multicolor immunofluorescence panels. Cytometry A 77, 814–818 (2010).
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).
Levine, J. H. et al. Data-driven phenotypic dissection of AML reveals progenitor-like cells that correlate with prognosis. Cell 162, 184–197 (2015).
Van Gassen, S. et al. FlowSOM: Using self-organizing maps for visualization and interpretation of cytometry data. Cytometry A 87, 636–645 (2015).
Maecker, H. T. & Trotter, J. Flow cytometry controls, instrument setup, and the determination of positivity. Cytometry A 69, 1037–1042 (2006).
Mahnke, Y. D. & Roederer, M. Optimizing a multicolor immunophenotyping assay. Clin. Lab. Med. 27, 469–485 (2007).
Cossarizza, A. et al. Guidelines for the use of flow cytometry and cell sorting in immunological studies. Eur. J. Immunol. 47, 1584–1797 (2017).
Lugli, E., Zanon, V., Mavilio, D. & Roberto, A. FACS analysis of memory T lymphocytes. Methods Mol. Biol. 1514, 31–47 (2017).
Roederer, M. Spectral compensation for flow cytometry: visualization artifacts, limitations, and caveats. Cytometry 45, 194–205 (2001).
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).
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).
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).
De Simone, M. et al. Transcriptional landscape of human tissue lymphocytes unveils uniqueness of tumor-infiltrating T regulatory cells. Immunity 45, 1135–1147 (2016).
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).
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).
Todorov, H. & Saeys, Y. Computational approaches for high-throughput single-cell data analysis. FEBS J. https://doi.org/10.1111/febs.14613 (2018).
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).
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).
Bagwell, C. B. Hyperlog-a flexible log-like transform for negative, zero, and positive valued data. Cytometry A 64, 34–42 (2005).
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).
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).
Hahne, F. et al. flowCore: a Bioconductor package for high throughput flow cytometry. BMC Bioinformatics 10, 106 (2009).
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).
Roederer, M. How many events is enough? Are you positive? Cytometry A 73, 384–385 (2008).
Chen, H. et al. Cytofkit: a bioconductor package for an integrated mass cytometry data analysis pipeline. PLoS Comput. Biol. 12, e1005112 (2016).
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).
Nowicka, M. et al. CyTOF workflow: differential discovery in high-throughput high-dimensional cytometry datasets. F1000Res 6, 748 (2017).
van der Maaten, L. & Hinton, G. Visualizing data using t-SNE. J. Mach. Learn. Res. 9, 2579–2605 (2008).
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).
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).
Farber, D. L., Yudanin, N. A. & Restifo, N. P. Human memory T cells: generation, compartmentalization and homeostasis. Nat. Rev. Immunol. 14, 24–35 (2014).
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).
Collin, M. & Bigley, V. Human dendritic cell subsets: an update. Immunology 154, 3–20 (2018).
See, P. et al. Mapping the human DC lineage through the integration of high-dimensional techniques. Science 356, eaag3009 (2017).
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).
Guilliams, M. et al. Unsupervised high-dimensional analysis aligns dendritic cells across tissues and species. Immunity 45, 669–684 (2016).
Villani, A. C. et al. Single-cell RNA-seq reveals new types of human blood dendritic cells, monocytes, and progenitors. Science 356 (2017).
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).
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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.
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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.
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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
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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
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DOI: https://doi.org/10.1038/s41596-019-0166-2
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