Abstract
The immune system comprises distinct functionally specialized cell populations, which can be characterized in depth by mass cytometry protein profiling. Unfortunately, the low-throughput nature of mass cytometry has made it challenging to analyze minor cell populations. This is the case for dendritic cells, which represent 0.2–2% of all immune cells in tissues and yet perform the critical task of initiating and modulating immune responses. Here, we provide an optimized step-by-step protocol for the characterization of well-known and emerging human dendritic cell populations in blood and tissues using mass cytometry. We provide detailed instructions for the generation of single-cell suspensions, sample enrichment, staining, acquisition and data analysis. We also include a barcoding option that reduces acquisition variability and allows the analysis of low numbers of dendritic cells, i.e., ~20,000. In contrast to other protocols, we emphasize the use of negative selection approaches to enrich for minor populations of immune cells while avoiding their activation. The entire procedure can be completed in 2–3 d and can be conveniently paused at several stages. The procedure described in this robust and reliable protocol allows the analysis of human dendritic cells in health and disease and during vaccination.
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Data availability
Data in this manuscript are available at http://flowrepository.org/ (accession number FR-FCM-Z453), including data shown in Figs. 4–6 and Supplementary Fig. 1. PBMC staining using CD88/CD89 is available upon request to the corresponding author.
References
Steinman, R. M. & Idoyaga, J. Features of the dendritic cell lineage. Immunol. Rev. 234, 5–17 (2010).
Lewis, K. L. & Reizis, B. Dendritic cells: arbiters of immunity and immunological tolerance. Cold Spring Harb. Perspect. Biol. 4, a007401 (2012).
Iwasaki, A. & Medzhitov, R. Control of adaptive immunity by the innate immune system. Nat. Immunol. 16, 343–353 (2015).
Böttcher, J. P. & Reis e Sousa, C. The role of type 1 conventional dendritic cells in cancer immunity. Trends Cancer 4, 784–792 (2018).
Alcántara-Hernández, M. et al. High-dimensional phenotypic mapping of human dendritic cells reveals interindividual variation and tissue specialization. Immunity 47, 1037–1050.e6 (2017).
Villani, A.-C. et al. Single-cellRNA-seq reveals new types of human blood dendritic cells, monocytes, and progenitors. Science 356, eaah4573 (2017).
See, P. et al. Mapping the human DC lineage through the integration of high-dimensional techniques. Science 356, eaag3009 (2017).
Heidkamp, G. F. et al. Human lymphoid organ dendritic cell identity is predominantly dictated by ontogeny, not tissue microenvironment. Sci. Immunol. 1, eaai7677 (2016).
Granot, T. et al. Dendritic cells display subset and tissue-specific maturation dynamics over human life. Immunity 46, 504–515 (2017).
Bosteels, C. & Scott, C. L. Transcriptional regulation of DC fate specification. Mol. Immunol. 121, 38–46 (2020).
Leylek, R. & Idoyaga, J. The versatile plasmacytoid dendritic cell: function, heterogeneity, and plasticity. Int. Rev. Cell Mol. Biol. 349, 177–211 (2019).
Leylek, R. et al. Chromatin landscape underpinning human dendritic cell heterogeneity. Cell Rep. 32, 108180 (2020).
Abbas, A. et al. The activation trajectory of plasmacytoid dendritic cells in vivo during a viral infection. Nat. Immunol. 21, 983–997 (2020).
Theisen, D. & Murphy, K. The role of cDC1s in vivo: CD8 T cell priming through cross-presentation. F1000Res 6, 98 (2017).
Eisenbarth, S. C. Dendritic cell subsets in T cell programming: location dictates function. Nat. Rev. Immunol. 19, 89–103 (2019).
Dutertre, C.-A. et al. Single-cell analysis of human mononuclear phagocytes reveals subset-defining markers and identifies circulating inflammatory dendritic cells. Immunity 51, 573–589.e8 (2019).
Cytlak, U. et al. Differential IRF8 transcription factor requirement defines two pathways of dendritic cell development in humans. Immunity 53, 353–370.e8 (2020).
Leylek, R. et al. Integrated cross-species analysis identifies a conserved transitional dendritic cell population. Cell Rep 29, 3736–3750.e8 (2019).
Bjornson, Z. B., Nolan, G. P. & Fantl, W. J. Single-cell mass cytometry for analysis of immune system functional states. Curr. Opin. Immunol. 25, 484–494 (2013).
Baca, Q., Cosma, A., Nolan, G. & Gaudilliere, B. The road ahead: implementing mass cytometry in clinical studies, one cell at a time. Cytom. B Clin. Cytom. 92, 10–11 (2017).
Böttcher, C. et al. Human microglia regional heterogeneity and phenotypes determined by multiplexed single-cell mass cytometry. Nat. Neurosci. 22, 78–90 (2019).
Leelatian, N., Diggins, K. E. & Irish, J. M. Characterizing phenotypes and signaling networks of single human cells by mass cytometry. Methods Mol. Biol. 1346, 99–113 (2015).
Di Palma, S. & Bodenmiller, B. Unraveling cell populations in tumors by single-cell mass cytometry. Curr. Opin. Biotechnol. 31, 122–129 (2015).
Kapoor, V. N. et al. Gremlin 1+ fibroblastic niche maintains dendritic cell homeostasis in lymphoid tissues. Nat. Immunol. 22, 571–585 (2021).
Inaba, K. et al. Isolation of dendritic cells. Curr. Protoc. Immunol. Ch. 3, Unit 3.7–3.7.19 (2009)..
Stoitzner, P., Romani, N., McLellan, A. D., Tripp, C. H. & Ebner, S. Isolation of skin dendritic cells from mouse and man. Methods Mol. Biol. 595, 235–248 (2010).
Leipold, M. D. & Maecker, H. T. Mass cytometry: protocol for daily tuning and running cell samples on a CyTOF mass cytometer. J. Vis. Exp. 69, e4398 (2012).
Pellerin, A. et al. Anti-BDCA2 monoclonal antibody inhibits plasmacytoid dendritic cell activation through Fc-dependent and Fc-independent mechanisms. EMBO Mol. Med. 7, 464–476 (2015).
Neurauter, A. A. et al. Cell isolation and expansion using Dynabeads. Adv. Biochem. Eng. Biotechnol. 106, 41–73 (2007).
Yu, H. et al. Human BDCA2+CD123+CD56+ dendritic cells (DCs) related to blastic plasmacytoid dendritic cell neoplasm represent a unique myeloid DC subset. Protein Cell 6, 297–306 (2015).
Ouchi, T., Nakato, G. & Udey, M. C. EpCAM expressed by murine epidermal Langerhans cells modulates immunization to an epicutaneously applied protein antigen. J. Invest. Dermatol. 136, 1627–1635 (2016).
Zunder, E. R. et al. Palladium-based mass tag cell barcoding with a doublet-filtering scheme and single-cell deconvolution algorithm. Nat. Protoc. 10, 316–333 (2015).
Stewart, J. C., Villasmil, M. L. & Frampton, M. W. Changes in fluorescence intensity of selected leukocyte surface markers following fixation. Cytometry A 71, 379–385 (2007).
Wagar, L. E. Live cell barcoding for efficient analysis of small samples by mass cytometry. Methods Mol. Biol. 1989, 125–135 (2019).
Mei, H. E., Leipold, M. D., Schulz, A. R., Chester, C. & Maecker, H. T. Barcoding of live human peripheral blood mononuclear cells for multiplexed mass cytometry. J. Immunol. 194, 2022–2031 (2015).
Han, G., Spitzer, M. H., Bendall, S. C., Fantl, W. J. & Nolan, G. P. Metal-isotope-tagged monoclonal antibodies for high-dimensional mass cytometry. Nat. Protoc. 13, 2121–2148 (2018).
Gullaksen, S.-E. et al. Titrating complex mass cytometry panels. Cytometry A 95, 792–796 (2019).
Picot, J., Guerin, C. L., Le Van Kim, C. & Boulanger, C. M. Flow cytometry: retrospective, fundamentals and recent instrumentation. Cytotechnology 64, 109–130 (2012).
Li, F. et al. Autofluorescence contributes to false-positive intracellular Foxp3 staining in macrophages: a lesson learned from flow cytometry. J. Immunol. Methods 386, 101–107 (2012).
Wizenty, J. et al. Autofluorescence: a potential pitfall in immunofluorescence-based inflammation grading. J. Immunol. Methods 456, 28–37 (2018).
Halldén, G., Sköld, C. M., Eklund, A., Forslid, J. & Hed, J. Quenching of intracellular autofluorescence in alveolar macrophages permits analysis of fluorochrome labelled surface antigens by flow cytofluorometry. J. Immunol. Methods 142, 207–214 (1991).
Brummelman, J. et al. Development, application and computational analysis of high-dimensional fluorescent antibody panels for single-cell flow cytometry. Nat. Protoc. 14, 1946–1969 (2019).
Herring, C. A. et al. Unsupervised trajectory analysis of single-cell RNA-Seq and imaging data reveals alternative tuft cell origins in the gut. Cell Syst. 6, 37–51.e9 (2018).
Liu, Y., Beyer, A. & Aebersold, R. On the dependency of cellular protein levels on mRNA abundance. Cell 165, 535–550 (2016).
Diggins, K. E., Greenplate, A. R., Leelatian, N., Wogsland, C. E. & Irish, J. M. Characterizing cell subsets using marker enrichment modeling. Nat. Methods 14, 275–278 (2017).
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).
Leipold, M. D. & Maecker, H. T. Phenotyping of live human PBMC using CyTOF™ mass cytometry. Bio Protoc. 5, e1382–e1382 (2015).
Gonder, S. et al. Method for the analysis of the tumor microenvironment by mass cytometry: application to chronic lymphocytic leukemia. Front. Immunol. 11, 578176 (2020).
Roussel, M., Bartkowiak, T. & Irish, J. M. Picturing polarized myeloid phagocytes and regulatory cells by mass cytometry. Methods Mol. Biol. 1989, 217–226 (2019).
Sander, J. et al. Cellular differentiation of human monocytes is regulated by time-dependent interleukin-4 signaling and the transcriptional regulator NCOR2. Immunity 47, 1051–1066.e12 (2017).
Baharlou, H., Canete, N. P., Cunningham, A. L., Harman, A. N. & Patrick, E. Mass cytometry imaging for the study of human diseases-applications and data analysis strategies. Front. Immunol. 10, 2657 (2019).
Gerner, M. Y., Casey, K. A., Kastenmuller, W. & Germain, R. N. Dendritic cell and antigen dispersal landscapes regulate T cell immunity. J. Exp. Med. 214, 3105–3122 (2017).
Czapiga, M., Kirk, A. D. & Lekstrom-Himes, J. Platelets deliver costimulatory signals to antigen-presenting cells: a potential bridge between injury and immune activation. Exp. Hematol. 32, 135–139 (2004).
Shoup, M. et al. The value of splenic preservation with distal pancreatectomy. Arch. Surg. 137, 164–168 (2002).
Fienberg, H. G., Simonds, E. F., Fantl, W. J., Nolan, G. P. & Bodenmiller, B. A platinum-based covalent viability reagent for single-cell mass cytometry. Cytometry A 81, 467–475 (2012).
Van Gassen, S., Gaudilliere, B., Angst, M. S., Saeys, Y. & Aghaeepour, N. CytoNorm: a normalization algorithm for cytometry data. Cytometry A 97, 268–278 (2020).
Rybakowska, P., Alarcón-Riquelme, M. E. & Marañón, C. Key steps and methods in the experimental design and data analysis of highly multi-parametric flow and mass cytometry. Comput. Struct. Biotechnol. J. 18, 874–886 (2020).
Becht, E. et al. Dimensionality reduction for visualizing single-cell data using UMAP. Nat. Biotechnol. 37, 38–44 (2018).
Chester, C. & Maecker, H. T. Algorithmic tools for mining high-dimensional cytometry data. J. Immunol. 195, 773–779 (2015).
Acknowledgements
This work was supported by NIH grants DP2AR069953, R01CA219994, R01AI158808 and R21AI163775 awarded to J.I. Data collection was performed on an instrument in the Shared FACS Facility obtained using NIH S10 Shared Instrument Grant S10OD016318-01. We thank the blood donors for their participation and the Idoyaga Lab members for technical support and discussions.
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M.A-H. and J.I. conceptualized and wrote the paper, M.A-H. performed the experiments and analyzed the data. J.I. is responsible for funding acquisition, supervision and project administration.
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Key references using this protocol
Alcántara-Hernández, M. et al. Immunity 47, 1037–1050.e6 (2017): https://doi.org/10.1016/j.immuni.2017.11.001
Leylek, R. et al. Cell Rep. 32, 108180 (2020): https://doi.org/10.1016/j.celrep.2020.108180
Leylek, R. et al. Cell Rep. 29, 3736–3750.e8 (2019): https://doi.org/10.1016/j.celrep.2019.11.042
Extended data
Extended Data Fig. 1 Frequencies of leukocytes in blood and skin samples before and after enrichment.
a, PBMCs were obtained from blood (Steps 1–10). b, Skin cell suspensions were obtained after enzymatic digestion (Steps 23–37). Myeloid cells or leukocytes were negatively enriched from blood and skin, respectively, following the procedures described in Steps 45–53. Cells were stained for flow cytometry before and after negative enrichment. One representative blood and skin sample is shown. Numbers represent frequency in the gate. Antibodies used are as follows: CD45 Brilliant Violet 785 (Biolegend, cat. no. 304049), CD3 PerCPCy5.5 (Biolegend, cat. no. 300430), CD19 PerCPCy5.5 (Biolegend, cat. no. 302230), CD335 PerCPCy5.5 (Biolegend, cat. no. 331920), CD66b PerCPCy5.5 (Biolegend, cat. no. 305108). Figure created for this protocol from data obtained as part of a primary publication5.
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Alcántara-Hernández, M., Idoyaga, J. Mass cytometry profiling of human dendritic cells in blood and tissues. Nat Protoc 16, 4855–4877 (2021). https://doi.org/10.1038/s41596-021-00599-x
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DOI: https://doi.org/10.1038/s41596-021-00599-x
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