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:

Mass cytometry analysis of immune cells in the brain

Nature Protocols volume 13, pages 377–391 (2018)Cite this article

Subjects

Abstract

Immune cells comprise a diverse and dynamic cell population that is responsible for a broad range of immunological activities. They act in concert with other immune and nonimmune cells via cytokine-mediated communication and direct cell–cell interactions. Understanding the complex immune network requires a broad characterization of its individual cellular components. This is especially relevant for the brain compartment, which is an active immunological site, composed of resident and infiltrating immune cells that affect brain development, tissue homeostasis and neuronal activity. Mass cytometry, or CyTOF (cytometry by time-of-flight), uses metal-conjugated antibodies to enable a high-dimensional description of tens of markers at the single-cell level, thereby providing a bird's-eye view of the immune system. This technique has been successfully applied to the discovery of novel immune populations in humans and rodents. Here, we provide a step-by-step description of a mass cytometry approach for the analysis of the mouse brain compartment. The different stages of the procedure include brain perfusion, extraction of the brain tissue and its dissociation into a single-cell suspension, followed by cell staining with metal-tagged antibodies, sample reading using a mass cytometer, and data analysis using SPADE and viSNE. This procedure takes <5 h (excluding data analysis) and can be applied to study modifications in the brain's immune populations under normal and pathological conditions.

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: Brain compartment analysis steps.
Figure 2: Perfusion quality evaluation steps.
Figure 3: Preliminary gating of a brain compartment mass cytometry sample.
Figure 4: The naive brain immune compartment.

Similar content being viewed by others

References

  1. Paolicelli, R.C. et al. Synaptic pruning by microglia is necessary for normal brain development. Science 333, 1456–1458 (2011).

    Article  CAS  PubMed  Google Scholar 

  2. Welberg, L. Synaptic plasticity: a synaptic role for microglia. Nat. Rev. Neurosci. 15, 68–69 (2014).

    Article  CAS  Google Scholar 

  3. Wu, Y., Dissing-Olesen, L., MacVicar, B.A. & Stevens, B. Microglia: dynamic mediators of synapse development and plasticity. Trends Immunol. 36, 605–613 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  4. Iori, V., Frigerio, F. & Vezzani, A. Modulation of neuronal excitability by immune mediators in epilepsy. Curr. Opin. Pharmacol. 26, 118–123 (2016).

    Article  CAS  PubMed  Google Scholar 

  5. Ransohoff, R.M., Schafer, D., Vincent, A., Blachère, N.E. & Bar-Or, A. Neuroinflammation: ways in which the immune system affects the brain. Neurotherapeutics 12, 896–909 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Lampron, A., Pimentel-Coelho, P.M. & Rivest, S. Migration of bone marrow-derived cells into the central nervous system in models of neurodegeneration. J. Comp. Neurol. 521, 3863–3876 (2013).

    Article  CAS  PubMed  Google Scholar 

  7. Dendrou, C.A., Fugger, L. & Friese, M.A. Immunopathology of multiple sclerosis. Nat. Rev. Immunol. 15, 545–558 (2015).

    Article  CAS  PubMed  Google Scholar 

  8. Heppner, F.L., Ransohoff, R.M. & Becher, B. Immune attack: the role of inflammation in Alzheimer disease. Nat. Rev. Neurosci. 16, 358–372 (2015).

    Article  CAS  PubMed  Google Scholar 

  9. Baruch, K. et al. PD-1 immune checkpoint blockade reduces pathology and improves memory in mouse models of Alzheimer's disease. Nat. Med. 22, 135–137 (2016).

    Article  CAS  PubMed  Google Scholar 

  10. Teeling, J.L. & Asuni, A.A. Immune to brain communication in health, age and disease: implications for understanding age-related neurodegeneration. in The Ageing Immune System and Health 125–139 (Springer, 2017).

  11. Arlehamn, C.S.L. et al. Immune response in Parkinson's disease driven by HLA display of α-synuclein peptides. J. Immunol. 198, 55.26 (2017).

    Google Scholar 

  12. Baruch, K. et al. Breaking immune tolerance by targeting Foxp3+ regulatory T cells mitigates Alzheimer's disease pathology. Nat. Commun. 6, 7967 (2015).

    Article  CAS  PubMed  Google Scholar 

  13. Stern, J.N.H. et al. B cells populating the multiple sclerosis brain mature in the draining cervical lymph nodes. Sci. Transl. Med. 6, 248ra107 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  14. Pikor, N.B., Prat, A., Bar-Or, A. & Gommerman, J.L. Meningeal tertiary lymphoid tissues and multiplesclerosis: a gathering place for diverse types of immune cells during CNS autoimmunity. Front. Immunol. 6, 2015.00657 (2016).

    Article  CAS  Google Scholar 

  15. Mosley, R.L., Hutter-Saunders, J.A., Stone, D.K. & Gendelman, H.E. Inflammation and adaptive immunity in Parkinson's disease. Cold Spring Harb. Perspect. Med. 2, a009381 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  16. Zhang, Y. et al. An RNA-sequencing transcriptome and splicing database of glia, neurons, and vascular cells of the cerebral cortex. J. Neurosci. 34, 11929–11947 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Grabert, K. et al. Microglial brain region-dependent diversity and selective regional sensitivities to aging. Nat. Neurosci. 19, 504–516 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Keren-Shaul, H. et al. A unique microglia type associated with restricting development of Alzheimer's disease. Cell 169, 1276–1290.e17 (2017).

    Article  CAS  PubMed  Google Scholar 

  19. Cheung, R.K. & Utz, P.J. Screening: CyTOF—the next generation of cell detection. Nat. Rev. Rheumatol. 7, 502–503 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  20. Bendall, S.C. et al. Single-cell trajectory detection uncovers progression and regulatory coordination in human B cell development. Cell 157, 714–725 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Setty, M. et al. Wishbone identifies bifurcating developmental trajectories from single-cell data. Nat. Biotechnol. 34, 637–645 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Simoni, Y. et al. Human innate lymphoid cell subsets possess tissue-type based heterogeneity in phenotype and frequency. Immunity 46, 148–161 (2017).

    Article  CAS  PubMed  Google Scholar 

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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  24. Yao, Y. & Montgomery, R.R. Role of immune aging in susceptibility to West Nile Virus. Methods Mol. Biol. 1435, 235–247 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Whiting, C.C. et al. Large-scale and comprehensive immune profiling and functional analysis of normal human aging. PLoS One 10, e0133627 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  26. Baughn, L.B. et al. Phenotypic and functional characterization of a bortezomib-resistant multiple myeloma cell line by flow and mass cytometry. Leuk. Lymphoma 58, 1931–1940 (2017).

    Article  CAS  PubMed  Google Scholar 

  27. Aquino-López, A., Senyukov, V.V., Vlasic, Z., Kleinerman, E.S. & Lee, D.A. Interferon gamma induces changes in natural killer (NK) cell ligand expression and alters NK cell-mediated lysis of pediatric cancer cell lines. Front. Immunol. 8, 391 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  28. Korin, B. et al. High-dimensional, single-cell characterization of the brain's immune compartment. Nat. Neurosci. 20, 1300–1309 (2017).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  30. Mrdjen, D., Hartmann, F. & Becher, B. High dimensional cytometry of central nervous system leukocytes during neuroinflammation. in Inflammation (eds. E. Clausen, B., Laman, J. D. & Clausen, B. E.) 321–332 (Springer, 2017).

  31. Garcia, J.A., Cardona, S.M. & Cardona, A.E. Isolation and analysis of mouse microglial cells. Curr. Protoc. Immunol. 104, Unit 14.35 (2014).

    Article  PubMed Central  Google Scholar 

  32. Beaudet, M.-J. et al. High yield extraction of pure spinal motor neurons, astrocytes and microglia from single embryo and adult mouse spinal cord. Sci. Rep. 5, srep16763 (2015).

    Article  CAS  Google Scholar 

  33. Mills, K., McManus, R. & Dungan, L. Isolation and FACS analysis on mononuclear cells from CNS tissue. Bio-Protoc. 4(18), 1240 (2014).

    Google Scholar 

  34. Günther, R. et al. Clinical testing and spinal cord removal in a mouse model for amyotrophic lateral sclerosis (ALS). J. Vis. Exp. (61), e3936 (2012).

  35. Pino, P.A. & Cardona, A.E. Isolation of brain and spinal cord mononuclear cells using Percoll gradients. J. Vis. Exp. (48), e2348 (2011).

  36. Zeisel, A. et al. Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq. Science 347, 1138–1142 (2015).

    Article  CAS  PubMed  Google Scholar 

  37. Ofengeim, D., Giagtzoglou, N., Huh, D., Zou, C. & Yuan, J. Single-cell RNA sequencing: unraveling the brain one cell at a time. Trends Mol. Med. 23, 563–576 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Matcovitch-Natan, O. et al. Microglia development follows a stepwise program to regulate brain homeostasis. Science 353, aad8670 (2016).

    Article  PubMed  CAS  Google Scholar 

  39. Gerner, M.Y., Kastenmuller, W., Ifrim, I., Kabat, J. & Germain, R.N. Histo-cytometry: a method for highly multiplex quantitative tissue imaging analysis applied to dendritic cell subset microanatomy in lymph nodes. Immunity 37, 364–376 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Gerdes, M.J. et al. Highly multiplexed single-cell analysis of formalin-fixed, paraffin-embedded cancer tissue. Proc. Natl. Acad. Sci. USA 110, 11982–11987 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  42. Kwon, S. Single-molecule fluorescence in situ hybridization: quantitative imaging of single RNA molecules. BMB Rep. 46, 65–72 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  43. Chen, K.H., Boettiger, A.N., Moffitt, J.R., Wang, S. & Zhuang, X. Spatially resolved, highly multiplexed RNA profiling in single cells. Science 348, aaa6090 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  44. Shah, S., Lubeck, E., Zhou, W. & Cai, L. In situ transcription profiling of single cells reveals spatial organization of cells in the mouse hippocampus. Neuron 92, 342–357 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. 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 

  46. Brodie, T.M. & Tosevski, V. High-dimensional single-cell analysis with mass cytometry. Curr. Protoc. Immunol. 118, 5.11.1–5.11.25 (2017).

    Article  CAS  Google Scholar 

  47. McCarthy, R.L., Mak, D.H., Burks, J.K. & Barton, M.C. Rapid monoisotopic cisplatin based barcoding for multiplexed mass cytometry. Sci. Rep. 7, 3779 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  48. Lai, L., Ong, R., Li, J. & Albani, S. A CD45-based barcoding approach to multiplex mass-cytometry (CyTOF). Cytometry A 87, 369–374 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Nassar, A.F., Wisnewski, A.V. & Raddassi, K. Automation of sample preparation for mass cytometry barcoding in support of clinical research: protocol optimization. Anal. Bioanal. Chem. 409, 2363–2372 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Bodenmiller, B. et al. Multiplexed mass cytometry profiling of cellular states perturbed by small-molecule regulators. Nat. Biotechnol. 30, 858–867 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Elghetany, M.T. & Davis, B.H. Impact of preanalytical variables on granulocytic surface antigen expression: a review. Cytometry B Clin. Cytom. 65, 1–5 (2005).

    Article  PubMed  Google Scholar 

  53. Kappelmayer, J. et al. Flow cytometric detection of intracellular myeloperoxidase, CD3 and CD79a. Interaction between monoclonal antibody clones, fluorochromes and sample preparation protocols. J. Immunol. Methods 242, 53–65 (2000).

    Article  CAS  PubMed  Google Scholar 

  54. 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).

    Article  PubMed  CAS  Google Scholar 

  55. Anderson, K.G. et al. Intravascular staining for discrimination of vascular and tissue leukocytes. Nat. Protoc. 9, 209–222 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Stern, A.D., Rahman, A.H. & Birtwistle, M.R. Cell size assays for mass cytometry. Cytometry A 91, 14–24 (2017).

    Article  CAS  PubMed  Google Scholar 

  57. Bennett, M.L. et al. New tools for studying microglia in the mouse and human CNS. Proc. Natl. Acad. Sci. USA 113, E1738–1746 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Butovsky, O. et al. Identification of a unique TGF-β-dependent molecular and functional signature in microglia. Nat. Neurosci. 17, 131–143 (2014).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Anchang, B. et al. Visualization and cellular hierarchy inference of single-cell data using SPADE. Nat. Protoc. 11, 1264–1279 (2016).

    Article  CAS  PubMed  Google Scholar 

  61. 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).

    Article  CAS  PubMed Central  Google Scholar 

  62. Samusik, N., Good, Z., Spitzer, M.H., Davis, K.L. & Nolan, G.P. Automated mapping of phenotype space with single-cell data. Nat. Methods 13, 493–496 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. 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 

  64. 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 

  65. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. 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 

  67. Newell, E.W. & Cheng, Y. Mass cytometry: blessed with the curse of dimensionality. Nat. Immunol. 17, 890–895 (2016).

    Article  CAS  PubMed  Google Scholar 

  68. Melchiotti, R., Gracio, F., Kordasti, S., Todd, A.K. & de Rinaldis, E. Cluster stability in the analysis of mass cytometry data. Cytometry A 91, 73–84 (2017).

    Article  CAS  PubMed  Google Scholar 

  69. Pennartz, S., Reiss, S., Biloune, R., Hasselmann, D. & Bosio, A. Generation of single-cell suspensions from mouse neural tissue. J. Vis. Exp. (29), e1267 (2009).

  70. Njie, E.G. et al. Ex vivo cultures of microglia from young and aged rodent brain reveal age-related changes in microglial function. Neurobiol. Aging 33, 195.e1–195.e12 (2012).

    Article  CAS  Google Scholar 

  71. Lee, J.-K. & Tansey, M.G. Microglia isolation from adult mouse brain. in Microglia 17–23 (Humana Press, 2013).

  72. Martin, E., El-Behi, M., Fontaine, B. & Delarasse, C. Analysis of microglia and monocyte-derived macrophages from the central nervous system by flow cytometry. J. Vis. Exp. (124), e55781 (2017).

  73. Nikodemova, M. & Watters, J.J. Efficient isolation of live microglia with preserved phenotypes from adult mouse brain. J. Neuroinflammation 9, 147 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Weigmann, B. et al. Isolation and subsequent analysis of murine lamina propria mononuclear cells from colonic tissue. Nat. Protoc. 2, 2307–2311 (2007).

    Article  CAS  PubMed  Google Scholar 

  75. Autengruber, A., Gereke, M., Hansen, G., Hennig, C. & Bruder, D. Impact of enzymatic tissue disintegration on the level of surface molecule expression and immune cell function. Eur. J. Microbiol. Immunol. 2, 112–120 (2012).

    Article  CAS  Google Scholar 

  76. Ford, A.L., Foulcher, E., Goodsall, A.L. & Sedgwick, J.D. Tissue digestion with dispase substantially reduces lymphocyte and macrophage cell-surface antigen expression. J. Immunol. Methods 194, 71–75 (1996).

    Article  CAS  PubMed  Google Scholar 

  77. Derecki, N., Derecki, N. & Kipnis, J. Mouse meninges isolation for FACS. Protoc. Exch. http://dx.doi.org/10.1038/protex.2014.030 (2014).

  78. Ornatsky, O. et al. Highly multiparametric analysis by mass cytometry. J. Immunol. Methods 361, 1–20 (2010).

    Article  CAS  PubMed  Google Scholar 

  79. Ornatsky, O.I. et al. Study of cell antigens and intracellular DNA by identification of element-containing labels and metallointercalators using inductively coupled plasma mass spectrometry. Anal. Chem. 80, 2539–2547 (2008).

    Article  CAS  PubMed  Google Scholar 

  80. Finck, R. et al. Normalization of mass cytometry data with bead standards. Cytometry A 83A, 483–494 (2013).

    Article  CAS  Google Scholar 

  81. Leipold, M.D., Newell, E.W. & Maecker, H.T. Multiparameter phenotyping of human PBMCs using mass cytometry. Methods Mol. Biol. 1343, 81–95 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Kay, A.W., Strauss-Albee, D.M. & Blish, C.A. Application of mass cytometry (CyTOF) for functional and phenotypic analysis of natural killer cells. Methods Mol. Biol. 1441, 13–26 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Louveau, A. et al. Structural and functional features of central nervous system lymphatic vessels. Nature 523, 337–341 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  84. Nassar, A.F., Wisnewski, A.V. & Raddassi, K. Automation of sample preparation for mass cytometry barcoding in support of clinical research: protocol optimization. Anal. Bioanal. Chem. 409, 1–10 (2017).

    Article  CAS  Google Scholar 

  85. Kaiser, O. et al. Dissociated neurons and glial cells derived from rat inferior colliculi after digestion with papain. PLoS ONE 8, e80490 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  86. Lehmann, M.L., Cooper, H.A., Maric, D. & Herkenham, M. Social defeat induces depressive-like states and microglial activation without involvement of peripheral macrophages. J. Neuroinflammation 13, 224 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

Download references

Acknowledgements

We thank S. Schwarzbaum and S. Urim for editing the paper; M. Schwartz, S. Shen-Orr, T. Ben-Shaanan and M. Schiller for their advice and helpful remarks; and A. Grau, Y. Sakuory and the Biomedical Core Facility at the Technion Faculty of Medicine for technical support and comments. This study was supported by the Israeli Ministry of Science, Technology & Space (MOST; 3-12070), the Prince Center for Neurodegenerative Diseases, Israeli Society for Science (ISF; 1862/15), an FP-7-CIG grant (618654) and the ADELIS Foundation. A.R. is an International Howard Hughes Medical Institute (HHMI)–Wellcome Trust researcher.

Author information

Authors and Affiliations

  1. Department of Immunology, Rappaport Faculty of Medicine, Technion – Israel Institute of Technology, Haifa, Israel

    Ben Korin, Tania Dubovik & Asya Rolls

  2. Department of Neuroscience, Rappaport Faculty of Medicine, Technion – Israel Institute of Technology, Haifa, Israel

    Ben Korin & Asya Rolls

Authors
  1. Ben Korin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tania Dubovik
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Asya Rolls
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

B.K. designed the protocol, acquired the data and wrote the manuscript; T.D. contributed to the development of the protocol and to writing of the manuscript; and A.R. led the experimental design and revised the manuscript.

Corresponding author

Correspondence to Asya Rolls.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Gating strategy for flow cytometry analysis.

Cells from the (a) blood and (b) brain compartment are selected according to the forward scatter (FSC) and side scatter (SSC) area (A) parameters. Then, doublet exclusion is performed using height (H) versus width (W) parameters of FSC and SSC accordingly, and live/dead discrimination is applied using Zombie dye.

Supplementary information

Supplementary Figures and Text

Supplementary Figure 1 and Supplementary Table 1. (PDF 592 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Korin, B., Dubovik, T. & Rolls, A. Mass cytometry analysis of immune cells in the brain. Nat Protoc 13, 377–391 (2018). https://doi.org/10.1038/nprot.2017.155

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nprot.2017.155

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

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