Article | Published:

Single-cell mass cytometry reveals distinct populations of brain myeloid cells in mouse neuroinflammation and neurodegeneration models

Nature Neurosciencevolume 21pages541551 (2018) | Download Citation

Abstract

Neuroinflammation and neurodegeneration may represent two poles of brain pathology. Brain myeloid cells, particularly microglia, play key roles in these conditions. We employed single-cell mass cytometry (CyTOF) to compare myeloid cell populations in the experimental autoimmune encephalomyelitis (EAE) model of multiple sclerosis, the R6/2 model of Huntington’s disease (HD) and the mutant superoxide dismutase 1 (mSOD1) model of amyotrophic lateral sclerosis (ALS). We identified three myeloid cell populations exclusive to the CNS and present in each disease model. Blood-derived monocytes comprised five populations and migrated to the brain in EAE, but not in HD and ALS models. Single-cell analysis resolved differences in signaling and cytokine production within similar myeloid populations in EAE compared to HD and ALS models. Moreover, these analyses highlighted α5 integrin on myeloid cells as a potential therapeutic target for neuroinflammation. Together, these findings illustrate how neuropathology may differ between inflammatory and degenerative brain disease.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Additional information

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

References

  1. 1.

    Prinz, M. & Priller, J. The role of peripheral immune cells in the CNS in steady state and disease. Nat. Neurosci. 20, 136–144 (2017).

  2. 2.

    Steinman, L. Immunology of relapse and remission in multiple sclerosis. Annu. Rev. Immunol. 32, 257–281 (2014).

  3. 3.

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

  4. 4.

    McGeer, P. L., Itagaki, S., Boyes, B. E. & McGeer, E. G. Reactive microglia are positive for HLA-DR in the substantia nigra of Parkinson’s and Alzheimer’s disease brains. Neurology 38, 1285–1291 (1988).

  5. 5.

    Crotti, A. et al. Mutant Huntingtin promotes autonomous microglia activation via myeloid lineage-determining factors. Nat. Neurosci. 17, 513–521 (2014).

  6. 6.

    Zhao, W., Beers, D. R. & Appel, S. H. Immune-mediated mechanisms in the pathoprogression of amyotrophic lateral sclerosis. J. Neuroimmune Pharmacol. 8, 888–899 (2013).

  7. 7.

    Aguzzi, A., Barres, B. A. & Bennett, M. L. Microglia: scapegoat, saboteur, or something else? Science 339, 156–161 (2013).

  8. 8.

    Graeber, M. B. Neuroinflammation: no rose by any other name. Brain Pathol. 24, 620–622 (2014).

  9. 9.

    Rogers, J. et al. Inflammation and Alzheimer’s disease pathogenesis. Neurobiol. Aging 17, 681–686 (1996).

  10. 10.

    Sudduth, T. L., Schmitt, F. A., Nelson, P. T. & Wilcock, D. M. Neuroinflammatory phenotype in early Alzheimer’s disease. Neurobiol. Aging 34, 1051–1059 (2013).

  11. 11.

    Zhang, B. et al. Integrated systems approach identifies genetic nodes and networks in late-onset Alzheimer’s disease. Cell 153, 707–720 (2013).

  12. 12.

    Bendall, S. C. et al. Single-cell mass cytometry of differential immune and drug responses across a human hematopoietic continuum. Science 332, 687–696 (2011).

  13. 13.

    Mangiarini, L. et al. Exon 1 of the HD gene with an expanded CAG repeat is sufficient to cause a progressive neurological phenotype in transgenic mice. Cell 87, 493–506 (1996).

  14. 14.

    Solomon, J. N. et al. Origin and distribution of bone marrow-derived cells in the central nervous system in a mouse model of amyotrophic lateral sclerosis. Glia 53, 744–753 (2006).

  15. 15.

    Karpuj, M. V. et al. Prolonged survival and decreased abnormal movements in transgenic model of Huntington disease, with administration of the transglutaminase inhibitor cystamine. Nat. Med. 8, 143–149 (2002).

  16. 16.

    Fourgeaud, L. et al. TAM receptors regulate multiple features of microglial physiology. Nature 532, 240–244 (2016).

  17. 17.

    Cardona, A. E., Huang, D., Sasse, M. E. & Ransohoff, R. M. Isolation of murine microglial cells for RNA analysis or flow cytometry. Nat. Protoc. 1, 1947–1951 (2006).

  18. 18.

    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).

  19. 19.

    Brendecke, S. M. & Prinz, M. Do not judge a cell by its cover-diversity of CNS resident, adjoining and infiltrating myeloid cells in inflammation. Semin. Immunopathol. 37, 591–605 (2015).

  20. 20.

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

  21. 21.

    Goldmann, T. et al. A new type of microglia gene targeting shows TAK1 to be pivotal in CNS autoimmune inflammation.Nat. Neurosci. 16, 1618–1626 (2013).

  22. 22.

    Wlodarczyk, A., Løbner, M., Cédile, O. & Owens, T. Comparison of microglia and infiltrating CD11c+ cells as antigen presenting cells for T cell proliferation and cytokine response. J. Neuroinflammation 11, 57 (2014).

  23. 23.

    Kim, H. et al. Increased phosphorylation of cyclic AMP response element-binding protein in the spinal cord of Lewis rats with experimental autoimmune encephalomyelitis. Brain Res. 1162, 113–120 (2007).

  24. 24.

    Liu, F. & Whitton, J. L. Cutting edge: re-evaluating the in vivo cytokine responses of CD8+ T cells during primary and secondary viral infections. J. Immunol. 174, 5936–5940 (2005).

  25. 25.

    Darrah, P. A. et al. Multifunctional TH1 cells define a correlate of vaccine-mediated protection against Leishmania major. Nat. Med. 13, 843–850 (2007).

  26. 26.

    Steinman, L. A brief history of TH17, the first major revision in the TH1/TH2 hypothesis of T cell-mediated tissue damage. Nat. Med. 13, 139–145 (2007).

  27. 27.

    Rosen, D. R. Mutations in Cu/Zn superoxide dismutase gene are associated with familial amyotrophic lateral sclerosis. Nature 364, 362 (1993).

  28. 28.

    Ajami, B., Bennett, J. L., Krieger, C., McNagny, K. M. & Rossi, F. M. Infiltrating monocytes trigger EAE progression, but do not contribute to the resident microglia pool. Nat. Neurosci. 14, 1142–1149 (2011).

  29. 29.

    Ajami, B., Bennett, J. L., Krieger, C., Tetzlaff, W. & Rossi, F. M. Local self-renewal can sustain CNS microglia maintenance and function throughout adult life. Nat. Neurosci. 10, 1538–1543 (2007).

  30. 30.

    Mildner, A. et al. Microglia in the adult brain arise from Ly-6ChiCCR2+ monocytes only under defined host conditions. Nat. Neurosci. 10, 1544–1553 (2007).

  31. 31.

    Salama, A. D. et al. Critical role of the programmed death-1 (PD-1) pathway in regulation of experimental autoimmune encephalomyelitis. J. Exp. Med. 198, 71–78 (2003).

  32. 32.

    Reiman, R., Gerard, C., Campbell, I. L. & Barnum, S. R. Disruption of the C5a receptor gene fails to protect against experimental allergic encephalomyelitis. Eur. J. Immunol. 32, 1157–1163 (2002).

  33. 33.

    Lu, J. Q., Power, C., Blevins, G., Giuliani, F. & Yong, V. W. The regulation of reactive changes around multiple sclerosis lesions by phosphorylated signal transducer and activator of transcription. J. Neuropathol. Exp. Neurol. 72, 1135–1144 (2013).

  34. 34.

    Yamasaki, R. et al. Differential roles of microglia and monocytes in the inflamed central nervous system. J. Exp. Med. 211, 1533–1549 (2014).

  35. 35.

    De Cesare, D., Jacquot, S., Hanauer, A. & Sassone-Corsi, P. Rsk-2 activity is necessary for epidermal growth factor-induced phosphorylation of CREB protein and transcription of c-fos gene. Proc. Natl Acad. Sci. USA 95, 12202–12207 (1998).

  36. 36.

    Ginhoux, F. & Merad, M. [Microglia arise from extra-embryonic yolk sac primitive progenitors]. Med. Sci. (Paris) 27, 719–724 (2011).

  37. 37.

    Le Varlet, B., Staquet, M. J., Dezutter-Dambuyant, C., Delorme, P. & Schmitt, D. In vitro adhesion of human epidermal Langerhans cells to laminin and fibronectin occurs through beta 1 integrin receptors. J. Leukoc. Biol. 51, 415–420 (1992).

  38. 38.

    Sobel, R. A. & Mitchell, M. E. Fibronectin in multiple sclerosis lesions. Am. J. Pathol. 135, 161–168 (1989).

  39. 39.

    Boroujerdi, A., Welser-Alves, J. V. & Milner, R. Extensive vascular remodeling in the spinal cord of pre-symptomatic experimental autoimmune encephalomyelitis mice; increased vessel expression of fibronectin and the α5β1 integrin. Exp. Neurol. 250, 43–51 (2013).

  40. 40.

    Estes, M. L. & McAllister, A. K. Alterations in immune cells and mediators in the brain: it’s not always neuroinflammation! Brain Pathol. 24, 623–630 (2014).

  41. 41.

    Salter, M. W. & Beggs, S. Sublime microglia: expanding roles for the guardians of the CNS. Cell 158, 15–24 (2014).

  42. 42.

    Obrietan, K. & Hoyt, K. R. CRE-mediated transcription is increased in Huntington’s disease transgenic mice. J. Neurosci. 24, 791–796 (2004).

  43. 43.

    Bannerman, P. G. et al. Motor neuron pathology in experimental autoimmune encephalomyelitis: studies in THY1-YFP transgenic mice. Brain 128, 1877–1886 (2005).

  44. 44.

    Steinman, L. Multiple sclerosis: a two-stage disease. Nat. Immunol. 2, 762–764 (2001).

  45. 45.

    Silvestroni, A., Faull, R. L., Strand, A. D. & Möller, T. Distinct neuroinflammatory profile in post-mortem human Huntington’s disease. Neuroreport 20, 1098–1103 (2009).

  46. 46.

    Zhou, Z., Peng, X., Insolera, R., Fink, D. J. & Mata, M. Interleukin-10 provides direct trophic support to neurons. J. Neurochem. 110, 1617–1627 (2009).

  47. 47.

    Ransohoff, R. M. A polarizing question: do M1 and M2 microglia exist? Nat. Neurosci. 19, 987–991 (2016).

  48. 48.

    Mildner, A. et al. CCR2+Ly-6Chi monocytes are crucial for the effector phase of autoimmunity in the central nervous system. Brain 132, 2487–2500 (2009).

  49. 49.

    Steinman, L. The discovery of natalizumab, a potent therapeutic for multiple sclerosis. J. Cell Biol. 199, 413–416 (2012).

  50. 50.

    Yednock, T. A. et al. Prevention of experimental autoimmune encephalomyelitis by antibodies against α4β1 integrin. Nature 356, 63–66 (1992).

  51. 51.

    Linthicum, D. S., Munoz, J. J. & Blaskett, A. Acute experimental autoimmune encephalomyelitis in mice. I. Adjuvant action of Bordetella pertussis is due to vasoactive amine sensitization and increased vascular permeability of the central nervous system. Cell. Immunol. 73, 299–310 (1982).

  52. 52.

    Hofstetter, H. H., Shive, C. L. & Forsthuber, T. G. Pertussis toxin modulates the immune response to neuroantigens injected in incomplete Freund’s adjuvant: induction of Th1 cells and experimental autoimmune encephalomyelitis in the presence of high frequencies of Th2 cells. J. Immunol. 169, 117–125 (2002).

  53. 53.

    Hou, W. et al. Pertussis toxin enhances Th1 responses by stimulation of dendritic cells. J. Immunol. 170, 1728–1736 (2003).

  54. 54.

    Chen, X. et al. Pertussis toxin as an adjuvant suppresses the number and function of CD4+CD25+ T regulatory cells. Eur. J. Immunol. 36, 671–680 (2006).

  55. 55.

    Weber, M. S. et al. Repetitive pertussis toxin promotes development of regulatory T cells and prevents central nervous system autoimmune disease. PLoS One 5, e16009 (2010).

  56. 56.

    Goldmann, T. et al. Origin, fate and dynamics of macrophages at central nervous system interfaces. Nat. Immunol. 17, 797–805 (2016).

  57. 57.

    Pino, P. A. & Cardona, A. E. Isolation of brain and spinal cord mononuclear cells using Percoll gradients. J. Vis. Exp. https://doi.org/10.3791/2348 (2011).

  58. 58.

    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).

  59. 59.

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

  60. 60.

    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).

  61. 61.

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

  62. 62.

    Spitzer, M. H. et al. An interactive reference framework for modeling a dynamic immune system. Science 349, 1259425 (2015).

Download references

Acknowledgements

We thank O. Butovsky (Harvard University) for the generous gift of 4D4 and FCRLS antibody. We thank A. Trejo and A. Jager for mass cytometry quality control and maintenance, and S. Douglas and V. Giangarra for technical assistance. This work was supported by grants to G.P.N.: U19 AI057229, 1U19AI100627, Department of Defense (CDMRP), Northrop-Grumman Corporation, R01CA184968, 1R33CA183654-01, R33CA183692, 1R01GM10983601, 1R21CA183660, 1R01NS08953304, OPP1113682, 5UH2AR067676, 1R01CA19665701, R01HL120724. G.P.N. is supported by the Rachford & Carlotta A. Harris Endowed Chair. B.A was supported by a three-year postdoctoral fellowship from the Canadian Institute for Health Research (201102MFE-246400-166230).

Author information

Affiliations

  1. Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA

    • Bahareh Ajami
    • , Peggy P. Ho
    •  & Lawrence Steinman
  2. Baxter Laboratory in Stem Cell Biology, Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, CA, USA

    • Nikolay Samusik
    • , Zach Bjornson
    • , Wendy J. Fantl
    •  & Garry P. Nolan
  3. Institute of Neuropathology, Medical Faculty, University of Freiburg, Freiburg, Germany

    • Peter Wieghofer
    •  & Marco Prinz
  4. Institute of Anatomy, University of Leipzig, Leipzig, Germany

    • Peter Wieghofer
  5. Department of Cellular and Molecular Medicine, University of California, San Diego, La Jolla, CA, USA

    • Andrea Crotti
  6. BIOSS Centre for Biological Signalling Studies, University of Freiburg, Freiburg, Germany

    • Marco Prinz

Authors

  1. Search for Bahareh Ajami in:

  2. Search for Nikolay Samusik in:

  3. Search for Peter Wieghofer in:

  4. Search for Peggy P. Ho in:

  5. Search for Andrea Crotti in:

  6. Search for Zach Bjornson in:

  7. Search for Marco Prinz in:

  8. Search for Wendy J. Fantl in:

  9. Search for Garry P. Nolan in:

  10. Search for Lawrence Steinman in:

Contributions

B.A. conceived the study; designed, directed and performed all experiments; analyzed and interpreted the data; and wrote the manuscript. N.S. developed the analysis algorithms and carried out analysis. P.W. and Z.B. designed experiments, performed data analysis and interpretation. P.P.H. designed experiments, performed data analysis and interpretation, and wrote the manuscript. A.C. provided advice on data analysis and interpretation and wrote the manuscript. M.P., W.J.F. and G.P.N. provided important advice on experimental design and data analysis and interpretation. L.S. conceived the study, directed the project and wrote the manuscript.

Competing interests

The authors declare no competing interests.

Corresponding authors

Correspondence to Bahareh Ajami or Lawrence Steinman.

Integrated supplementary information

  1. Supplementary Figure 1 Composite minimum spanning tree (MST) of CNS samples reveals distinct CD45+ immune cell populations.

    Composite MST represents X-shift clusters constructed by combining CNS samples from all the disease conditions and their biological replicates (n = 37). All the samples in this experiment were barcoded and analyzed together on CyTOF (Methods). All CD45pos immune cells are circled in the center panel of this figure. Lymphocytes are depicted to the left, with clusters of CD3, CD4, and B220 populations circled in individual panels. CD11b myeloid cells are shown to the right, with clusters of Ly6G, Ly6C and CD11c populations circled in individual panels. The color code shows the expression level (zero to high) of each expression marker in each panel.

  2. Supplementary Figure 2 Expression of CD86 and CD39 in CNS-resident myeloid population.

    Composite MSTs (n = 37) of CNS samples demonstrate CD86 and CD39 expression on populations A, B, C. a) The color code shows the expression level of CD86. b) The color code shows the expression level of CD39.

  3. Supplementary Figure 3 Comparisons of the expression of several markers in three CNS-resident myeloid populations.

    a) Similarities. Populations A, B, and C expressed different levels of CD88, MHC class I (H-2), TAM receptor tyrosine kinases Mer (MerTK), and the newly introduced microglia markers 4D4 and FCRLS. This panel represents data from Peak EAE from 10 independent experiments. b) Variations. Differential expression of a number of markers were detected in three CNS-resident myeloid populations. Populations B and C expressed different levels of CD80, TAM receptor Axl, T-cell immunoglobulin mucin protein 4 (TIM4), CD274 (PD-L1), CD195 (CCR5), CD194 (CCR4), and low levels of CD206 and TREM2. Population A lacked the expression of all these markers. This panel represents data from Peak EAE from 10 independent experiments.

  4. Supplementary Figure 4 Variation in expression of several markers depending on disease conditions.

    MSTs from each disease condition (n = 5) demonstrates the expression level of a) CD80 and CD274 (PD-L1); b) CD194 (CCR4) and CD195 (CCR5); and c) TAM receptor Axl and T-cell immunoglobulin mucin protein 4 (TIM4).

  5. Supplementary Figure 5 Expression of FCRLS and 4D4 in CNS-resident myeloid populations.

    Composite MSTs of CNS samples (n = 37) and MSTs from each disease condition (n = 5) demonstrate that FCRLS and 4D4 (new antibodies that mark microglia) are only expressed on CNS-resident myeloid populations A, B, and C. a) The color code shows the expression level of FCRLS. b) The color code show the expression level of 4D4.

  6. Supplementary Figure 6 CNS-resident myeloid populations under healthy conditions.

    a) Expression of YFP. In healthy conditional Cx3cr1CreER Rosa26-YFP mice, populations A and B (the only two populations that exist in the healthy condition) were manually gated and the expression of YFP was confirmed. The gating strategy is described in Fig. 2b. Cells were stained with a similar CyTOF panel as the rest of the experiment. GFP conjugated antibody was used to detect YFP. Histogram from a representative experiment (n = 5). b) MSTs from healthy condition wild-type mice (n = 5) suggests that populations A and B are the two prominent CNS-resident myeloid populations. Population C was barely detectable in the healthy CNS.

  7. Supplementary Figure 7 Expression of CD11b and CD11c under different disease conditions.

    a) Expression of CD11c in population C under different disease conditions. MSTs from different disease conditions (n = 5) demonstrates that CD11c is only expressed in population C among the three CNS-resident myeloid populations. CD11c expression in population C is only detected in the active phase of the EAE disease (presymptomatic, onset and peak). This marker is not expressed in population C in end-stage HD, chronic EAE, or recovered EAE. b) Expansion rate of each CD11b CNS-resident myeloid population under each disease condition. Representative biaxial dot plot showing the fraction of Ki-67+ proliferating cells within each CNS-resident myeloid population under each disease condition (n = 5).

  8. Supplementary Figure 8 Expression of intracellular cytokine levels in the CNS-resident myeloid population by disease condition.

    a) Distribution plots (Violin plots) shows the expression levels of indicated intracellular cytokines, grouped by disease condition and in each CNS-resident myeloid population. The gating strategy for each population is described in Fig. 2b. Plots were created in Mathematica. This plot is a representative from three independent experiments. b) Box-and-whisker charts showing the same distribution of intracellular cytokine levels in individual cells, grouped by cluster and disease state. The distribution plots (Violin plots from above) have been converted to box-and-whisker charts, which allow for quantitative evaluation of the median and quantiles of cytokine expression for the individual cells. Center line is median; boxes extend to 25th and 75th quantile; whiskers extend to 1.5x the interquartile range. This chart is a representative from three independent experiments.

  9. Supplementary Figure 9 Single-cell analysis of cytokine production in CNS-resident myeloid populations in early time points of HD.

    a) Frequency of populations A, B, and C based on manual gating (Fig. 2b) confirms that populations A, B, and C are present in early time points of HD. Center line is average; boxes extend to 25th and 75th percentile; whiskers extend to 5th and 95th percentiles. This graph presents data from four independent experiments. b) Single-cell analysis of cytokine production by different CNS-resident myeloid populations in early time points of HD. X-shift analysis of the co-expression of cytokines in CNS-resident myeloid populations suggests that each population contains heterogeneous subsets depending on each disease conditions. Percentages of single-cells expressing zero, one or two cytokines are represented in a stacked bar graph. This graph presents data from four independent experiments.

  10. Supplementary Figure 10 Single-cell analysis of cytokine production in CNS-resident myeloid populations in mSOD1 mice, the transgenic model of amyotrophic lateral sclerosis (ALS).

    a) Frequency of populations A, B, and C based on manual gating (Fig. 2b) confirms that populations A, B, and C are present in ALS. Center line is average; boxes extend to 25th and 75th percentile; whiskers extend to 5th and 95th percentiles. mSOD1 onset represents six independent experiments, mSOD1 disease end stage represents five independent experiments. b) Single-cell analysis of cytokine production by different CNS-resident myeloid populations in ALS. X-shift analysis of the co-expression of cytokines in CNS-resident myeloid populations reveals that each population contains heterogeneous subsets depending on each disease conditions. Percentages of single-cells expressing zero, one or two cytokines are represented in a stacked bar graph. This graph presents data from four independent experiments.

  11. Supplementary Figure 11 Variation in expression of several markers in five peripheral monocyte populations.

    Differential expression of a number of markers were detected in peripheral monocyte populations. Populations D and E compared to the other three populations have a higher expression of phagocytic receptors like TAM receptor tyrosine kinases (Mer, Axl), costimulatory molecules (CD80, CD86), receptors involved in purinergic signaling (CD38, CD39), and TREM2 as well as CD206. This panel represents data from Peak EAE from 10 independent experiments.

  12. Supplementary Figure 12 Expression of different cytokines in five peripheral monocyte populations.

    a) Distribution plots (Violin plots) shows the expression levels of indicated intracellular cytokines grouped by disease condition and in each peripheral monocyte population. The gating strategy for each populations is described in Fig. 4b. Plots were created in Mathematica. This plot is a representative from three independent experiments. b) Box-and-whisker charts showing the same distribution of intracellular cytokine levels in individual cells, grouped by cluster and disease state. The distribution plots (Violin plots from above) have been converted to box-and-whisker charts, which allow quantitative evaluation of the median and quantiles of cytokine expression for the individual cells. Center line is median; boxes extend to 25th and 75th quantile, whiskers extend to 1.5x the interquartile range. This chart is a representative from three independent experiments.

  13. Supplementary Figure 13 Confirmation of samples by CyTOF.

    a) Expression of YFP in peripheral blood cells. The lack of YFP expression in peripheral blood cells of conditional Cx3cr1CreER Rosa26-YFP mice was confirmed by CyTOF. Both peripheral blood cells and CNS were stained with a similar CyTOF panel. Histogram is a representative experiment (n = 5). b) Gating strategy on CyTOF samples for singlets and live/dead. Samples are labeled with iridium nucleic acid (intercalator 191/193Ir DNA intercalator, DVS Sciences/Fluidigm, Markham, ON). For each event, many features are recorded, including signal duration (called Event Length) and iridium intensity. Single events have lower iridium intensity (since they have less DNA) and lower Event Length values compared to aggregates. These characteristics enable gating of single cells (singlets). Live cells are identified by the lack of cleaved poly-(ADP)-ribose polymerase (c-PARP) binding as previously reported60.

Supplementary information

  1. Supplementary Text and Figures

    Supplementary Figures 1–13 and Supplementary Tables 1 and 2

  2. Life Sciences Reporting Summary

About this article

Publication history

Received

Accepted

Published

DOI

https://doi.org/10.1038/s41593-018-0100-x

Further reading