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

Nature Neuroscience volume 21, pages 541–551 (2018)Cite this article

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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.

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Fig. 1: Schematic representation of the experimental strategy.
Fig. 2: Data-driven, unsupervised clustering defines three distinct CNS-resident myeloid populations.
Fig. 3: CyTOF analysis reveals the signaling and cytokine molecular signatures in the three CNS-resident myeloid populations under different clinical conditions.
Fig. 4: Kinetics of peripheral monocytes in CNS under inflammatory versus degenerative conditions.
Fig. 5: Single-cell analysis of signaling molecules and cytokine production in different peripheral monocyte populations in response to different disease conditions.
Fig. 6: CyTOF analysis reveals a therapeutic target on infiltrating myeloid cells in inflammatory conditions.

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References

  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).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  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).

    Article  CAS  PubMed  Google Scholar 

  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).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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).

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  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).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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).

    Article  CAS  PubMed  Google Scholar 

  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).

    Article  PubMed  Google Scholar 

  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).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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).

    Article  CAS  PubMed  Google Scholar 

  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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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).

    Article  CAS  PubMed  Google Scholar 

  20. 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 

  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).

    Article  CAS  PubMed  Google Scholar 

  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).

    Article  PubMed  PubMed Central  Google Scholar 

  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).

    Article  CAS  PubMed  Google Scholar 

  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).

    Article  CAS  PubMed  Google Scholar 

  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).

    Article  CAS  PubMed  Google Scholar 

  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).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  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).

    Article  CAS  PubMed  Google Scholar 

  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).

    Article  CAS  PubMed  Google Scholar 

  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).

    Article  CAS  PubMed  Google Scholar 

  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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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).

    Article  CAS  PubMed  Google Scholar 

  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).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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).

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  Google Scholar 

  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).

    Article  PubMed  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  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).

    Article  PubMed  Google Scholar 

  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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  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).

    Article  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  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).

    Article  CAS  PubMed  Google Scholar 

  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).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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. 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 

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

    Article  PubMed  PubMed Central  Google Scholar 

  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).

    Article  PubMed  Google Scholar 

  61. 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 

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

    Article  PubMed  PubMed Central  Google Scholar 

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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).

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Authors and 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

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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.

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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.

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.

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.

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).

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.

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.

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).

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.

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.

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.

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.

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.

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.

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Ajami, B., Samusik, N., Wieghofer, P. et al. Single-cell mass cytometry reveals distinct populations of brain myeloid cells in mouse neuroinflammation and neurodegeneration models. Nat Neurosci 21, 541–551 (2018). https://doi.org/10.1038/s41593-018-0100-x

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