Abstract

Microglia, the specialized innate immune cells of the CNS, play crucial roles in neural development and function. Different phenotypes and functions have been ascribed to rodent microglia, but little is known about human microglia (huMG) heterogeneity. Difficulties in procuring huMG and their susceptibility to cryopreservation damage have limited large-scale studies. Here we applied multiplexed mass cytometry for a comprehensive characterization of postmortem huMG (103 – 104 cells). We determined expression levels of 57 markers on huMG isolated from up to five different brain regions of nine donors. We identified the phenotypic signature of huMG, which was distinct from peripheral myeloid cells but was comparable to fresh huMG. We detected microglia regional heterogeneity using a hybrid workflow combining Cytobank and R/Bioconductor for multidimensional data analysis. Together, these methodologies allowed us to perform high-dimensional, large-scale immunophenotyping of huMG at the single-cell level, which facilitates their unambiguous profiling in health and disease.

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Source data associated with Figs. 47 can be accessed at https://flowrepository.org/id/FR-FCM-ZYM6.

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Acknowledgements

We thank C. Böttcher for excellent technical assistance with FACS analysis. We also acknowledge the assistance of the BCRT Flow Cytometry Lab (Charité – Universitätsmedizin Berlin, Germany). C.B. and J.P. were supported by the German Research Foundation (SFB TRR167, B05 & B07). J.P. received additional funding from the Berlin Institute of Health (CRG2aSP6) and the UK DRI (Momentum Award). S.S. was partially funded by the EU-H2020 project PACE (grant agreement number 733006). A.K. and E.P. were supported by stipends from the NeuroMac School (SFB TRR167, IRTG). A.K. received additional funding from the Cluster of Excellence NeuroCure. H.E.M. and A.R.S. were supported through grant Me3644/5-1. B.S. was supported by the German Research Foundation (SI 749/9-1, 749/10-1, CRC-TRR 241). B.S. and R.G. were supported by the Deutsche Krebhilfe (70112011). M.A.M.S. was supported by a 2014 NARSAD Young Investigator Grant from the Brain & Behavior Research Foundation and L.D.D.W. by the Virgo Consortium, funded by the Dutch government, project number FES0908. The psychiatric donor program of the Netherlands Brain Bank (NBB-Psy) is financially supported by the Netherlands Organization for Scientific Research (NWO). We acknowledge the Leibniz Science Campus for Chronic Inflammation for general support.

Author information

Author notes

  1. These authors contributed equally: Chotima Böttcher, Stephan Schlickeiser, Marjolein A. M. Sneeboer.

  2. These authors jointly supervised this work: Lot D. de Witte, Josef Priller.

Affiliations

  1. Department of Neuropsychiatry and Laboratory of Molecular Psychiatry, Charité—Universitätsmedizin Berlin, Berlin, Germany

    • Chotima Böttcher
    • , Anniki Knop
    • , Evdokia Paza
    • , Eike J Spruth
    •  & Josef Priller
  2. Berlin-Brandenburg Center for Regenerative Therapies, Berlin, Germany

    • Stephan Schlickeiser
    •  & Desiree Kunkel
  3. Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, the Netherlands

    • Marjolein A. M. Sneeboer
    • , Gijsje J. L. Snijders
    • , Elly M. Hol
    •  & Lot D de Witte
  4. Epilepsy-Center Berlin-Brandenburg, Department of Neurology, Charité – Universitätsmedizin Berlin, Berlin, Germany

    • Pawel Fidzinski
    •  & Larissa Kraus
  5. Berlin Institute of Health, Berlin, Germany

    • Larissa Kraus
    •  & Josef Priller
  6. Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    • René S Kahn
    •  & Lot D de Witte
  7. German Rheumatism Research Center, Berlin, Germany

    • Axel R Schulz
    •  & Henrik E Mei
  8. Netherlands Brain Bank, Amsterdam, the Netherlands

    • Department of Neuroimmunology, Netherlands Institute for Neuroscience, An Institute of the Royal Academy of Arts and Sciences, Amsterdam, the Netherlands

      • Elly M. Hol
    • Medical Department for Gastroenterology, Division of Gastroenterology, Infectiology and Rheumatology, Charité – Universitätsmedizin Berlin, Berlin, Germany

      • Britta Siegmund
      •  & Rainer Glauben
    • DZNE, Berlin, Germany

      • Eike J Spruth
      •  & Josef Priller
    • Cluster of Excellence NeuroCure, Berlin, Germany

      • Josef Priller
    • University of Edinburgh and UK Dementia Research Institute, Edinburgh, UK

      • Josef Priller

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    Consortia

    1. NBB-Psy

      Contributions

      C.B. and J.P. conceived and designed the project. C.B., S.S., D.K., B.S. and R.G. designed the antibody panels for mass cytometry. M.A.M.S., G.J.L.S., E.M.H., R.S.K. and L.D.D.W. established and performed the isolation of postmortem huMG. P.F. and L.K. provided biopsy tissues from temporal lobe resections. A.R.S. and H.E.M. set up the fixation approach for cryostorage of human leucocytes and provided guidance in using the system. C.B. established the protocol for cryopreservation of isolated huMG. A.K. and E.P. performed barcoding and antibody staining for CyTOF. A.K. conducted FACS analysis of postmortem huMG. D.K. performed CyTOF measurements. E.J.S. and J.P. provided peripheral blood and cerebrospinal fluid samples. C.B. and S.S. analyzed the data. C.B., S.S., L.D.D.W. and J.P. wrote the manuscript.

      Competing interests

      The authors declare no competing interests.

      Corresponding authors

      Correspondence to Chotima Böttcher or Josef Priller.

      Integrated supplementary information

      1. Supplementary Figure 1 Immunophenotypic profiling by single-cell mass cytometry.

        (a) Two-dimensional projections of single-cell data (Panel B) generated by t-SNE of PBMCs (n =4 biologically independent samples), CSF cells (n = 4 biologically independent samples) and brain mononuclear cells (n = 36 biologically independent samples). Each dot represents one cell. The color spectrum represents expression of TMEM119 (red denotes high expression, blue denotes no expression). TMEM119+ cells were gated as huMG (blue) and TMEM119- cells were gated as different circulating immune cells. (b) Representative scatter plots and histograms (two independently repeated experiments with similar results) show expression level of P2Y12 in TMEM119+ cells (upper panel) and vice versa of TMEM119 in P2Y12+ cells (lower panel). (c) The graph shows the quantitative frequencies of P2Y12+ cells in TMEM119+ cell population (P2Y12+TMEM119+, n = 20 biologically independent samples) and vice versa of TMEM119+ cells in P2Y12+cell population (TMEM119+P2Y12+, n = 20 biologically independent samples). Black lines show mean values of the data sets. The values show mean ± SD.

      2. Supplementary Figure 2 Differential marker expressions of PBMCs, CSF cells, and human microglia.

        (a) Histogram plots show expression levels of 55 markers analysed in HLA-DR+CD11c+ PBMCs (blue), CSF cells (orange) and huMG from SVZ (green), THA (red), CER (purple), GTS (brown) and GFM (pink). (b) Histogram plots compare expression levels of CD11b and CD115 of huMG and CD3+ T cells (negative for both markers). Expression levels of CD11b and CD115 of individual donors are also shown as histogram plots.

      3. Supplementary Figure 3 Differential marker expressions of PBMCs, CSF cells, and human microglia.

        Mean expression levels of selected markers in PBMCs (n = 4), CSF cells (n = 4) and huMG from SVZ (n = 8), THA (n = 8), Cer (n = 5), GTS (n = 8) and GFM (n = 7). Black lines show mean values of the data sets. Each dot represents one individual.

      4. Supplementary Figure 4 CD11c and HLA-DR expression on human microglia.

        (a) An overlaid dot plot shows the expression of HLA-DR and CD11c of PBMCs (blue), CSF cells (orange) and P2Y12+ huMG (green). (b) Individual contour plots show the expression of HLA-DR and CD11c of PBMCs, CSF cells, P2Y12+ and TMEM119+ huMG (two independently repeated experiments with similar results).

      5. Supplementary Figure 5 Differential immunophenotypes of postmortem and fresh human microglia.

        (a) Heat map and cluster dendrogram demonstrates the mean expression of all analyzed markers and relationships between postmortem GTS-huMG (green), postmortem GFM-huMG (orange) and huMG from fresh biopsies (blue). Heat colours have been scaled per marker (red denotes high and blue denotes low expression). (b) Heat map and cluster dendrogram demonstrates the mean expression of analyzed markers (excluded IRF-8 and P2Y12) and relationships between postmortem GTS-, GFM-huMG and huMG from fresh biopsies, respectively. (c) An overlaid t-SNE plot (left image) of all cells from all samples (green = postmortem GTS-huMG (n = 10), orange = postmortem GFM-huMG (n = 9) and blue = huMG from fresh biopsies (n = 3)). Two main clusters, G1 (blue) and G2 (orange), are detected. Overlaid histograms and single dot plots show mean expression levels of selected markers showing differential marker expressions between the two gates (G1 & G2 in a) in huMG from GTS (green), GFM (orange) and fresh biopsies (blue). Black lines show the mean of data sets.

      6. Supplementary Figure 6

        CD206 expression on human microglia and perivascular macrophages. (a) A dot plot graph shows mean expression levels of P2Y12 in CD206-negative huMG (filled dots) from GTS (blue, n = 8) and GFM (orange, n = 7) compared with CD206hi perivascular macrophage (pmΦ, circles) from GTS (blue, n = 8) and GFM (orange, n = 7). Black lines show the mean of data acquired by mass cytometry (CyTOF). (b) An overlaid histogram plot displays low expression of CD206 in P2Y12+ huMG from GTS (blue) and GFM (orange) showing the high autofluorescent background of postmortem samples analyzed by flow cytometry (FACS). Two experiments were independently repeated with similar results.

      7. Supplementary Figure 7 CD206 expression by a subpopulation of human microglia as assessed by antibody Panel B.

        (a) High-dimensional tSNE plots of concatenated FCS file (n = 36 biologically independent samples, Panel B). Each dot represents one cell. The color spectrum represents an expression level of CD206 (upper left), TMEM119 (upper middle), HLA-DR (upper right), CD86 (lower left) and CD36 (lower right). Red color denotes high expression, blue color denotes no expression. CD206low cell population is gated as “G1” (red circle) and “G2” (green circle). (b) Frequencies of each CD206-positive population in different brain regions (SVZ, n = 8; THA, n = 8; CER, n = 5; GTS, n = 8 & GFM, n = 7). The values of an individual donor were plotted in the same color. The black line represents the mean value.

      8. Supplementary Figure 8 Donor-specific phenotypic differences in human microglia.

        (a) The tSNE plots of concatenated FCS files from 36 huMG samples analysed by antibody Panel A with colours indicating distribution of each donor’s cells (same as in Fig. 5a). (b) Assessment of subject-to-subject variability by probability binning and bin-wise testing for donor-specific differences between 8 donors on n = 35 biologically independent samples – SVZ (n = 8); THA (n = 8); CER (n = 5); GTS (n = 8) and GFM (n =6) – (using negative binomial generalized linear model and quasi-likelihood test of edgeR). The colour spectrum indicates FDR-adjusted p-values thresholded to 0.05 (blue). Top significant bins were automatically gated (G1) to identify donor #6-specific huMG phenotype. (c) Median bin-expression levels as shown in boxplot representation for bins inside (red boxes, n = 24 bins) and outside (green boxes, n = 488 bins) of G1 and all markers indicate a specifically higher expression of CD64 and EMR1 in huMG samples of donor #6. Box center line and limits represent median, 16th and 84th percentiles; whiskers define the data minimum and maximum. (d) Heatmap representing the pairwise earth mover’s distances (EMD) between cellular density distributions among 31 huMG samples after t-SNE embedding without the 5 samples from donor #6 or after t-SNE-embedding of 36 huMG samples excluding the outlier markers CD64 and EMR1 (e). (f) The tSNE plots of concatenated FCS files (n = 31 biologically independent samples). The colouring indicates five brain regions (left image) and eight donors (middle image, without donor #6). The right image shows the smoothed representation of statistical tSNE map after thresholding to 0.05 FDR-adjusted p-values (blue). Three highly differentially abundant subsets with distinct phenotypes (indicated by arrows) were detected when donor #6 was excluded before embedding. The phenotypes of these subsets are equivalent to subset 1, 2 & 3 seen in Figs. 6 & 7, as shown with expression levels of the key markers (lower boxplot graph representing median bin-expression levels across 31 concatenated samples with subset 1 (n = 73 bins), 2 (n = 23 bins), 3 (n = 8 bins), and all cells – subsets (n = 408 bins). (g) The t-SNE plots of concatenated FCS files (n = 36 biologically independent samples). The colouring indicates five brain regions (left image) and nine donors (middle image). The tSNE embedding was performed without CD64 and EMR1. The right image shows the smoothed representation of statistical tSNE map after thresholding to 0.05 FDR-adjusted p-values (blue). Boxplots represent median bin-expression levels across 36 concatenated samples with subset 1 (n = 56 bins), 2 (n = 53 bins) and all cells – subsets (n = 403 bins). Two highly significant subsets (white lines) were detected when CD64 and EMR1 were excluded from embedding. Subset 1 is phenotypically identical to subset 1 shown in Figs. 6 & 7 with regard to co-expression of CD11c, CD45, CD68, CX3CR1, and HLA-DR, whereas the CD206+ subset 2 is equivalent to subsets 2 & 3 in Figs. 6 & 7, as shown in the boxplot graph below. For the boxplot graph shown in both Supplementary Fig. 8f, g, box center line and limits represent median, 16th and 84th percentiles; whiskers define the data minimum and maximum. (h) Comparable results (as shown in Fig. 6c and Supplementary Fig. 8f, g) are obtained when the analysis was performed using the cydar/edgeR framework for detection of differentially abundant (DA) subsets as hyperspheres in original multiparameter space (on same set of all markers or excluding CD64 and EMR1 in n = 35 biologically independent samples or in n= 30 independent samples excluding samples from donor #6). The colour spectrum indicates FDR-adjusted P-values (edgeR quasi-likelihood test) thresholded to 0.05, overlaid onto the tSNE map (non-significant hyperspheres in blue). (i) Mean signal intensity levels of Ki-67, Cyclin A and Cyclin B staining across four different defined subsets (subset 1, n = 36; subset 2, n = 34; subset 3, n = 36 and subset 4, n = 35). The black line represents the mean value. *P < 0.05, **P < 0.01, ***P < 0.001, ****P <0.0001, one-way ANOVA with Bonferroni correction.

      9. Supplementary Figure 9 Assessment of regional differences in human microglia phenotypes by probability binning using the antibody Panel B.

        The top panel shows the same tSNE map of concatenated FCS files (n = 36 biologically independent samples). The coloring in (a) indicates nine donors and five brain regions (left and right plot, respectively, legends shown in (e)), (b) overall cellular density of the concatenated files (left plot, spectrum from blue, low density, to red, high density) with a superimposed binning grid (512 bins) and unadjusted P-values (right plot) of bin-wise Skillings-Mack test for frequency differences between five brain regions, and (c) unadjusted p-values in differentially abundant hyperspheres (using cydar/edgeR framework), overlaid onto the tSNE map. The testing was performed on n = 35 independent huMG samples (SVZ (n = 8); THA (n = 8); CER (n = 5); GTS (n = 8) and GFM (n =6)) from 8 biologically independent donors. (d) tSNE plots of concatenated FCS files of each brain region. (e) Heatmap representing the pairwise earth mover’s distances (EMD) between cellular density distributions over the tSNE-space among all huMG samples. Hierarchical clustering highlights samples that have a similar phenotype, as indicated by low EMD values (top left colour bar).

      10. Supplementary Figure 10 Region-dependent human microglia subpopulations analysed by antibody Panel B.

        (a) Smoothed representation of statistical tSNE map (show in Supplementary Fig. 9b) after thresholding to 0.05 FDR-adjusted p-values (blue) shows selection of four differentially abundant subsets by automated gating (black contour lines). Skilling-Mack testing was performed on n = 35 biologically independent huMG samples (SVZ (n = 8); THA (n = 8); CER (n = 5); GTS (n = 8) and GFM (n =6)) from 8 biologically independent donors. (b) Snail plot shows mean marker expression levels of each huMG subset (subset 1 = red; 2 = orange; 3 = green and 4 = purple). The snail shell represents transversal (perpendicular) axis mapping marker expression levels on exponential scale. Each line denotes each sample (total of 36 samples). (c) Gates of four detected subsets overlaid on cell density tSNE plots of concatenated FCS files for each brain region (biologically independent samples of SVZ = 8; THA = 8; CER = 5; GTS = 8 and GFM = 7). (d) Top panel shows median bin-expression levels in boxplot representation of markers that discriminate between the four subsets (subset 1 = red box (n = 6 bins); 2 = orange box plot (n = 95 bins); 3 = green box (n = 4 bins); 4 = purple box (n = 12 bins); remaining cells (cells – subsets) = blue box (n = 395 bins) according to robust separation score. Box center line and limits represent median, 16th and 84th percentiles; whiskers define the data minimum and maximum.

      11. Supplementary Figure 11 Aged-related marker expression on human microglia.

        Scatter plots showing correlation between mean marker expression and age of all nine biologically independent donors analyzed (SVZ = 8; THA = 8; CER = 5; GTS = 8 and GFM = 7). *P < 0.05, **P < 0.01, non-parametric Spearman correlation test (r), two-sided.

      Supplementary information

      1. Supplementary Figures 1–11

        Supplementary Figs. 1–11 and Supplementary Tables 1–5

      2. Reporting Summary

      3. Supplementary Software

        Supplementary Software.

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      https://doi.org/10.1038/s41593-018-0290-2