High-dimensional analysis of the murine myeloid cell system

Abstract

Advances in cell-fate mapping have revealed the complexity in phenotype, ontogeny and tissue distribution of the mammalian myeloid system. To capture this phenotypic diversity, we developed a 38-antibody panel for mass cytometry and used dimensionality reduction with machine learning–aided cluster analysis to build a composite of murine (mouse) myeloid cells in the steady state across lymphoid and nonlymphoid tissues. In addition to identifying all previously described myeloid populations, higher-order analysis allowed objective delineation of otherwise ambiguous subsets, including monocyte-macrophage intermediates and an array of granulocyte variants. Using mice that cannot sense granulocyte macrophage–colony stimulating factor GM-CSF (Csf2rb−/−), which have discrete alterations in myeloid development, we confirmed differences in barrier tissue dendritic cells, lung macrophages and eosinophils. The methodology further identified variations in the monocyte and innate lymphoid cell compartment that were unexpected, which confirmed that this approach is a powerful tool for unambiguous and unbiased characterization of the myeloid system.

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Figure 1: Mass cytometry analysis of mouse lung myeloid cell subsets defined by traditional criteria.
Figure 2: tSNE analysis objectively delineates myeloid cell subsets of lung, spleen and bone marrow.
Figure 3: tSNE-guided analysis of ambiguous lung cell populations.
Figure 4: tSNE-guided analysis of tissue-resident granulocytes.
Figure 5: Myeloid cell populations integrated across eight tissues.
Figure 6: Broad comparison of myeloid cell subset composition in C57BL/6 and Csf2rb−/− mice.

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Acknowledgements

The authors thank the SIgN community, the SIgN Flow Cytometry Facility and members of E.W.N.'s, F.G.'s and B.B.'s labs, and L.G. Ng for helpful discussion. Some antibodies used for generating the mass cytometry panels were provided by R. Balderas and A. Tiong (Becton Dickinson). The antibodies generated by BioXcell were provided for this analysis free of charge. B.B. performed this work while on sabbatical at A*STAR/SIgN. Supported by A*STAR/SIgN (P.R.-C., F.G., M.P., E.W.N.) and the Swiss National Science Foundation (PP03P3_144781 (M.G.), 316030_150768, 310030_146130 and CRSII3_136203 (B.B.)), European Union FP7 project TargetBraIn, NeuroKine, Advanced T-cell Engineered for Cancer Therapy (ATECT) and the University Research Priority Project 'Translational Cancer Research' (B.B.).

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Authors

Contributions

E.W.N. and B.B. designed the experiments and directed the study jointly. B.B., A.S., F.M., H.S., K.T., and D.L. performed the experiments. B.B., A.S., J.C., F.M., H.S., K.T., M.G., F.G. and E.W.N. analyzed the data and generated the figures. C.R., P.R.-C., M.P. and M.G. provided reagents and/or critical analysis support. B.B., F.G., A.S., J.C. and E.W.N. wrote the manuscript.

Corresponding authors

Correspondence to Burkhard Becher or Evan W Newell.

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The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Examples of staining for each antibody used for the mass cytometry analysis.

To illustrate the functionality of each antibody probe, representative plots illustrating the expected staining profiles of each antibody are shown. Cells gated as shown (annotated above each plot) are represented as two parameter dot plots.

Supplementary Figure 2 DensVM analysis flowchart.

This flow chart describes how tSNE transformed data are clustered using the DensVM approach. Using the first two dimensions of tSNE map coordinates, a peak detection algorithm is applied that results in some cells assigned to clusters as previously described (see methods). These classified events together with their 38-antibody expression profiles are used to train Support Vector Machine (SVM) algorithm. Using remaining unclassified cells are then classified based on the SVM leaving an output with all cells classified.

Supplementary Figure 3 tSNE analysis of an independently collected data set comprising four mice and six tissues (spleen, liver, mesenteric lymph nodes, lung, bone marrow, brain).

Two C57Bl6 wild-type mice and two Csf2rb−/− mice were analyzed as described for the main dataset resulting in a new two-dimensional tSNE map of all myeloid cells derived from all tissues of these four mice. (a, b) As in Figure 2 manual traditional gating was applied to cell events to annotate the major clusters of cells derived from (a) lung and (b) bone marrow. For cluster 6-, 8- and 28-like cells, manual gating as illustrated in Figure 3a was used to annotate these cells. Note that the dominant clusters of cells display similar arrangements as in Fig. 2. (c) As in Figure 6a, the distributions and composition of lung cells are compared between two C57Bl6 wildtype mice and two Csf2rb−/− mice. Arrows point to clusters of cells (corresponding to alveolar macrophages in magenta, cluster 6-like eosinophils in orange, and cluster 28-like innate lymphoid cells in black) where reductions in frequencies were observed in Csf2rb−/− mice as described and further validated in Figure 6.

Supplementary Figure 4 tSNE analysis objectively delineates myeloid cell subsets of lymph node, thymus and liver.

As in figure 2, cells are plotted as tSNE composite dimensions 1 vs. 2. In the left panels, cells are colored according to traditional/biased definitions using gating strategies similar to that in Figure 1. gray color indicates cells that are remaining and unaccounted for by these definitions and predominant clusters are indicated with arrows. Cells in the right panels are colored according to their unbiased cluster designation (see results). Notable clusters including those that correspond to subsets not accounted for by traditional gating are labeled with cluster numbers. Frequencies as percentage of total CD45+CD90-CD19-CD3- are shown for each subset.

Supplementary Figure 5 Use of fluorescence flow cytometry–based cell sorting to analyze the morphology of ambiguous cell subsets delineated by higher-order analysis.

(a) For flow cytometric cell sorting of cells from lung tissue, matching characteristics of cluster 8 (eosinophils), cluster 6 (Ly6C- monocytes) and cluster 28 (NK cells) were gated and sorted at shown. Representative Geimsa stained images for each are shown in Figure 3. (b) For flow cytometric cell sorting of bone marrow, cells representative of cluster 4 (Ly6G+ neutrophils), cluster 11 and 12 (Ly6C+ monocytes, clusters 11 and 12 not distinguished by these fluorescent markers), and cluster 5 (Ly6G-Ly6G/C+Ly6Cint granulocyte precursors) were gated as shown. Clusters 5 and 11-12 differ in the expression of CD48 as shown in the histogram on the right. Representative Giemsa stained cells are shown for cluster 11-12 (Ly6C+ monocytes). Images of clusters 4 and 5 (neutrophils and granulocyte precursors) in are shown in Figure 4. (c) For flow cytometric cell sorting of thymus, cells representative of clusters 18-21 (DCs), cluster 11-12 (Ly6C+ monocytes) and cluster 7 (CD11c+ eosinophils) were gated as shown, sorted, mounted and subsequently Giemsa stained. Representative cells from cluster 18-21 (DCs) and clusters 11-12 (Ly6C+ monocytes) are shown. A representative cell from cluster 7 (eosinophil) is shown in Figure 3. (d) Additional fluorescence flow cytometry analysis of a thymus-derived ambiguous cell subset. Representative plots used to identify cluster 11-12 (Ly6C+ monocytes, red) and cluster 7 (CD11c+ eosinophils, green) are shown. Parallel staining of Siglec-F is also shown. Histograms of Siglec-F staining cells derived from red and green gates are also shown, which demonstrate strong Siglec-F staining on cells similar to those identified as cluster 7.

Supplementary Figure 6 tSNE analysis used to compare cellular composition of tissues derived from individual Csf2rb−/− and Csf2rb+/+ mice.

(a-c) As in figure 6a, bone marrow-, spleen-, and liver-derived cells color-coded by DensVM cluster number are plotted by tSNE scores. For each panel, the three upper plots represent three replicate C57Bl6 wild-type tissue cells and the three lower plots represent three Csf2rb−/− tissue cells run in parallel.

Supplementary Figure 7 Biaxial plots comparing Csf2rb−/− and Csf2rb+/+ lung myeloid subsets.

(a) Standard biaxial plots to were used to delineate lung alveolar macrophages (CD11c+MHC II-/low) and their quantities were compared between Csf2rb−/− vs. Csf2rb+/+ mice (Average ± S.E.M. frequencies are provided, n=3 mice each). (b) Based on the gate in (a) to exclude AM and DCs, frequencies of Ly6C-Ly6G- (non-monocyte, non-neutrophils) were quantified. These cells are composed of both eosinophils (cluster 6) and NK cells (cluster 28), both found to be reduced in Csf2rb−/− mice and here their frequencies are compared between Csf2rb−/− vs. Csf2rb+/+ mice (Average ± S.E.M. frequencies are provided, n=3 mice each).

Supplementary Figure 8 Comparison of granulocyte composition in various tissues derived from Csf2rb−/− and Csf2rb+/+ mice.

(a) As in figure 4, the five different populations of neutrophil-like cells and two different populations of eosinophil-like cells were used for ISOMAP dimensionality reduction analysis to compare their overall phenotypic relatedness. For each cell cluster (from Csf2rb+/+ tissues, left and Csf2rb−/− tissues, right), the median values in ISOMAP dimension 1 are plotted against the relative frequencies of each cluster for each tissue (expressed as percentage of total granulocytes for each tissue).

Supplementary Figure 9 Florescence flow cytometric gating strategy to quantify and validate changes in composition observed in tissues from Csf2rb−/− (bottom) and Csf2rb+/+ mice (top).

(a) Representative plots are shown as they were used to identify cells corresponding to cluster 16 (MHCIIlo/CD11c+: AM), cluster 28 was interrogated by further gating on CD45+MHCIILy6Clow/–Ly6GCD11blow (NK cells) and clusters 6 and 8 were further gated on by F4/80+CD11b+ and specifically interrogated for CD11c (Cluster 8: Ly6Clow monocytes) and CD24 (Cluster 6: eosinophils). The same gating strategy was used to analyze (b) liver and (c) spleen. In panels b and c, for cluster 28, we further excluded more abundant CD3+ and B220+ cells.

Supplementary Figure 10 Comparison of DC composition in lung tissue derived from Csf2rb−/− and Csf2rb+/+ mice.

(a) To compare CD103 expression in the DC compartment of Csf2rb−/− vs. Csf2rb+/+ CD11c+MHCII+ cells are shown. Note, reduction of CD103 expression on CD11b- cells in Csf2rb-/- lung tissue. (b) Gating strategy to identify CD11b/CD24+ (also referred to as CD103+ DCs red), CD11b+CD24+CD301+ DCs (green) and CD11b+CD24CD301 interstitial macrophages (orange). (c) To specifically focus on DC phenotypic profiles, tSNE analysis was repeated using CD11c+MHCII+ cells derived from spleen, liver, bone marrow and lung. From this analysis two new tSNE dimensions were used to segregate lung DCs with better resolution. To annotate this representation, cells gated in (a) were color coded and overlaid on tSNE contour plots (CD11b-CD24+ DCs in red, CD11b+CD24-CD301+ DCs in green and CD11b+CD24-CD301- interstitial macrophages in orange). Note the prominence of the two DC subsets in Csf2rb+/+ mostly lost in Csf2rb−/− replaced by a prominent population of interstitial macrophage-like cells and an additional unknown population. (d) To compare the phenotypes of the four cell populations defined by zoomed-in tSNE analysis in (c), the median intensities of each marker for each population are summarized as a heat-plot.

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Becher, B., Schlitzer, A., Chen, J. et al. High-dimensional analysis of the murine myeloid cell system. Nat Immunol 15, 1181–1189 (2014). https://doi.org/10.1038/ni.3006

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