Social network architecture of human immune cells unveiled by quantitative proteomics

Abstract

The immune system is unique in its dynamic interplay between numerous cell types. However, a system-wide view of how immune cells communicate to protect against disease has not yet been established. We applied high-resolution mass-spectrometry-based proteomics to characterize 28 primary human hematopoietic cell populations in steady and activated states at a depth of >10,000 proteins in total. Protein copy numbers revealed a specialization of immune cells for ligand and receptor expression, thereby connecting distinct immune functions. By integrating total and secreted proteomes, we discovered fundamental intercellular communication structures and previously unknown connections between cell types. Our publicly accessible (http://www.immprot.org/) proteomic resource provides a framework for the orchestration of cellular interplay and a reference for altered communication associated with pathology.

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Figure 1: Comprehensive proteome atlas of 28 hematopoietic cell types by single shot LC-MS/MS analysis.
Figure 2: Immune cell relationships are defined by lineage-specific signature proteins.
Figure 3: Functional modules of the immune system.
Figure 4: Communication network of immune cells.
Figure 5: Intercellular connections between immune cells and tissues.
Figure 6: Dynamics of senders and receivers of biological information.
Figure 7: Cell-type- and context-dependent communication.

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Acknowledgements

We thank R. Scheltema for MS assistance, K. Mayr, I. Paron, S. Gabriele, S. Dewitz and M. Dodel for technical assistance, M. Wierer and P. Sinitcyn for support with RNAseq analysis, M. Oroshi and C. Schaab for computer and database support, A. Zychlinsky for critical review of the manuscript, and J. Geddes, A. Frauenstein, M. Phulphagar and L. Kühn for helpful discussions. The work was funded by the Max Planck Society for the Advancement of Science. R.G. was supported by a grant from the Swiss SystemsX.ch initiative, evaluated by the Swiss National Science Foundation. A.L. is supported by the Helmut Horten Foundation.

Author information

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Authors

Contributions

J.C.R., R.G., D.J. and T.W. performed flow and cell culture experiments. J.C.R. developed and implemented the bioinformatics methods. J.C.R. and F.M. conceived the data analysis and interpreted the data. D.H. assisted in data analysis and implemented the website. S.S.S. and K.K. assisted in data analysis and provided the Textbook and ImmuneXpresso data sets. F.M. and M.M. conceived the study. F.M., A.L. and F.S. supervised the experiments. F.M., J.C.R. and M.M. wrote the manuscript.

Corresponding authors

Correspondence to Matthias Mann or Felix Meissner.

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The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Representative flow cytometry scatter plots of the sorting panels for each immune cell population measured by LC-MS/MS.

Total CD4 T cells, Tregs, TH1, TH2 and TH17 were enriched with anti-CD4 coated magnetic beads; CD8 T cells with anti-CD8 coated beads, dendritic cells with anti-CD1c and anti-CD304 coated beads, B cells with anti-CD19 coated beads and NK cells with anti-CD56 coated beads.

Supplementary Figure 2 Quality measures of proteome data set.

(a) Left: Number of identified protein groups for each cell type. Total identification is displayed in black, solely MS/MS based identification is displayed in grey (NOT: steady state, ACT: activated). Right: Percent transferred identifications from other MS/MS measurements.(b) Sequence coverage, unique peptides and protein score for all identified protein group.(c) Pearson correlation coefficients between cell types replicates.(d) Comparison of protein identifications for individual cell lineages and relative coverage of relevant immune processes. Reference datasets are indicated.(e) Pearson correlation coefficients of all total proteome measurements. Samples are arranged according to their lineage relationship.(f) Heat map of protein markers used for cell sorting. Gene names are indicated in parentheses.

Supplementary Figure 3 Proteome differences and marker proteins.

(a) Proteome comparison of naive CD4 with naive CD8 T cells (Welch’s t-test, FDR<5%, S0=1). Significant proteins are marked in blue containing the lineage markers CD4 and CD8A.(b) Proteome comparison of naive CD4 with classical monocytes, respectively (Welch’s t-test, FDR<5%, S0=1). Significant proteins are marked in blue and the top 50 differentially abundant proteins are named.(c) Number of significantly (Welch’s t-test, FDR<5%, S0=1) different proteins by pairwise proteome comparison (NOT: steady state, ACT: activated).(d) Expression comparison (z-scored MaxLFQ values) of marker proteins for B cell plasma blasts (top) and for CD8 TEMRA cells (bottom). Potential unique marker proteins are indicated below the grey horizontal line. For clarity z-score values of other cell lineages were removed except for cell lineage markers (CD19, CD8B) and potential exclusive cell type markers. The isoforms of CD45 could not be resolved from the proteome analysis, therefore CD45RO and CD45RA are not included in the plot.(e) Median fluorescence intensity (MFI) of PLVAP (intracellular staining) in B cells determined by flow cytometry and normalized by isotype control MFI (n=2, two independent donors, left). Representative histograms of fluorescence intensities of isotype control and PLVAP (right).

Supplementary Figure 4 Module relationship, annotation enrichment maps and CD4 TEMRA cytotoxicity.

(a) PCA of proteins in module 29 and its representative cell types. Projections and loadings are displayed. Cell types are color-coded as in Fig. 1. Cytolytic proteins shared between CD4 TEMRA, NK and CD8 T cells are labelled in red. Expression profile of GZMB is displayed as an example.(b) Heat map of module correlation coefficients.(c) Enrichment map of module 17. Nodes are annotation terms, edges represent gene overlap between terms. Node size represent annotation enrichment, fill color indicates adjusted p value.(d) Enrichment map of module 19. Labels as in panel B.

Supplementary Figure 5 Communication network assembly and validation of novel connections.

(a) Relative coverage of immune annotated transcription factors (T), adaptor molecules (A), receptors (R), and ligands (L).(b) Histogram of intercellular interaction scores. Intercellular interaction scores of our study were reduced to cell types and cytokines identified in the textbook (light grey). Intercellular interaction scores covered by the textbook are displayed in (dark grey).(c) Intercellular interaction score cutoff (Online Methods).(d) Percentage of ingoing connections for cytokines in our study compared to ‘Textbook’ and ‘ImmuneXpresso’.(e) Understudied cell types and cytokines from ingoing connections. Significantly enriched cell types and cytokines in our study compared to ‘Textbook’ and ‘ImmuneXpresso’ were identified by Fisher’s exact test with BH FDR of 5%.(f) Resistin (RETN) secretion of B cells. B cells were stimulated with a cocktail (Goat F(ab’)2 anti-human Lambda/Kappa, F(ab’)2 Goat anti-mouse IgG Fc, CpG, anti-CD40) or LPS and anti-CD40 for 4 days and the resistin concentration in the supernatant was determined by ELISA (n=4, cell culture replicates from 2 independent donors).(g) T4 memory cells respond to IL-34 by total proteome changes. T4 memory cells were activated with aCD3/aCD28 for 2 days and then cultured for another 2 days with IL-2 in the presence of different amounts of IL-34 (0, 1, 10, or 100 ng/ml). The volcano plot (top) shows the protein differences between T4 memory cells treated with 0 ng/ml and 100 ng/ml IL-34 (significantly changed proteins are shown in blue, S0=1, FDR < 5%, n=4, cell culture replicates from 2 independent donors). Examples for significantly shifted Keyword annotations (determined by 1D annotation enrichment, BH FDR < 1%) are highlighted in violet (calcium transport) or green (immunglobulinVregion). Density distribution (bottom) show the shift of the Keyword annotations in dependency of IL-34 concentration.

Supplementary Figure 6 Transcriptome to proteome comparison.

(a) Pearson correlation coefficients of comparable cell types from RNAseq (columns) and proteome (rows) measurements. Correlation coefficients were computed from RPKM and iBAQ values (s.s.: steady state, act: activated).(b) Pearson correlation coefficients (RNAseq vs Proteome) of only matched cell type pairs.(c) Pearson correlation coefficients (Microarray vs Proteome) of protein-gene expression profiles.(d) Comparison of WGCNA modules from microarray and proteome data. Modules were matched by Pearson correlation of their module eigengenes and the best two correlating microarray (R) modules for each proteome (P) module are displayed and plotted against their gene overlap. The gene overlap represents the percentage of genes in the proteome module covered by the corresponding microarray module.(e) Comparison of intercellular receptor-ligand connections in proteome (blue) and microarray (green) data. For this comparison the intercellular signaling networks were constructed by only shared cell types.

Supplementary Figure 7 Differential protein secretion patterns and intracellular signaling networks.

(a, b) Volcano plots of secreted proteins from mDCs (a) and pDCs (b). Significantly secreted proteins are colored in blue (FDR 0.05, S0=1, n=4 from independent donors). Known cytokines are labelled in green.(c) Intracellular signaling adaptors and transcription factors (columns) for each cell type (rows) that propagate intercellular signals upon receptor ligation by proteins secreted by mDCs and pDCs.(d, e) Volcano plots of secreted proteins from classical monocytes activated with lipopolysaccharide (LPS) (d) or zymosan (ZYM) (e). Significantly secreted proteins are colored in blue (FDR 0.01, S0=1, n=5 from independent donors). Known cytokines are labelled in green.(f) Intracellular signaling adaptors and transcription factors (columns) for each cell type (rows) that propagate intercellular signals upon receptor ligation by proteins secreted by cMO.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–7, Supplementary Table 1, 2 and 12, and Supplementary Note 1 (PDF 2591 kb)

Supplementary Table 3

Quantitative proteomics data of total proteomes (XLSX 60899 kb)

Supplementary Table 4

Annotation enrichment analysis of principal components (XLSX 126 kb)

Supplementary Table 5

Annotation enrichment analysis of lineage signature genes (XLSX 54 kb)

Supplementary Table 6

Protein copy numbers of immune cell types (XLSX 10930 kb)

Supplementary Table 7

Annotation enrichment analysis of WGCNA modules (XLSX 226 kb)

Supplementary Table 8

RNAseq data of immune cell types (XLSX 2557 kb)

Supplementary Table 9

Annotation enrichment analysis of WGCNA modules microarray vs proteome (XLSX 166 kb)

Supplementary Table 10

Quantitative proteomics data of dendritic cell secreteomes (XLSX 2699 kb)

Supplementary Table 11

Quantitative proteomics data of classical monocyte secreteomes (XLSX 2239 kb)

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Rieckmann, J., Geiger, R., Hornburg, D. et al. Social network architecture of human immune cells unveiled by quantitative proteomics. Nat Immunol 18, 583–593 (2017). https://doi.org/10.1038/ni.3693

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