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Social network architecture of human immune cells unveiled by quantitative proteomics

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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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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

Affiliations

  1. Experimental Systems Immunology, Max Planck Institute of Biochemistry, Bayern, Germany.

    • Jan C Rieckmann
    • , Daniel Hornburg
    •  & Felix Meissner
  2. Institute for Research in Biomedicine, Università della Svizzera italiana, Bellinzona, Switzerland.

    • Roger Geiger
    • , Tobias Wolf
    • , David Jarrossay
    • , Federica Sallusto
    •  & Antonio Lanzavecchia
  3. Institute of Microbiology, ETH Zürich, Zürich, Switzerland.

    • Roger Geiger
    • , Tobias Wolf
    •  & Antonio Lanzavecchia
  4. Department of Immunology, Faculty of Medicine, Technion-Israel Institute of Technology, Haifa, Israel.

    • Ksenya Kveler
    •  & Shai S Shen-Orr
  5. Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Bayern, Germany.

    • Matthias Mann

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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.

Competing interests

The authors declare no competing financial interests.

Corresponding authors

Correspondence to Matthias Mann or Felix Meissner.

Integrated supplementary information

Supplementary information

PDF files

  1. 1.

    Supplementary Text and Figures

    Supplementary Figures 1–7, Supplementary Table 1, 2 and 12, and Supplementary Note 1

Excel files

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    Supplementary Table 3

    Quantitative proteomics data of total proteomes

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    Supplementary Table 4

    Annotation enrichment analysis of principal components

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    Supplementary Table 5

    Annotation enrichment analysis of lineage signature genes

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    Supplementary Table 6

    Protein copy numbers of immune cell types

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    Supplementary Table 7

    Annotation enrichment analysis of WGCNA modules

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    Supplementary Table 8

    RNAseq data of immune cell types

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    Supplementary Table 9

    Annotation enrichment analysis of WGCNA modules microarray vs proteome

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    Supplementary Table 10

    Quantitative proteomics data of dendritic cell secreteomes

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    Supplementary Table 11

    Quantitative proteomics data of classical monocyte secreteomes