Social network architecture of human immune cells unveiled by quantitative proteomics

Nature Immunology

Volumn 18, Issue 5, 2017, Pages 583-593

  Rieckmann, Jan C ^a   Geiger, Roger ^b,c   Hornburg, Daniel ^a   Wolf, Tobias ^b,c   Kveler, Ksenya ^d   Jarrossay, David ^b   Sallusto, Federica ^b   Shen Orr, Shai S ^d   Lanzavecchia, Antonio ^b,c   Mann, Matthias ^a   Meissner, Felix ^a  

^a MAX PLANCK INSTITUTE OF BIOCHEMISTRY   (Germany)

^b INSTITUTE FOR RESEARCH IN BIOMEDICINE   (Switzerland)

^c ETH ZURICH   (Switzerland)

^d TECHNION ISRAEL INSTITUTE OF TECHNOLOGY   (Israel)

Author keywords

[No Author keywords available]

Indexed keywords

PROTEOME;

ARTICLE; B LYMPHOCYTE; CD4+ T LYMPHOCYTE; CD8+ T LYMPHOCYTE; CELL COMMUNICATION; CELL JUNCTION; CELL POPULATION; CONTROLLED STUDY; CYTOARCHITECTURE; DENDRITIC CELL; ERYTHROCYTE; FLOW CYTOMETRY; GRANULOCYTE; HEMATOPOIETIC CELL; HUMAN; HUMAN CELL; IMMUNE SYSTEM; IMMUNOCOMPETENT CELL; MASS SPECTROMETRY; MONOCYTE; NATURAL KILLER CELL; PRIORITY JOURNAL; PROTEIN EXPRESSION; PROTEOMICS; STEADY STATE; ANIMAL; BLOOD CELL; BODILY SECRETIONS; CELLULAR IMMUNITY; COMPUTER SIMULATION; PHYSIOLOGY; PROTEIN PROTEIN INTERACTION; SOCIAL SUPPORT;

ANIMALS; BLOOD CELLS; BODILY SECRETIONS; CELL COMMUNICATION; COMPUTER SIMULATION; HUMANS; IMMUNITY, CELLULAR; MASS SPECTROMETRY; PROTEIN INTERACTION MAPS; PROTEOME; PROTEOMICS; SOCIAL SUPPORT;

EID: 85014542002     PISSN: 15292908     EISSN: 15292916     Source Type: Journal    
DOI: 10.1038/ni.3693     Document Type: Article

References (62)

1. Subramanian, N., Torabi-Parizi, P., Gottschalk, R.A., Germain, R.N. &Dutta, B. Network representations of immune system complexity. Wiley Interdiscip. Rev. Syst. Biol. Med. 7, 13-38 (2015).
2. Rivera, A., Siracusa, M.C., Yap, G.S. &Gause, W.C. Innate cell communication kick-starts pathogen-specific immunity. Nat. Immunol. 17, 356-363 (2016).
3. Hotson, A.N. et al. Coordinate actions of innate immune responses oppose those of the adaptive immune system during Salmonella infection of mice. Sci. Signal. 9, ra4 (2016).
4. Dobrin, R. et al. Multi-tissue coexpression networks reveal unexpected subnetworks associated with disease. Genome Biol. 10, R55 (2009).
5. Frankenstein, Z., Alon, U. &Cohen, I.R. The immune-body cytokine network defines a social architecture of cell interactions. Biol. Direct 1, 32 (2006).
6. Shen-Orr, S.S. et al. Towards a cytokine-cell interaction knowledgebase of the adaptive immune system. Pac. Symp. Biocomput. 2009, 439-450 (2009).
7. Heng, T.S., Painter, M.W. &Immunological Genome Project, C. The Immunological Genome Project: networks of gene expression in immune cells. Nat. Immunol. 9, 1091-1094 (2008).
8. Novershtern, N. et al. Densely interconnected transcriptional circuits control cell states in human hematopoiesis. Cell 144, 296-309 (2011).
9. 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).
10. Parnas, O. et al. A genome-wide CRISPR screen in primary immune cells to dissect regulatory networks. Cell 162, 675-686 (2015).
11. Paul, F. et al. Transcriptional heterogeneity and lineage commitment in myeloid progenitors. Cell 163, 1663-1677 (2015).
12. Schwanhausser, B. et al. Global quantification of mammalian gene expression control. Nature 473, 337-342 (2011).
13. Jovanovic, M. et al. Immunogenetics. Dynamic profiling of the protein life cycle in response to pathogens. Science 347, 1259038 (2015).
14. Meissner, F., Scheltema, R.A., Mollenkopf, H.J. &Mann, M. Direct proteomic quantification of the secretome of activated immune cells. Science 340, 475-478 (2013).
15. Altelaar, A.F., Munoz, J. &Heck, A.J. Next-generation proteomics: towards an integrative view of proteome dynamics. Nat. Rev. Genet. 14, 35-48 (2013).
16. Aebersold, R. &Mann, M. Mass-spectrometric exploration of proteome structure and function. Nature 537, 347-355 (2016).
17. Meissner, F. &Mann, M. Quantitative shotgun proteomics: considerations for a high-quality workflow in immunology. Nat. Immunol. 15, 112-117 (2014).
18. Nagaraj, N. et al. System-wide perturbation analysis with nearly complete coverage of the yeast proteome by single-shot ultra HPLC runs on a bench top Orbitrap. Mol. Cell Proteomics 11, M111 013722 (2012).
19. Schaab, C., Geiger, T., Stoehr, G., Cox, J. &Mann, M. Analysis of high accuracy, quantitative proteomics data in the MaxQB database. Mol. Cell Proteomics 11, M111 014068 (2012).
20. Scheltema, R.A. et al. The Q Exactive HF, a Benchtop mass spectrometer with a pre-filter, high-performance quadrupole and an ultra-high-field Orbitrap analyzer. Mol. Cell. Proteomics 13, 3698-3708 (2014).
21. Cox, J. &Mann, M. MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat. Biotechnol. 26, 1367-1372 (2008).
22. Cox, J. et al. Accurate proteome-wide label-free quantification by delayed normalization and maximal peptide ratio extraction, termed MaxLFQ. Mol. Cell. Proteomics 13, 2513-2526 (2014).
23. Kim, M.S. et al. A draft map of the human proteome. Nature 509, 575-581 (2014).
24. Jojic, V. et al. Identification of transcriptional regulators in the mouse immune system. Nat. Immunol. 14, 633-643 (2013).
25. Cox, J. &Mann, M. 1D and 2D annotation enrichment: a statistical method integrating quantitative proteomics with complementary high-throughput data. BMC Bioinformatics 13 (Suppl. 16), S12 (2012).
26. Vivier, E., Tomasello, E., Baratin, M., Walzer, T. &Ugolini, S. Functions of natural killer cells. Nat. Immunol. 9, 503-510 (2008).
27. Zhang, N. &Bevan, M.J. CD8(+) T cells: foot soldiers of the immune system. Immunity 35, 161-168 (2011).
28. Wisniewski, J.R., Hein, M.Y., Cox, J. &Mann, M. A 'proteomic ruler' for protein copy number and concentration estimation without spike-in standards. Mol. Cell. Proteomics 13, 3497-3506 (2014).
29. Friedman, J., Hastie, T. &Tibshirani, R. Regularization paths for generalized linear models via coordinate descent. J. Stat. Softw. 33, 1-22 (2010).
30. Bottcher, J.P. et al. Functional classification of memory CD8+ T cells by CX3CR1 expression. Nat. Commun. 6, 8306 (2015).
31. Langfelder, P. &Horvath, S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 9, 559 (2008).
32. Bieber, K. &Autenrieth, S.E. Insights how monocytes and dendritic cells contribute and regulate immune defense against microbial pathogens. Immunobiology 220, 215-226 (2015).
33. Appay, V. The physiological role of cytotoxic CD4+ T cells: the holy grail? Clin. Exp. Immunol. 138, 10-13 (2004).
34. Gordon, S.M. et al. The transcription factors T-bet and Eomes control key checkpoints of natural killer cell maturation. Immunity 36, 55-67 (2012).
35. Townsend, M.J. et al. T-bet regulates the terminal maturation and homeostasis of NK and Valpha14i NKT cells. Immunity 20, 477-494 (2004).
36. Bezman, N.A. et al. Molecular definition of the identity and activation of natural killer cells. Nat. Immunol. 13, 1000-1009 (2012).
37. Albrecht, I. et al. Persistence of effector memory Th1 cells is regulated by Hopx. Eur. J. Immunol. 40, 2993-3006 (2010).
38. Sharma, K. et al. Cell-type-and brain-region-resolved mouse brain proteome. Nat. Neurosci. 18, 1819-1831 (2015).
39. Azimifar, S.B., Nagaraj, N., Cox, J. &Mann, M. Cell-type-resolved quantitative proteomics of murine liver. Cell Metab. 20, 1076-1087 (2014).
40. Szklarczyk, D. et al. STRING v10: protein-protein interaction networks, integrated over the tree of life. Nucleic Acids Res. 43, D447-D452 (2015).
41. Dembic, Z. The Cytokines of the Immune System: The Role of Cytokines in Disease Related to Immune Response (Elsevier Science, 2015).
42. Uhlen, M. et al. Proteomics. Tissue-based map of the human proteome. Science 347, 1260419 (2015).
43. Gerstein, M.B. et al. Architecture of the human regulatory network derived from ENCODE data. Nature 489, 91-100 (2012).
44. Forster, R., Davalos-Misslitz, A.C. &Rot, A. CCR7 and its ligands: balancing immunity and tolerance. Nat. Rev. Immunol. 8, 362-371 (2008).
45. Ponath, P.D. et al. Molecular cloning and characterization of a human eotaxin receptor expressed selectively on eosinophils. J. Exp. Med. 183, 2437-2448 (1996).
46. Kim, C.C. &Lanier, L.L. Beyond the transcriptome: completion of act one of the Immunological Genome Project. Curr. Opin. Immunol. 25, 593-597 (2013).
47. Brosseron, F. et al. Stepwise isolation of human peripheral erythrocytes, T lymphocytes, and monocytes for blood cell proteomics. Proteomics Clin. Appl. 6, 497-501 (2012).
48. Trichler, S.A., Bulla, S.C., Thomason, J., Lunsford, K.V. &Bulla, C. Ultra-pure platelet isolation from canine whole blood. BMC Vet. Res. 9, 144 (2013).
49. Scheltema, R.A. &Mann, M. SprayQc: a real-time LC-MS/MS quality monitoring system to maximize uptime using off the shelf components. J. Proteome Res. 11, 3458-3466 (2012).
50. Cox, J. et al. Andromeda: a peptide search engine integrated into the MaxQuant environment. J. Proteome Res. 10, 1794-1805 (2011).
51. Tyanova, S., Temu, T. &Cox, J. The MaxQuant computational platform for mass spectrometry-based shotgun proteomics. Nat. Protoc. 11, 2301-2319 (2016).
52. Tyanova, S. et al. The Perseus computational platform for comprehensive analysis of (prote)omics data. Nat. Methods 13, 731-740 (2016).
53. RStudio, I. Shiny: Easy web applications in R (2014).
54. Team, R.D.C. R: a Language and Environment for Statistical Computing (2008).
55. Gentleman, R.C. et al. Bioconductor: open software development for computational biology and bioinformatics. Genome Biol. 5, R80 (2004).
56. Tusher, V.G., Tibshirani, R. &Chu, G. Significance analysis of microarrays applied to the ionizing radiation response. Proc. Natl. Acad. Sci. USA 98, 5116-5121 (2001).
57. Ashburner, M. et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat. Genet. 25, 25-29 (2000).
58. Kanehisa, M., Goto, S., Sato, Y., Furumichi, M. &Tanabe, M. KEGG for integration and interpretation of large-scale molecular data sets. Nucleic Acids Res. 40, D109-D114 (2012).
59. Magrane, M. &UniProt, C. UniProt Knowledgebase: a hub of integrated protein data. Database (Oxford) 2011, bar009 (2011).
60. Kikugawa, S. et al. PCDq: human protein complex database with quality index which summarizes different levels of evidences of protein complexes predicted from h-invitational protein-protein interactions integrative dataset. BMC Syst. Biol. 6 (Suppl, 2), S7 (2012).
61. Gu, Z., Gu, L., Eils, R., Schlesner, M. &Brors, B. circlize Implements and enhances circular visualization in R. Bioinformatics 30, 2811-2812 (2014).
62. Vizcaino, J.A. et al. The PRoteomics IDEntifications (PRIDE) database and associated tools: status in 2013. Nucleic Acids Res. 41, D1063-D1069 (2013).