Advertisement

Characterization of Dendritic Cell Subsets Through Gene Expression Analysis

  • Thien-Phong Vu ManhEmail author
  • Marc Dalod
Part of the Methods in Molecular Biology book series (MIMB, volume 1423)

Abstract

Dendritic cells (DCs) are immune sentinels of the body and play a key role in the orchestration of the communication between the innate and the adaptive immune systems. DCs can polarize innate and adaptive immunity toward a variety of functions, sometimes with opposite roles in the overall control of immune responses (e.g., tolerance or immunosuppression versus immunity) or in the balance between various defense mechanisms promoting the control of different types of pathogens (e.g., antiviral versus antibacterial versus anti-worm immunity). These multiple DC functions result both from the plasticity of individual DC to exert different activities and from the existence of various DC subsets specialized in distinct functions. Functional genomics represents a powerful, unbiased, approach to better characterize these two levels of DC plasticity and to decipher its molecular regulation. Indeed, more and more experimental immunologists are generating high-throughput data in order to better characterize different states of DC based, for example, on their belonging to a specific subpopulation and/or on their exposure to specific stimuli and/or on their ability to exert a specific function. However, the interpretation of this wealth of data is severely hampered by the bottleneck of their bioinformatics analysis. Indeed, most experimental immunologists lack advanced computational or bioinformatics expertise and do not know how to translate raw gene expression data into potential biological meaning. Moreover, subcontracting such analyses is generally disappointing or financially not sustainable, since companies generally propose canonical analysis pipelines that are often unadapted for the structure of the data to analyze or for the precise type of questions asked. Hence, there is an important need of democratization of the bioinformatics analyses of gene expression profiling studies, in order to accelerate interpretation of the results by the researchers at the origin of the research project, of the data and who know best the underlying biology. This chapter will focus on the analysis of DC subset transcriptomes as measured by microarrays. We will show that simple bioinformatics procedures, applied one after the other in the framework of a pipeline, can lead to the characterization of DC subsets. We will develop two tutorials based on the reanalysis of public gene expression data. The first tutorial aims at illustrating a strategy for establishing the identity of DC subsets studied in a novel context, here their in vitro generation in cultures of human CD34+ hematopoietic progenitors. The second tutorial aims at illustrating how to perform a posteriori bioinformatics analyses in order to evaluate the risk of contamination or of improper identification of DC subsets during preparation of biological samples, such that this information is taken into account in the final interpretation of the data and can eventually help to redesign the sampling strategy.

Key words

Dendritic cell subsets Microarray analysis for beginners Workflow analysis Cell identity characterization Transcriptomic signatures Gene set enrichment approach 

Notes

Acknowledgements

The laboratory of Marc Dalod receives funding from the European Research Council under the European Community’s Seventh Framework Programme (FP7/2007–2013 Grant Agreement no. 281225, including salary support for T.-P.V.M.); from the I2HD collaborative project between CIML, AVIESAN, and SANOFI; from Institut National du Cancer (INCa grant #2011-155); from Agence Nationale de la Recherche (project PhyloGenDC, ANR-09-BLAN-0073-02); and from FRM (Equipe labellisée to M.D.), as well as institutional support from Inserm and CNRS. We also acknowledge support from the DCBIOL Labex (ANR-11-LABEX-0043, grant ANR-10-IDEX-0001-02PSL*) and the A*MIDEX project (ANR-11-IDEX-0001-02) funded by the French Government's "Investissements d'Avenir" program managed by the ANR.

References

  1. 1.
    Pascual V, Chaussabel D, Banchereau J (2010) A genomic approach to human autoimmune diseases. Annu Rev Immunol 28:535–571CrossRefPubMedPubMedCentralGoogle Scholar
  2. 2.
    Chaussabel D, Baldwin N (2014) Democratizing systems immunology with modular transcriptional repertoire analyses. Nat Rev Immunol 14(4):271–280CrossRefPubMedPubMedCentralGoogle Scholar
  3. 3.
    Pandey G, Cohain A, Miller J, Merad M (2013) Decoding dendritic cell function through module and network analysis. J Immunol Methods 387(1–2):71–80CrossRefPubMedGoogle Scholar
  4. 4.
    Mabbott NA, Kenneth Baillie J, Hume DA, Freeman TC (2010) Meta-analysis of lineage-specific gene expression signatures in mouse leukocyte populations. Immunobiology 215(9–10):724–736CrossRefPubMedGoogle Scholar
  5. 5.
    Zak DE, Tam VC, Aderem A (2014) Systems-level analysis of innate immunity. Annu Rev Immunol 32:547–577CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    Pulendran B, Li S, Nakaya HI (2010) Systems vaccinology. Immunity 33(4):516–529CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
    Weeraratna AT, Taub DD (2007) Microarray data analysis: an overview of design, methodology, and analysis. Methods Mol Biol 377:1–16CrossRefPubMedGoogle Scholar
  8. 8.
    Imbeaud S, Auffray C (2005) ‘The 39 steps’ in gene expression profiling: critical issues and proposed best practices for microarray experiments. Drug Discov Today 10(17):1175–1182CrossRefPubMedGoogle Scholar
  9. 9.
    Theocharidis A, van Dongen S, Enright AJ, Freeman TC (2009) Network visualization and analysis of gene expression data using BioLayout Express (3D). Nat Protoc 4(10):1535–1550CrossRefPubMedGoogle Scholar
  10. 10.
    Reich M, Liefeld T, Gould J, Lerner J, Tamayo P, Mesirov JP (2006) GenePattern 2.0. Nat Genet 38(5):500–501CrossRefPubMedGoogle Scholar
  11. 11.
    da Huang W, Sherman BT, Lempicki RA (2009) Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc 4(1):44–57CrossRefGoogle Scholar
  12. 12.
    Balan S, Ollion V, Colletti N, Chelbi R, Montanana-Sanchis F, Liu H, Vu Manh TP, Sanchez C, Savoret J, Perrot I, Doffin AC, Fossum E, Bechlian D, Chabannon C, Bogen B, Asselin-Paturel C, Shaw M, Soos T, Caux C, Valladeau-Guilemond J, Dalod M (2014) Human XCR1+ dendritic cells derived in vitro from CD34+ progenitors closely resemble blood dendritic cells, including their adjuvant responsiveness, contrary to monocyte-derived dendritic cells. J Immunol 193(4):1622–1635CrossRefPubMedPubMedCentralGoogle Scholar
  13. 13.
    Qiu CH, Miyake Y, Kaise H, Kitamura H, Ohara O, Tanaka M (2009) Novel subset of CD8a + dendritic cells localized in the marginal zone is responsible for tolerance to cell-associated antigens. J Immunol 182(7):4127–4136CrossRefPubMedGoogle Scholar
  14. 14.
    Edgar R, Domrachev M, Lash AE (2002) Gene Expression Omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res 30(1):207–210CrossRefPubMedPubMedCentralGoogle Scholar
  15. 15.
    Brazma A, Parkinson H, Sarkans U, Shojatalab M, Vilo J, Abeygunawardena N, Holloway E, Kapushesky M, Kemmeren P, Lara GG, Oezcimen A, Rocca-Serra P, Sansone SA (2003) ArrayExpress—a public repository for microarray gene expression data at the EBI. Nucleic Acids Res 31(1):68–71CrossRefPubMedPubMedCentralGoogle Scholar
  16. 16.
    Hijikata A, Kitamura H, Kimura Y, Yokoyama R, Aiba Y, Bao Y, Fujita S, Hase K, Hori S, Ishii Y, Kanagawa O, Kawamoto H, Kawano K, Koseki H, Kubo M, Kurita-Miki A, Kurosaki T, Masuda K, Nakata M, Oboki K, Ohno H, Okamoto M, Okayama Y, O-Wang J, Saito H, Saito T, Sakuma M, Sato K, Seino K, Setoguchi R, Tamura Y, Tanaka M, Taniguchi M, Taniuchi I, Teng A, Watanabe T, Watarai H, Yamasaki S, Ohara O (2007) Construction of an open-access database that integrates cross-reference information from the transcriptome and proteome of immune cells. Bioinformatics 23(21):2934–2941CrossRefPubMedGoogle Scholar
  17. 17.
    Vu Manh TP, Marty H, Sibille P, Le Vern Y, Kaspers B, Dalod M, Schwartz-Cornil I, Quere P (2014) Existence of conventional dendritic cells in Gallus gallus revealed by comparative gene expression profiling. J Immunol 192(10):4510–4517CrossRefPubMedGoogle Scholar
  18. 18.
    Saeed AI, Sharov V, White J, Li J, Liang W, Bhagabati N, Braisted J, Klapa M, Currier T, Thiagarajan M, Sturn A, Snuffin M, Rezantsev A, Popov D, Ryltsov A, Kostukovich E, Borisovsky I, Liu Z, Vinsavich A, Trush V, Quackenbush J (2003) TM4: a free, open-source system for microarray data management and analysis. Biotechniques 34(2):374–378PubMedGoogle Scholar
  19. 19.
    Spinelli L, Carpentier S, Montañana Sanchis F, Dalod M, Vu Manh TP (2015) BubbleGUM: automatic extraction of phenotype molecular signatures and comprehensive visualization of multiple Gene Set Enrichment Analyses. BMC Genomics 16(1):814CrossRefPubMedPubMedCentralGoogle Scholar
  20. 20.
    Gentleman RC, Carey VJ, Bates DM, Bolstad B, Dettling M, Dudoit S, Ellis B, Gautier L, Ge Y, Gentry J, Hornik K, Hothorn T, Huber W, Iacus S, Irizarry R, Leisch F, Li C, Maechler M, Rossini AJ, Sawitzki G, Smith C, Smyth G, Tierney L, Yang JY, Zhang J (2004) Bioconductor: open software development for computational biology and bioinformatics. Genome Biol 5(10):R80CrossRefPubMedPubMedCentralGoogle Scholar
  21. 21.
    Bolstad BM, Irizarry RA, Astrand M, Speed TP (2003) A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics 19(2):185–193CrossRefPubMedGoogle Scholar
  22. 22.
    Efron B, Tibshirani R (1991) Statistical data analysis in the computer age. Science 253(5018):390–395CrossRefPubMedGoogle Scholar
  23. 23.
    Pearson K (1901) On lines and planes of closest fit to systems of points in space. Philos Mag 2:559–572CrossRefGoogle Scholar
  24. 24.
    Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES, Mesirov JP (2005) Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A 102(43):15545–15550CrossRefPubMedPubMedCentralGoogle Scholar
  25. 25.
    Churchill GA (2002) Fundamentals of experimental design for cDNA microarrays. Nat Genet 32(Suppl):490–495CrossRefPubMedGoogle Scholar
  26. 26.
    Li C, Wong WH (2001) Model-based analysis of oligonucleotide arrays: expression index computation and outlier detection. Proc Natl Acad Sci U S A 98(1):31–36CrossRefPubMedPubMedCentralGoogle Scholar
  27. 27.
    Irizarry RA, Bolstad BM, Collin F, Cope LM, Hobbs B, Speed TP (2003) Summaries of Affymetrix GeneChip probe level data. Nucleic Acids Res 31(4):e15CrossRefPubMedPubMedCentralGoogle Scholar
  28. 28.
    Amaratunga D, Cabrera J, Shkedy Z (2014) Exploration of analysis of DNA microarray and other high-dimensional data. In: Amaratunga D, Cabrera J, Shkedy Z (eds) Exploration of analysis of DNA microarray and other high-dimensional data, Wiley series in probability and statistics. Wiley, Hoboken, NJCrossRefGoogle Scholar
  29. 29.
    Johnson WE, Li C, Rabinovic A (2007) Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics 8(1):118–127CrossRefPubMedGoogle Scholar
  30. 30.
    Bezman NA, Kim CC, Sun JC, Min-Oo G, Hendricks DW, Kamimura Y, Best JA, Goldrath AW, Lanier LL (2012) Molecular definition of the identity and activation of natural killer cells. Nat Immunol 13(10):1000–1009CrossRefPubMedPubMedCentralGoogle Scholar
  31. 31.
    Guilliams M, Henri S, Tamoutounour S, Ardouin L, Schwartz-Cornil I, Dalod M, Malissen B (2010) From skin dendritic cells to a simplified classification of human and mouse dendritic cell subsets. Eur J Immunol 40(8):2089–2094CrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Centre d’Immunologie de Marseille-Luminy, UNIV UM2Aix Marseille UniversitéMarseilleFrance
  2. 2.U1104INSERMMarseilleFrance
  3. 3.UMR7280CNRSMarseilleFrance

Personalised recommendations