RNA Sequencing of FACS-Sorted Immune Cell Populations from Zebrafish Infection Models to Identify Cell Specific Responses to Intracellular Pathogens

  • Julien Rougeot
  • Ania Zakrzewska
  • Zakia Kanwal
  • Hans J. Jansen
  • Herman P. Spaink
  • Annemarie H. MeijerEmail author
Part of the Methods in Molecular Biology book series (MIMB, volume 1197)


The zebrafish (Danio rerio) is increasingly used as a model for studying infectious diseases. This nonmammalian vertebrate host, which is transparent at the early life stages, is especially attractive for live imaging of interactions between pathogens and host cells. A number of useful fluorescent reporter lines have recently been developed and significant advances in RNA sequencing technology have been made, which now make it possible to apply the zebrafish model for investigating changes in transcriptional activity of specific immune cell types during the course of an infection process.

Here we describe how to sequence RNA extracted from fluorescently labeled macrophages obtained by cell-sorting of 5-day-old zebrafish larvae of the transgenic Tg(mpeg1:Gal4-VP16);Tg(UAS-E1b:Kaede) line. This technique showed reproducible results and allowed to detect specific expression of macrophage markers in the mpeg1 positive cell population, whereas no markers specific for neutrophils or lymphoid cells were detected. This protocol has been also successfully extended to other immune cell types as well as cells infected by Mycobacterium marinum.

Key words

RNA sequencing FACS Immune cells Zebrafish larvae dissociation Transcriptome analysis 



This work was supported by the Marie Curie Initial Training Network FishForPharma (PITN-GA-2011-289209) and the project ZF-HEALTH (HEALTH-F4-2010-242048) funded by European Commission 7th Framework Programme, and by the SmartMix programme of the Netherlands Ministry of Economic Affairs and the Ministry of Education, Culture, and Science. Additionally, Z.K. was supported by the Higher Education Commission of Pakistan, and A.Z. was supported by a Horizon grant of the Netherlands Genomics Initiative.


  1. 1.
    Westermann AJ, Gorski SA, Vogel J (2012) Dual RNA-seq of pathogen and host. Nat Rev Microbiol 10(9):618–630PubMedCrossRefGoogle Scholar
  2. 2.
    Wang Z, Gerstein M, Snyder M (2009) RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet 10(1):57–63PubMedCentralPubMedCrossRefGoogle Scholar
  3. 3.
    Fullwood MJ, Wei CL, Liu ET et al (2009) Next-generation DNA sequencing of paired-end tags (PET) for transcriptome and genome analyses. Genome Res 19(4):521–532PubMedCentralPubMedCrossRefGoogle Scholar
  4. 4.
    Trapnell C, Williams BA, Pertea G et al (2010) Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat Biotechnol 28(5):511–515PubMedCentralPubMedCrossRefGoogle Scholar
  5. 5.
    Mortazavi A, Williams BA, McCue K et al (2008) Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat Methods 5(7):621–628PubMedCrossRefGoogle Scholar
  6. 6.
    Cann GM, Gulzar ZG, Cooper S et al (2012) mRNA-Seq of single prostate cancer circulating tumor cells reveals recapitulation of gene expression and pathways found in prostate cancer. PLoS One 7(11):e49144PubMedCentralPubMedCrossRefGoogle Scholar
  7. 7.
    Lao KQ, Tang F, Barbacioru C et al (2009) mRNA-sequencing whole transcriptome analysis of a single cell on the SOLiD system. J Biomol Tech 20(5):266–271PubMedCentralPubMedGoogle Scholar
  8. 8.
    Ramskold D, Luo S, Wang YC et al (2012) Full-length mRNA-Seq from single-cell levels of RNA and individual circulating tumor cells. Nat Biotechnol 30(8):777–782PubMedCentralPubMedCrossRefGoogle Scholar
  9. 9.
    Tang F, Barbacioru C, Nordman E et al (2010) RNA-Seq analysis to capture the transcriptome landscape of a single cell. Nat Protoc 5(3):516–535PubMedCrossRefGoogle Scholar
  10. 10.
    Meijer AH, Spaink HP (2011) Host-pathogen interactions made transparent with the zebrafish model. Curr Drug Targets 12(7):1000–1017PubMedCentralPubMedCrossRefGoogle Scholar
  11. 11.
    Ramakrishnan L (2012) Revisiting the role of the granuloma in tuberculosis. Nat Rev Immunol 12(5):352–366PubMedGoogle Scholar
  12. 12.
    Renshaw SA, Trede NS (2012) A model 450 million years in the making: zebrafish and vertebrate immunity. Dis Model Mech 5(1):38–47PubMedCentralPubMedCrossRefGoogle Scholar
  13. 13.
    van der Vaart M, Spaink HP, Meijer AH (2012) Pathogen recognition and activation of the innate immune response in zebrafish. Adv Hematol 2012:159807PubMedCentralPubMedGoogle Scholar
  14. 14.
    Davis JM, Clay H, Lewis JL et al (2002) Real-time visualization of mycobacterium-macrophage interactions leading to initiation of granuloma formation in zebrafish embryos. Immunity 17(6):693–702PubMedCrossRefGoogle Scholar
  15. 15.
    Levraud JP, Disson O, Kissa K et al (2009) Real-time observation of Listeria monocytogenes-phagocyte interactions in living zebrafish larvae. Infect Immun 77(9):3651–3660PubMedCentralPubMedCrossRefGoogle Scholar
  16. 16.
    Prajsnar TK, Hamilton R, Garcia-Lara J et al (2012) A privileged intraphagocyte niche is responsible for disseminated infection of Staphylococcus aureus in a zebrafish model. Cell Microbiol 14(10):1600–1619PubMedCentralPubMedCrossRefGoogle Scholar
  17. 17.
    van der Sar AM, Spaink HP, Zakrzewska A et al (2009) Specificity of the zebrafish host transcriptome response to acute and chronic mycobacterial infection and the role of innate and adaptive immune components. Mol Immunol 46(11–12):2317–2332PubMedGoogle Scholar
  18. 18.
    Vergunst AC, Meijer AH, Renshaw SA et al (2010) Burkholderia cenocepacia creates an intramacrophage replication niche in zebrafish embryos, followed by bacterial dissemination and establishment of systemic infection. Infect Immun 78(4):1495–1508PubMedCentralPubMedCrossRefGoogle Scholar
  19. 19.
    Encinas P, Rodriguez-Milla MA, Novoa B et al (2010) Zebrafish fin immune responses during high mortality infections with viral haemorrhagic septicemia rhabdovirus. A proteomic and transcriptomic approach. BMC Genomics 11:518PubMedCentralPubMedCrossRefGoogle Scholar
  20. 20.
    Hegedus Z, Zakrzewska A, Agoston VC et al (2009) Deep sequencing of the zebrafish transcriptome response to mycobacterium infection. Mol Immunol 46(15):2918–2930PubMedCrossRefGoogle Scholar
  21. 21.
    Ordas A, Hegedus Z, Henkel CV et al (2011) Deep sequencing of the innate immune transcriptomic response of zebrafish embryos to Salmonella infection. Fish Shellfish Immunol 31(5):716–724PubMedCrossRefGoogle Scholar
  22. 22.
    Stockhammer OW, Rauwerda H, Wittink FR et al (2010) Transcriptome analysis of Traf6 function in the innate immune response of zebrafish embryos. Mol Immunol 48(1–3):179–190PubMedCrossRefGoogle Scholar
  23. 23.
    Yang D, Liu Q, Yang M et al (2012) RNA-seq liver transcriptome analysis reveals an activated MHC-I pathway and an inhibited MHC-II pathway at the early stage of vaccine immunization in zebrafish. BMC Genomics 13:319PubMedCentralPubMedCrossRefGoogle Scholar
  24. 24.
    Ellett F, Pase L, Hayman JW et al (2011) mpeg1 promoter transgenes direct macrophage-lineage expression in zebrafish. Blood 117(4):e49–e56PubMedCentralPubMedCrossRefGoogle Scholar
  25. 25.
    Hall C, Flores MV, Chien A et al (2009) Transgenic zebrafish reporter lines reveal conserved Toll-like receptor signaling potential in embryonic myeloid leukocytes and adult immune cell lineages. J Leukoc Biol 85(5):751–765PubMedCrossRefGoogle Scholar
  26. 26.
    Langenau DM, Ferrando AA, Traver D et al (2004) In vivo tracking of T cell development, ablation, and engraftment in transgenic zebrafish. Proc Natl Acad Sci U S A 101(19):7369–7374PubMedCentralPubMedCrossRefGoogle Scholar
  27. 27.
    Renshaw SA, Loynes CA, Trushell DM et al (2006) A transgenic zebrafish model of neutrophilic inflammation. Blood 108(13):3976–3978PubMedCrossRefGoogle Scholar
  28. 28.
    Wittamer V, Bertrand JY, Gutschow PW et al (2011) Characterization of the mononuclear phagocyte system in zebrafish. Blood 117(26):7126–7135PubMedCrossRefGoogle Scholar
  29. 29.
    Garber M, Grabherr MG, Guttman M et al (2011) Computational methods for transcriptome annotation and quantification using RNA-seq. Nat Methods 8(6):469–477PubMedCrossRefGoogle Scholar
  30. 30.
    Trapnell C, Roberts A, Goff L et al (2012) Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks. Nat Protoc 7(3):562–578PubMedCentralPubMedCrossRefGoogle Scholar
  31. 31.
    Dillies MA, Rau A, Aubert J, Hennequet-Antier C, Jeanmougin M, Servant N, Keime C, Marot G, Castel D, Estelle J, Guernec G, Jagla B, Jouneau L, Laloë D, Le Gall C, Schaëffer B, Le Crom S, Guedj M, Jaffrézic F (2012) French StatOmique Consortium. A comprehensive evaluation of normalization methods for Illumina high-throughput RNA sequencing data analysis. Brief Bioinform. 14(6):671–83Google Scholar
  32. 32.
    Elsalini OA, Rohr KB (2003) Phenylthiourea disrupts thyroid function in developing zebrafish. Dev Genes Evol 212(12):593–598PubMedGoogle Scholar
  33. 33.
    Li Z, Ptak D, Zhang L et al (2012) Phenylthiourea specifically reduces zebrafish eye size. PLoS One 7(6):e40132PubMedCentralPubMedCrossRefGoogle Scholar
  34. 34.
    Westerfield M (1993) The zebrafish book: a guide for the laboratory use of zebrafish (Brachydanio rerio). M. Westerfield, Eugene, ORGoogle Scholar
  35. 35.
    Anders S, Huber W (2010) Differential expression analysis for sequence count data. Genome Biol 11(10):R106PubMedCentralPubMedCrossRefGoogle Scholar
  36. 36.
    Covassin L, Amigo JD, Suzuki K et al (2006) Global analysis of hematopoietic and vascular endothelial gene expression by tissue specific microarray profiling in zebrafish. Dev Biol 299(2):551–562PubMedCentralPubMedCrossRefGoogle Scholar
  37. 37.
    Cummings M, McGinley CV, Wilkinson N et al (2011) A robust RNA integrity-preserving staining protocol for laser capture microdissection of endometrial cancer tissue. Anal Biochem 416(1):123–125PubMedCrossRefGoogle Scholar
  38. 38.
    Blankenberg D, Gordon A, Von Kuster G et al (2010) Manipulation of FASTQ data with Galaxy. Bioinformatics 26(14):1783–1785PubMedCentralPubMedCrossRefGoogle Scholar
  39. 39.
    Langmead B, Trapnell C, Pop M et al (2009) Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol 10(3):R25PubMedCentralPubMedCrossRefGoogle Scholar
  40. 40.
    Trapnell C, Pachter L, Salzberg SL (2009) TopHat: discovering splice junctions with RNA-Seq. Bioinformatics 25(9):1105–1111PubMedCentralPubMedCrossRefGoogle Scholar
  41. 41.
    Robinson MD, McCarthy DJ, Smyth GK (2010) edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26(1):139–140PubMedCentralPubMedCrossRefGoogle Scholar
  42. 42.
    Hardcastle TJ, Kelly KA (2010) baySeq: empirical Bayesian methods for identifying differential expression in sequence count data. BMC Bioinformatics 11:422PubMedCentralPubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Julien Rougeot
    • 1
  • Ania Zakrzewska
    • 1
  • Zakia Kanwal
    • 1
  • Hans J. Jansen
    • 2
  • Herman P. Spaink
    • 1
  • Annemarie H. Meijer
    • 1
    Email author
  1. 1.Institute of BiologyLeiden UniversityLeidenThe Netherlands
  2. 2.ZF-screens B.V.LeidenThe Netherlands

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