Skip to main content

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

  • Protocol
  • First Online:
Book cover Host-Bacteria Interactions

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1197))

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Protocol
USD 49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 109.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 139.00
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Westermann AJ, Gorski SA, Vogel J (2012) Dual RNA-seq of pathogen and host. Nat Rev Microbiol 10(9):618–630

    Article  CAS  PubMed  Google Scholar 

  2. Wang Z, Gerstein M, Snyder M (2009) RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet 10(1):57–63

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  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):521–532

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  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):511–515

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  5. Mortazavi A, Williams BA, McCue K et al (2008) Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat Methods 5(7):621–628

    Article  CAS  PubMed  Google Scholar 

  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):e49144

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  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):266–271

    PubMed Central  PubMed  Google Scholar 

  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):777–782

    Article  PubMed Central  PubMed  Google Scholar 

  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):516–535

    Article  CAS  PubMed  Google Scholar 

  10. Meijer AH, Spaink HP (2011) Host-pathogen interactions made transparent with the zebrafish model. Curr Drug Targets 12(7):1000–1017

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  11. Ramakrishnan L (2012) Revisiting the role of the granuloma in tuberculosis. Nat Rev Immunol 12(5):352–366

    CAS  PubMed  Google Scholar 

  12. Renshaw SA, Trede NS (2012) A model 450 million years in the making: zebrafish and vertebrate immunity. Dis Model Mech 5(1):38–47

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  13. van der Vaart M, Spaink HP, Meijer AH (2012) Pathogen recognition and activation of the innate immune response in zebrafish. Adv Hematol 2012:159807

    PubMed Central  PubMed  Google Scholar 

  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):693–702

    Article  CAS  PubMed  Google Scholar 

  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):3651–3660

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  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):1600–1619

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  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):2317–2332

    PubMed  Google Scholar 

  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):1495–1508

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  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 11:518

    Article  PubMed Central  PubMed  Google Scholar 

  20. Hegedus Z, Zakrzewska A, Agoston VC et al (2009) Deep sequencing of the zebrafish transcriptome response to mycobacterium infection. Mol Immunol 46(15):2918–2930

    Article  CAS  PubMed  Google Scholar 

  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):716–724

    Article  CAS  PubMed  Google Scholar 

  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):179–190

    Article  CAS  PubMed  Google Scholar 

  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 13:319

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  24. Ellett F, Pase L, Hayman JW et al (2011) mpeg1 promoter transgenes direct macrophage-lineage expression in zebrafish. Blood 117(4):e49–e56

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  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):751–765

    Article  CAS  PubMed  Google Scholar 

  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):7369–7374

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  27. Renshaw SA, Loynes CA, Trushell DM et al (2006) A transgenic zebrafish model of neutrophilic inflammation. Blood 108(13):3976–3978

    Article  CAS  PubMed  Google Scholar 

  28. Wittamer V, Bertrand JY, Gutschow PW et al (2011) Characterization of the mononuclear phagocyte system in zebrafish. Blood 117(26):7126–7135

    Article  CAS  PubMed  Google Scholar 

  29. Garber M, Grabherr MG, Guttman M et al (2011) Computational methods for transcriptome annotation and quantification using RNA-seq. Nat Methods 8(6):469–477

    Article  CAS  PubMed  Google Scholar 

  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):562–578

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  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):671–83

    Google Scholar 

  32. Elsalini OA, Rohr KB (2003) Phenylthiourea disrupts thyroid function in developing zebrafish. Dev Genes Evol 212(12):593–598

    CAS  PubMed  Google Scholar 

  33. Li Z, Ptak D, Zhang L et al (2012) Phenylthiourea specifically reduces zebrafish eye size. PLoS One 7(6):e40132

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  34. Westerfield M (1993) The zebrafish book: a guide for the laboratory use of zebrafish (Brachydanio rerio). M. Westerfield, Eugene, OR

    Google Scholar 

  35. Anders S, Huber W (2010) Differential expression analysis for sequence count data. Genome Biol 11(10):R106

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  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):551–562

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  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):123–125

    Article  CAS  PubMed  Google Scholar 

  38. Blankenberg D, Gordon A, Von Kuster G et al (2010) Manipulation of FASTQ data with Galaxy. Bioinformatics 26(14):1783–1785

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  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):R25

    Article  PubMed Central  PubMed  Google Scholar 

  40. Trapnell C, Pachter L, Salzberg SL (2009) TopHat: discovering splice junctions with RNA-Seq. Bioinformatics 25(9):1105–1111

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  41. Robinson MD, McCarthy DJ, Smyth GK (2010) edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26(1):139–140

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  42. Hardcastle TJ, Kelly KA (2010) baySeq: empirical Bayesian methods for identifying differential expression in sequence count data. BMC Bioinformatics 11:422

    Article  PubMed Central  PubMed  Google Scholar 

Download references

Acknowledgments

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.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Annemarie H. Meijer .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer Science+Business Media New York

About this protocol

Cite this protocol

Rougeot, J., Zakrzewska, A., Kanwal, Z., Jansen, H.J., Spaink, H.P., Meijer, A.H. (2014). RNA Sequencing of FACS-Sorted Immune Cell Populations from Zebrafish Infection Models to Identify Cell Specific Responses to Intracellular Pathogens. In: Vergunst, A., O'Callaghan, D. (eds) Host-Bacteria Interactions. Methods in Molecular Biology, vol 1197. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-1261-2_15

Download citation

  • DOI: https://doi.org/10.1007/978-1-4939-1261-2_15

  • Published:

  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-1260-5

  • Online ISBN: 978-1-4939-1261-2

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics