Skip to main content

Fluorescence-Activated Cell Sorting and Gene Expression Profiling of GFP-Positive Cells from Transgenic Zebrafish Lines

  • Protocol
  • First Online:
Zebrafish

Abstract

Gene expression profiling is a useful approach for deeper understanding of the specificity of cells, tissues, and organs in the transcriptional level. Recent development of high-throughput next-generation sequence (NGS) allows the RNA-seq method for this profiling. This method provides precise information of transcripts about the quantitation and the structure such as the splicing variants. In this chapter, we describe a method for gene expression profiling of GFP-positive cells from transgenic zebrafish by RNA-seq. We labeled specific cells in the brain with GFP by crossing a Gal4 driver line with the UAS:GFP line, isolated those cells by fluorescence-activated cell sorting (FACS), and analyzed by RNA-seq.

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 99.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 129.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 179.99
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. Kawakami K, Takeda H, Kawakami N, Kobayashi M, Matsuda N, Mishina M (2004) A transposon-mediated gene trap approach identifies developmentally regulated genes in zebrafish. Dev Cell 7:133–144

    Article  CAS  PubMed  Google Scholar 

  2. Nagayoshi S, Hayashi E, Abe G, Osato N, Asakawa K, Urasaki A, Horikawa K, Ikeo K, Takeda H, Kawakami K (2008) Insertional mutagenesis by the Tol2 transposon-mediated enhancer trap approach generated mutations in two developmental genes: tcf7 and synembryn-like. Development 135:159–169

    Article  CAS  PubMed  Google Scholar 

  3. Scott EK, Mason L, Arrenberg AB, Ziv L, Gosse NJ, Xiao T, Chi NC, Asakawa K, Kawakami K, Baier H (2007) Targeting neural circuitry in zebrafish using GAL4 enhancer trapping. Nat Methods 4:323–326

    CAS  PubMed  Google Scholar 

  4. Asakawa K, Suster ML, Mizusawa K, Nagayoshi S, Kotani T, Urasaki A, Kishimoto Y, Hibi M, Kawakami K (2008) Genetic dissection of neural circuits by Tol2 transposon-mediated Gal4 gene and enhancer trapping in zebrafish. Proc Natl Acad Sci U S A 105:1255–1260

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Kawakami K, Abe G, Asada T, Asakawa K, Fukuda R, Ito A, Lal P, Mouri N, Muto A, Suster ML et al (2010) zTrap: zebrafish gene trap and enhancer trap database. BMC Dev Biol 10:105

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Ozsolak F, Milos PM (2011) NIH public access. Nat Rev Genet 12:87–98

    Article  CAS  PubMed  Google Scholar 

  8. Sultan M, Schulz MH, Richard H, Magen A, Klingenhoff A, Scherf M, Seifert M, Borodina T, Soldatov A, Parkhomchuk D et al (2008) A global view of gene activity and alternative splicing by deep sequencing of the Human Transcriptome. Science 321:956–960

    Article  CAS  PubMed  Google Scholar 

  9. Zhao S, Fung-Leung WP, Bittner A, Ngo K, Liu X (2014) Comparison of RNA-Seq and microarray in transcriptome profiling of activated T cells. PLoS One 9:e78644

    Article  PubMed  PubMed Central  Google Scholar 

  10. Carninci P, Kasukawa T, Katayama S, Gough J, Frith MC, Maeda N, Oyama R, Ravasi T, Lenhard B, Wells C et al (2005) The transcriptional landscape of the mammalian genome. Science 309:1559–1563

    Article  CAS  PubMed  Google Scholar 

  11. Cloonan N, Forrest ARR, Kolle G, Gardiner BBA, Faulkner GJ, Brown MK, Taylor DF, Steptoe AL, Wani S, Bethel G et al (2008) Stem cell transcriptome profiling via massive-scale mRNA sequencing. Nat Methods 5:613–619

    Article  CAS  PubMed  Google Scholar 

  12. Mortazavi A, Williams BA, McCue K, Schaeffer L, Wold B (2008) Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat Methods 5:621–628

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Kim D, Pertea G, Trapnell C, Pimentel H, Kelley R, Salzberg SL (2013) TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biol 14:R36

    Article  PubMed  PubMed Central  Google Scholar 

  16. Wang K, Singh D, Zeng Z, Coleman SJ, Huang Y, Savich GL, He X, Mieczkowski P, Grimm SA, Perou CM et al (2010) MapSplice: accurate mapping of RNA-seq reads for splice junction discovery. Nucleic Acids Res 8:1–14

    Google Scholar 

  17. Trapnell C, Williams BA, Pertea G, Mortazavi A, Kwan G, van Baren MJ, Salzberg SL, Wold BJ, Pachter L (2010) Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat Biotechnol 28:511–515

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Guttman M, Garber M, Levin JZ, Donaghey J, Robinson J, Adiconis X, Fan L, Koziol MJ, Gnirke A, Nusbaum C et al (2010) Ab initio reconstruction of cell type-specific transcriptomes in mouse reveals the conserved multi-exonic structure of lincRNAs. Nat Biotechnol 28:503–510

    Google Scholar 

  19. Robinson MD, McCarthy DJ, Smyth GK (2009) edgeR: a bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26:139–140

    Article  PubMed  PubMed Central  Google Scholar 

  20. Love MI, Huber W, Anders S (2014) Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 15:550

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgments

We thank Naoyuki Inagaki for the helpful advice about cell dissociation methods. This work was partly supported by the National BioResource Project (to KK), Grant-in-Aids for Scientific Research on Innovative Areas “Genome Science” (221S0002 to YS), and Grant-in-Aids for Scientific Research (A) (23241063 and 15H02370 to KK) from the Ministry of Education, Culture, Sports, Science and Technology of Japan.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Koichi Kawakami .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer Science+Business Media New York

About this protocol

Cite this protocol

Tanabe, H. et al. (2016). Fluorescence-Activated Cell Sorting and Gene Expression Profiling of GFP-Positive Cells from Transgenic Zebrafish Lines. In: Kawakami, K., Patton, E., Orger, M. (eds) Zebrafish. Methods in Molecular Biology, vol 1451. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-3771-4_7

Download citation

  • DOI: https://doi.org/10.1007/978-1-4939-3771-4_7

  • Published:

  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-3769-1

  • Online ISBN: 978-1-4939-3771-4

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics