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RNA-Seq as a Tool to Study the Tumor Microenvironment

  • Pudchalaluck Panichnantakul
  • Mathieu Bourgey
  • Alexandre Montpetit
  • Guillaume Bourque
  • Yasser Riazalhosseini
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1458)

Abstract

The transcriptome is composed of different types of RNA molecules including mRNAs, tRNAs, rRNAs, and other noncoding RNAs that are found inside a cell at a given time. Analyzing transcriptome patterns can shed light on the functional state of the cell as well as on the dynamics of cellular behavior associated with genomic and environmental changes. Likewise, transcriptome analysis has been a major help in solving biological issues and understanding the molecular basis of many diseases including human cancers. Specifically, since targeted and whole genome sequencing studies are becoming more common in identifying the driving factors of cancer, a comprehensive and high-resolution analysis of the transcriptome, as provided by RNA-Sequencing (RNA-Seq), plays a key role in investigating the functional relevance of the identified genomic aberrations. Here, we describe experimental procedures of RNA-Seq and downstream data processing and analysis, with a focus on the identification of abnormally expressed transcripts and genes.

Key words

RNA-Seq Tumor microenvironment Next-generation sequencing Bioinformatics 

References

  1. 1.
    Johnson BE, Mazor T, Hong C, Barnes M, Aihara K, McLean CY, Fouse SD, Yamamoto S, Ueda H, Tatsuno K, Asthana S, Jalbert LE, Nelson SJ, Bollen AW, Gustafson WC, Charron E, Weiss WA, Smirnov IV, Song JS, Olshen AB, Cha S, Zhao Y, Moore RA, Mungall AJ, Jones SJM, Hirst M, Marra MA, Saito N, Aburatani H, Mukasa A, Berger MS, Chang SM, Taylor BS, Costello JF (2014) Mutational analysis reveals the origin and therapy-driven evolution of recurrent glioma. Science (New York, NY) 343(6167):189–193CrossRefGoogle Scholar
  2. 2.
    Yates LR, Gerstung M, Knappskog S, Desmedt C, Gundem G, Van Loo P, Aas T, Alexandrov LB, Larsimont D, Davies H, Li Y, Ju YS, Ramakrishna M, Haugland HK, Lilleng PK, Nik-Zainal S, McLaren S, Butler A, Martin S, Glodzik D, Menzies A, Raine K, Hinton J, Jones D, Mudie LJ, Jiang B, Vincent D, Greene-Colozzi A, Adnet P-Y, Fatima A, Maetens M, Ignatiadis M, Stratton MR, Sotiriou C, Richardson AL, Lonning PE, Wedge DC, Campbell PJ (2015) Subclonal diversification of primary breast cancer revealed by multiregion sequencing. Nat Med 21(7):751–759CrossRefPubMedPubMedCentralGoogle Scholar
  3. 3.
    Finak G, Bertos N, Pepin F, Sadekova S, Souleimanova M, Zhao H, Chen H, Omeroglu G, Meterissian S, Omeroglu A, Hallett M, Park M (2008) Stromal gene expression predicts clinical outcome in breast cancer. Nat Med 14(5):518–527CrossRefPubMedGoogle Scholar
  4. 4.
    Trimboli AJ, Cantemir-Stone CZ, Li F, Wallace JA, Merchant A, Creasap N, Thompson JC, Caserta E, Wang H, Chong JL, Naidu S, Wei G, Sharma SM, Stephens JA, Fernandez SA, Gurcan MN, Weinstein MB, Barsky SH, Yee L, Rosol TJ, Stromberg PC, Robinson ML, Pepin F, Hallett M, Park M, Ostrowski MC, Leone G (2009) Pten in stromal fibroblasts suppresses mammary epithelial tumors. Nature 461(7267):1084–1091CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Lee S, Seo CH, Lim B, Yang JO, Oh J, Kim M, Lee S, Lee B, Kang C, Lee S (2010) Accurate quantification of transcriptome from RNA-Seq data by effective length normalization. Nucleic Acids Res 39(2):e9CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    Wagner G, Kin K, Lynch V (2012) Measurement of mRNA abundance using RNA-seq data: RPKM measure is inconsistent among samples. Theory Biosci 131(4):281–285CrossRefPubMedGoogle Scholar
  7. 7.
    Cloonan N, Forrest ARR, Kolle G, Gardiner BBA, Faulkner GJ, Brown MK, Taylor DF, Steptoe AL, Wani S, Bethel G, Robertson AJ, Perkins AC, Bruce SJ, Lee CC, Ranade SS, Peckham HE, Manning JM, McKernan KJ, Grimmond SM (2008) Stem cell transcriptome profiling via massive-scale mRNA sequencing. Nat Methods 5(7):613–619CrossRefPubMedGoogle Scholar
  8. 8.
    Wang Z, Gerstein M, Snyder M (2009) RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet 10(1):57–63CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    Okoniewski MJ, Miller CJ (2006) Hybridization interactions between probesets in short oligo microarrays lead to spurious correlations. BMC Bioinformatics 7:276CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Zhao S, Fung-Leung W-P, Bittner A, Ngo K, Liu X (2014) Comparison of RNA-seq and microarray in transcriptome profiling of activated T cells. PLoS One 9(1):e78644CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Auer PL, Doerge RW (2010) Statistical design and analysis of RNA sequencing data. Genetics 185(2):405–416CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    Mills JD, Kawahara Y, Janitz M (2013) Strand-specific RNA-seq provides greater resolution of transcriptome profiling. Curr Genomics 14(3):173–181CrossRefPubMedPubMedCentralGoogle Scholar
  13. 13.
    Sigurgeirsson B, Emanuelsson O, Lundeberg J (2014) Analysis of stranded information using an automated procedure for strand specific RNA sequencing. BMC Genomics 15(1):631CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    Johnsson P, Ackley A, Vidarsdottir L, Lui W-O, Corcoran M, Grandér D, Morris KV (2013) A pseudogene long noncoding RNA network regulates PTEN transcription and translation in human cells. Nat Struct Mol Biol 20(4):440–446CrossRefPubMedPubMedCentralGoogle Scholar
  15. 15.
    Poliseno L, Salmena L, Zhang J, Carver B, Haveman WJ, Pandolfi PP (2010) A coding-independent function of gene and pseudogene mRNAs regulates tumour biology. Nature 465(7301):1033–1038CrossRefPubMedPubMedCentralGoogle Scholar
  16. 16.
    Grun D, Lyubimova A, Kester L, Wiebrands K, Basak O, Sasaki N, Clevers H, van Oudenaarden A (2015) Single-cell messenger RNA sequencing reveals rare intestinal cell types. Nature 525(7568):251–255CrossRefPubMedGoogle Scholar
  17. 17.
    Miyamoto DT, Zheng Y, Wittner BS, Lee RJ, Zhu H, Broderick KT, Desai R, Fox DB, Brannigan BW, Trautwein J, Arora KS, Desai N, Dahl DM, Sequist LV, Smith MR, Kapur R, Wu C-L, Shioda T, Ramaswamy S, Ting DT, Toner M, Maheswaran S, Haber DA (2015) RNA-Seq of single prostate CTCs implicates noncanonical Wnt signaling in antiandrogen resistance. Science 349(6254):1351–1356CrossRefPubMedPubMedCentralGoogle Scholar
  18. 18.
    Grabherr MG, Haas BJ, Yassour M, Levin JZ, Thompson DA, Amit I, Adiconis X, Fan L, Raychowdhury R, Zeng Q, Chen Z, Mauceli E, Hacohen N, Gnirke A, Rhind N, di Palma F, Birren BW, Nusbaum C, Lindblad-Toh K, Friedman N, Regev A (2011) Trinity: reconstructing a full-length transcriptome without a genome from RNA-Seq data. Nat Biotechnol 29(7):644–652CrossRefPubMedPubMedCentralGoogle Scholar
  19. 19.
    Bolger AM, Lohse M, Usadel B (2014) Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30(15):2114–2120CrossRefPubMedPubMedCentralGoogle Scholar
  20. 20.
    Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, Batut P, Chaisson M, Gingeras TR (2013) STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29(1):15–21CrossRefPubMedGoogle Scholar
  21. 21.
    DeLuca DS, Levin JZ, Sivachenko A, Fennell T, Nazaire M-D, Williams C, Reich M, Winckler W, Getz G (2012) RNA-SeQC: RNA-seq metrics for quality control and process optimization. Bioinformatics 28(11):1530–1532CrossRefPubMedPubMedCentralGoogle Scholar
  22. 22.
    Trapnell C, Roberts A, Goff L, Pertea G, Kim D, Kelley DR, Pimentel H, Salzberg SL, Rinn JL, Pachter L (2012) Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks. Nat Protoc 7(3):562–578CrossRefPubMedPubMedCentralGoogle Scholar
  23. 23.
    Trapnell C, Hendrickson DG, Sauvageau M, Goff L, Rinn JL, Pachter L (2013) Differential analysis of gene regulation at transcript resolution with RNA-seq. Nat Biotechnol 31(1):46–53CrossRefPubMedGoogle Scholar
  24. 24.
    Mortazavi A, Williams BA, McCue K, Schaeffer L, Wold B (2008) Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat Methods 5(7):621–628CrossRefPubMedGoogle Scholar
  25. 25.
    Robinson MD, McCarthy DJ, Smyth GK (2010) edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26(1):139–140CrossRefPubMedGoogle Scholar
  26. 26.
    Anders S, Huber W (2010) Differential expression analysis for sequence count data. Genome Biol 11(10):R106CrossRefPubMedPubMedCentralGoogle Scholar
  27. 27.
    Young MD, Wakefield MJ, Smyth GK, Oshlack A (2010) Gene ontology analysis for RNA-seq: accounting for selection bias. Genome Biol 11(2):R14CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Pudchalaluck Panichnantakul
    • 1
    • 2
  • Mathieu Bourgey
    • 2
  • Alexandre Montpetit
    • 2
  • Guillaume Bourque
    • 1
    • 2
  • Yasser Riazalhosseini
    • 1
    • 2
  1. 1.Department of Human GeneticsMcGill UniversityMontrealCanada
  2. 2.McGill University and Genome Quebec Innovation CentreMontrealCanada

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