Skip to main content
SpringerLink
Log in
Book cover

Genomics and Systems Biology of Mammalian Cell Culture pp 1–25Cite as

Transcriptome Analysis

  • Chapter
  • First Online:

Part of the book series: Advances in Biochemical Engineering Biotechnology ((ABE,volume 127))

Abstract

Transcriptome analysis technologies are important systems-biology methods for the investigation and optimization of mammalian cell cultures concerning with regard to growth rates and productivity. For the production of recombinant proteins, knowledge of the expression conditions of the influencing genes is a major issue in the improvement of cell lines by means of genome engineering. This chapter presents two main techniques for transcriptome analysis: microarray technology and next-generation sequencing. Protein-based methods are also briefly outlined. Furthermore, the impact of these technologies on mammalian cell culture improvement is discussed.

Graphical Abstract

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

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   349.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   449.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   449.99
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

Learn about institutional subscriptions

Notes

  1. 1.

    http://www.ncbi.nlm.nih.gov/guide/genomes/.

  2. 2.

    www.appliedbiosystems.com.

  3. 3.

    www.454.com.

  4. 4.

    www.illumina.com.

References

  1. Khan J et al (1999) DNA microarray technology: the anticipated impact on the study of human disease. Biochimica Biophysica Acta Rev Cancer 1423(2):M17–M28

    Article  CAS  Google Scholar 

  2. Duggan DJ et al (1999) Expression profiling using cDNA microarrays. Nat Genet 21:10–14

    Article  CAS  Google Scholar 

  3. Brown PO, Botstein D (1999) Exploring the new world of the genome with DNA microarrays. Nat Genet 21:33–37

    Article  CAS  Google Scholar 

  4. DeRisi JL, Iyer VR, Brown PO (1997) Exploring the metabolic and genetic control of gene expression on a genomic scale. Science 278(5338):680–686

    Article  CAS  Google Scholar 

  5. Harrington CA, Rosenow C, Retief J (2000) Monitoring gene expression using DNA microarrays. Curr Opin Microbiol 3(3):285–291

    Article  CAS  Google Scholar 

  6. Park JH et al (2004) Oligonucleotide microarray-based mutation detection of the K-rasgene in colorectal cancers with use of competitive DNA hybridization. Clin Chem 50(9):1688–1691

    Article  CAS  Google Scholar 

  7. Hegde MR et al (2008) Microarray-based mutation detection in the dystrophin gene. Hum Mutat 29(9):1091–1099

    Article  CAS  Google Scholar 

  8. Walter G et al (2000) Protein arrays for gene expression and molecular interaction screening. Curr Opin Microbiol 3(3):98–302

    Article  Google Scholar 

  9. Wang DG et al (1998) Large-scale identification, mapping, and genotyping of single-nucleotide polymorphisms in the human genome. Science 280(5366):1077–1082

    Article  CAS  Google Scholar 

  10. Strauss KA et al (2008) Clinical application of DNA microarrays: Molecular diagnosis and HLA matching of an Amish child with severe combined immune deficiency. Clin Immunol 128(1):31–38

    Article  CAS  Google Scholar 

  11. Gunn SR, Robetorye RS, Mohammed MS (2007) Comparative genomic hybridization arrays in clinical pathology—progress and challenges. Mol Diagn Ther 11(2):73–77

    CAS  Google Scholar 

  12. Sebat JL, Colwell FS, Crawford RL (2003) Metagenomic profiling: microarray analysis of an environmental genomic library. Appl Environ Microbiol 69(8):4927–4934

    Article  CAS  Google Scholar 

  13. Wang RL et al (2008) DNA microarray application in ecotoxicology: experimental design, microarray scanning, and factors affecting transcriptional profiles in a small fish species. Environ Toxicol Chem 27(3):652–663

    Article  CAS  Google Scholar 

  14. Richmond CS et al (1999) Genome-wide expression profiling in Escherichia coli K-12. Nucleic Acids Res 27(19):3821–3835

    Article  CAS  Google Scholar 

  15. Oh MK, Liao JC (2000) DNA microarray detection of metabolic responses to protein overproduction in Escherichia coli. Metab Eng 2(3):201–209

    Article  CAS  Google Scholar 

  16. Wang M et al (2009) Microarray-based gene expression analysis as a process characterization tool to establish comparability of complex biological products: scale-up of a whole-cell immunotherapy product. Biotechnol Bioeng 104(4):796–808

    CAS  Google Scholar 

  17. Fodor SP et al (1991) Light-directed, spatially addressable parallel chemical synthesis. Science 251(4995):767–773

    Article  CAS  Google Scholar 

  18. McGall GH et al (1997) The efficiency of light-directed synthesis of DNA arrays on glass substrates. J Am Chem Soc 119(22):5081–5090

    Article  CAS  Google Scholar 

  19. Gao X, Gulari E, Zhou X (2004) In situ synthesis of oligonucleotide microarrays. Biopolymers 73(5):579–596

    Article  CAS  Google Scholar 

  20. Yang YH, Speed T (2002) Design issues for cDNA microarray experiments. Nat Rev Genet 3(8):579–588

    CAS  Google Scholar 

  21. Kerr MK, Churchill GA (2001) Statistical design and the analysis of gene expression microarray data. Genet Res 77(2):123–128

    CAS  Google Scholar 

  22. Dombkowski AA et al (2004) Gene-specific dye bias in microarray reference designs. FEBS Lett 560(1–3):120–124

    Article  CAS  Google Scholar 

  23. Landgrebe J, Bretz F, Brunner E (2004) Efficient two-sample designs for microarray experiments with biological replications. In Silico Biol 4(4):61–70

    Google Scholar 

  24. Kim SY, Lee JW, Sohn IS (2006) Comparison of various statistical methods for identifying differential gene expression in replicated microarray data. Stat Methods Med Res 15(1):3–20

    Article  Google Scholar 

  25. Klebanov L et al (2007) Statistical methods and microarray data. Nat Biotechnol 25(1):25–26 author reply 26–27

    Article  CAS  Google Scholar 

  26. Brazma A et al (2001) Minimum information about a microarray experiment (MIAME)-toward standards for microarray data. Nat Genet 29(4):365–371

    Article  CAS  Google Scholar 

  27. Stekel D (2003) Microarray bioinformatics. Cambridge University Press, Cambridge

    Book  Google Scholar 

  28. Adams R, Bischof L (1994) Seeded Region Growing. IEEE Trans Pattern Anal Machine Intell 16(6):641–647

    Article  Google Scholar 

  29. Quackenbush J (2002) Microarray data normalization and transformation. Nat Genet 32(Suppl):496–501

    Article  CAS  Google Scholar 

  30. Eisen MB et al (1998) Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci USA 95(25):14863–14868

    Article  CAS  Google Scholar 

  31. Bloom JS et al (2009) Measuring differential gene expression by short read sequencing: quantitative comparison to 2-channel gene expression microarrays. BMC Genomics 10:221

    Article  Google Scholar 

  32. van Vliet AH (2009) Next generation sequencing of microbial transcriptomes: challenges and opportunities. FEMS Microbiol Lett 302(1):1–7

    Article  Google Scholar 

  33. Hinton JCD et al (2004) Benefits and pitfalls of using microarrays to monitor bacterial gene expression during infection. Curr Opin Microbiol 7(3):277–282

    Article  CAS  Google Scholar 

  34. Jiang H, Wong WH (2009) Statistical inferences for isoform expression in RNA-seq. Bioinformatics 25(8):1026–1032

    Article  CAS  Google Scholar 

  35. Oshlack A, Wakefield MJ (2009) Transcript length bias in RNA-seq data confounds systems biology. Biol Direct 4:14

    Article  Google Scholar 

  36. Sanger F et al (1977) Nucleotide sequence of bacteriophage phi X174 DNA. Nature 265(5596):687–695

    Article  CAS  Google Scholar 

  37. Marsh S (2007) Pyrosequencing applications. Methods Mol Biol 373:15–24

    CAS  Google Scholar 

  38. Sanger F, Nicklen S, Coulson AR (1977) DNA sequencing with chain-terminating inhibitors. Proc Natl Acad Sci USA 74(12):5463–5467

    Article  CAS  Google Scholar 

  39. Ronaghi M, Uhlen M, Nyren P (1998) A sequencing method based on real-time pyrophosphate. Science 281(5375):5363

    Article  Google Scholar 

  40. Blow N (2009) Transcriptomics: the digital generation. Nature 458(7235):239–242

    Article  CAS  Google Scholar 

  41. Denoeud F et al (2008) Annotating genomes with massive-scale RNA sequencing. Genome Biol 9(12):R175

    Article  Google Scholar 

  42. Morozova O, Marra MA (2008) Applications of next-generation sequencing technologies in functional genomics. Genomics 92(5):255–264

    Article  CAS  Google Scholar 

  43. Margulies M et al (2005) Genome sequencing in microfabricated high-density picolitre reactors. Nature 437(7057):376–380

    CAS  Google Scholar 

  44. Wang L et al (2009) DEGseq: an R package for identifying differentially expressed genes from RNA-seq data. Bioinformatics 26(1):136–138

    Article  CAS  Google Scholar 

  45. Sultan M et al (2008) A global view of gene activity and alternative splicing by deep sequencing of the human transcriptome. Science 321(5891):956–960

    Article  CAS  Google Scholar 

  46. Cloonan N et al (2008) Stem cell transcriptome profiling via massive-scale mRNA sequencing. Nat Methods 5(7):613–619

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  48. de Magalhaes JP, Finch CE, Janssens G (2010) Next-generation sequencing in aging research: emerging applications, problems, pitfalls and possible solutions. Ageing Res Rev 9(3):315–323

    Article  Google Scholar 

  49. Zhou XG et al (2010) The next-generation sequencing technology: a technology review and future perspective. Sci China Life Sci 53(1):44–57

    Article  CAS  Google Scholar 

  50. Nowrousian M (2010) Next-generation sequencing techniques for eukaryotic microorganisms: sequencing-based solutions to biological problems. Eukaryot Cell 9(9):1300–1310

    Article  CAS  Google Scholar 

  51. Simon SA et al (2009) Short-read sequencing technologies for transcriptional analyses. Annu Rev Plant Biol 60:305–333

    Article  CAS  Google Scholar 

  52. Morozova O, Hirst M, Marra MA (2009) Applications of new sequencing technologies for transcriptome analysis. Annu Rev Genomics Hum Genet 10:135–151

    Article  CAS  Google Scholar 

  53. Morin RD et al (2008) Profiling the HeLa S3 transcriptome using randomly primed cDNA and massively parallel short-read sequencing. Biotechniques 45(1):81

    Article  CAS  Google Scholar 

  54. Graf A et al (2009) Yeast systems biotechnology for the production of heterologous proteins. FEMS Yeast Res 9(3):335–348

    Article  CAS  Google Scholar 

  55. Wang B et al (2010) Survey of the transcriptome ofAspergillus oryzaevia massively parallel mRNA sequencing. Nucleic Acids Res 38(15):5075–5087

    Article  CAS  Google Scholar 

  56. Simon C, Daniel R (2009) Achievements and new knowledge unraveled by metagenomic approaches. Appl Microbiol Biotechnol 85(2):265–276

    Article  CAS  Google Scholar 

  57. Rubina AY et al (2005) Quantitative immunoassay of biotoxins on hydrogel-based protein microchips. Anal Biochem 340(2):317–329

    Article  CAS  Google Scholar 

  58. Zhu H, Snyder M (2003) Protein chip technology. Curr Opin Chem Biol 7(1):55–63

    Article  CAS  Google Scholar 

  59. Zong Y et al (2007) Forward-phase and reverse-phase protein microarray. Methods Mol Biol 381:363–374

    Article  CAS  Google Scholar 

  60. Wu P, Castner DG, Grainger DW (2008) Diagnostic devices as biomaterials: a review of nucleic acid and protein microarray surface performance issues. J Biomater Sci Polym Ed 19(6):725–753

    Article  CAS  Google Scholar 

  61. Kukar T et al (2002) Protein microarrays to detect protein–protein interactions using red and green fluorescent proteins. Anal Biochem 306(1):50–54

    Article  CAS  Google Scholar 

  62. Tonkinson JL, Stillman BA (2002) Nitrocellulose: a tried and true polymer finds utility as a post-genomic substrate. Front Biosci 7:c1–c12

    Article  CAS  Google Scholar 

  63. Grainger DW et al (2007) Current microarray surface chemistries. Methods Mol Biol 381:37–57

    Article  CAS  Google Scholar 

  64. Reck M et al (2007) Optimization of a microarray sandwich-ELISA against hINF-gamma on a modified nitrocellulose membrane. Biotechnol Prog 23(6):1498–1505

    Article  CAS  Google Scholar 

  65. Walter J-G, Reck M, Praulich I (2010) Protein microarrays: reduced autofluorescence and improved LOD. Eng Life Sci 10(2):103–108

    CAS  Google Scholar 

  66. Walter JG et al (2008) Systematic investigation of optimal aptamer immobilization for protein-microarray applications. Anal Chem 80(19):7372–7378

    Article  CAS  Google Scholar 

  67. Doyle A, Griffiths J, Newel D (1994) Cell & tissue culture: laboratory procedures. Wiley, New York, pp 3:01–3:03

    Google Scholar 

  68. Wilkening S, Stahl F, Bader A (2003) Comparison of primary human hepatocytes and hepatoma cell line Hepg2 with regard to their biotransformation properties. Drug Metab Dispos 31(8):1035–1042

    Article  CAS  Google Scholar 

  69. Castro-Melchor ML, Le H, Hu W-S (2011) Transcriptome data analysis for cell culture process. Adv Biochem Eng Biotechnol

    Google Scholar 

  70. Achilles J et al (2007) Isolation of intact RNA from cytometrically sorted Saccharomyces cerevisiae for the analysis of intrapopulation diversity of gene expression. Nat Protoc 2(9):2203–2211

    Article  CAS  Google Scholar 

  71. Browne SM, Al-Rubeai M (2007) Selection methods for high-producing mammalian cell lines. Trends Biotechnol 25(9):425–432

    Article  CAS  Google Scholar 

  72. Brezinsky SC et al (2003) A simple method for enriching populations of transfected CHO cells for cells of higher specific productivity. J Immunol Methods 277(1–2):141–155

    Article  CAS  Google Scholar 

  73. Kantardjieff A et al (2009) Transcriptome and proteome analysis of Chinese hamster ovary cells under low temperature and butyrate treatment. J Biotechnol 145(2):143–159

    Article  Google Scholar 

  74. Jayat C, Ratinaud MH (1993) Cell cycle analysis by flow cytometry: principles and applications. Biol Cell 78(1–2):15–25

    Article  CAS  Google Scholar 

  75. Majore I et al (2009) Identification of subpopulations in mesenchymal stem cell-like cultures from human umbilical cord. Cell Commun Signal 7:6

    Article  Google Scholar 

  76. Moretti P et al (2010) Characterization and improvement of cell line performance via flow cytometry and cell sorting. Eng Life Sci 10(2):130–138

    CAS  Google Scholar 

  77. Spellman PT, Sherlock G (2004) Reply: whole-culture synchronization—effective tools for cell cycle studies. Trends Biotechnol 22(6):270–273

    Article  CAS  Google Scholar 

  78. Dutton RL, Scharer J, Moo-Young M (2006) Cell cycle phase dependent productivity of a recombinant Chinese hamster ovary cell line. Cytotechnology 52(2):55–69

    CAS  Google Scholar 

  79. Fu X et al (2009) Estimating accuracy of RNA-seq and microarrays with proteomics. BMC Genomics 10:161

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute for Technical Chemistry, Leibniz University, Hannover Callinstr. 5, 30167, Hannover, Germany

    Frank Stahl, Kai Mutz, Daniel Landgrebe, Miriam Lübbecke, Cornelia Kasper, Johanna Walter & Thomas Scheper

  2. Fg Prozessanalytik und Getreidetechnologie, Universität Hohenheim, Garbenstr. 23, 70599, Stuttgart, Germany

    Bernd Hitzmann

Authors
  1. Frank Stahl
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bernd Hitzmann
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kai Mutz
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Daniel Landgrebe
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Miriam Lübbecke
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Cornelia Kasper
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Johanna Walter
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Thomas Scheper
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Frank Stahl .

Editor information

Editors and Affiliations

  1. , Dept. Chemical Engineering and Material, University of Minnesota, Washington Ave. SE 421, Minneapolis, 55455, Minnesota, USA

    Wei Shou Hu

  2. , Institut für Bioprozess- und Biosystemte, Technische Universität Hamburg-Harburg, Denickestr. 15, Hamburg, 21073, Germany

    An-Ping Zeng

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Stahl, F. et al. (2011). Transcriptome Analysis. In: Hu, W., Zeng, AP. (eds) Genomics and Systems Biology of Mammalian Cell Culture. Advances in Biochemical Engineering Biotechnology, vol 127. Springer, Berlin, Heidelberg. https://doi.org/10.1007/10_2011_102

Download citation

Publish with us

Policies and ethics

Search

Navigation