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RNA Stabilization of Peripheral Blood and Profiling by Bead Chip Analysis

  • Svenja Debey-Pascher
  • Daniela Eggle
  • Joachim L. Schultze
Part of the METHODS IN MOLECULAR BIOLOGY™ book series (MIMB, volume 496)

Abstract

Gene expression profiling of peripheral blood is a very attractive approach for the development of new diagnostic markers of blood-borne but also systemic diseases as well as the development of biomarkers for drug development. Since most cellular components of peripheral blood are specialized to quickly respond to exogenous stimuli, sample procurement approaches are required that reduce the overall impact of ex vivo changes in gene expression due to technical issues such as prolonged sample handling or temperature changes during transportation of the blood prior to genome-wide analysis. To address these needs, a whole blood RNA stabilization technology was combined with a bead-based oligonucleotide microarray technology for genome-wide transcriptome analysis. Cells, and thereby also RNA is immediately stabilized after the blood draw by a commercially available device (PAXgene). Total RNA is then extracted from PAXgene-stabilized blood and subjected to microarray analysis. In our hands, the Illumina BeadChip array platform outperformed other microarray platforms. Combining RNA stabilization of peripheral blood with bead-based oligonucleotide microarray technology is not only applicable to small single-center studies with optimized infrastructure but also to large scale multi-center trials that are mandatory for the development of predictive markers for disease and treatment outcome.

Key Words

Transcriptome gene expression profiling peripheral blood RNA stabilization bead chip arrays 

Notes

Acknowledgment

This work was mainly supported by the Alexander von Humboldt Foundation via a Sofja-Kovalevskaja Award to JLS, JLS is a member of the National Genome Research Network (NGFN) in Germany.

References

  1. 1.
    Branca, M. (2003) Genetics and medicine. Putting gene arrays to the test. Science 300, 238.CrossRefPubMedGoogle Scholar
  2. 2.
    Schubert, C. M. (2003) Microarray to be used as routine clinical screen. Nat Med 9, 9.CrossRefPubMedGoogle Scholar
  3. 3.
    Alizadeh, A. A., Eisen, M. B., Davis, R. E., Ma, C., Lossos, I. S., Rosenwald, A., Boldrick, J. C., Sabet, H., Tran, T., Yu, X., Powell, J. I., Yang, L., Marti, G. E., Moore, T., Hudson, J. Jr., Lu, L,, Lewis, D. B., Tibshirani, R., Sherlock, G., Chan, W. C,, Greiner, T. C., Weisenburger, D. D., Armitage, J. O., Warnke, R., Levy, R., Wilson, W., Grever, M. R., Byrd, J. C., Botstein, D., Brown, P. O., Staudt, L. M. (2000) Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature 403, 503–511.CrossRefPubMedGoogle Scholar
  4. 4.
    Bittner, M., Meltzer, P., Chen, Y., Jiang, Y., Seftor, E., Hendrix, M., Radmacher, M., Simon, R., Yakhini, Z., Ben-Dor, A., Sampas, N., Dougherty, E., Wang, E., Marincola, F., Gooden, C., Lueders, J., Glatfelter, A., Pollock, P., Carpten, J., Gillanders, E., Leja, D., Dietrich, K., Beaudry, C., Berens, M., Alberts, D., Sondak, V. (2000) Molecular classification of cutaneous malignant melanoma by gene expression profiling. Nature 406, 536–540.CrossRefPubMedGoogle Scholar
  5. 5.
    Golub, T. R., Slonim, D. K., Tamayo, P., Huard, C., Gaasenbeek, M., Mesirov, J. P., Coller, H., Loh, M. L., Downing, J. R., Caligiuri, M. A., Bloomfield, C. D., Lander, E. S. (1999) Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286, 531–537.CrossRefPubMedGoogle Scholar
  6. 6.
    Bullinger, L., Döhner, K., Bair, E., Fröhling, S., Schlenk, R. F., Tibshirani, R., Döhner, H., Pollack, J. R. (2004) Use of gene-expression profiling to identify prognostic subclasses in adult acute myeloid leukemia. N Engl J Med 350, 1605–1616.CrossRefPubMedGoogle Scholar
  7. 7.
    Rosenwald, A., et al., (2002) The use of molecular profiling to predict survival after chemotherapy for diffuse large-B-cell lymphoma. N Engl J Med 346, 1937–1947.CrossRefPubMedGoogle Scholar
  8. 8.
    Shipp, M. A., Ross, K. N., Tamayo, P., Weng, A. P., Kutok, J. L., Aguiar, R. C., Gaasenbeek, M., Angelo, M., Reich, M., Pinkus, G. S., Ray, T. S., Koval, M. A., Last, K. W., Norton, A., Lister, T. A., Mesirov, J., Neuberg, D. S., Lander, E. S., Aster, J. C., Golub, T. R. (2002) Diffuse large B-cell lymphoma outcome prediction by gene-expression profiling and supervised machine learning. Nat Med 8, 68–74.CrossRefPubMedGoogle Scholar
  9. 9.
    Valk, P. J., Verhaak, R. G., Beijen, M. A., Erpelinck, C. A., Barjesteh van Waalwijk van Doorn-Khosrovani, S., Boer, J. M., Beverloo, H. B., Moorhouse, M. J., van der Spek, P. J., Löwenberg, B., Delwel, R. (2004) Prognostically useful gene-expression profiles in acute myeloid leukemia. N Engl J Med 350, 1617–1628.Google Scholar
  10. 10.
    van de Vijver, M. J., He, Y. D., van’t Veer, L. J., Dai, H., Hart, A. A., Voskuil, D. W., Schreiber, G. J., Peterse, J. L., Roberts, C., Marton, M. J., Parrish, M., Atsma, D., Witteveen, A., Glas, A., Delahaye, L., van der Velde, T., Bartelink, H., Rodenhuis, S., Rutgers, E. T., Friend, S. H., Bernards, R. (2002) A gene-expression signature as a predictor of survival in breast cancer. N Engl J Med 347, 1999–2009.Google Scholar
  11. 11.
    Gerhold, D. L., Jensen, R. V., Gullans, S. R. (2002) Better therapeutics through microarrays. Nat Genet 32 suppl, 547–551.CrossRefPubMedGoogle Scholar
  12. 12.
    Gunther, E. C., Stone, D. J., Gerwien, R. W., Bento, P., Heyes, M. P. (2003) Prediction of clinical drug efficacy by classification of drug-induced genomic expression profiles in vitro. Proc Natl Acad Sci USA 100, 9608–9613.CrossRefPubMedGoogle Scholar
  13. 13.
    Scherf, U., Ross, D. T., Waltham, M., Smith, L. H., Lee, J. K., Tanabe, L., Kohn, K. W., Reinhold, W. C., Myers, T. G., Andrews, D. T., Scudiero, D. A., Eisen, M. B., Sausville, E. A., Pommier, Y., Botstein, D., Brown, P. O., Weinstein, J. N. (2000) A gene expression database for the molecular pharmacology of cancer. Nat Genet 24, 236–244.CrossRefPubMedGoogle Scholar
  14. 14.
    Bennett, L., Palucka, A. K., Arce, E., Cantrell, V., Borvak, J., Banchereau, J., Pascual, V. (2003) Interferon and granulopoiesis signatures in systemic lupus erythematosus blood. J Exp Med 197, 711–723.CrossRefPubMedGoogle Scholar
  15. 15.
    Burczynski, M. E., Twine, N. C., Dukart, G., Marshall, B., Hidalgo, M., Stadler, W. M., Logan, T., Dutcher, J., Hudes, G., Trepicchio, W. L., Strahs, A., Immermann, F., Slonim, D. K., Dorner, A. J. (2005) Transcriptional profiles in peripheral blood mononuclear cells prognostic of clinical outcomes in patients with advanced renal cell carcinoma. Clin Cancer Res 11, 1181–1189.PubMedGoogle Scholar
  16. 16.
    Palucka, A. K., Blanck, J. P., Bennett, L., Pascual, V., Banchereau, J. (2005) Cross-regulation of TNF and IFN-alpha in autoimmune diseases. Proc Natl Acad Sci USA 102, 3372–3377.CrossRefPubMedGoogle Scholar
  17. 17.
    Pascual, V., Allantaz, F., Arce, E., Punaro, M., Banchereau, J. (2005) Role of interleukin-1 (IL-1) in the pathogenesis of systemic onset juvenile idiopathic arthritis and clinical response to IL-1 blockade. J Exp Med 201, 1479–1486.CrossRefPubMedGoogle Scholar
  18. 18.
    Osman, I., Bajorin, D. F., Sun, T. T., Zhong, H., Douglas, D., Scattergood, J., Zheng, R., Han, M., Marshall, K. W., Liew, C. C. (2006) Novel blood biomarkers of human urinary bladder cancer. Clin Cancer Res 12, 3374–3380.CrossRefPubMedGoogle Scholar
  19. 19.
    Ramilo, O., Allman, W., Chung, W., Mejias, A., Ardura, M., Glaser, C., Wittkowski, K. M., Piqueras, B., Banchereau, J., Palucka, A. K., Chaussabel, D. (2007) Gene expression patterns in blood leukocytes discriminate patients with acute infections. Blood 109, 2066–2077.CrossRefPubMedGoogle Scholar
  20. 20.
    Brazma, A., Hingamp, P., Quackenbush, J., Sherlock, G., Spellman, P., Stoeckert, C., Aach, J., Ansorge, W., Ball, C. A., Causton, H. C., Gaasterland, T., Glenisson, P., Holstege, F. C., Kim, I. F., Markowitz, V., Matese, J. C., Parkinson, H., Robinson, A., Sarkans, U., Schulze-Kremer, S., Stewart, J., Taylor, R., Vilo, J., Vingron, M. (2001) Minimum information about a microarray experiment (MIAME)-toward standards for microarray data. Nat Genet 29, 365–371.CrossRefPubMedGoogle Scholar
  21. 21.
    Bammler, T., et al., (2005) Standardizing global gene expression analysis between laboratories and across platforms. Nat Methods 2, 351–356.CrossRefPubMedGoogle Scholar
  22. 22.
    Canales, R. D., Luo, Y., Willey, J. C., Austermiller, B., Barbacioru, C. C., Boysen, C., Hunkapiller, K., Jensen, R. V., Knight, C. R., Lee, K. Y., Ma, Y., Maqsodi, B., Papallo, A., Peters, E. H., Poulter, K., Ruppel, P. L., Samaha, R. R., Shi, L., Yang, W., Zhang, L., Goodsaid, F. M. (2006) Evaluation of DNA microarray results with quantitative gene expression platforms. Nat Biotechnol 24, 1115–1122.Google Scholar
  23. 23.
    Guo, L., Lobenhofer, E. K., Wang, C., Shippy, R., Harris, S. C., Zhang, L., Mei, N., Chen, T., Herman, D., Goodsaid, F. M., Hurban, P., Phillips, K. L., Xu, J., Deng, X., Sun, Y. A., Tong, W., Dragan, Y.P., Shi, L. (2006) Rat toxicogenomic study reveals analytical consistency across microarray platforms. Nat Biotechnol 24, 1162–1169.CrossRefPubMedGoogle Scholar
  24. 24.
    Irizarry, R. A., Warren, D., Spencer, F., Kim, I. F., Biswal, S., Frank, B. C., Gabrielson, E., Garcia, J. G., Geoghegan, J., Germino, G., Griffin, C., Hilmer, S. C., Hoffman, E., Jedlicka, A. E., Kawasaki, E., Martínez-Murillo, F., Morsberger, L., Lee, H., Petersen, D., Quackenbush, J., Scott, A., Wilson, M., Yang, Y., Ye, S.Q., Yu, W. (2005) Multiple-laboratory comparison of microarray platforms. Nat Methods 2, 345–350.CrossRefPubMedGoogle Scholar
  25. 25.
    Larkin, J. E., Frank, B. C., Gavras, H., Sultana, R., Quackenbush, J. (2005) Independence and reproducibility across microarray platforms. Nat Methods 2, 337–344.CrossRefPubMedGoogle Scholar
  26. 26.
    Patterson, T. A., Lobenhofer, E. K., Fulmer-Smentek, S. B., Collins, P. J., Chu, T. M., Bao, W., Fang, H., Kawasaki, E. S., Hager, J., Tikhonova, I. R., Walker, S. J., Zhang, L., Hurban, P., de Longueville, F., Fuscoe, J. C., Tong, W., Shi, L., Wolfinger, R.D. (2006) Performance comparison of one-color and two-color platforms within the MicroArray Quality Control (MAQC) project. Nat Biotechnol 24, 1140–1150.Google Scholar
  27. 27.
    Shi, L., et al., (2006) The MicroArray Quality Control (MAQC) project shows inter- and intraplatform reproducibility of gene expression measurements. Nat Biotechnol 24, 1151–1161.CrossRefPubMedGoogle Scholar
  28. 28.
    Tong, W., Lucas, A. B., Shippy, R., Fan, X., Fang, H., Hong, H., Orr, M. S., Chu, T. M., Guo, X., Collins, P. J., Sun, Y. A., Wang, S. J., Bao, W., Wolfinger, R. D., Shchegrova, S., Guo, L., Warrington, J. A., Shi, L. (2006) Evaluation of external RNA controls for the assessment of microarray performance. Nat Biotechnol 24, 1132–1139.CrossRefPubMedGoogle Scholar
  29. 29.
    Debey, S., Schoenbeck, U., Hellmich, M., Gathof, B. S., Pillai, R., Zander, T., Schultze, J. L. (2004) Comparison of different isolation techniques prior gene expression profiling of blood derived cells: impact on physiological responses, on overall expression and the role of different cell types. Pharmacogenomics J 4, 193–207.CrossRefPubMedGoogle Scholar
  30. 30.
    Debey, S., Zander, T., Brors, B., Popov, A., Eils, R., Schultze, J. L. (2006) A highly standardized, robust, and cost-effective method for genome-wide transcriptome analysis of peripheral blood applicable to large-scale clinical trials. Genomics 87, 653–664.CrossRefPubMedGoogle Scholar
  31. 31.
    Rainen, L., Oelmueller, U., Jurgensen, S., Wyrich, R., Ballas, C., Schram, J., Herdman, C., Bankaitis-Davis, D., Nicholls, N., Trollinger, D., Tryon, V. (2002) Stabilization of mRNA expression in whole blood samples. Clin Chem 48, 1883–1890.PubMedGoogle Scholar
  32. 32.
    Cobb, J. P., et al., (2005) Application of genome-wide expression analysis to human health and disease. Proc Natl Acad Sci USA 102, 4801–4806.CrossRefPubMedGoogle Scholar
  33. 33.
    Kacharmina, J. E., Crino, P. B., Eberwine, J. (1999) Preparation of cDNA from single cells and subcellular regions. Methods Enzymol 303, 3–18.CrossRefPubMedGoogle Scholar
  34. 34.
    Pabon, C., Modrusan, Z., Ruvolo, M. V., Coleman, I. M., Daniel, S., Yue, H., Arnold, L. J. Jr. (2001) Optimized T7 amplification system for microarray analysis. Biotechniques 31, 874–879.PubMedGoogle Scholar
  35. 35.
    Van Gelder, R. N., von Zastrow, M. E., Yool, A., Dement, W. C., Barchas, J. D., Eberwine, J. H. (1990) Amplified RNA synthesized from limited quantities of heterogeneous cDNA. Proc Natl Acad Sci USA 87, 1663–1667.CrossRefPubMedGoogle Scholar
  36. 36.
    Schultze, J. L., Eggle, D. (2007) IlluminaGUI: graphical user interface for analyzing gene expression data generated on the Illumina platform. Bioinformatics 23, 1431–1433.CrossRefPubMedGoogle Scholar
  37. 37.
    Dudoit, S., et al., (2002) Statistical methods for identifying differentially expressed genes in replicated cDNA microarray experiments. Statistica Sinica 12, 111–139.Google Scholar
  38. 38.
    Bolstad, B. M., Irizarry, R. A., Astrand, M., Speed, T. P. (2003) A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics 19, 185–193.CrossRefPubMedGoogle Scholar
  39. 39.
    Huber, W., von Heydebreck, A., Sültmann, H., Poustka, A., Vingron, M. (2002) Variance stabilization applied to microarray data calibration and to the quantification of differential expression. Bioinformatics 18 Suppl (1), S96–104.Google Scholar
  40. 40.
    Workman, C., Jensen, L. J., Jarmer, H., Berka, R., Gautier, L., Nielser, H. B., Saxild, H. H., Nielsen, C., Brunak, S., Knudsen, S. (2002) A new non-linear normalization method for reducing variability in DNA microarray experiments. Genome Biol 3, research0048.Google Scholar
  41. 41.
    Tibshirani, R., Hastie, T., Narasimhan, B., Chu, G. (2002) Diagnosis of multiple cancer types by shrunken centroids of gene expression. Proc Natl Acad Sci USA 99, 6567–6572.CrossRefPubMedGoogle Scholar
  42. 42.
    Affymetrix Technical Note: Globin Reduction Protocol: A Method for Processing Whole Blood RNA Samples for Improved Array Results. (2003).Google Scholar

Copyright information

© Humana Press, a part of Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • Svenja Debey-Pascher
    • 1
  • Daniela Eggle
    • 1
  • Joachim L. Schultze
    • 1
  1. 1.Department for Genomics, Life and Medical SciencesUniversity of BonnGermany

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