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Licensed Unlicensed Requires Authentication Published by De Gruyter February 20, 2007

Quantification of relative changes in specific mRNAs from frozen whole blood – methodological considerations and clinical implications

  • Reidun Øvstebø , Knut Lande , Peter Kierulf and Kari Bente Foss Haug

Abstract

Background: Based on quantification of relative changes in lipopolysaccharide (LPS)-regulated mRNA transcripts, the present study aimed to establish a robotic method to isolate RNA from stabilized frozen whole blood suitable for gene expression analysis.

Methods: Whole blood (±LPS) was stored in EasyLyse™ solution or PAXgene® tubes (room temperature and −70°C) for comparison of storage methods, then subjected to robotic isolation of total RNA. Yield, quality and relative changes in 11 selected mRNA transcripts were examined. Method precision (% coefficient of variation) for a longitudinal control was established. The influence of globin mRNA from reticulocytes in quantitative RT-PCR was examined.

Results: All storage methods gave a similar high-quality RNA yield. The differences in the 11 specific mRNA quantities stored in EasyLyse or PAXgene® at −70°C were small: mean −0.01 (95% CI –0.19 to 0.17). The CV for mRNAs in the longitudinal control ranged from 3% to 150%. Thus, the number of replicates necessary to detect a 20% difference (power 0.8) ranged from 2–50. Globin mRNA had no influence on quantitative RT-PCR

Conclusions: Based on measuring the relative changes in specific mRNAs in LPS-exposed whole blood, we conclude that PAXgene® tubes stored at −70°C could preferentially be used. This may open opportunities for monitoring gene expression changes in clinical settings.

Clin Chem Lab Med 2007;45:171–6.

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Corresponding author: Reidun Øvstebø, R&D Group, Department of Clinical Chemistry, Ullevål University Hospital, 0407 Oslo, Norway Phone: +47-22119490, Fax: +47-22118189,

References

1. Whitney AR, Diehn M, Popper SJ, Alizadeh AA, Boldrick JC, Relman DA, et al. Individuality and variation in gene expression patterns in human blood. Proc Natl Acad Sci USA2003;100:1896–901.10.1073/pnas.252784499Search in Google Scholar

2. Debey S, Schoenbeck U, Hellmich M, Gathof BS, Pillai R, Zander T, et al. 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 J2004;4:193–207.10.1038/sj.tpj.6500240Search in Google Scholar

3. Gibbons GH, Liew CC, Goodarzi MO, Rotter JI, Hsueh WA, Siragy HM, et al. Genetic markers: progress and potential for cardiovascular disease. Circulation2004;109:IV47–58.10.1161/01.CIR.0000133440.86427.26Search in Google Scholar

4. Pahl A. Gene expression profiling using RNA extracted from whole blood: technologies and clinical applications. Expert Rev Mol Diagn2005;5:43–52.10.1586/14737159.5.1.43Search in Google Scholar

5. Radich JP, Mao M, Stepaniants S, Biery M, Castle J, Ward T, et al. Individual-specific variation of gene expression in peripheral blood leukocytes. Genomics2004;83:980–8.10.1016/j.ygeno.2003.12.013Search in Google Scholar

6. Pahl A, Brune K. Stabilization of gene expression profiles in blood after phlebotomy. Clin Chem2002;48:2251–3.10.1093/clinchem/48.12.2251Search in Google Scholar

7. Muirhead KA, Wallace PK, Schmitt TC, Frescatore RL, Franco JA, Horan PK. Methodological considerations for implementation of lymphocyte subset analysis in a clinical reference laboratory. Ann NY Acad Sci1986;468:113–27.10.1111/j.1749-6632.1986.tb42034.xSearch in Google Scholar

8. Stordeur P, Zhou L, Byl B, Brohet F, Burny W, de Groote D, et al. Immune monitoring in whole blood using real-time PCR. J Immunol Methods2003;276:69–77.10.1016/S0022-1759(03)00074-7Search in Google Scholar

9. Feder JN, Gnirke A, Thomas W, Tsuchihashi Z, Ruddy DA, Basava A, et al. A novel MHC class I-like gene is mutated in patients with hereditary haemochromatosis. Nat Genet1996;13:399–408.10.1038/ng0896-399Search in Google Scholar PubMed

10. Ovstebo R, Haug KB, Lande K, Kierulf P. PCR-based calibration curves for studies of quantitative gene expression in human monocytes: development and evaluation. Clin Chem2003;49:425–32.10.1373/49.3.425Search in Google Scholar PubMed

11. Goulter AB, Harmer DW, Clark KL. Evaluation of low density array technology for quantitative parallel measurement of multiple genes in human tissue. BMC Genomics2006;7:34.10.1186/1471-2164-7-34Search in Google Scholar

12. Livak KJ, Schmittgen TD. Analysis of relative gene expression data using real-time quantitative PCR and the 2(–ΔΔC(T)) method. Methods2001;25:402–8.10.1006/meth.2001.1262Search in Google Scholar

13. Duran EM, Shapshak P, Worley J, Minagar A, Ziegler F, Haliko S, et al. Presenilin-1 detection in brain neurons and FOXP3 in peripheral blood mononuclear cells: normalizer gene selection for real time reverse transcriptase PCR using the ΔΔCt method. Front Biosci2005;10:2955–65.10.2741/1751Search in Google Scholar

14. Bland JM, Altman DG. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet1986;1:307–10.10.1016/S0140-6736(86)90837-8Search in Google Scholar

15. Chai V, Vassilakos A, Lee Y, Wright JA, Young AH. Optimization of the PAXgene blood RNA extraction system for gene expression analysis of clinical samples. J Clin Lab Anal2005;19:182–8.10.1002/jcla.20075Search in Google Scholar PubMed PubMed Central

16. Tumor Analysis Best Practices Working Group. Expression profiling – best practices for data generation and interpretation in clinical trials. Nat Rev Genet 2004;5:229–37.10.1038/nrg1297Search in Google Scholar PubMed

17. Shou J, Dotson C, Qian HR, Tao W, Lin C, Lawrence F, et al. Optimized blood cell profiling method for genomic biomarker discovery using high-density microarray. Biomarkers2005;10:310–20.10.1080/13547500500218583Search in Google Scholar PubMed

18. Spijker S, van de Leemput JC, Hoekstra C, Boomsma DI, Smit AB. Profiling gene expression in whole blood samples following an in-vitro challenge. Twin Res2004;7:564–70.10.1375/1369052042663878Search in Google Scholar PubMed

19. Imbeaud S, Graudens E, Boulanger V, Barlet X, Zaborski P, Eveno E, et al. Towards standardization of RNA quality assessment using user-independent classifiers of microcapillary electrophoresis traces. Nucleic Acids Res2005;33:e56.10.1093/nar/gni054Search in Google Scholar PubMed PubMed Central

20. Fleige S, Pfaffl MW. RNA integrity and the effect on the real-time qRT-PCR performance. Mol Aspects Med2006;27:126–39.10.1016/j.mam.2005.12.003Search in Google Scholar PubMed

21. Li D, Butt A, Clarke S, Swaminathana R. Real-time quantitative PCR measurement of thyroglobulin mRNA in peripheral blood of thyroid cancer patients and healthy subjects. Ann NY Acad Sci2004;1022:147–51.10.1196/annals.1318.024Search in Google Scholar

22. Wurfel MM, Park WY, Radella F, Ruzinski J, Sandstrom A, Strout J, et al. Identification of high and low responders to lipopolysaccharide in normal subjects: an unbiased approach to identify modulators of innate immunity. J Immunol2005;175:2570–8.10.4049/jimmunol.175.4.2570Search in Google Scholar

23. Boldrick JC, Alizadeh AA, Diehn M, Dudoit S, Liu CL, Belcher CE, et al. Stereotyped and specific gene expression programs in human innate immune responses to bacteria. Proc Natl Acad Sci USA2002;99:972–7.10.1073/pnas.231625398Search in Google Scholar

24. Fan H, Hegde PS. The transcriptome in blood: challenges and solutions for robust expression profiling. Curr Mol Med2005;5:3–10.10.2174/1566524053152861Search in Google Scholar

25. McPhail S, Goralski TJ. Overcoming challenges of using blood samples with gene expression microarrays to advance patient stratification in clinical trials. Drug Discov Today2005;10:1485–7.10.1016/S1359-6446(05)03644-5Search in Google Scholar

26. Debey S, Zander T, Brors B, Popov A, Eils R, Schultze JL. A highly standardized, robust, and cost-effective method for genome-wide transcriptome analysis of peripheral blood applicable to large-scale clinical trials. Genomics2005;87:653–64.10.1016/j.ygeno.2005.11.010Search in Google Scholar PubMed

Published Online: 2007-02-20
Published in Print: 2007-02-01

©2007 by Walter de Gruyter Berlin New York

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