Separation of whole blood cells and its impact on gene expression

  • Andreas Grützkau
  • Andreas Radbruch
Part of the Progress in Inflammation Research book series (PIR)


Ease of sample collection predestines peripheral blood cells, and their transcriptional or translational products, to become surrogate markers for inflammatory processes in several diseases, including cancer, autoimmune, genetic or metabolic disorders. Therefore, peripheral blood mononuclear cells (PBMCs) and whole blood have been commonly used for genome-wide expression analyses. In comparison to whole blood, which primarily consists of erythrocytes, reticulocytes, platelets, granulocytes, T and B lymphocytes, NK cells and monocytes, PBMCs were pre-enriched for lymphocyte populations, NK cells, and monocytes by density gradient centrifugation, such as Ficoll or Percoll. But the cellular composition of blood shows inter-individual variations and is intensely influenced by pathophysiological processes, such as inflammation. Thus, success in terms of reproducibility and interpretability of a microarray experiment greatly depends on samples being comparable in quality and in quantitative cellular composition.

In this context, some theoretical considerations will be bestowed upon problems arising from samples of heterogeneous composition. To overcome these limitations, cell sorting strategies will be presented that have been optimized with respect to the special requirements necessary for global gene expression studies.


Rheumatic Disease Global Gene Expression Cellular Composition Gene Expression Array LYMPHO Cyte 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Grant GR, Manduchi E, Pizarro A, Stoeckert CJ,Jr. Maintaining data integrity in microarray data management. Biotechnol Bioeng 2003; 84(7): 795–800PubMedCrossRefGoogle Scholar
  2. 2.
    Wilkes T, Laux H, Foy CA. Microarray data quality — review of current developments. OMICS 2007; 11(1): 1–13PubMedCrossRefGoogle Scholar
  3. 3.
    Brazma A, Hingamp P, Quackenbush J, Sherlock G, Spellman P, Stoeckert C et al. Minimum information about a microarray experiment (MIAME)-toward standards for microarray data. Nat Genet 2001; 29(4): 365–71PubMedCrossRefGoogle Scholar
  4. 4.
    Geschwind DH. Sharing gene expression data: an array of options. Nat Rev Neurosci 2001; 2(6): 435–8PubMedCrossRefGoogle Scholar
  5. 5.
    Imbeaud S, Auffray C. ‘The 39 steps’ in gene expression profiling: critical issues and proposed best practices for microarray experiments. Drug Discov Today 2005; 10(17): 1175–82PubMedCrossRefGoogle Scholar
  6. 6.
    Shi L, Reid LH, Jones WD, Shippy R, Warrington JA, Baker SC et al. The MicroArray Quality Control (MAQC) project shows inter-and intraplatform reproducibility of gene expression measurements. Nat Biotechnol 2006; 24(9): 1151–61PubMedCrossRefGoogle Scholar
  7. 7.
    Bakay M, Chen YW, Borup R, Zhao P, Nagaraju K, Hoffman EP. Sources of variability and effect of experimental approach on expression profiling data interpretation. BMC Bioinformatics 2002; 3: 4PubMedCrossRefGoogle Scholar
  8. 8.
    Spruill SE, Lu J, Hardy S, Weir B. Assessing sources of variability in microarray gene expression data. Biotechniques 2002; 33(4): 916-20Google Scholar
  9. 9.
    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 USA 2003; 100(4): 1896–901PubMedCrossRefGoogle Scholar
  10. 10.
    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 J 2004; 4(3): 193–207PubMedCrossRefGoogle Scholar
  11. 11.
    Goronzy JJ, Weyand CM. Rheumatoid arthritis. Immunol Rev 2005; 204: 55–73PubMedCrossRefGoogle Scholar
  12. 12.
    Hoffman, E.P. Expression profiling — best practices for data generation and interpretation in clinical trials. Nat Rev Genet 2004; 5(3): 229–37CrossRefGoogle Scholar
  13. 13.
    Han ES, Wu Y, McCarter R, Nelson JF, Richardson A, Hilsenbeck SG. Reproducibility, sources of variability, pooling, and sample size: important considerations for the design of high-density oligonucleotide array experiments. J Gerontol A Biol Sci Med Sci 2004; 59(4): 306–15PubMedGoogle Scholar
  14. 14.
    Macgregor S. Most pooling variation in array-based DNA pooling is attributable to array error rather than pool construction error. Eur J Hum Genet 2007; 15(4): 501–4PubMedCrossRefGoogle Scholar
  15. 15.
    Viale A, Li J, Tiesman J, Hester S, Massimi A, Griffin C et al. Big results from small samples: evaluation of amplification protocols for gene expression profiling. J Biomol Tech 2007; 18(3): 150–61PubMedGoogle Scholar
  16. 16.
    Lyons PA, Koukoulaki M, Hatton A, Doggett K, Woffendin HB, Chaudhry AN et al. Microarray analysis of human leucocyte subsets: the advantages of positive selection and rapid purification. BMC Genomics 2007; 8: 64PubMedCrossRefGoogle Scholar
  17. 17.
    Galbraith DW, Elumalai R, Gong FC. Integrative flow cytometric and microarray approaches for use in transcriptional profiling. Methods Mol Biol 2004; 263: 259–80PubMedGoogle Scholar
  18. 18.
    Szaniszlo P, Wang N, Sinha M, Reece LM, Van Hook JW, Luxon BA et al. Getting the right cells to the array: Gene expression microarray analysis of cell mixtures and sorted cells. Cytometry A 2004; 59(2): 191–202PubMedCrossRefGoogle Scholar
  19. 19.
    Haeupl T, Gruetzkau A, Gruen J, Radbruch A, Burmetser GR. Expression analysis of rheumatic diseases, prospects and problems. In: Holmdahl R (ed): The Hereditary Basis of Rheumatic Diseases. Basel, Boston, Berlin: Birkhäuser; 2007; 119–30Google Scholar
  20. 20.
    Mahr S, Burmester GR, Hilke D, Gobel U, Grutzkau A, Haupl T et al.. Cis-and transacting gene regulation is associated with osteoarthritis. Am J Hum Genet 2006; 78(5): 793–803PubMedCrossRefGoogle Scholar
  21. 21.
    Sethu P, Moldawer LL, Mindrinos MN, Scumpia PO, Tannahill CL, Wilhelmy J et al. Microfluidic isolation of leukocytes from whole blood for phenotype and gene expression analysis. Anal Chem 2006; 78(15): 5453–61PubMedCrossRefGoogle Scholar

Copyright information

© Birkhäuser Verlag Basel/Switzerland 2008

Authors and Affiliations

  • Andreas Grützkau
    • 1
  • Andreas Radbruch
    • 1
  1. 1.German Arthritis Research Center (DRFZ)BerlinGermany

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