Abstract
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.
Keywords
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.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Grant GR, Manduchi E, Pizarro A, Stoeckert CJ,Jr. Maintaining data integrity in microarray data management. Biotechnol Bioeng 2003; 84(7): 795–800
Wilkes T, Laux H, Foy CA. Microarray data quality — review of current developments. OMICS 2007; 11(1): 1–13
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): 365–71
Geschwind DH. Sharing gene expression data: an array of options. Nat Rev Neurosci 2001; 2(6): 435–8
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): 1175–82
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): 1151–61
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: 4
Spruill SE, Lu J, Hardy S, Weir B. Assessing sources of variability in microarray gene expression data. Biotechniques 2002; 33(4): 916-20
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): 1896–901
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): 193–207
Goronzy JJ, Weyand CM. Rheumatoid arthritis. Immunol Rev 2005; 204: 55–73
Hoffman, E.P. Expression profiling — best practices for data generation and interpretation in clinical trials. Nat Rev Genet 2004; 5(3): 229–37
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): 306–15
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): 501–4
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): 150–61
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: 64
Galbraith DW, Elumalai R, Gong FC. Integrative flow cytometric and microarray approaches for use in transcriptional profiling. Methods Mol Biol 2004; 263: 259–80
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): 191–202
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; 119–30
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): 793–803
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): 5453–61
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Birkhäuser Verlag Basel/Switzerland
About this chapter
Cite this chapter
Grützkau, A., Radbruch, A. (2008). Separation of whole blood cells and its impact on gene expression. In: Bosio, A., Gerstmayer, B. (eds) Microarrays in Inflammation. Progress in Inflammation Research. Birkhäuser Basel. https://doi.org/10.1007/978-3-7643-8334-3_3
Download citation
DOI: https://doi.org/10.1007/978-3-7643-8334-3_3
Publisher Name: Birkhäuser Basel
Print ISBN: 978-3-7643-8333-6
Online ISBN: 978-3-7643-8334-3
eBook Packages: Biomedical and Life SciencesBiomedical and Life Sciences (R0)