Global transcriptome analysis of whole blood RNA using microarrays has been proven to be challenging due to the high abundance of globin transcripts that constitute 70% of whole blood mRNA. This is a particular problem in patients with sickle cell disease, secondary to the high abundance of globin-expressing nucleated red blood cells and reticulocytes in the circulation. In order to accurately measure the steady state blood transcriptome in sickle cell patients we evaluated the efficacy of reducing globin transcripts in PAXgene stabilized RNA for genome-wide transcriptome analyses using microarrays. We demonstrate here by both microarrays and Q-PCR that the globin mRNA depletion method resulted in 55–65 fold reduction in globin transcripts in whole blood collected from healthy volunteers and sickle cell disease patients. This led to an improvement in microarray data quality by reducing data variability, with increased detection rate of expressed genes and improved overlap with the expression signatures of isolated peripheral blood mononuclear (PBMC) preparations. Analysis of differences between the whole blood transcriptome and PBMC transcriptome revealed important erythrocyte genes that participate in sickle cell pathogenesis and compensation. The combination of globin mRNA reduction after whole-blood RNA stabilization represents a robust clinical research methodology for the discovery of biomarkers for hematologic diseases.
References
[1]
Jison ML, Munson PJ, Barb JJ, Suffredini AF, Talwar S, et al. (2004) Blood mononuclear cell gene expression profiles characterize the oxidant, hemolytic, and inflammatory stress of sickle cell disease. Blood 104: 270–280.
[2]
Burczynski ME, Twine NC, Dukart G, Marshall B, Hidalgo M, et al. (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.
[3]
Chai V, Vassilakos A, Lee Y, Wright JA, Young AH (2005) Optimization of the PAXgene blood RNA extraction system for gene expression analysis of clinical samples. J Clin Lab Anal 19: 182–188.
[4]
Debey S, Schoenbeck U, Hellmich M, Gathof BS, Pillai R, et al. (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.
[5]
DePrimo SE, Wong LM, Khatry DB, Nicholas SL, Manning WC, et al. (2003) Expression profiling of blood samples from an SU5416 Phase III metastatic colorectal cancer clinical trial: a novel strategy for biomarker identification. BMC Cancer 3: 3.
[6]
McPhail S, Goralski TJ (2005) Overcoming challenges of using blood samples with gene expression microarrays to advance patient stratification in clinical trials. Drug Discov Today 10: 1485–1487.
[7]
Whitley P, M S, Santiago J, Johnson C, Setterquist R (2005) Improved microarray sensitivity using blood RNA samples. Ambion Technotes 20–22.
[8]
Baechler EC, Gregersen PK, Behrens TW (2004) The emerging role of interferon in human systemic lupus erythematosus. Curr Opin Immunol 16: 801–807.
[9]
Rainen L, Oelmueller U, Jurgensen S, Wyrich R, Ballas C, et al. (2002) Stabilization of mRNA expression in whole blood samples. Clin Chem 48: 1883–1890.
[10]
Klein U, Tu Y, Stolovitzky GA, Mattioli M, Cattoretti G, et al. (2001) Gene expression profiling of B cell chronic lymphocytic leukemia reveals a homogeneous phenotype related to memory B cells. J Exp Med 194: 1625–1638.
[11]
Locati M, Deuschle U, Massardi ML, Martinez FO, Sironi M, et al. (2002) Analysis of the gene expression profile activated by the CC chemokine ligand 5/RANTES and by lipopolysaccharide in human monocytes. J Immunol 168: 3557–3562.
[12]
Affymetrix (2003) An analysis of blood processing methods to prepare samples for genechip expression profiling. Affymetrix Technical Note.
[13]
Pahl A, Brune K (2002) Gene expression changes in blood after phlebotomy: implications for gene expression profiling. Blood 100: 1094–1095.
