Literature DB >> 16387473

A highly standardized, robust, and cost-effective method for genome-wide transcriptome analysis of peripheral blood applicable to large-scale clinical trials.

Svenja Debey1, Thomas Zander, Benedikt Brors, Alexey Popov, Roland Eils, Joachim L Schultze.   

Abstract

The use of peripheral blood mononuclear cells (PBMC) for transcriptome analysis has already been proven valuable for assessing disease-associated and drug-response-related gene signatures. While these proof-of-principle studies have been critically important, the instability of RNA within PBMC prohibits their use in large-scale multicenter trials for which samples have to be transported for a prolonged time prior to RNA isolation. Therefore, a prerequisite for transcriptome analysis of peripheral blood in clinical trials will be a standardized and valid method to stabilize the RNA profile immediately after blood withdrawal. Moreover, to be able to perform such large-scale clinical studies routinely in several hundred patients more cost-effective array technologies are required. To address these critical issues, we have combined a whole-blood RNA stabilization technology with a method to reduce globin mRNA, followed by genome-wide transcriptome analysis using a newly introduced BeadChip oligonucleotide technology. We demonstrate that the globin mRNA reduction method results in significantly improved data quality of stabilized RNA samples with low intragroup variance and a detection rate of expressed genes similar to that in PBMC. More important, even small differences in gene expression such as are observed between females and males were detected and sufficient to predict gender in whole-blood samples. We therefore propose the combination of globin mRNA reduction after whole-blood RNA stabilization with a newly introduced cost-effective BeadChip array as the preferred approach for large-scale multicenter trials, especially when establishing predictive markers for disease and treatment outcome in peripheral blood.

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Year:  2006        PMID: 16387473     DOI: 10.1016/j.ygeno.2005.11.010

Source DB:  PubMed          Journal:  Genomics        ISSN: 0888-7543            Impact factor:   5.736


  30 in total

Review 1.  Quality assurance of RNA expression profiling in clinical laboratories.

Authors:  Weihua Tang; Zhiyuan Hu; Hind Muallem; Margaret L Gulley
Journal:  J Mol Diagn       Date:  2011-10-20       Impact factor: 5.568

Review 2.  The end of the microarray Tower of Babel: will universal standards lead the way?

Authors:  Ernest S Kawasaki
Journal:  J Biomol Tech       Date:  2006-07

3.  Effects of globin mRNA reduction methods on gene expression profiles from whole blood.

Authors:  Jinny Liu; Elizabeth Walter; David Stenger; Dzung Thach
Journal:  J Mol Diagn       Date:  2006-11       Impact factor: 5.568

4.  The challenges for molecular nutrition research 2: quantification of the nutritional phenotype.

Authors:  Ben van Ommen; Jaap Keijer; Robert Kleemann; Ruan Elliott; Christian A Drevon; Harry McArdle; Mike Gibney; Michael Müller
Journal:  Genes Nutr       Date:  2008-06-25       Impact factor: 5.523

5.  RNA-stabilized whole blood samples but not peripheral blood mononuclear cells can be stored for prolonged time periods prior to transcriptome analysis.

Authors:  Svenja Debey-Pascher; Andrea Hofmann; Fatima Kreusch; Gerold Schuler; Beatrice Schuler-Thurner; Joachim L Schultze; Andrea Staratschek-Jox
Journal:  J Mol Diagn       Date:  2011-07       Impact factor: 5.568

Review 6.  Design and application of single-cell RNA sequencing to study kidney immune cells in lupus nephritis.

Authors:  Deepak A Rao; Arnon Arazi; David Wofsy; Betty Diamond
Journal:  Nat Rev Nephrol       Date:  2019-12-18       Impact factor: 28.314

7.  Deciphering normal blood gene expression variation--The NOWAC postgenome study.

Authors:  Vanessa Dumeaux; Karina S Olsen; Gregory Nuel; Ruth H Paulssen; Anne-Lise Børresen-Dale; Eiliv Lund
Journal:  PLoS Genet       Date:  2010-03-12       Impact factor: 5.917

8.  The effects of globin on microarray-based gene expression analysis of mouse blood.

Authors:  Mary E Winn; Matthew A Zapala; Iiris Hovatta; Victoria B Risbrough; Elizabeth Lillie; Nicholas J Schork
Journal:  Mamm Genome       Date:  2010-05-16       Impact factor: 2.957

9.  A practical platform for blood biomarker study by using global gene expression profiling of peripheral whole blood.

Authors:  Ze Tian; Nathan Palmer; Patrick Schmid; Hui Yao; Michal Galdzicki; Bonnie Berger; Erxi Wu; Isaac S Kohane
Journal:  PLoS One       Date:  2009-04-17       Impact factor: 3.240

10.  Gene expression profiling of whole blood: comparison of target preparation methods for accurate and reproducible microarray analysis.

Authors:  Kristina Vartanian; Rachel Slottke; Timothy Johnstone; Amanda Casale; Stephen R Planck; Dongseok Choi; Justine R Smith; James T Rosenbaum; Christina A Harrington
Journal:  BMC Genomics       Date:  2009-01-05       Impact factor: 3.969

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