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Open Access Research article

Variability of gene expression profiles in human blood and lymphoblastoid cell lines

Josine L Min1*, Amy Barrett2, Tim Watts3, Fredrik H Pettersson1, Helen E Lockstone4, Cecilia M Lindgren1, Jennifer M Taylor4, Maxine Allen2, Krina T Zondervan1 and Mark I McCarthy2*

Author Affiliations

1 Genetic and Genomic Epidemiology Unit, Wellcome Trust Centre for Human Genetics, Oxford, UK

2 Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, UK

3 Genomics Laboratory, Wellcome Trust Centre for Human Genetics, Oxford, UK

4 Bioinformatics Core, Wellcome Trust Centre for Human Genetics, Oxford, UK

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BMC Genomics 2010, 11:96  doi:10.1186/1471-2164-11-96

Published: 8 February 2010

Abstract

Background

Readily accessible samples such as peripheral blood or cell lines are increasingly being used in large cohorts to characterise gene expression differences between a patient group and healthy controls. However, cell and RNA isolation procedures and the variety of cell types that make up whole blood can affect gene expression measurements. We therefore systematically investigated global gene expression profiles in peripheral blood from six individuals collected during two visits by comparing five of the following cell and RNA isolation methods: whole blood (PAXgene), peripheral blood mononuclear cells (PBMCs), lymphoblastoid cell lines (LCLs), CD19 and CD20 specific B-cell subsets.

Results

Gene expression measurements were clearly discriminated by isolation method although the reproducibility was high for all methods (range ρ = 0.90-1.00). The PAXgene samples showed a decrease in the number of expressed genes (P < 1*10-16) with higher variability (P < 1*10-16) compared to the other methods. Differentially expressed probes between PAXgene and PBMCs were correlated with the number of monocytes, lymphocytes, neutrophils or erythrocytes. The correlations (ρ = 0.83; ρ = 0.79) of the expression levels of detected probes between LCLs and B-cell subsets were much lower compared to the two B-cell isolation methods (ρ = 0.98). Gene ontology analysis of detected genes showed that genes involved in inflammatory responses are enriched in B-cells CD19 and CD20 whereas genes involved in alcohol metabolic process and the cell cycle were enriched in LCLs.

Conclusion

Gene expression profiles in blood-based samples are strongly dependent on the predominant constituent cell type(s) and RNA isolation method. It is crucial to understand the differences and variability of gene expression measurements between cell and RNA isolation procedures, and their relevance to disease processes, before application in large clinical studies.