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Open Access Methodology article

Robust computational reconstitution – a new method for the comparative analysis of gene expression in tissues and isolated cell fractions

Martin Hoffmann1*, Dirk Pohlers2, Dirk Koczan3, Hans-Jürgen Thiesen3, Stefan Wölfl4 and Raimund W Kinne2

Author Affiliations

1 Leibniz Institute for Natural Products Research and Infection Biology – Hans Knöll Institute, Beutenbergstr. 11a, Jena, Germany

2 Experimental Rheumatology Unit, Department of Orthopedics, Friedrich Schiller University Jena, Jena, Germany

3 Institute of Immunology, University of Rostock, Rostock, Germany

4 Department of Pharmacy and Molecular Biotechnology, Ruprecht Karls University Heidelberg, Heidelberg, Germany

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BMC Bioinformatics 2006, 7:369  doi:10.1186/1471-2105-7-369

Published: 4 August 2006

Abstract

Background

Biological tissues consist of various cell types that differentially contribute to physiological and pathophysiological processes. Determining and analyzing cell type-specific gene expression under diverse conditions is therefore a central aim of biomedical research. The present study compares gene expression profiles in whole tissues and isolated cell fractions purified from these tissues in patients with rheumatoid arthritis and osteoarthritis.

Results

The expression profiles of the whole tissues were compared to computationally reconstituted expression profiles that combine the expression profiles of the isolated cell fractions (macrophages, fibroblasts, and non-adherent cells) according to their relative mRNA proportions in the tissue. The mRNA proportions were determined by trimmed robust regression using only the most robustly-expressed genes (1/3 to 1/2 of all measured genes), i.e. those showing the most similar expression in tissue and isolated cell fractions. The relative mRNA proportions were determined using several different chip evaluation methods, among which the MAS 5.0 signal algorithm appeared to be most robust. The computed mRNA proportions agreed well with the cell proportions determined by immunohistochemistry except for a minor number of outliers. Genes that were either regulated (i.e. differentially-expressed in tissue and isolated cell fractions) or robustly-expressed in all patients were identified using different test statistics.

Conclusion

Robust Computational Reconstitution uses an intermediate number of robustly-expressed genes to estimate the relative mRNA proportions. This avoids both the exclusive dependence on the robust expression of individual, highly cell type-specific marker genes and the bias towards an equal distribution upon inclusion of all genes for computation.