Research Article: Tissue Compartment Analysis for Biomarker Discovery by Gene Expression Profiling

Date Published: November 10, 2009

Publisher: Public Library of Science

Author(s): Antoine Disset, Lydie Cheval, Olga Soutourina, Jean-Paul Duong Van Huyen, Guorong Li, Christian Genin, Jacques Tostain, Alexandre Loupy, Alain Doucet, Rabary Rajerison, Ulrich Zanger.

Abstract: Although high throughput technologies for gene profiling are reliable tools, sample/tissue heterogeneity limits their outcomes when applied to identify molecular markers. Indeed, inter-sample differences in cell composition contribute to scatter the data, preventing detection of small but relevant changes in gene expression level. To date, attempts to circumvent this difficulty were based on isolation of the different cell structures constituting biological samples. As an alternate approach, we developed a tissue compartment analysis (TCA) method to assess the cell composition of tissue samples, and applied it to standardize data and to identify biomarkers.

Partial Text: A central goal in biomedicine is to identify specific markers for diagnosis and prognosis of diseases and for evaluating treatment efficiency. Identification of molecular biomarkers is often based on differential profiling of gene expression [1], [2]. Although powerful technologies for gene expression analysis, e.g. microarrays and SAGE [3], are nowadays well systematized and highly reliable, the overall procedure for differential gene expression profiling still suffers from several flaws. One seldom solved relates to the very nature of the biological samples, especially when studying heterogeneous tissues or organs [4], [5]. As a matter of fact, random sampling of a heterogeneous tissue yields samples with different cell compositions. Thus, differences in gene expression levels observed between samples may be accounted for not only by true changes in gene expression, but also by differences in their cell composition. This artefact increases data scatter and may prevent detection of small amplitude changes in gene expression, as those expected for early biomarkers.

This paper describes a tissue compartment analysis method to quantify the proportion of the different cellular structures in a kidney tissue sample, such as a surgical piece of kidney or a renal biopsy. The method was initially designed and validated using normal kidney tissue because it is a readily available source of tissue, and because we disposed of published data regarding the segmental expression of thousands of genes in such tissue. However, TCA proved also efficient for analyzing kidney needle biopsies from patients with a wide variety of kidney diseases.



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