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Tissue Compartment Analysis for Biomarker Discovery by Gene Expression Profiling

BACKGROUND: 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 bu...

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Autores principales: Disset, Antoine, Cheval, Lydie, Soutourina, Olga, Duong Van Huyen, Jean-Paul, Li, Guorong, Genin, Christian, Tostain, Jacques, Loupy, Alexandre, Doucet, Alain, Rajerison, Rabary
Formato: Texto
Lenguaje:English
Publicado: Public Library of Science 2009
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2771357/
https://www.ncbi.nlm.nih.gov/pubmed/19901995
http://dx.doi.org/10.1371/journal.pone.0007779
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author Disset, Antoine
Cheval, Lydie
Soutourina, Olga
Duong Van Huyen, Jean-Paul
Li, Guorong
Genin, Christian
Tostain, Jacques
Loupy, Alexandre
Doucet, Alain
Rajerison, Rabary
author_facet Disset, Antoine
Cheval, Lydie
Soutourina, Olga
Duong Van Huyen, Jean-Paul
Li, Guorong
Genin, Christian
Tostain, Jacques
Loupy, Alexandre
Doucet, Alain
Rajerison, Rabary
author_sort Disset, Antoine
collection PubMed
description BACKGROUND: 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. METHODOLOGY/PRINCIPAL FINDINGS: TCA is based on the comparison of mRNA expression levels of specific markers of the different constitutive structures in pure isolated structures, on the one hand, and in the whole sample on the other. TCA method was here developed with human kidney samples, as an example of highly heterogeneous organ. It was validated by comparison of the data with those obtained by histo-morphometry. TCA demonstrated the extreme variety of composition of kidney samples, with abundance of specific structures varying from 5 to 95% of the whole sample. TCA permitted to accurately standardize gene expression level amongst >100 kidney biopsies, and to identify otherwise imperceptible molecular disease markers. CONCLUSIONS/SIGNIFICANCE: Because TCA does not require specific preparation of sample, it can be applied to all existing tissue or cDNA libraries or to published data sets, inasmuch specific operational compartments markers are available. In human, where the small size of tissue samples collected in clinical practice accounts for high structural diversity, TCA is well suited for the identification of molecular markers of diseases, and the follow up of identified markers in single patients for diagnosis/prognosis and evaluation of therapy efficiency. In laboratory animals, TCA will interestingly be applied to central nervous system where tissue heterogeneity is a limiting factor.
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spelling pubmed-27713572009-11-10 Tissue Compartment Analysis for Biomarker Discovery by Gene Expression Profiling Disset, Antoine Cheval, Lydie Soutourina, Olga Duong Van Huyen, Jean-Paul Li, Guorong Genin, Christian Tostain, Jacques Loupy, Alexandre Doucet, Alain Rajerison, Rabary PLoS One Research Article BACKGROUND: 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. METHODOLOGY/PRINCIPAL FINDINGS: TCA is based on the comparison of mRNA expression levels of specific markers of the different constitutive structures in pure isolated structures, on the one hand, and in the whole sample on the other. TCA method was here developed with human kidney samples, as an example of highly heterogeneous organ. It was validated by comparison of the data with those obtained by histo-morphometry. TCA demonstrated the extreme variety of composition of kidney samples, with abundance of specific structures varying from 5 to 95% of the whole sample. TCA permitted to accurately standardize gene expression level amongst >100 kidney biopsies, and to identify otherwise imperceptible molecular disease markers. CONCLUSIONS/SIGNIFICANCE: Because TCA does not require specific preparation of sample, it can be applied to all existing tissue or cDNA libraries or to published data sets, inasmuch specific operational compartments markers are available. In human, where the small size of tissue samples collected in clinical practice accounts for high structural diversity, TCA is well suited for the identification of molecular markers of diseases, and the follow up of identified markers in single patients for diagnosis/prognosis and evaluation of therapy efficiency. In laboratory animals, TCA will interestingly be applied to central nervous system where tissue heterogeneity is a limiting factor. Public Library of Science 2009-11-10 /pmc/articles/PMC2771357/ /pubmed/19901995 http://dx.doi.org/10.1371/journal.pone.0007779 Text en Disset et al. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Disset, Antoine
Cheval, Lydie
Soutourina, Olga
Duong Van Huyen, Jean-Paul
Li, Guorong
Genin, Christian
Tostain, Jacques
Loupy, Alexandre
Doucet, Alain
Rajerison, Rabary
Tissue Compartment Analysis for Biomarker Discovery by Gene Expression Profiling
title Tissue Compartment Analysis for Biomarker Discovery by Gene Expression Profiling
title_full Tissue Compartment Analysis for Biomarker Discovery by Gene Expression Profiling
title_fullStr Tissue Compartment Analysis for Biomarker Discovery by Gene Expression Profiling
title_full_unstemmed Tissue Compartment Analysis for Biomarker Discovery by Gene Expression Profiling
title_short Tissue Compartment Analysis for Biomarker Discovery by Gene Expression Profiling
title_sort tissue compartment analysis for biomarker discovery by gene expression profiling
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2771357/
https://www.ncbi.nlm.nih.gov/pubmed/19901995
http://dx.doi.org/10.1371/journal.pone.0007779
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