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

Hoffmann M, Pohlers D, Koczan D, Thiesen HJ, Wölfl S, Kinne RW - BMC Bioinformatics (2006)

Bottom Line: 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.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Leibniz Institute for Natural Products Research and Infection Biology - Hans Knöll Institute, Beutenbergstr, 11a, Jena, Germany. martin.hoffmann@hki-jena.de

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.

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Results for mixing experiment 2. Mean and standard deviation of the computed mRNA proportions of macrophages (macro), fibroblasts (fibro) and non-adherent cells (nadc) for the second mixing experiment as a function of the number of included genes. This experiment corresponds to Figure 2, except that the tissue expression profile was not obtained by preparing mRNA from the whole synovial tissue, but by mixing mRNA samples from the isolated cell fractions according to the relative proportions pM = 0.86, pF = 0.11, and pN = 0.03. These proportions were almost perfectly determined when no further normalization of the reconstituted tissue profile was performed (dashed curves): pM = 0.86, pF = 0.10, pN = 0.04. Applying additional stepwise normalization (lr) (solid curves) resulted in pM = 0.85, pF = 0.13, pN = 0.02. All proportions were determined at 4000 included genes.
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Figure 3: Results for mixing experiment 2. Mean and standard deviation of the computed mRNA proportions of macrophages (macro), fibroblasts (fibro) and non-adherent cells (nadc) for the second mixing experiment as a function of the number of included genes. This experiment corresponds to Figure 2, except that the tissue expression profile was not obtained by preparing mRNA from the whole synovial tissue, but by mixing mRNA samples from the isolated cell fractions according to the relative proportions pM = 0.86, pF = 0.11, and pN = 0.03. These proportions were almost perfectly determined when no further normalization of the reconstituted tissue profile was performed (dashed curves): pM = 0.86, pF = 0.10, pN = 0.04. Applying additional stepwise normalization (lr) (solid curves) resulted in pM = 0.85, pF = 0.13, pN = 0.02. All proportions were determined at 4000 included genes.

Mentions: The target values for the mRNA proportions of the first two mixing experiments were set according to the range of values computed for patient 2 (Table 1). Figure 3 shows the results for the second mixing experiment using trimmed mean normalized MAS-S probe set summaries. The proportions are virtually fixed from the beginning and the standard deviations drop at only a few thousand included genes. This picture was typical for all mixing experiments, probe set summaries, and chip normalization methods of the present study. The number of included genes, at which the computed mRNA proportions were determined, was between 2000 and 4000 in all cases.


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

Hoffmann M, Pohlers D, Koczan D, Thiesen HJ, Wölfl S, Kinne RW - BMC Bioinformatics (2006)

Results for mixing experiment 2. Mean and standard deviation of the computed mRNA proportions of macrophages (macro), fibroblasts (fibro) and non-adherent cells (nadc) for the second mixing experiment as a function of the number of included genes. This experiment corresponds to Figure 2, except that the tissue expression profile was not obtained by preparing mRNA from the whole synovial tissue, but by mixing mRNA samples from the isolated cell fractions according to the relative proportions pM = 0.86, pF = 0.11, and pN = 0.03. These proportions were almost perfectly determined when no further normalization of the reconstituted tissue profile was performed (dashed curves): pM = 0.86, pF = 0.10, pN = 0.04. Applying additional stepwise normalization (lr) (solid curves) resulted in pM = 0.85, pF = 0.13, pN = 0.02. All proportions were determined at 4000 included genes.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC1574358&req=5

Figure 3: Results for mixing experiment 2. Mean and standard deviation of the computed mRNA proportions of macrophages (macro), fibroblasts (fibro) and non-adherent cells (nadc) for the second mixing experiment as a function of the number of included genes. This experiment corresponds to Figure 2, except that the tissue expression profile was not obtained by preparing mRNA from the whole synovial tissue, but by mixing mRNA samples from the isolated cell fractions according to the relative proportions pM = 0.86, pF = 0.11, and pN = 0.03. These proportions were almost perfectly determined when no further normalization of the reconstituted tissue profile was performed (dashed curves): pM = 0.86, pF = 0.10, pN = 0.04. Applying additional stepwise normalization (lr) (solid curves) resulted in pM = 0.85, pF = 0.13, pN = 0.02. All proportions were determined at 4000 included genes.
Mentions: The target values for the mRNA proportions of the first two mixing experiments were set according to the range of values computed for patient 2 (Table 1). Figure 3 shows the results for the second mixing experiment using trimmed mean normalized MAS-S probe set summaries. The proportions are virtually fixed from the beginning and the standard deviations drop at only a few thousand included genes. This picture was typical for all mixing experiments, probe set summaries, and chip normalization methods of the present study. The number of included genes, at which the computed mRNA proportions were determined, was between 2000 and 4000 in all cases.

Bottom Line: 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.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Leibniz Institute for Natural Products Research and Infection Biology - Hans Knöll Institute, Beutenbergstr, 11a, Jena, Germany. martin.hoffmann@hki-jena.de

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.

Show MeSH
Related in: MedlinePlus