Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2009;4(2):e4427.
doi: 10.1371/journal.pone.0004427. Epub 2009 Feb 12.

Detection of gene expression in an individual cell type within a cell mixture using microarray analysis

Penelope A Bryant  1 , Gordon K Smyth, Roy Robins-Browne, Nigel Curtis
Affiliations

Detection of gene expression in an individual cell type within a cell mixture using microarray analysis

Penelope A Bryant et al. PLoS One. 2009.

Abstract

Background: A central issue in the design of microarray-based analysis of global gene expression is the choice between using cells of single type and a mixture of cells. This study quantified the proportion of lipopolysaccharide (LPS) induced differentially expressed monocyte genes that could be measured in peripheral blood mononuclear cells (PBMC), and determined the extent to which gene expression in the non-monocyte cell fraction diluted or obscured fold changes that could be detected in the cell mixture.

Methodology/principal findings: Human PBMC were stimulated with LPS, and monocytes were then isolated by positive (Mono+) or negative (Mono-) selection. The non-monocyte cell fraction (MonoD) remaining after positive selection of monocytes was used to determine the effect of non-monocyte cells on overall expression. RNA from LPS-stimulated PBMC, Mono+, Mono- and MonoD samples was co-hybridised with unstimulated RNA for each cell type on oligonucleotide microarrays. There was a positive correlation in gene expression between PBMC and both Mono+ (0.77) and Mono- (0.61-0.67) samples. Analysis of individual genes that were differentially expressed in Mono+ and Mono- samples showed that the ability to detect expression of some genes was similar when analysing PBMC, but for others, differential expression was either not detected or changed in the opposite direction. As a result of the dilutional or obscuring effect of gene expression in non-monocyte cells, overall about half of the statistically significant LPS-induced changes in gene expression in monocytes were not detected in PBMC. However, 97% of genes with a four fold or greater change in expression in monocytes after LPS stimulation, and almost all (96-100%) of the top 100 most differentially expressed monocyte genes were detected in PBMC.

Conclusions/significance: The effect of non-responding cells in a mixture dilutes or obscures the detection of subtle changes in gene expression in an individual cell type. However, for studies in which only the most highly differentially expressed genes are of interest, separating and analysing individual cell types may be unnecessary.

PubMed Disclaimer

Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Figure 1
Figure 1. Matrix of scatter plots showing estimated log2-fold changes for each cell population.
The log-fold changes for each sample are plotted against those for each of the other samples for gene expression after stimulation with LPS for 3 hours (top right of diagonal) and 24 hours (bottom left). Correlation coefficients between each pair of samples are shown in the bottom right of each box.
Figure 2
Figure 2. Hierarchical cluster analysis using average linkage of the linear model estimated log-expression of all genes and cell types at 3 and 24 hours.
Figure 3
Figure 3. Scatterplots showing log2-fold changes for monocytes (Mono+) compared with non-monocytes (MonoD) and PBMC at 3 and 24 h.
Each point corresponds to a gene and represents its differential expression after stimulation with LPS for 3 or 24 h compared to 0 h. The lines represent the cut-off of 50% fold change. Each of the 9 regions separated by the cut-off lines per graph corresponds to a cell in Table 2.
Figure 4
Figure 4. The proportion of differentially expressed genes in PBMC in relation to (a) the number selected of top ranked differentially expressed genes in the monocyte population, and (b) the cut-off selected for fold change in monocyte genes after LPS stimulation.
Figure 5
Figure 5. Comparison of IL-1a, IL-1b, IL-6 and IL-10 gene expression in different samples after stimulation with LPS for 3 and 24 hours.
The log2-fold change (M value) and standard error for the expression of each gene are plotted for each sample.
Figure 6
Figure 6. Genes expressed in common between monocytes and PBMC.
Examples of gene expression that is (a) upregulated and (b) downregulated in both PBMC and monocyte samples after stimulation with LPS for 3 and 24 hours. The log2-fold change (M value) and standard error for the expression of each gene are plotted for each sample.
Figure 7
Figure 7. Genes expressed differentially between monocytes and PBMC.
Examples of gene expression that is (a) upregulated in monocytes but not PBMC, (b) upregulated in monocytes and remains detectable in PBMC and (c) downregulated in monocytes but not PBMC after stimulation with LPS for 3 hours. The log2-fold change (M value) and standard error for the expression of each gene are plotted for each sample.
Figure 8
Figure 8. The fold change in gene expression after LPS-stimulation predicted in PBMC as a function of fold change in monocytes.
The top curve is for genes expressed only in monocytes with no attenuation of fold change. The other curves assume that the gene is expressed in monocytes at double, equal and half the level that it is in non-monocytes. Expression of the IL-1a gene at 3 and 24 hours is plotted as an example.
Figure 9
Figure 9. Examples of genes that are expressed in opposite directions in monocytes and PBMC after stimulation with LPS for 3 hours.
The log2-fold change (M value) and standard error for the expression of each gene are plotted for each sample.
See this image and copyright information in PMC

References

    1. Detweiler CS, Cunanan DB, Falkow S. Host microarray analysis reveals a role for the Salmonella response regulator phoP in human macrophage cell death. Proc Natl Acad Sci U S A. 2001;98:5850–5855. - PMC - PubMed
    1. Huang Q, Liu D, Majewski P, Schulte LC, Korn JM, et al. The plasticity of dendritic cell responses to pathogens and their components. Science. 2001;294:870–875. - PubMed
    1. Skelton L, Cooper M, Murphy M, Platt A. Human immature monocyte-derived dendritic cells express the G protein-coupled receptor GPR105 (KIAA0001, P2Y14) and increase intracellular calcium in response to its agonist, uridine diphosphoglucose. J Immunol. 2003;171:1941–1949. - PubMed
    1. Viemann D, Schulze-Osthoff K, Roth J. Potentials and pitfalls of DNA array analysis of the endothelial stress response. Biochim Biophys Acta. 2005;1746:73–84. - PubMed
    1. Boldrick JC, Alizadeh AA, Diehn M, Dudoit S, Liu CL, et al. Stereotyped and specific gene expression programs in human innate immune responses to bacteria. Proc Natl Acad Sci U S A. 2002;99:972–977. - PMC - PubMed

Publication types

Substances