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. 2007 Mar 5;8:64.
doi: 10.1186/1471-2164-8-64.

Microarray Analysis of Human Leucocyte Subsets: The Advantages of Positive Selection and Rapid Purification

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Free PMC article

Microarray Analysis of Human Leucocyte Subsets: The Advantages of Positive Selection and Rapid Purification

Paul A Lyons et al. BMC Genomics. .
Free PMC article

Abstract

Background: For expression profiling to have a practical impact in the management of immune-related disease it is essential that it can be applied to peripheral blood cells. Early studies have used total peripheral blood mononuclear cells, and as a consequence the majority of the disease-related signatures identified have simply reflected differences in the relative abundance of individual cell types between patients and controls. To identify cell-specific changes in transcription it would be necessary to profile purified leucocyte subsets.

Results: We have used sequential rounds of positive selection to isolate CD4 and CD8 T cells, CD19 B cells, CD14 monocytes and CD16 neutrophils for microarray analysis from a single blood sample. We compared gene expression in cells isolated in parallel using either positive or negative selection and demonstrate that there are no significant consistent changes due to positive selection, and that the far inferior results obtained by negative selection are largely due to reduced purity. Finally, we demonstrate that storing cells prior to separation leads to profound changes in expression, predominantly in cells of the myeloid lineage.

Conclusion: Leukocyte subsets should be prepared for microarray analysis by rapid positive selection.

Figures

Figure 1
Figure 1
Microarray analysis of purified cell populations from six normal controls. (A) Hierarchical clustering of microarray data generated for five cell populations isolated from six normal controls was performed using expression data from 12,022 genes and clusters samples according to cell lineage. (B) Relative expression levels of CD14, CD16, CD19, CD4 and CD8 mRNA in each of the five cell types.
Figure 2
Figure 2
Positive selection is associated with increased cell purity. Proportion of cell types (CD4, CD8 and CD14) in PBMC as assessed by flow cytometry prior to separation (yellow boxes). Purity of these cells assessed by flow cytometry following positive (red boxes) or negative (green boxes) selection.
Figure 3
Figure 3
Differential gene expression following positive or negative selection reflects cellular contamination rather than activation. (A) Venn diagrams showing number and overlap of statistically significant, differentially expressed genes (as defined in materials and methods) in each independent experiment (roman numerals). The number in the bottom right of each panel is the number of genes whose expression does not change with selection. Genes whose expression changes upon positive (+) or negative (-) selection are shown for each cell type. (B) Heat diagrams show the relative expression pattern of genes significantly changed in 2 out of 3 replicates in 3A on arrays of purified cell subsets from Figure 1A. Red indicates over-expression and green indicates under-expression. (C) Representative flow cytometry profiles for each positively and negatively purified cell type. Putative contaminating cell populations that correlate with the gene expression patterns observed in 3A are ringed (------).
Figure 4
Figure 4
Delaying separation leads to significant changes in gene expression especially in cells of the myeloid lineage. Expression profiles were obtained from RNA samples extracted from cells separated immediately following venesection compared to those separated after a four hour delay on ice. Box plots (A) show the change in gene expression between 0 and 4 hours for independent experiments (I – III) and combined self versus self hybridisation data. The Venn diagrams (B) show the number and overlap between genes showing statistically significant differential expression (as defined in the materials and methods).

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