Table 1. Percentage of monocytes in different samples. CD14+ cells (%) Subject 1 100 70.8 14.2 0.1 Subject 2 100 85.0 10.5 0.0 Mono+ Mono2 PBMC MonoD Results doi:10.1371/journal.pone.0004427.t001 information about the effect on gene expression in PBMC. The proportion of LPS-induced differentially expressed monocyte genes that can be detected in PBMC was successfully quantified. Global gene expression in LPS-stimulated PBMC from two individuals was compared to expression in monocytes isolated from the same blood sample. Monocytes separated by two different methods – positive isolation (Mono+) and negative isolation (Mono2) – were compared to PBMC. Duplicate PBMC samples (PBMC1 and PBMC2) were stimulated in parallel for 3 and 24 hours to estimate the variability between independent stimulations of the same cell population. The PBMC and monocyte samples were compared to the non-monocyte cell fraction remaining after positive isolation (MonoD), comprising PBMC depleted of Mono+ cells, to analyse the effect of the non- monocyte cells in the mixture on gene expression. The proportion of monocytes in the PBMC samples was 10– 14% (table 1). This proportion was greater after both positive and negative monocyte isolation. The monocyte component of Mono+ could not be measured directly, but was implied to be 100%, as the MonoD sample contained 0% CD14-positive cells. RNA from each stimulation time point was competitively hybridised with RNA from 0 hours of the same cell type (eg Mono+ at 3 h with Mono+ at 0 h). The 0 hour sample was chosen as the reference as it was biologically relevant and could be used to compare changes between cell types. Overall patterns of expression The broad relationship between gene expression in the different samples was illustrated by plotting the log ratios of differential expression between LPS-stimulated and unstimulated samples for the individual for whom there were repeat PBMC samples. There was a positive correlation between overall gene expression detected between all the samples containing monocytes after both 3 and 24 hours’ stimulation with LPS (figure 1). The pattern of gene expression was most similar between the two PBMC samples with a correlation coefficient of 0.85 (3 hours) and 0.89 (24 hours). There was also a positive correlation between both of the PBMC samples and the monocyte samples, with the Mono+ sample having greater correlation at 3 hours (0.77) than the Mono– sample (0.61–0.67). There was a low correlation at both time points (0.15–0.35) between both the Mono+ and Mono2 samples and the MonoD samples as expected. The fold changes in gene expression between unstimulated and LPS-stimulated samples tended to be lower in the PBMC than in the monocytes, consistent with the dilutional effect of non-monocyte cell expression in PBMC. Hierarchical clustering analysis of gene expression after LPS stimulation at both time points confirmed that the two PBMC Gene Expression & Cell Subsets samples clustered together most closely at each time point, followed by the Mono+ and Mono2 samples (figure 2). The 3 hour samples and the 24 hour samples clustered together respectively, indicating that there was more similarity at each time point between gene expression in the different samples that contained monocytes, than there was between the same sample types at different times. The PBMC and monocyte samples from both time points clustered together more closely than either MonoD sample, indicating these samples are the most dissimilar. Differentially expressed genes A linear model was fit to the data from both individuals to compare differential gene expression induced by LPS in monocytes and PBMC. The Mono+ samples were used as the ‘gold standard’as they comprised the purest monocyte populations. The MonoD samples were used to provide information about the effect of the non-monocyte cell fraction on gene expression in PBMC. After 3 hours’ stimulation with LPS, 1716 genes were significantly differentially expressed in the Mono+ samples (872 up, 844 down), underlining the marked effect of LPS on monocytes (table 2, figure 3). Of these 1716 genes, only 791(46%, shaded yellow) were detected as differentially expressed in PBMC after stimulation with LPS, and 3 of these genes (0.2%) were expressed in the opposite direction to the Mono+ sample. The dilutional effect of the lack of gene expression in non- monocytes on the differential expression detected in PBMC was explored. This was done by examining the proportion of genes that did not change in response to LPS in the MonoD sample. Of the 1716 genes that were differentially expressed in the Mono+ sample, most (87%, shaded violet) did not change in the MonoD sample. The influence of gene expression in non-monocytes in the opposite direction to that in monocytes was also explored, and found to occur in only a small minority (0.8%) of the differentially expressed genes in the Mono+ sample. A further 361 genes (shaded blue) were differentially expressed after LPS stimulation in PBMC but not in the Mono+ sample, reflecting changes in expression in the non-monocyte cells. The findings were similar at 24 hours: 1453 genes were significantly differentially expressed in response to LPS in the Mono+ sample (695 up and 758 down). Of these 1453 monocyte genes, only 593 (41%) were detected as being differentially expressed in PBMC. A further 471 genes were differentially expressed after LPS stimulation in PBMC but not in the Mono+ sample. Again, both of these findings were due to the mainly dilutional effects of expression in the non-monocytes: of the 1453 monocyte genes, 94% were not differentially expressed in the non- monocytes in response to LPS while 0.3% changed in the opposite direction. To determine the effect on detection in PBMC in the most highly differentially expressed monocyte genes, genes were ranked by fold change in the Mono+ sample and the most differentially expressed genes were selected. Detection of these genes in PBMC was investigated by selecting different numbers of the topmost differentially expressed genes: from the top 10 genes to the top 1000 genes (figure 4a). As an example, all of the 100 monocyte genes most differentially expressed in response to LPS were detected in PBMC at 3 hours, and 96 of the top 100 monocyte genes at 24 hours. The proportion of genes detected in PBMC decreased with increasing numbers of genes selected, confirming that magnitude of expression in monocytes affected detection in PBMC. This is also illustrated by relating the proportion of LPS- induced gene expression changes in monocytes detectable in PBMC to their fold change (figure 4b). PLoS ONE | www.plosone.org 2 February 2009 | Volume 4 | Issue 2 | e4427