Gene Expression & Cell Subsets there was no differential expression in the MonoD sample, the marked expression in monocytes resulted in the gene remaining detectable in PBMC, despite the dilutional effects of the non- monocytes. There were in addition genes in which downregulation of expression in monocytes was more significant than in PBMC. These included histone deacetylase 4 (HDAC4)[14], and acylox- yacyl hydrolase (neutrophil) (AOAH)[15], where in both cases the differential expression in the MonoD sample was negligible or even slightly upregulated. These examples highlight the different effects of non-monocyte cells on overall expression in PBMC, obscuring or diluting the expression detectable in monocytes. The known proportion of monocytes (a mean of 12.4% in this study) and non-monocytes within the PBMC was used to predict gene expression in PBMC. This was done by calculating the reduction in fold change in gene expression that would occur in PBMC with different expression levels in non-monocytes (figure 8). Increased expression in non-monocytes leads to greater dilution of the fold change in PBMC. This is illustrated by expression of the IL-1a gene. At 24 hours, IL-1a is expressed only in monocytes (figure 5) and the fold change is therefore the same in monocytes and PBMC (figures 8). In contrast, at 3 hours IL-1a is expressed in non-monocytes at about one sixth of the level in monocytes and this results in a reduction in fold change in PBMC (figure 8). There were a few genes that were differentially expressed in opposite directions in monocytes and PBMC after stimulation with LPS. Examples included granzyme A (GZMA) and c-type lectin (CLECSF2) (figure 9). In both cases, expression in the MonoD sample was in the opposite direction to the expression in the monocyte samples, which resulted in opposite overall expression in PBMC. These are the most extreme examples of the effect of differential expression in non-monocyte cells. For these few genes, analysis of PBMC provides misleading information about expression in monocytes. Several functional gene ontology (GO) terms were found to be highly statistically over-represented amongst the differentially expressed genes. Not surprisingly GO terms that were similarly over-represented in genes differentially expressed after LPS stimula- tion in both monocytes and PBMC were related to the immune response – for example: response to biotic stimulus, defense response, inflammatory response and immune response, and these include genes such as TNF and MCP2. The GO terms statistically over- represented in the genes that were expressed most differently between monocytes and PBMC included more specific immune activities – for example: regulation of T cell activation (eg LAT), chemotaxis (eg RANTES), lymphocyte differentiation (eg HDAC4) and B cell differentiation (eg HDAC5). In addition to the activity being more 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. doi:10.1371/journal.pone.0004427.g002 Table 2. Comparison of change in expression between Mono+ and MonoD and PBMC samples at 3 and 24 hours (each cell corresponds to a region in Figure 3). Mono+ Down No change Up Down No change Up doi:10.1371/journal.pone.0004427.t002 Time point 3 h 24 h MonoD Down 243 214 19 114 151 9 No change 1382 14519 1086 1384 15895 857 Up 43 881 277 23 156 75 PBMC Down 433 87 0 390 171 4 No change 1227 15381 736 1125 15533 495 Up 8 146 646 6 498 442 PLoS ONE | www.plosone.org 4 February 2009 | Volume 4 | Issue 2 | e4427