Variability of gene expression profiles in human blood and lymphoblastoid cell lines

Abstract

Background: Readily accessible samples such as peripheral blood or cell lines are increasingly being used in large cohorts to characterise gene expression differences between a patient group and healthy controls. However, cell and RNA isolation procedures and the variety of cell types that make up whole blood can affect gene expression measurements. We therefore systematically investigated global gene expression profiles in peripheral blood from six individuals collected during two visits by comparing five of the following cell and RNA isolation methods: whole blood (PAXgene), peripheral blood mononuclear cells (PBMCs), lymphoblastoid cell lines (LCLs), CD19 and CD20 specific B-cell subsets.

Results: Gene expression measurements were clearly discriminated by isolation method although the reproducibility was high for all methods (range rho = 0.90-1.00). The PAXgene samples showed a decrease in the number of expressed genes (P < 1*10(-16)) with higher variability (P < 1*10(-16)) compared to the other methods. Differentially expressed probes between PAXgene and PBMCs were correlated with the number of monocytes, lymphocytes, neutrophils or erythrocytes. The correlations (rho = 0.83; rho = 0.79) of the expression levels of detected probes between LCLs and B-cell subsets were much lower compared to the two B-cell isolation methods (rho = 0.98). Gene ontology analysis of detected genes showed that genes involved in inflammatory responses are enriched in B-cells CD19 and CD20 whereas genes involved in alcohol metabolic process and the cell cycle were enriched in LCLs.

Conclusion: Gene expression profiles in blood-based samples are strongly dependent on the predominant constituent cell type(s) and RNA isolation method. It is crucial to understand the differences and variability of gene expression measurements between cell and RNA isolation procedures, and their relevance to disease processes, before application in large clinical studies.

Figure 1

Study design. We obtained gene expression profiles of five different post venipuncture methods of cell and RNA isolation. The pie charts illustrate the different cellular composition of the five methods whereas the arrows show the laboratory processes.

Figure 2

Scatterplots of mean expression levels across individuals. Gene expression levels are averaged for the two visits of the overlapping detected probes.

Figure 3

Principal components analysis of the samples.

Figure 4

Hierarchical clustering of 2,072 probes with 5% lowest and 5% highest PLS variable weights expressed across all 56 samples.

Figure 5

Venn diagrams of the number of detected probes between A) PAX and PBMCs B) B-cell CD19 and LCLs C) B-cell CD19 and LCLs D) B-cell CD19 and B-cell CD20.

Figure 6

Hierarchical clustering of 374 differentially expressed probes for the PAX and PBMCs.

Figure 7

Clusters of differentially expressed probes are correlated with several parameters of blood counts (number of neutrophils, number of lymphocytes, number of erythrocytes, number of monocytes and mean cell volume). Open circles indicate outlying values in PAX. Significance levels are indicated at the top: * p-values ≤ 0.05 and > 10^-5,** p-values are ≤ 10^-5 and >10^-10,*** p-values are ≤ 10^-10.