BMC Res Notes

BackgroundBlood is a convenient sample and increasingly used for quantitative gene expression measurements with a variety of diseases including chronic fatigue syndrome (CFS). Quantitative gene expression measurements require normalization of target genes to reference genes that are stable and independent from variables being tested in the experiment. Because there are no genes that are useful for all situations, reference gene selection is an essential step to any quantitative reverse transcription-PCR protocol. Many publications have described appropriate genes for a wide variety of tissues and experimental conditions, however, reference genes that may be suitable for the analysis of CFS, or human blood RNA derived from whole blood as well as isolated peripheral blood mononuclear cells (PBMCs), have not been described.FindingsLiterature review and analyses of our unpublished microarray data were used to narrow down the pool of candidate reference genes to six. We assayed whole blood RNA from Tempus tubes and cell preparation tube (CPT)-collected PBMC RNA from 46 subjects, and used the geNorm and NormFinder algorithms to select the most stable reference genes. Phosphoglycerate kinase 1 (PGK1) was one of the optimal normalization genes for both whole blood and PBMC RNA, however, additional genes differed for the two sample types; Ribosomal protein large, P0 (RPLP0) for PBMC RNA and Peptidylprolyl isomerase B (PPIB) for whole blood RNA. We also show that the use of a single reference gene is sufficient for normalization when the most stable candidates are used.ConclusionsWe have identified PGK1 as a stable reference gene for use with whole blood RNA and RNA derived from PBMC. When stable genes are selected it is possible to use a single gene for normalization rather than two or three. Optimal normalization will improve the ability of results from PBMC RNA to be compared with those from whole blood RNA and potentially allows comparison of gene expression results from blood RNA collected and processed by different methods with the intention of biomarker discovery. Results of this study should facilitate large-scale molecular epidemiologic studies using blood RNA as the target of quantitative gene expression measurements