Reticulocytes are present in small numbers in peripheral blood, can be readily isolated, and still harbour remnants of genetic material that likely represent gene expression profiles of patients. This makes them a good source of biological material in which to study differential gene expression. Quantitative trait loci involved in the phenotypic outcome of haemoglobinopathies, which are mapped and localised by linkage and association studies, usually exert their effect through subtle changes in gene expression. To detect these subtle changes, a sensitive method of detection is needed. Real time PCR is one of the most sensitive and reproducible quantification methods for gene expression analysis. It provides simultaneous measurement of gene expression in many different samples for a number of genes. However, many different factors in real-time PCR may effect the results, including the selection of the housekeeping genes. The 'ideal' housekeeping gene should be constantly transcribed in all cell types and tissues and remain stable between samples taken from different time points and under different experimental conditions. However, it is impossible to find a 'universal' housekeeping gene having stable expression under all these conditions [4, 16]. For example, ACTB, GAPDH and 18S are the most commonly used housekeeping genes, but a number of studies have provided evidence that their transcription levels vary considerably between different individuals, different cell types, different developmental stages and under different experimental conditions [4, 5, 9]. Therefore, for accurate analysis of gene expression, thorough validation of candidate housekeeping genes is crucial.
Another crucial factor in gene expression analysis concerns using the most appropriate method for housekeeping gene selection. A significant consideration when it comes to this is the small quantities of mRNA obtained from reticulocytes. Attempts to find the most appropriate method has involved an approach developed by Vandesompele et al . This approach relies on the principle that two perfect housekeeping genes would be identical in all samples in all experimental conditions or cell types. Variation in expression ratios between different samples reflects the fact that one or both of the genes are not stably expressed. Therefore, increasing variation in this ratio corresponds to decreasing expression stability. A visual basic application for Microsoft Excel – termed geNorm – has been written that uses an algorithm to calculate M, a gene expression stability measure, which is the mean pairwise variation for a gene compared with all other tested control genes. Genes with higher M values have greater variation in expression, and via a stepwise exclusion process genes can be ranked. For an accurate measure of expression levels, normalisation by multiple housekeeping genes is suggested. Consequently, a normalisation factor based on the expression levels of the best performing housekeeping genes must be calculated via averaging of the control genes using the geometric mean. It is suggested that 3 stable control genes should suffice for accurate normalisation of samples with relatively low expression variation, whereas other tissue panels require a fourth, or even a fifth control gene to capture the variation. Further software based approaches include BestKeeper  and Normfinder . BestKeeper, an Excel-based tool also uses 'pair-wise' correlations, and can determine the best suited standards, out of 10 candidates, and combine them into an index. Where the earlier presented geNorm software is restricted to housekeeping gene analysis only, the index generated by BestKeeper can then be compared with a further 10 genes to decide whether they are differentially expressed, for example, under an applied treatment. Here, all data processing is based on crossing points . Normfinder, whose strategy is rooted in a mathematical model of gene expression, enables estimation not only of the overall variation of the candidate normalisation genes but also of the variation between sample subgroups of the sample set. Notably, the strategy provides a direct measure for the expression variation, enabling the user to evaluate the systematic error introduced when using the gene. It has also been reported to show relatively low sensitivity towards coregulation of the candidate normalisation genes . Although very accurate, these methods rely on the design of specialist software programs, and where the use of multiple housekeeping genes are suggested, it is difficult to obtain enough reticulocyte sample to realistically use 3 or more housekeeping genes for normalisation. A further common strategy for the investigation of gene expression involves the standardisation of starting mRNA whereby a constant amount of total RNA is added to each reverse transcription reaction . In some instances however, it is impossible to quantify this parameter, for example, because only minimal amounts of RNA are obtained from patient samples, errors in measurement can occur due to protein contamination in the RNA sample. We have therefore adopted a ΔCt approach; a method similar to that described by Vandesompele et al, whereby 'pairs of genes' are compared. This simple ΔCt approach bypasses the need to accurately quantify input RNA and instead uses ΔCt comparisons between genes.
Results show that the level of RNA transcription for some of the candidate housekeeping genes varied considerably and in some cases RNA transcription levels were too low to quantify accurately or reproducibly. This was particularly the case with ACTB, and this would therefore be completely inappropriate to use as a housekeeping gene. Out of all the genes tested, those that scored highest in their requirements as housekeeping genes were GAPDH, SDHA and HPRT1. However, when carrying out ΔCt comparisons, in order to provide the highest level of accuracy, the Ct values compared should not be vastly different. HPRT1 expression was found to be relatively low and it would therefore be inappropriate to compare this against a relatively highly expressed gene, for example, against γ-globin. GAPDH fulfilled most criteria as a suitable housekeeping gene in that it was strongly expressed, displayed minimal fluctuation and is likely to be independent of genes (i.e. not co-ordinately expressed) commonly involved in reticulocyte studies. In addition, B2M, which has been found to be a stable housekeeping gene in other biological systems [14, 17] showed one of the highest levels of variation in this tissue. To confirm the validity of the approach used in the housekeeping gene selection process, γ-globin levels normalised against the gene panel were compared with their corresponding protein level. The strongest correlation was observed between GAPDH-normalised γ-globin expression and HPLC-measured Hb F levels, supporting GAPDH as an appropriate housekeeping gene for reticulocytes. The second highest ranking housekeeping gene from the gene panel was SDHA; SDHA-normalised γ-globin expression was then correlated with Hb F levels.
A large number of studies have been carried out concerning the validation of housekeeping genes in many different tissues and cell types. However, it has been difficult to find information on appropriate housekeeping genes for use in reticulocytes. To our knowledge, the only reported gene for normalisation in reticulocytes is RPS19 . For accurate quantitation of a target gene, it is recommended that the housekeeper should have similar expression levels. This increases the sensitivity to detect subtle differences in gene expression. However, RPS19 is very highly expressed, as are many of the ribosomal genes, and would therefore be more appropriate for normalising very highly expressed genes such as α and β globins, but preferably not for genes with lower expression. Reticulocytes are often used in haematological studies and as a result, this information on alternative housekeeping genes may be very useful, if not essential for any future studies carried out involving this cell type.