by Prof. Dr Joachim Schultze

Gene expression profiling with microarrays allows thousands of genes to be measured, but a number of improvements are needed before this technology can be implemented in a clinical setting. Traditional cryopreservation techniques affect the transcriptome but an alternative technique is ideal for microarray analysis of blood samples.

Gene expression profiling allows researchers to analyse the genetic make-up of an individual and to measure the up- or down-regulation of specific biomarkers. This method has great potential for a variety of very different clinical applications. The ultimate goal is to be able to identify disease-associated genes and pathways in order to employ a targeted therapy, which may form the basis of personalised healthcare. However, gene expression analysis requires careful considerations, particularly if applied in routine diagnostic settings. Its successful implementation will depend on standardisation of procurement of the clinical specimens, an improvement of the clinical annotation of datasets available in public databases and an agreement on a standardised set of bioinformatical tools. In addition, the handling and storage of the sample, and the use of certain RNA isolation technologies, is of particular importance. In this article we have evaluated the effect of different sample preservation strategies for blood, which is one of the most attractive sample types from a practical perspective. We show that peripheral blood mononuclear cells (PBMCs) are not suitable for prolonged storage prior to gene expression profiling studies. We present an alternative technique: RNA-stabilising sample preservation, which overcomes this problem and also allows the use of whole blood as the sample type and enables samples to be stored for longer periods of time.

How it works

A microarray experiment consists of multiple steps in order to move from the gene expression experiment or patient sample through to the final result. The initial step is the preparation of the microarray by synthesising thousands of probes in situ and spotting them on the microchip. The spotted probes can be either specific genes of interest or all genes of the organism’s genome. The messenger RNA is isolated from the cells of the sample. After isolation, the mRNA is labeled with a fluorescent dye, one with the mRNA from the experimental condition and one as control mRNA. Both mRNAs are simultaneously added to the microarray. The mRNA hybridises on the spotted probe RNA and leads to red and green fluorescent signals for the sample and control, respectively. The microarray is scanned optically and the ratios of the red and green fluorescence are computed. In a typical microarray experiment, the expression of thousands of genes is measured across many conditions. This huge amount of data makes it practically impossible to analyse visually in such a multidimensional matrix. One way to make sense of these data is to reduce the complexity by mathematical models. This is where unsupervised hierarchical clustering is useful. This mathematical analysis looks at ratios of expression levels of control and experimental conditions and relationships among genes are mathematically computed. The genes that behave similarly are clustered together, leading to a clustered display of all genes analysed.

Storage & preservation does impact gene expression

RNA analysis relies on very careful handling, procurement and processing of the cells. This is particularly important when working with cells that are strongly reactive towards exogenous signals, and blood contains many cells that are specialised to respond to exogenous stimuli. One study from our lab has shown that simple technical issues, wsuch as prolonged sample transport or temperature, have a strong influence on the overall transcriptional profile of peripheral blood [1]. Moreover, the different methods used to obtain peripheral blood are mainly incompatible, making it very difficult to compare different studies.

One often used technique is cryopreservation of the samples before analysis, so we investigated to what extent the standard freezing procedure of primary cells influences the transcriptome. A microarray analysis was performed on cells that were cryopreserved as well as those that were freshly lysed and analysed. Unsupervised hierarchical analysis of gene expression profiles is used to cluster these genes that show a similar behaviour. Figure 1 shows that the genes of frozen cells (cf) cluster together as do those from freshly lysed and analysed cells (c). This clear distinction of these groups, with one exceptional case, indicates a strong effect of the cell cryopreservation procedure on gene expression profiles. In conclusion, cryopreservation affects gene expression and is not suitable for gene expression profiling. We also performed an analysis that identified the genes and pathways that were affected. The annotated Toll-like receptor signaling pathway was identified to be affected with a decline of IRF3 and increase of CCL5. This indicates that either the freezing or the subsequent thawing might interfere with pattern recognition receptor signalling processes or are related to variations in cell populations expressing these transcripts at constant level. In addition, membrane-associated genes were affected.

We also tested the effects of long-term cryopreservation – ranging from 20 to 60 months – and found that it did not maintain stability of gene expression profiles over time. Rather, substantial changes were identified, characterised by pronounced loss of signal of time for many transcripts.

Alternative storage technique

An alternative technique involves RNA stabilisation with PAXgene Blood RNA Tubes (Qiagen), which contain a stabilisation solution for immediate cell lysis and denaturation of all proteins. This freezes the in vivo gene transcription profile by reducing in vitro RNA degradation and preventing gene induction. To test the influence of long-term storage, PAXgene-stabilised blood was collected from healthy volunteers and stored at room temperature for 24 hours before processing. Afterwards, RNA was either directly isolated or PAXgene Blood RNA Tubes were frozen for different time periods of up to 12 months at -20°C prior to further processing. The gene expression profiles were compared using unsupervised hierarchical cluster analysis as mentioned above. This resulted in gender and individual dependent clustering of the samples in which the individuals and the gender clustered together, while there was no clustering pattern observed for the time period of storage. This indicates that the variability of the data depends predominantly on inter-individual differences rather than prolonged freezing periods and that the storage of PAXgene Blood RNA stabilised samples does not affect gene expression profiling experiments. The technology is therefore highly suitable for gene expression studies.

Summary

Gene expression profiling with microarrays represents a great opportunity to measure thousands of genes. A number of improvements are needed to implement this technology in a clinical setting. In biomedical research, but in particular in clinical applications, the stability of the transcriptome and reproducibility of the profiling analysis is crucial. Traditional cryopreservation techniques affect the transcriptome and are therefore not suitable for microarray studies, however, we tested PAXgene Blood RNA stabilising as an alternative technique. The results demonstrated high stability of the transcriptome, which is ideal for microarray analysis of blood samples, and for future clinical settings.

The author

Prof. Dr Joachim Schultze

Director of Genomics and
Immunoregulation LIMES-Institut

Carl-Troll-Str. 31, Bonn, Germany

Tel: +49 (0)2 28 / 73 - 6 27 86

e-mail: j.schultze@uni-bonn.de