Type 1 diabetes mellitus (T1DM) is an autoimmune disease caused by the selective destruction of the pancreatic beta-cells causing impaired insulin secretion. Beta-cell dysfunction and death in T1DM is the result of direct contact with activated macrophages and T-lymphocytes, and/or exposure to soluble mediators secreted by these cells, such as cytokines, oxygen free radicals and nitric oxide (NO) 1. There is increasing evidence that apoptosis is the main cause of beta-cell death at the onset of T1DM 1234 and after islet transplantation 156 Apoptosis is a regulated process, affected by expression of diverse pro- and anti-apoptotic genes 178 Cytokines play a role in the inflammatory destruction of islet grafts immediately after transplantation 91011 a process that hampers the success of islet transplantation in patients with T1DM. In vitro beta-cell exposure to the cytokine interleukin (IL)-1β induces functional impairment, whereas exposure to IL-1β in combination with interferon (IFN)-γ and/or tumor necrosis factor (TNF)-α, induces beta-cell death by apoptosis in rodent and human islet cells after a period of 3–9 days 123 These cytokines modify the expression of several hundreds of genes in beta-cells, including stress response genes that are either protective or deleterious for beta-cell survival, whereas genes related to differentiated beta-cell functions are mostly down-regulated 1213

DNA microarrays have become a standard tool for several applications in molecular biology and provide a way to monitor the expression of thousands of genes in a single assay. The two major microarray platforms presently in use are the high density microarrays produced by in situ synthesis and the arrays produced by deposition of pre-synthesized DNA onto a solid surface. One widely used implementation is the Affymetrix GeneChip which uses photolithography and solid-phase chemistry to produce high density arrays of 25-mer oligonucleotides 14. Spotted long oligonucleotides arrays were recently introduced as an alternative to cDNA arrays and in situ synthesized oligonucleotide arrays 15. Utilizing this technology we have prepared a custom oligonucleotide array representing 574 genes chosen for their putative involvement in beta cell death, the APOCHIP. Gene selection was based on the analysis of a large number of array determinations of cytokine- and double stranded RNA-treated primary beta cells or insulin-producing INS-1 cells using Affymetrix chips 5161718. This targeted and low cost array to be made freely available to the research community will allow the performance of detailed time-course studies and thus contribute to the understanding of the molecular events leading to beta cell dysfunction and death in diabetes mellitus.

To evaluate the performance of the spotted oligonucleotide array, we presently used two approaches. First we investigated the ability of the individual probes to respond to changes in target concentration. We expected that the M-value (log₂ fold-change of test versus reference) would be proportional to the target concentration on a logarithmic scale and that slopes ideally would be close to one. We performed a weighted regression of M on concentration (log₂ scale) using data from hybridisations at five different target concentrations. Next we used ten dissimilar RNA samples to compare the gene expression between the spotted array and Affymetrix platforms. We expected that this would yield a sufficient number of differentially expressed genes to allow for meaningful conclusions to be drawn about the concordance between the two platforms.

Our data suggest a good reproducibility for technical replications both within and between chips. High concordance to the Affymetrix GeneChip in terms of relative gene expression indicates that the APOCHIP is a reliable tool for studying the molecular mechanisms involved in beta cell apoptosis.

Microarrays have been widely used for expression profiling 1423, discovery of gene function 2425, pathway dissection 26, classification of clinical samples 2728 as well as investigation of RNA splice variants 29. Several studies have been conducted comparing gene expression across platforms with varying results 30313233343536373839. Whereas quantitative RT-PCR are usually found to agree well with corresponding array data concerns have been raised in some studies comparing different array formats 29323337. Thus, Kuo et al. 32 compared cDNA and Affymetrix 25-mer arrays and reported little concordance. The data in this study, however, was originated from two different laboratories and it is not clear whether the poor agreement was due to differences in the array types. Moreover, these results were based on absolute measurements which may be misleading 40. Li et al. 29 and Kothapalli et al. 33 also used cDNA and Affymetrix arrays and in both cases found substantial discrepancies; based on these findings, it was inferred that cDNA arrays often fail to identify differentially expressed genes. On the other hand, strong support for the use of long oligonucleotide microarrays comes from two independent studies 3034, and several recent studies suggest a robust concordance between the different microarray platforms 404142. Hughes et al. 30 reported high concordance utilizing data from 60-mer oligonucleotide arrays synthesized by an ink-jet oligonucleotide synthesizer, cDNA arrays and Affymetrix GeneChip arrays. Barczak et al. 34 compared relative gene expression measurements of a large collection of spotted 70-mers against Affymetrix GeneChips and found good agreement.

Although, the majority of the most differentially expressed probes yielded high correlations, there were exceptions (Table 5). There was also a group of genes exhibiting relatively large log₂ fold-change variation in one, but not the other, platform (Figure 6). These findings may partly be explained by differences in sensitivity and specificity and other probe specific effects. Of note, in some cases differences in transcript annotation and/or RNA splicing may be more important than discrepancies in array performance. Several factors may influence the reproducibility when comparing data across platforms. Proper gene identification is essential as genes can only be compared if they are accurately identified on both platforms 43. This can be difficult as transcript information often comes from different sources and are continuously being improved. The starting material must be consistent and procedures for RNA handling standardized. There are several labelling procedures in use, amplification versus no amplification, direct versus indirect dye incorporation which may contribute to downstream biases 43. In this study the samples were treated identically prior to RNA amplification and similar amplification and labelling protocols were used for both array types. Pre-processing and methods for data handling may also influence the final results 44. As stated in the Results section, there were differences using different spot identification software and normalisation algorithms, but these differences were substantially reduced by removing low intensity data and by comparing only the most varying genes (data not shown). Moreover, when comparing gene expression data across platforms it is essential to do so using relative measurements, since absolute measurements are affected by probe and platform specific properties that may cause misleading interpretations 40. As discussed above, low signal intensities are prone to increased variation 21 a phenomenon that is well established for most array formats, including spotted 30 mer arrays 45, in situ synthesized 24 mer arrays 46 and GeneChips 4748. Thus, it was not surprising to find that the correlation between differential measurements improved significantly when low-intensity measurements were excluded. Although intensities between two identical samples labelled to different dyes are rarely equal across all spots, we find that much of this variation is removed after proper normalisation (Figure 4 subplot 3). Two-colour hybridisations are generally used for spotted arrays, and many study designs involve comparison of the test sample to a common reference sample. Accurate quantification of a particular gene requires that the reference sample contains sufficient RNA to produce a clear signal for the corresponding probe. Reference samples may be generated from a pool of several cell lines, or as here, by pooling of all samples obtained from different tissues. The rationale for pooling the samples is that differentially expressed transcripts will also be present in the reference. Reference pools may not always produce sufficient signal intensity to allow for accurate quantification of some of the probes. When using Affymetrix MAS 5.0 software to analyse the pool reference for the subset of genes associated to both platforms, 76 % of the probe sets were called "present", as compared to 65 % "present" calls on average in the present data. Different designs such as a reference-free setup where pairs of test samples are compared directly may be preferable depending on the application 49.

Oligonucleotide probe design may also be important for signal intensity and for measuring differential gene expression. Oligonucleotide probes are designed on the basis of sequence. Several criteria, such as GC content and melting point, are used in the design but it is not possible to accurately account for differences in structure which may lead to unwanted steric effects. We observed that there were sometimes large numerical differences in the signal intensity of different spotted probes corresponding to the same gene (data not shown) a phenomenon that has been noted by others 1434. In a few cases long oligonucleotides representing the same gene gave discordant results. Such differences between probes may depend on several factors, including low sensitivity of some probes, alternative splicing, nucleic acid structure, distance from the 3' end of the RNA transcripts, GC content, and cross-hybridisation to unknown or poorly characterized mRNAs including pseudo genes and non-coding RNAs. Hence, the use of standardised sets of probes and protocols is an important issue when data from different laboratories and array platforms are compared 4041425051. Selection of a suitable microarray platform is influenced by several considerations. The Affymetrix system has been widely used for several applications and holds the advantage of standardisation in terms of probes and hybridisation protocols and, to some extent, data quantification 40. However, this technology has been limited by cost considerations for projects involving a large number of samples. Spotted arrays are labour intensive, but they can be made in large quantities by individual laboratories at a lower cost. Moreover, sequences with high homology to other genes can be avoided and probes for novel genes and gene variants may readily be designed.

In conclusion, we have constructed and validated the APOCHIP, a spotted microarray designed for the study of beta cell death in diabetes mellitus that may be of use to the scientific community. Designing and printing in-house arrays offers a flexible mean to carry out combinations of extensive multipoint and detailed time course gene expression analysis, following exposure of pancreatic beta-cells to different pro-apoptotic stimuli. We expect that this array will help research in the field enabling the performance of more detailed and complete experiments.

We have validated a rat oligonucleotide microarray constructed for the study of beta cell death in diabetes mellitus. We evaluated the technical reproducibility of the array by estimating the variance associated with the internal and external replication. We then used a fold-change regression model to estimate the ability of the probes to respond to changes in target concentration. Finally, we used ten dissimilar RNA samples to compare the relative gene expression between the spotted array and Affymetrix platforms. We found a high reproducibility for technical replications both within arrays and between arrays, with most oligonucleotide probes responding to target concentration in a manner close to that predicted by the model. There was a clear relation between successive data filtering and concordance between the two array types; by comparing only the most variable genes on both platforms we found that there was a high concordance between the APOCHIP and the GeneChip platform, supporting the validity of this approach.

NEM initiated the present study and was responsible for the design and construction of the APOCHIP and for the handling of microarrays. TFØ and MK supervised the microarray procedures. NEM, AKC and DLE selected the genes for the APOPCHIP. JLJ did the mathematical/statistical work. NEM and JLJ interpreted the results and wrote the article. DLE, TFØ, and AKC made improvements and suggestions to the manuscript.