- Methodology article
- Open Access
Construction and validation of the APOCHIP, a spotted oligo-microarray for the study of beta-cell apoptosis
© Magnusson et al; licensee BioMed Central Ltd. 2005
- Received: 16 September 2005
- Accepted: 29 December 2005
- Published: 29 December 2005
Type 1 diabetes mellitus (T1DM) is a autoimmune disease caused by a long-term negative balance between immune-mediated beta-cell damage and beta-cell repair/regeneration. Following immune-mediated damage the beta-cell fate depends on several genes up- or down-regulated in parallel and/or sequentially. Based on the information obtained by the analysis of several microarray experiments of beta-cells exposed to pro-apoptotic conditions (e.g. double stranded RNA (dsRNA) and cytokines), we have developed a spotted rat oligonucleotide microarray, the APOCHIP, containing 60-mer probes for 574 genes selected for the study of beta-cell apoptosis.
The APOCHIP was validated by a combination of approaches. First we performed an internal validation of the spotted probes based on a weighted linear regression model using dilution series experiments. Second we profiled expression measurements in ten dissimilar rat RNA samples for 515 genes that were represented on both the spotted oligonucleotide collection and on the in situ-synthesized 25-mer arrays (Affymetrix GeneChips). Internal validation showed that most of the spotted probes displayed a pattern of reaction close to that predicted by the model. By using simple rules for comparison of data between platforms we found strong correlations (rmedian= 0.84) between relative gene expression measurements made with spotted probes and in situ-synthesized 25-mer probe sets.
In conclusion our data suggest that there is a high reproducibility of the APOCHIP in terms of technical replication and that relative gene expression measurements obtained with the APOCHIP compare well to the Affymetrix GeneChip. The APOCHIP is available to the scientific community and is a useful tool to study the molecular mechanisms regulating beta-cell apoptosis.
- Beta Cell Death
- Spot Variance
- Internal Replication
- Spotted Array
- RNeasy Spin Column
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) . There is increasing evidence that apoptosis is the main cause of beta-cell death at the onset of T1DM [1–4] and after islet transplantation [1, 5, 6] Apoptosis is a regulated process, affected by expression of diverse pro- and anti-apoptotic genes [1, 7, 8] Cytokines play a role in the inflammatory destruction of islet grafts immediately after transplantation [9–11] 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 [1–3] 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 [12, 13]
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 . Spotted long oligonucleotides arrays were recently introduced as an alternative to cDNA arrays and in situ synthesized oligonucleotide arrays . 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 [5, 16–18]. 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.
The Model based approach
Standard deviations for the various random terms in the log2 fold-change for all the chips in the dilution series.
concentration μg/20 μL
Additional variance when determining fold-changes on two chips (technical replication).
concentration μg/20 μL
Variance in a one-colour system.
concentration μg/20 μL
One-color variance, block
As depicted in Table 1 we observed a discrepancy between the log2 concentration and the median log2 fold-change. This may partly be accounted for by the scanner settings which were set to fixed but arbitrary values. Considering the self-self hybridisations (concentration 1, Table 1) it is evident that the settings for the test channel were too high compared to the settings for the reference channel. This effect may be minimized by using automated settings generated by the scanning software (data not shown). However, the ratios between two consecutive concentrations are close to the expected values except for the highest concentration where it is lower than expected (Table 1).
Cross platform comparison
We compared the relative expression of 515 genes present on both the APOCHIP and Affymetrix GeneChip 230A arrays. These genes, corresponding to 949 probes on the APOCHIP, were used to compare the relative gene expression profiles in ten rat RNA samples. On average, 93 % of the spots were called "good" by the Scanarray Express software and 7 % was called either "bad" or "not found". The samples and the pooled reference was analysed separately on GeneChip 230A arrays, since this system utilizes single colour hybridisations. Normalised M-values (sample vs. reference) were calculated for each probe set on the array using RMA  and Affymetrix MAS 5.0 algorithm that compares signal intensity from perfect-match and mis-match 25-mers . On average, 65 % of the genes surveyed on these arrays were called "present" and 34 % were called "absent" and the remainder "marginal" using MAS 5.0. This software also reports calls for "increased" (I), "decreased" (D) and "no change" (NC) for the relative gene expression. To take into account possible differences due to normalisation methods we compared the results obtained by our approach (MAS 5.0/median centering) to those obtained using RMA and a LOWESS (LOcally WEighted Scatterplot Smoothing) procedure implemented in MIDAS . We found similar results particularly when low intensity data was excluded, as described below (data not shown).
Representation of the cross-platform comparisons.
Coefficient of correlation rmedian
1. All data
2. No data associated with absent, bad, not found
3. Most varying probes
Without this quality filtering of the probes the median of the weighted Pearson correlation was 0.39, whereas the filtering increased this value to 0.64 (first two lines of Table 4). A further filtering of the probes may be relevant. If a gene has no differential expression between the ten samples there is no possibility of estimating the correlation. Similarly, if the probe does not respond at all in one of the two platforms, the estimated correlation is unreliable. In an attempt to avoid this we removed probes that had a low variation over the ten samples in either one or both of the two platforms. The Affymetrix GeneChips showed the largest range of the log2 ratios. To compare a large number of probes and include only the most varying we set an arbitrary cut-off of 0.25 for the Affymetrix platform. To include a similar number of probes for the APOCHIP we set an arbitrary cut-off of 0.0625 for the variance of this platform. This reduced the number of probes to 267 (164 genes) (Figure 4). For this reduced set of probes the median correlation was 0.84 (Table 4), indicating a tight concordance between the two array types.
Representation of the spotted probes exhibiting negative correlation coefficients with Affymetrix probes.
Long oligonucleotide sequence 5'-3'
GenBank accession no.
Probe Set ID RAE230A
Microarrays have been widely used for expression profiling [14, 23], discovery of gene function [24, 25], pathway dissection , classification of clinical samples [27, 28] as well as investigation of RNA splice variants . Several studies have been conducted comparing gene expression across platforms with varying results [30–39]. 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 [29, 32, 33, 37]. Thus, Kuo et al.  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 . Li et al.  and Kothapalli et al.  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 [30, 34], and several recent studies suggest a robust concordance between the different microarray platforms [40–42]. Hughes et al.  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.  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 . 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 . 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 . 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 . As discussed above, low signal intensities are prone to increased variation  a phenomenon that is well established for most array formats, including spotted 30 mer arrays , in situ synthesized 24 mer arrays  and GeneChips [47, 48]. 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 .
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 [14, 34]. 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 [40–42, 50, 51]. 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 . 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.
Isolation of total RNA
Total RNA was isolated from snap frozen cells and tissue using Trizol. Each sample was dissolved in 1 mL Trizol® reagent (Invitrogen) on ice and homogenised using a Fastprep homogeniser (Bio 101 Savant Instruments Inc.) according to the manufacturer's instructions. Trizol was removed by addition of chloroform followed by isopropanol precipitation. The precipitates were washed using 75 % ethanol. The amount and purity of RNA was quantified photo-spectrometrically by measuring the optical density at 260 and 280 nm and the integrity was checked by agarose gel electrophoresis.
For each hybridisation reverse transcription was performed on 5 μg total RNA for 1 hour at 42°C using a T7 oligo(dT)24-primer and reverse transcriptase (SuperScript II; Life Technologies Inc.). Second-strand cDNA synthesis was performed for 2 hours at 16°C using Escherichia coli DNA polymerase I, DNA ligase, and RNase H (Life Technologies Inc.) followed by incubation in 50 mM NaOH and 0.1 mM EDTA for 10 minutes at 65°C to degrade the RNA. After phenol-chloroform extraction and ethanol precipitation, in vitro transcription was performed for 6 hours at 37°C using biotin-16-UTP and biotin-11-CTP with an RNA transcript labelling kit (BioArray; Enzo Diagnostics). cRNA was purified on RNeasy spin columns (Qiagen), followed by fragmentation for 30 minutes at 95°C.
Spotted oligonucleotide chip
Total RNA extraction, reverse transcription on 5 μg total RNA and second strand cDNA synthesis were performed as described above. In vitro transcription was performed for 6 h at 37°C using amino-allyl-UTP and T7 Megascript Kit (Ambion). The produced cRNA was purified using Rneasy spin columns (Quiagen) followed by coupling of Cy3 and Cy5 fluorescent dyes in water-free DMSO for 2.5 h at room temperature. The labelled cRNA was fragmented for 30 min at 60°C in a 50 mM ZnCl2 solution and excess dyes were removed by ethanol precipitation of the cRNA.
Spotted oligonucleotide microarray procedures
Oligonucleotide probe design
The genes on the spotted array were selected based on our large data set obtained with GeneChip (Affymetrix) analyses of two different treatments that induce beta cell apoptosis, namely cytokines and double stranded RNA [13, 16–18]. We used three criteria to select genes to grid in our custom microarray: First, largest numerical alterations in gene expression; Second, representing informative gene clusters (e.g. genes involved in NO production, signal transduction/transcription factors, bcl-2 family, ER stress, etc); Third, genes showing distinct expression patterns over a time course (identified by self organizing maps). The complete list of genes present in the APOCHIP is provided in Additional file 1. Moreover a number of genes were selected for normalisation purposes. These genes were chosen to cover a range of signal intensities from low, medium to high. For each gene on the array one to three 60-mer oligonucleotides were designed using the Array Designer software (Premier Biosoft International).
Hybridisation, washing and scanning
The probes were spotted in duplicate on Codelink slides (Amersham Biosciences Inc.) at 30 % relative humidity and 20°C using a VersArray Chipwriter from BioRad. For a standard hybridisation one μg of each Cy3 and Cy5 labelled target sample was applied to the microarray slide in a volume of 20 μL for 16 h at 42°C. Before scanning all slides were washed as previously described . The two replicates were spotted below one another on the chips and all hybridisations were carried out twice on separate arrays. The samples were labelled with Cy3 and a common reference pool was labelled with Cy5. Following scanning of the glass slides the fluorescent intensities were quantified and background adjusted using an "adaptive circle" method implemented in the Scanarray Express software (PerkinElmer). Data was normalised by a blockwise median centering within individual hybridisation pairs and mean log2-expression ratios were calculated from the four measurements of each probe. Probes exhibiting expression values higher than 60000 (arbitrary units) in one chip within any comparison were discarded from the analyses. Probes exhibiting negative expression values in more than four chips were discarded from the analyses and remaining negative values were set to 1.
A model-based approach for internal validation of spotted oligonucleotide probes
Dilution series hybridisations
Total RNA and cRNA from rat kidney, heart, liver, and muscle tissue was prepared as described above. Equal amounts of cRNA from all samples were pooled and divided for fluorescent labelling to the dyes Cy3 and Cy5 as described above. Hybridisations were performed at five concentrations of Cy3 labelled target (0.3 μg/20 μL, 1 μg/20 μL, 2 μg/20 μL, 3 μg/20 μL, 4 μg/20 μL). The Cy5 material was used as reference and was kept at constant concentration of 1 μg/20 μL in all hybridisations. Arrays were scanned at identical laser (100 %) and PMT (50 for Cy5 and 65 for Cy3) settings.
In the spotted array the total variation contains contributions from: a. variations in the spots; b. variations in the two channels; c. variations between arrays. To study the variation in the system we modelled the log2 expression value xgcj for gene g, channel c = 1, 2, and internal replicate j = 1, 2 as a sum of terms representing the different variations. Terms that are used to model the mean value structure are denoted levels and terms that are used to model the variance structure are called random. We wrote the log expression as a gene level (μg), plus an overall channel and replication level (ψcj), plus a random spot variation (ugj with variance σ2s), plus a random gene specific channel difference (υgc with variance σ2c), plus, finally, a random measurement error (εgcj with variance σ2ωgcj2, where ωgcj2 is a known term). Here σ2s (s for spot) reflects the difference in morphology of the spots and is not related to the gene. Similarly, σ2c (c for channel) reflects that the two channels react differently depending on the gene, the variation in this gene specific channel difference is then given by σ2c.
Mathematically we write the model as xgcj = μg + ξcj + ugj + λgc + εgcj. As suggested by Churchill et al. , we model some of the variation as random components. To take into account the larger variances associated with small expression values  we scaled the variances using the standard deviations sgcj for the pixel intensities of each spot supplied by the software. Transforming sgcj to the log2 scale we used ωgcj = sgcj/[exp(xgcj ln(2))ln(2)]. The overall levels xicj were estimated by median values and we let ygcj = xgcj-xicj be the remainder when the estimated overall level was subtracted.
We first considered the variance of the measurement error. The measurement variance can be evaluated by looking at the difference dg = (yg11 - yg21) - (yg12 - yg22) between the two log2 fold-changes corresponding to the internal replication. The variance of this difference is σ2sg2 where sg2 is the sum of the four terms of ωgcj2 for gene g. A natural estimate for σ2 is then the average of the squared scaled differences dg/sg.
Having estimated the measurement variance we could next estimate the spot variance σs2 and the channel variance σc2. For the spot variance we considered the sum over the two channels of the difference between the two replicates: (yg11 - yg12) + (yg21 - yg22). The variance of this term is 8σs2 + σ2sg2, and having found the measurement variance σ2 above we then used the observed variance of these terms to estimate the spot variance σs2. Similarly, for the channel variance we considered the sum over the two internal replicates of the log2 fold-changes (yg11 - yg21) - (yg12 - yg22), which has variance 8σc2 + σ2sg2. As above we estimated σc2 from the observed variance of these terms.
To examine the reproducibility of the external replication we calculated a log2 fold-change for each of the two chips and considered the difference of these. We compared the variance of these differences with that predicted by the model.
For each probe and concentration we calculated a common log2 fold-change from the two internal and the two external replicates. The variances of these are τg2 rgi2, where g is gene and i is concentration, and where rgi2 is given through σ2ω2 above. Next, for each gene we performed a regression of log2 fold-change against the median of the log2 fold-changes, where the factor τg2 in the variance describes how well the linear relation fits the data.
Cross platform comparison of gene expression
Total RNA and double stranded cDNA from ten dissimilar rat tissues were prepared as described above. To minimise the variation associated with preparation of double stranded cDNA, each sample of double stranded cDNA was divided in two equal volumes that were used to prepare cRNA for hybridisation to Affymetrix GeneChips-RAE230A and for hybridisation to the spotted arrays.
A common reference pool was prepared by pooling equal amounts of cRNA from all samples investigated. We analysed 10 samples and common reference cRNA on GeneChips RAE-230A (Affymetrix Inc.). These arrays were hybridised with 15 μg of labelled cRNA for 16 h at 45°C while rotating. The chips were stained in an Affymetrix Fluidics station with streptavidin/phycoerythrin, followed by staining with an antistreptavidin antibody and streptavidin/phycoerythrin. The chips were scanned using a HP-laser scanner and the readings from the quantitative scanning were analyzed by the Affymetrix Gene Expression Analysis microarray Suite Software (MAS) 5.0. Each microarray was scaled to "150" as previously described . Data was also normalised using the Robust Multiarray Analysis (RMA) normalisation approach in the Bioconductor Affymetrix package to the R project for statistical computing .
Spotted oligonucleotide chip
A common reference pool was prepared by pooling equal amounts of cRNA from all investigated samples. The reference pool was labelled to Cy5 and the ten samples were labelled to Cy3 as described above. For each sample one μg of each Cy3 and Cy5 labelled target was applied to the microarray slide. Data was normalised as described in the Hybridisation, washing and scanning section.
Comparison of relative gene expression between platforms
To identify genes common to both platforms we used a combination of publicly available databases, DAVID  and Affymetrix , to identify UniGene clusters (build 99) and GenBank accessions. Based on this information, we were able to compare gene expression measurements for 515 genes represented on both platforms (Figure 1). For each gene we calculated the correlation between the values from the two platforms using a weighted Pearson correlation. The weighted Pearson correlation is obtained from the usual Pearson correlation by replacing all the sums entering this formula by weighted sums, using the same weights as those used in the regression above (see Additional file 2). These weights were obtained from the spotted arrays and are also used for the GeneChip values.
This work was supported by a grant from the Juvenile Diabetes Foundation International to Decio L. Eizirik and Torben Ørntoft. We gratefully acknowledge Ms. Hanne Steen and Ms. Gitte Høj at the Molecular Diagnostic Laboratory, University Hospital of Aarhus, for excellent technical assistance.
- Eizirik DL, Mandrup-Poulsen T: A choice of death-the signal-transduction of immune-mediated beta-cell apoptosis. Diabetologia 2001, 44(12):2115–33. Review. Erratum in: Diabetologia. 2002 Jun;45(6):936. 10.1007/s001250100021View ArticlePubMedGoogle Scholar
- Suarez-Pinzon W, Sorensen O, Bleackley RC, Elliott JF, Rajotte RV, Rabinovitch A: Beta-cell destruction in NOD mice correlates with Fas (CD95) expression on beta-cells and proinflammatory cytokine expression in islets. Diabetes 1999, 48(1):21–8.View ArticlePubMedGoogle Scholar
- Kurrer MO, Pakala SV, Hanson HL, Katz JD: Beta cell apoptosis in T cell-mediated autoimmune diabetes. Proc Natl Acad Sci U S A 94(1):213–8. 1997 Jan 7 10.1073/pnas.94.1.213PubMed CentralView ArticlePubMedGoogle Scholar
- O'Brien BA, Harmon BV, Cameron DP, Allan DJ: Apoptosis is the mode of beta-cell death responsible for the development of IDDM in the nonobese diabetic (NOD) mouse. Diabetes 1997, 46(5):750–7.View ArticlePubMedGoogle Scholar
- Moriwaki M, Itoh N, Miyagawa J, Yamamoto K, Imagawa A, Yamagata K, Iwahashi H, Nakajima H, Namba M, Nagata S, Hanafusa T, Matsuzawa Y: Fas and Fas ligand expression in inflamed islets in pancreas sections of patients with recent-onset Type I diabetes mellitus. Diabetologia 1999, 42(11):1332–40. 10.1007/s001250051446View ArticlePubMedGoogle Scholar
- Davalli AM, Scaglia L, Zangen DH, Hollister J, Bonner-Weir S, Weir GC: Vulnerability of islets in the immediate posttransplantation period. Dynamic changes in structure and function. Diabetes 1996, 45(9):1161–7.View ArticlePubMedGoogle Scholar
- Biarnes M, Montolio M, Nacher V, Raurell M, Soler J, Montanya E: Beta-cell death and mass in syngeneically transplanted islets exposed to short-and long-term hyperglycemia. Diabetes 2002, 51(1):66–72.View ArticlePubMedGoogle Scholar
- Friedlander RM: Apoptosis and caspases in neurodegenerative diseases. N Engl J Med 348(14):1365–75. 2003 Apr 3 10.1056/NEJMra022366View ArticleGoogle Scholar
- Newmeyer DD, Ferguson-Miller S: Mitochondria: releasing power for life and unleashing the machineries of death. Cell 112(4):481–90. 2003 Feb 21. Review. Erratum in: Cell. 2003 Mar21;(112)6:873. 10.1016/S0092-8674(03)00116-8Google Scholar
- Suarez-Pinzon W, Rajotte RV, Mosmann TR, Rabinovitch A: Both CD4+ and CD8+ T-cells in syngeneic islet grafts in NOD mice produce interferon-gamma during beta-cell destruction. Diabetes 1996, 45(10):1350–7.View ArticlePubMedGoogle Scholar
- Sandberg JO, Eizirik DL, Sandler S: IL-1 receptor antagonist inhibits recurrence of disease after syngeneic pancreatic islet transplantation to spontaneously diabetic non-obese diabetic (NOD) mice. Clin Exp Immunol 1997, 108(2):314–7. 10.1046/j.1365-2249.1997.3771275.xPubMed CentralView ArticlePubMedGoogle Scholar
- Eizirik DL, Darville MI: beta-cell apoptosis and defense mechanisms: lessons from type 1 diabetes. Diabetes 2001, 50(Suppl 1):S64–9.View ArticlePubMedGoogle Scholar
- Kutlu B, Cardozo AK, Darville MI, Kruhoffer M, Magnusson N, Orntoft T, Eizirik DL: Discovery of gene networks regulating cytokine-induced dysfunction and apoptosis in insulin-producing INS-1 cells. Diabetes 2003, 52(11):2701–19.View ArticlePubMedGoogle Scholar
- Lockhart DJ, Dong H, Byrne MC, Follettie MT, Gallo MV, Chee MS, Mittmann M, Wang C, Kobayashi M, Horton H, Brown EL: Expression monitoring by hybridization to high-density oligonucleotide arrays. Nat Biotechnol 1996, 14(13):1675–80. 10.1038/nbt1296-1675View ArticlePubMedGoogle Scholar
- Kane MD, Jatkoe TA, Stumpf CR, Lu J, Thomas JD, Madore SJ: Assessment of the sensitivity and specificity of oligonucleotide (50 mer) microarrays. Nucleic Acids Res 28(22):4552–7. 2000 Nov 15 10.1093/nar/28.22.4552Google Scholar
- Cardozo AK, Proost P, Gysemans C, Chen MC, Mathieu C, Eizirik DL: IL-1beta and IFN-gamma induce the expression of diverse chemokines and IL-15 in human and rat pancreatic islet cells, and in islets from pre-diabetic NOD mice. Diabetologia 2003, 46(2):255–66.PubMedGoogle Scholar
- Cardozo AK, Heimberg H, Heremans Y, Leeman R, Kutlu B, Kruhoffer M, Orntoft T, Eizirik DL: A comprehensive analysis of cytokine-induced and nuclear factor-kappa B-dependent genes in primary rat pancreatic beta-cells. J Biol Chem 276(52):48879–86. 2001 Dec 28 10.1074/jbc.M108658200View ArticlePubMedGoogle Scholar
- Rasschaert J, Liu D, Kutlu B, Cardozo AK, Kruhoffer M, ORntoft TF, Eizirik DL: Global profiling of double stranded RNA- and IFN-gamma-induced genes in rat pancreatic beta cells. Diabetologia 2003, 46(12):1641–57. 10.1007/s00125-003-1245-yView ArticlePubMedGoogle Scholar
- Irizarry RA, Hobbs B, Collin F, Beazer-Barclay YD, Antonellis KJ, Scherf U, Speed TP: Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics 4: 249–264. 10.1093/biostatistics/4.2.249Google Scholar
- Saeed AI, Sharov V, White J, Li J, Liang W, Bhagabati N, Braisted J, Klapa M, Currier T, Thiagarajan M, Sturn A, Snuffin M, Rezantsev A, Popov D, Ryltsov A, Kostukovich E, Borisovsky I, Liu Z, Vinsavich A, Trush V, Quackenbush J: TM4: a free, open-source system for microarray data management and analysis. Biotechniques 2003, 34(2):374–8.PubMedGoogle Scholar
- Yang MC, Ruan QG, Yang JJ, Eckenrode S, Wu S, McIndoe RA, She JX: A statistical method for flagging weak spots improves normalization and ratioestimates in microarrays. Physiol Genomics 7(1):45–53. 2001 Oct 10View ArticlePubMedGoogle Scholar
- Schena M, Shalon D, Davis RW, Brown PO: Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science 270(5235):467–70. 1995 Oct 20View ArticlePubMedGoogle Scholar
- Chu S, DeRisi J, Eisen M, Mulholland J, Botstein D, Brown PO, Herskowitz I: The transcriptional program of sporulation in budding yeast. Science 282(5389):699–705. 1998 Oct 23. Erratum in: Science 1998 Nov 20;282(5393):1421. 10.1126/science.282.5389.699Google Scholar
- Hughes TR, Marton MJ, Jones AR, Roberts CJ, Stoughton R, Armour CD, Bennett HA, Coffey E, Dai H, He YD, Kidd MJ, King AM, Meyer MR, Slade D, Lum PY, Stepaniants SB, Shoemaker DD, Gachotte D, Chakraburtty K, Simon J, Bard M, Friend SH: Functional discovery via a compendium of expression profiles. Cell 102(1):109–26. 2000 Jul 7 10.1016/S0092-8674(00)00015-5Google Scholar
- Roberts CJ, Nelson B, Marton MJ, Stoughton R, Meyer MR, Bennett HA, He YD, Dai H, Walker WL, Hughes TR, Tyers M, Boone C, Friend SH: Signaling and circuitry of multiple MAPK pathways revealed by a matrix of global gene expression profiles. Science 287(5454):873–80. 2000 Feb 4 10.1126/science.287.5454.873Google Scholar
- Khan J, Simon R, Bittner M, Chen Y, Leighton SB, Pohida T, Smith PD, Jiang Y, Gooden GC, Trent JM, Meltzer PS: Gene expression profiling of alveolar rhabdomyosarcoma with cDNA microarrays. Cancer Res 58(22):5009–13. 1998 Nov 15Google Scholar
- Golub TR, Slonim DK, Tamayo P, Huard C, Gaasenbeek M, Mesirov JP, Coller H, Loh ML, Downing JR, Caligiuri MA, Bloomfield CD, Lander ES: Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286(5439):531–7. 1999 Oct 15 10.1126/science.286.5439.531Google Scholar
- Li J, Pankratz M, Johnson JA: Differential gene expression patterns revealed by oligonucleotide versus long cDNA arrays. Toxicol Sci 2002, 69(2):383–90. 10.1093/toxsci/69.2.383View ArticlePubMedGoogle Scholar
- Hughes TR, Mao M, Jones AR, Burchard J, Marton MJ, Shannon KW, Lefkowitz SM, Ziman M, Schelter JM, Meyer MR, Kobayashi S, Davis C, Dai H, He YD, Stephaniants SB, Cavet G, Walker WL, West A, Coffey E, Shoemaker DD, Stoughton R, Blanchard AP, Friend SH, Linsley PS: Expression profiling using microarrays fabricated by an ink-jet oligonucleotide synthesizer. Nat Biotechnol 2001, 19(4):342–7. 10.1038/86730View ArticlePubMedGoogle Scholar
- Yuen T, Wurmbach E, Pfeffer RL, Ebersole BJ, Sealfon SC: Accuracy and calibration of commercial oligonucleotide and custom cDNA microarrays. Nucleic Acids Res 30(10):e48. 2002 May 15 10.1093/nar/30.10.e48Google Scholar
- Kuo WP, Jenssen TK, Butte AJ, Ohno-Machado L, Kohane IS: Analysis of matched mRNA measurements from two different microarray technologies. Bioinformatics 2002, 18(3):405–12. 10.1093/bioinformatics/18.3.405View ArticlePubMedGoogle Scholar
- Kothapalli R, Yoder SJ, Mane S, Loughran TP Jr: Microarray results: how accurate are they? BMC Bioinformatics 3(1):22. 2002 Aug 23 10.1186/1471-2105-3-22Google Scholar
- Barczak A, Rodriguez MW, Hanspers K, Koth LL, Tai YC, Bolstad BM, Speed TP, Erle DJ: Spotted long oligonucleotide arrays for human gene expression analysis. Genome Res 2003, 13(7):1775–85. 10.1101/gr.1048803PubMed CentralView ArticlePubMedGoogle Scholar
- Carter MG, Hamatani T, Sharov AA, Carmack CE, Qian Y, Aiba K, Ko NT, Dudekula DB, Brzoska PM, Hwang SS, Ko MS: In situ-synthesized novel microarray optimized for mouse stem cell and early developmental expression profiling. Genome Res 2003, 13(5):1011–21. 10.1101/gr.878903PubMed CentralView ArticlePubMedGoogle Scholar
- Wang HY, Malek RL, Kwitek AE, Greene AS, Luu TV, Behbahani B, Frank B, Quackenbush J, Lee NH: Assessing unmodified 70-mer oligonucleotide probe performance on glass-slide microarrays. Genome Biol 2003, 4(1):R5. Epub 2003 Jan 6. 10.1186/gb-2003-4-1-r5PubMed CentralView ArticlePubMedGoogle Scholar
- Tan PK, Downey TJ, Spitznagel EL Jr, Xu P, Fu D, Dimitrov DS, Lempicki RA, Raaka BM, Cam MC: Evaluation of gene expression measurements from commercial microarray platforms. Nucleic Acids Res 31(19):5676–84. 2003 Oct 1 10.1093/nar/gkg763Google Scholar
- Shi L, Tong W, Fang H, Scherf U, Han J, Puri RK, Frueh FW, Goodsaid FM, Guo L, Su Z, Han T, Fuscoe JC, Xu ZA, Patterson TA, Hong H, Xie Q, Perkins RG, Chen JJ, Casciano DA: Cross-platform comparability of microarray technology: intra-platform consistency and appropriate data analysis procedures are essential. BMC Bioinformatics 6(Suppl 2):S12. 2005 Jul 15 10.1186/1471-2105-6-S2-S12Google Scholar
- Shippy R, Sendera TJ, Lockner R, Palaniappan C, Kaysser-Kranich T, Watts G, Alsobrook J: Performance evaluation of commercial short-oligonucleotide microarrays and the impact of noise in making cross-platform correlations. BMC Genomics 5(1):61. 2004 Sep 2 10.1186/1471-2164-5-61Google Scholar
- Irizarry RA, Warren D, Spencer F, Kim IF, Biswal S, Frank BC, Gabrielson E, Garcia JG, Geoghegan J, Germino G, Griffin C, Hilmer SC, Hoffman E, Jedlicka AE, Kawasaki E, Martinez-Murillo F, Morsberger L, Lee H, Petersen D, Quackenbush J, Scott A, Wilson M, Yang Y, Ye SQ, Yu W: Multiple-laboratory comparison of microarray platforms. Nat Methods 2005, 2(5):345–50. Epub 2005 Apr 21. 10.1038/nmeth756View ArticlePubMedGoogle Scholar
- Larkin JE, Frank BC, Gavras H, Sultana R, Quackenbush J: Independence and reproducibility across microarray platforms. Nat Methods 2005, 2(5):337–44. Epub 2005 Apr 21. 10.1038/nmeth757View ArticlePubMedGoogle Scholar
- Bammler T, Beyer RP, Bhattacharya S, Boorman GA, Boyles A, Bradford BU, Bumgarner RE, Bushel PR, Chaturvedi K, Choi D, Cunningham ML, Deng S, Dressman HK, Fannin RD, Farin FM, Freedman JH, Fry RC, Harper A, Humble MC, Hurban P, Kavanagh TJ, Kaufmann WK, Kerr KF, Jing L, Lapidus JA, Lasarev MR, Li J, Li YJ, Lobenhofer EK, Lu X, Malek RL, Milton S, Nagalla SR, O'malley JP, Palmer VS, Pattee P, Paules RS, Perou CM, Phillips K, Qin LX, Qiu Y, Quigley SD, Rodland M, Rusyn I, Samson LD, Schwartz DA, Shi Y, Shin JL, Sieber SO, Slifer S, Speer MC, Spencer PS, Sproles DI, Swenberg JA, Suk WA, Sullivan RC, Tian R, Tennant RW, Todd SA, Tucker CJ, Van Houten B, Weis BK, Xuan S, Zarbl H, Members of the Toxicogenomics Research Consortium: Standardizing global gene expression analysis between laboratories and across platforms. Nat Methods 2005, 2(5):351–6. Epub 2005 Apr 21. 10.1038/nmeth754View ArticlePubMedGoogle Scholar
- Park PJ, Cao YA, Lee SY, Kim JW, Chang MS, Hart R, Choi S: Current issues for DNA microarrays: platform comparison, double linear amplification, and universal RNA reference. J Biotechnol 112(3):225–45. 2004 Sep 9 10.1016/j.jbiotec.2004.05.006Google Scholar
- Jarvinen AK, Hautaniemi S, Edgren H, Auvinen P, Saarela J, Kallioniemi OP, Monni O: Are data from different gene expression microarray platforms comparable? Genomics 2004, 83(6):1164–8. 10.1016/j.ygeno.2004.01.004View ArticlePubMedGoogle Scholar
- Ramakrishnan R, Dorris D, Lublinsky A, Nguyen A, Domanus M, Prokhorova A, Gieser L, Touma E, Lockner R, Tata M, Zhu X, Patterson M, Shippy R, Sendera TJ, Mazumder A: An assessment of Motorola CodeLink microarray performance for gene expression profiling applications. Nucleic Acids Res 30(7):e30. 2002 Apr 1 10.1093/nar/30.7.e30Google Scholar
- Nuwaysir EF, Huang W, Albert TJ, Singh J, Nuwaysir K, Pitas A, Richmond T, Gorski T, Berg JP, Ballin J, McCormick M, Norton J, Pollock T, Sumwalt T, Butcher L, Porter D, Molla M, Hall C, Blattner F, Sussman MR, Wallace RL, Cerrina F, Green RD: Gene expression analysis using oligonucleotide arrays produced by maskless photolithography. Genome Res 2004, 12: 1749–1755. 10.1101/gr.362402View ArticleGoogle Scholar
- Mills JC, Gordon JI: A new approach for filtering noise from high-density oligonucleotide microarray datasets. Nucleic Acids Res 2001, 29: e72. 10.1093/nar/29.15.e72PubMed CentralView ArticlePubMedGoogle Scholar
- Grundschober C, Malosio ML, Astolfi L, Giordano T, Nef P, Meldolesi J: Neurosecretion competence. A comprehensive gene expression program identified in PC12 cells. J Biol Chem 2002, 277: 36715–36724. 10.1074/jbc.M203777200View ArticlePubMedGoogle Scholar
- Yang YH, Speed T: Design issues for cDNA microarray experiments. Nat Rev Genet 2002, 3(8):579–88.PubMedGoogle Scholar
- Wright MA, Church GM: An open-source oligomicroarray standard for human and mouse. Nat Biotechnol 2002, 20: 1082–1083. 10.1038/nbt1102-1082View ArticlePubMedGoogle Scholar
- Li F, Stormo GD: Selection of optimal DNA oligos for gene expression arrays. Bioinformatics 2001, 17: 1067–1076. 10.1093/bioinformatics/17.11.1067View ArticlePubMedGoogle Scholar
- Kerr MK, Martin M, Churchill GA: Analysis of variance for gene expression microarray data. J Comput Biol 2000, 7(6):819–37. 10.1089/10665270050514954View ArticlePubMedGoogle Scholar
- Thykjaer T, Workman C, Kruhoffer M, Demtroder K, Wolf H, Andersen LD, Frederiksen CM, Knudsen S, Orntoft TF: Identification of gene expression patterns in superficial and invasive human bladder cancer. Cancer Res 2001, 61(6):2492–2499.PubMedGoogle Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.