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1From the Guerrieri Center at the Wilmer Eye Institute, the 3Departments of Molecular Biology and Genetics and 2Neuroscience, and the 4McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland.
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Abstract |
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METHODS. A cDNA microarray was constructed based on the predicted human retina gene expression profile according to expressed sequence tag (EST) databases. Gene expression profiles were obtained from five human retinas, two livers, and the cerebral cortical regions of two brains. Each sample was studied in duplicate, using a reference sample experimental design. Retina-enriched genes were identified by using the significance analysis for microarray (SAM) algorithm. Quantitative real time PCR was used to confirm microarray results. Bioinformatic analysis was performed to compare the array results with expression data available from public databases.
RESULTS. The cDNA microarray contains 10,034 sequences: 67% represent known genes and 33% represent ESTs. Differential hybridization with the array identified, in addition to known retinal genes, 186 retina-enriched genes that do not have known retinal function. Of these, 96 represent novel genes. Quantitative real-time PCR of 11 of the identified genes and ESTs confirmed their retina-enriched expression pattern. Bioinformatic analysis of EST databases suggests that of the 186 genes, approximately 40% are predominantly expressed in the retina, whereas the remainder show significant expression in other tissues. Comparison of this study’s microarray-based retina-enriched gene set with three published similar sets identified using complementary high-throughput approaches demonstrated only limited overlap of the identified genes.
CONCLUSIONS. Because previous studies have demonstrated that many retina-enriched genes are crucial for maintaining normal retinal function, the genes identified here are likely to include ones that have important roles in the retina and ones that when mutated can cause or modulate retinal disease. In addition, the retina custom array should provide a useful resource for comparing expression profiles between normal and diseased human retinas.
Sequencing and data mining of cDNA libraries is one of the high-throughput expression profiling approaches that has been applied to identify retina-enriched genes.4 5 6 7 8 However, potential bias and limited cost effectiveness has hampered its application for gene expression level comparison across multiple samples.10 Two additional recent techniques, serial analyses of gene expression (SAGE) and microarrays, have gained popularity for high-throughput gene expression profiling.11 12 13 Both methods have been efficient in identifying novel genes and in providing insights into complex pathologic processes, such as cancer and disease of the central nervous system.14 15 Unlike microarrays, SAGE is not dependent on preselection of sequences for analysis. It theoretically can identify all mRNA molecules in a sample. In practice, however, SAGE is often limited by the presence of orphan and ambiguous tags and by the considerable sequencing costs involved in its application for the study of multiple samples. By contrast, microarrays can be more readily applied to analyses of multiple samples, and follow-up studies on particular sequences are simplified by the immediate availability of the relevant cDNA clones. In contrast to SAGE, a disadvantage of microarrays is that they are limited to analyses of the sequences represented on the array. This limitation is particularly problematic when studying highly specialized tissues such as the retina, because the differentially expressed genes that underlie the unique structure and function of the retina are underrepresented on commercial microarrays.
One approach to circumvent this problem of limited representation of genes of importance to the retina is to construct custom cDNA microarrays enriched for genes that are likely to be relevant for retinal research. Recently, the feasibility of constructing a mouse retina custom cDNA microarray was reported.16 17 Herein, we report construction of a custom human cDNA microarray that allows for comprehensive expression profiling of genes that are likely to be of interest in studies of the normal and diseased human retina. As one potential application of this microarray, we describe its use to identify human genes with a retina-enriched expression pattern.
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Methods |
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Probe Labeling
Probes were prepared by minor modification of the method described by Hedge et al.18 RNA was extracted using isolation reagent (TRIzol; Invitrogen) according to the manufacturer’s protocol, and 20-µg aliquots of total RNA were treated with DNase (DNAfree; Ambion, Austin, TX), followed by RNA purification (RNeasy kit; Qiagen, Valencia, CA). Purified RNA samples were then incubated for 10 minutes at 70°C with 6 µg random hexamers (Invitrogen), cooled on ice, and used as a template for first-strand cDNA synthesis (5 µL first-strand buffer, 3 µL dithiothreitol [DTT], 2 µL reverse transcriptase (SuperScript II; Invitrogen) and 0.6 µL of a mixture of 10 mM aminoallyl-UTP (Sigma-Aldrich Corp., St. Louis, MO), 15 mM dTTP and 25 mM of dATP, dCTP, and dGTP (Invitrogen)). The 30-µL reverse transcription reaction was incubated overnight at 42°C. The 2:3 aminoallyl-UTP to dTTP ratio used in the RT reaction was selected after testing various ratios. For human RNA, the 2:3 ratio yielded the best results in terms of dye incorporation and hybridization (data not shown). After the RT reaction, 10 µL 1 M NaOH and 10 µL 0.5 M EDTA (pH 8) were added. After incubation for 15 minutes at 65°C, 25 µL of Tris–HCl (pH 7.4) was added followed by a purification step (PCR purification kit; Qiagen) in which the PE wash buffer in the kit was replaced with a phosphate wash buffer (1 M KPO4 [pH 8] in 80% ethanol).
The purified cDNA was dried, resuspended in 4.5 µL of 0.1 M carbonate buffer (pH 9), and incubated for 2 hours in room temperature with 12.3 µg of either Cy3 or Cy5 monoreactive dye (Amersham Biosciences, Inc., Piscataway, NJ) suspended in 4.5 µL of DMSO. The labeled probe was then purified (PCR purification kit; Qiagen) and dried in a heated speed vac. The total amount of dye incorporation (measured in picomoles of dye per probe) and the ratio of unlabeled to fluorescent labeled nucleotide in the probe were assessed by measuring probe absorbance at 260, 550, and 650 nm to assess DNA, Cy3, and Cy5 concentration, respectively.18 In typical reactions, dye incorporation is more than 300 picomoles and the unlabeled/labeled nucleotide ratio is in the range of 10 to 20.
Slide Printing, Postprocessing, and Hybridization
Amine-coated slides (SuperAmine; TeleChem, Sunnyvale, CA) were printed using an arrayer with 100-µm-tip pins (MicroGrid II; Biorobotics, Cambridge, UK) in a 60% humidity and 22°C environment. Slides were stored in a desiccator for up to 6 months and treated with 60 mJ of UV light (UV Stratalinker 2400; Stratagene, La Jolla, CA) before use.
Prehybridization and hybridization were preformed as described by Hedge et al.18 Briefly, slides were incubated for 40 minutes in 1% bovine serum albumin (BSA), 5x SSC, and 0.1% SDS at 42°C, washed in water and isopropanol, and dried using compressed air. Cy3 and Cy5 labeled probes were combined in 40 µL hybridization buffer (50% formamide, 5x SSC, 0.1% SDS buffer, 10 µg poly dA, and 10 µg Cot-1 DNA) and heated at 95°C for 3 minutes. Hybridization was performed under a coverslip (Fisher Scientific, Suwanee, GA) in a humidified hybridization chamber (GeneMachine, San Carlos, CA) for 18 hours in a water bath at 42°C. After hybridization, slides were washed in 1x SSC/0.2% SDS at 42°C, followed by washes in 0.1x SSC/0.2% SDS, and 0.1x SSC both at room temperature. Each wash lasted for 4 minutes, and then the slides were dried with compressed air.
Data Acquisition and Analysis
Slides were scanned using the a confocal laser scanner (ScanArray 5000; Perkin Elmer, Boston, MA). Scans using the maximum laser power setting that did not produce saturated spots were used for analyses. Spot finding and quantification of fluorescence intensity were performed on computer (Imagene software; Biodiscovery, Inc., Marina del Rey, CA). Local background subtraction and normalization were also performed on computer (Genesight software; Biodiscovery, Inc.) by dividing each spot intensity by the mean intensity value of that particular fluorescent channel (either Cy3 or Cy5).
Normalized study sample–reference sample ratios were analyzed using the significant analyses for microarray (SAM) algorithm.19 Two-class unpaired analysis was applied to compare the five retina samples to the liver and cortex samples separately. SAM calculates a false discovery rate (FDR), which is the median percentage of genes from a list that are likely to be mistakenly identified as differentially expressed. According to the SAM algorithm, genes are identified as differentially expressed based on the difference in expression among the sample groups and the consistency of this expression difference. We used similar, but not identical, FDRs to identify common genes in the retina–cortex and retina–liver expression pattern comparisons, because the SAM algorithm does not produce continuous FDR values but rather produces increments that depend on the imputed data.
In this study novel genes were defined as EST or group of ESTs that are not part of a cloned gene, regardless of whether the EST was assigned to a Unigene cluster.
Hierarchical clustering was performed and viewed using the Cluster and Treeview (both softwares are available at http://rna.lbl.gov.Eisensoftware.htm; softwares were developed by M. Eisen and provided in the public domain by the Lawrence Berkeley National lab and the University of California at Berkeley, Berkeley, CA) programs, respectively.20 Principal component analyses (PCAs) were performed on computer (Partek; Partek Inc., St. Charles, MO). Assessment of the tissue distribution of the expressed sequences was performed by BLAST search of each sequence against the human EST database (http://www.ncbi.nlm.nih.gov/dbest/ NCBI, Bethesda, MD). Hits with an E-value less than 1e-10 were accepted and mapped to the library name and further to the tissue of origin (http://www.ncbi.nlm.nih.gov/UniLib). For each clone the number of unique tissues in which matches were found were counted.
Donor Tissue
Five eyes were obtained through the National Disease Research Interchange (NDRI, Philadelphia, PA). Whole retinas were dissected and RNA extracted as described under probe labeling. RNA was also extracted from two liver and cortex tissues that were obtained from three donors (Table 1) .
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Results |
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We were able to obtain plasmids representing 10,034 of the sequences on our master list. Of these, 67% are from known genes and 33% are from ESTs. About half of the known genes on the array have been characterized. Their encoded proteins are involved in variety of biological (Fig. 1A) and molecular (Fig. 1B) processes.
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To assess overall performance and reliability of our microarray analyses, we performed self–self hybridizations. Forty micrograms of reference sample total RNA was divided into two aliquots: Half was labeled with Cy3, and the other half was labeled with Cy5, and then the two were mixed and hybridized together (Fig. 2) . A high degree of correlation was achieved between the two channels (correlation coefficient, R2 = 0.9432), demonstrating the reproducibility of the labeling, hybridization, and image analysis processes. However, it should be noted that a small fraction of the genes artifactually appeared to be differentially expressed: 21 of the 10,034 sequences showed twofold expression difference in the hybridization presented in Figure 2 . This is a common finding in microarray studies that underscores the importance of performing replicate arrays with dye swapping for each sample. The signal-to-background ratios varied across the spots on each slide and across arrays, with average signal-to-background ratios of the Cy3 and Cy5 channels on different slides varying from 2 ± 1.9 to 9.8 ± 6.7, and the percentage of spots with signal-to-background ratios higher than 1.4-fold varied from 36% to 85%.
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As would be expected based on their shared neuronal identity, the expression pattern of the retina was more similar to that of cortex than that of liver. This was reflected by the finding that, at similar significance levels, more than twice as many genes were preferentially expressed in the retina compared with liver than in the retina compared with cortex based on the SAM algorithm (Web Tables 3 and 4 are accessible at http://www.iovs.org/cgi/content/full/44/9/3732/DC1). As outlined in the Methods section, we have assessed the statistical significance of the differential gene expression patterns by using the FDR as determined with the SAM algorithm.19 The FDR is the median number of genes likely to be falsely identified as differentially expressed between samples. For example, at a moderately stringent FDR of 26% for the retina–cortex and 27% for the retina–liver comparisons (the design of the SAM program makes it difficult to achieve identical FDRs), there were 452 and 1097 genes that were preferentially expressed in the retina in the retina–cortex and retina–liver comparison, respectively (Web Tables 3 and 4). Fig. 1 shows the functional annotation of genes from Web Tables 3 and 4. Known photoreceptor genes, which constitute the best-defined group of retina-specific genes, composed a larger portion of the identified retina-enriched genes from the retina–cortex versus retina–liver comparison (46% vs. 26%, respectively; Fig. 1 ). In contrast, a larger absolute number of known photoreceptor genes were correctly identified by the retina–liver versus the retina–cortex comparison (31 vs. 25 genes, respectively). These findings presumably stem from the higher retina–cortex than retina–liver similarity and reflect better specificity for the retina–cortex comparison, and better sensitivity for the retina–liver comparison for identifying retina-specific genes. Of note, both inflammation and immune response genes were rare or absent among the retina-enriched genes compared with their significant representation among the sequences on the array (Fig. 1) , perhaps reflecting the so-called immune-privileged status of the retina.
To focus more on individual differentially expressed genes, so as to select genes of potential interest for further evaluation, we reanalyzed the data with a more stringent FDR. With FDRs of 10.3% and 9.5% for the retina–cortex and retina–liver comparisons, 345 and 720 retina-enriched genes were identified, respectively. Of these genes, 211 were common to both lists (Table 5 lists the top 40 of these genes; the full list is available in Web Table 5 at http://www.iovs.org/cgi/content/full/44/9/3732/DC1). Of these, 46% (98 genes) were ESTs representing novel retina-enriched genes, and 36% (77 genes) and 17% (36 genes) were cloned genes with known or unknown function, respectively. Of those with known function, 32% (25 genes) represent already characterized photoreceptor-enriched or photoreceptor-specific genes (Fig. 3) , compared with 10% percent of known photoreceptor-enriched genes of the genes with known function on the array (Fig. 1) . This was a reassuring finding and suggests that a significant fraction of the remaining 186 genes in the group (Table 5) are likely to represent retina-enriched genes.
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We included in the analysis the genes from the retina–cortex comparison at an FDR of 26% (Web Table 3 in the present study), genes in Table S11 from Blackshaw et al.,1 genes in Table 11 from Sharon et al.,3 and genes in Table 2 from Stohr et al.4 In all cases the genes from these tables were identified by the authors as retina-enriched, but it should be noted that not only different methods but also different species (human and mouse) and different statistical criteria were used to identify retina-enriched genes in each of the studies. Known photoreceptor genes were excluded from our analysis, both because they are generally the easiest to detect and because they were not included in two of the studies.1 4 In addition, because we used Unigene clusters to compare findings across the different studies, only known genes and ESTs that were assigned to such clusters were compared.
Table 8A shows the number of genes that were identified in two or more studies. The degree of overlap ranged from 23.9% (percent of genes from Sharon et al.3 that were identified by us as well) to 0% (no overlap between Blackshaw et al.1 and Stohr et al.4 ). Overall, the present study showed more overlap with the other studies than any of the other studies showed with each other. We identified 44 retina-enriched genes that were also identified in one of the other studies, and three genes that were identified in two of them (Table 8B) . No genes were identified that were shared by two other studies but not by ours, and, perhaps surprisingly, there were not any genes that were identified as differentially expressed in all four studies.
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Discussion |
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We have constructed a cDNA microarray designed for analysis of human retinal gene expression under normal and pathologic conditions. As an initial use for the microarray, we have applied it to characterize gene expression pattern differences between retina, another central nervous system–derived tissue (cerebral cortex) and liver. This comparison enabled the identification of more than 100 novel retina-enriched genes, as well as a large group of known genes that were not previously reported to have a retina-enriched expression pattern. Several of these known genes may play important roles in the physiology of the retina. For example, thrombomodulin, an endothelial cell surface glycoprotein that forms a complex with thrombin and converts it into a potent anticoagulant, was previously detected in chorioretinal blood vessels and in the anterior eye segment.24 25 The finding of its preferential expression in the retina suggests a possibly significant role in maintaining retinal and choroidal blood flow.
Both bioinformatic and experimental data support the validity of the microarray-derived expression results. First, the microarrays correctly identified multiple known retina-enriched genes. Second, the bioinformatic analysis we used, based on the SAM algorithm, is tested for internal consistency in the microarray data by looking for expression differences that were consistent across the five retina samples compared with the brain and liver samples.19 Third, the microarray analysis identified 47 genes that have been reported recently as retina-enriched by other investigators.1 3 4 Fourth, we confirmed the microarray findings for 10 of the newly identified differentially expressed genes and ESTs with an independent experimental method, QPCR. Consistent with previous reports, the QPCR data in fact suggest that the microarray results tended to underestimate the identified differential expression patterns.22 23 26 Fifth, analysis of available databases revealed that ESTs corresponding to the microarray identified retina-enriched genes were identified in fewer tissue sources than the overall clones represented on the array, which in turn were present in fewer tissues than sequences randomly selected from the genome. Last, clustering of the microarray data by two different algorithms successfully identified the five retina samples as being distinct from the liver and cortex samples.
Several caveats about our study should be acknowledged. First, although the retina custom microarray contains a substantial representation of the genes (both novel and known) that have been identified in retina cDNA libraries, additional retina-enriched genes not represented on the microarray are likely to exist. Second, we have studied whole retinas, but because the retina contains approximately 50 different cell types,27 conclusions regarding the gene expression patterns in particular retinal cell types cannot be directly drawn from our study. This problem may be addressed by expression profiling of homogeneous cell populations isolated by techniques such as laser capture microscopy28 29 or single-cell isolation methods30 (Wahlin et al., manuscript in preparation). A challenge in using these methods for microarray studies, however, is that they require amplification, a process that potentially introduces significant bias to the analysis.
Although there was considerable overlap between our microarray results and the previous SAGE and bioinformatic studies,1 3 4 it seems significant that many of the novel retina-enriched genes were identified by only one of the studies. Several factors may account for this surprising finding. Species differences in gene expression pattern may contribute to the relatively small overlap between the mouse SAGE study1 and the human SAGE, EST data mining, and microarray studies.3 4 As an example of potential species differences, we have identified several genes that are highly expressed in bovine and human RPE, but only minimally if at all in murine RPE, as well as a human and bovine retinal transcription factor that appears not to even exist in the mouse genome (Wang et al., manuscript submitted). However, given the major structural and functional similarities between murine and human retina, species differences probably account for only a small fraction of the observed expression profile differences. A more likely factor is the probable existence of a large number of unknown retina-enriched genes, together with the likely possibility that each method and approach is sampling only a limited and partially overlapping subset of this larger set. Additional microarray, SAGE, and bioinformatic studies, together with proteomic and other approaches, should eventually more definitively define the unique expression profiles of the murine and human retina.
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Acknowledgements |
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Footnotes |
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Submitted for publication October 23, 2002; revised April 8, 2003; accepted April 18, 2003.
Disclosure: I. Chowers (P); T.L. Gunatilaka, None; R.H. Farkas, None; J. Qian, None; A.S. Hackam, None; E. Duh, None; M. Kageyama, Santen Pharmaceutical (E, P); C. Wang, None; A. Vora, None; P.A. Campochiaro, None; D.J. Zack, Santen Pharmaceutical (F, P)
The publication costs of this article were defrayed in part by page charge payment. This article must therefore be marked "advertisement" in accordance with 18 U.S.C. 
1734 solely to indicate this fact.
Corresponding author: Donald J. Zack, 809 Maumenee Building, Johns Hopkins University School of Medicine, 600 N. Wolfe Street, Baltimore, MD 21287; dzack{at}bs.jhmi.edu.
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), and retina-enriched genes compared with cortex (
; FDR = 26%) and liver (
; FDR = 27%), based on biological (A) and molecular (B) function. Gene function was classified based on the gene ontology database (http://www.geneontology.org/). Only those genes that were present in the gene ontology database were classified. In (A), the number of genes classified from the array, retina–cortex, and retina–liver comparisons was 1106, 49, and 86, respectively. In (B), the number of genes classified from the array, retina–cortex, and retina–liver comparisons was 2098, 71, and 177, respectively. As indicated, the known photoreceptor gene set is enriched for genes that are differentially expressed in the retina, whereas the inflammation and immune response categories are deficient in such differentially expressed genes.
), cortex (•), liver (