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Published online before print
June 12, 2003, 10.1101/gr.1048803 Genome Res. 13:1775-1785, 2003 ©2003 by Cold Spring Harbor Laboratory Press; ISSN 1088-9051/03 $5.00
Methods Spotted Long Oligonucleotide Arrays for Human Gene Expression Analysis1 Department of Medicine, University of California, San Francisco, San Francisco, California 94143, USA 2 Gladstone Institute of Cardiovascular Disease, San Francisco, California 94141, USA 3 Group in Biostatistics, University of California, Berkeley, California 94720, USA 4 Department of Statistics, University of California, Berkeley, California 94720, USA 5 Division of Genetics and Bioinformatics, The Walter and Eliza Hall Institute of Medical Research, Parkville, Vic 3050, Australia
DNA microarrays produced by deposition (or `spotting')of a single long oligonucleotide probe for each gene may be an attractive alternative to other types of arrays. We produced spotted oligonucleotide arrays using two large collections of  70-mer probes, and used these arrays to analyze gene
expression in two dissimilar human RNA samples. These samples were also
analyzed using arrays produced by in situ synthesis of sets of multiple short
(25-mer)oligonucleotides for each gene (Affymetrix GeneChips). We compared
expression measurements for 7344 genes that were represented in both long
oligonucleotide probe collections and the in situ-synthesized 25-mer arrays.
We found strong correlations (r = 0.8–0.9)between relative gene
expression measurements made with spotted long oligonucleotide probes and in
situ-synthesized 25-mer probe sets. Spotted long oligonucleotide arrays were
suitable for use with both unamplified cDNA and amplified RNA targets, and are
a cost-effective alternative for many functional genomics applications. Most
previously reported evaluations of microarray technologies have focused on
expression measurements made on a relatively small number of genes. The
approach described here involves far more gene expression measurements and
provides a useful method for comparing existing and emerging techniques for
genome-scale expression analysis.
Microarrays can be produced by deposition (or spotting) of DNA or by in
situ synthesis of oligonucleotides on a solid substrate. Spotted cDNA arrays
are typically produced by depositing PCR amplicons, made from cDNA clones, on
modified glass slides (Schena et al.
1996
Various approaches have been used to verify the accuracy of microarray
data. Microarray assay technology can be calibrated by spiking known
quantities of one or several RNA transcripts into test samples. Alternatively,
independent methods including Northern blotting or quantitative PCR can be
used to verify array measurements (Yuen et
al. 2002
Spotted long oligonucleotide arrays were recently introduced as an
alternative to spotted cDNA arrays and in situ-synthesized oligonucleotide
arrays (Kane et al. 2000
To evaluate the performance of spotted oligonucleotide arrays, we used
these arrays to compare gene expression in two dissimilar RNA samples. We
expected that this would result in a large number of differentially expressed
genes, which would allow us to draw meaningful conclusions about how well
measurements made using different array types were correlated across a large
set of genes. We produced arrays using two large commercially available
collections of
We produced two different sets of spotted arrays using two collections of long oligonucleotide probes (Operon Human Genome Oligo Set Versions 1 and 2,Table 1). There were 10,801 UniGene clusters that were represented in both groups of probes, but the sequences of these two groups of probes were largely independent: Version 1 and Version 2 probes overlapped significantly (by at least 25 identical bases) for just 1935 of the 10,801 gene clusters that were represented in both versions. We also used commercially produced arrays containing sets of 25-mer probes synthesized in situ (Affymetrix U95Av2 GeneChips). We used all three groups of probes to compare gene expression in two total RNA samples, one made from K562 erythroleukemia cells and one made from a pool of 10 different cell lines.
For spotted long oligonucleotide arrays, the RNA samples were used to produce labeled cDNA targets. Two color hybridizations were performed using Cy3- and Cy5-labeled targets derived from the two RNA samples, and gene expression ratios were calculated for each hybridization. Six independent replicates were performed for Version 1 probe hybridizations, and four replicates were performed for Version 2 probe hybridizations. Two values, M and A, were calculated for each element on each array. M is a normalized, log2-transformed measure of differential gene expression. Positive M values indicate higher normalized signal intensity in the K562 RNA sample, negative M values indicate higher intensity in the pool RNA sample, and M values of zero indicate equal intensity in the two samples. A is a log2-transformed measure of total signal intensity for both samples. Higher A values indicate brighter signals. M and A values for all long oligonucleotide probes are shown in Figure 1A, B. These M and A values are means of six replicate arrays. Compared with the human gene probes, randomized negative control long oligonucleotide probes all produced dim signals (low A values) and M values close to zero. For example, for the Version 1 arrays, the 29 randomized negative control probes had A values of 6.8 ± 0.2 and M values of 0.08 ± 0.12 (mean ± standard deviation).
We analyzed the same two RNA samples using in situ-synthesized 25-mer oligonucleotide arrays. We analyzed K562 and pool RNA samples separately, because this technology has been optimized for single-color hybridizations. Normalized log2-transformed absolute signal intensities were calculated for each probe set on each array using RMA software. After averaging across replicate hybridizations, intensity values from K562 and pool arrays were combined to calculate M and A values for each probe set (Fig. 1C). Absolute signal intensities (A values) differ between long oligonucleotide arrays and 25-mer arrays, because these values were calculated from raw data obtained using different technologies. When we used another algorithm (Affymetrix MAS 5) that compares signal intensity from perfect-match and mismatch 25-mers, about half of the genes surveyed on these arrays were called "present" in these samples. For example, for the three replicate arrays used to analyze the pool sample,48%–53% of probe sets were called "present," 45%–49% "absent," and the remainder "marginal." We compared measurements obtained using the three different array types. Because probe designs were based on different sets of GenBank cDNA sequences, we assigned each long oligonucleotide probe or 25-mer probe set to a cluster using the UniGene database (Build 155). The numbers of Uni-Gene clusters represented in each probe group are listed in Table 1. The distribution of UniGene clusters between the three array types is shown in Figure 2. There were 7344 gene clusters that were represented on each of the three array types we used. We used this large set of genes as a basis for comparing expression data from the three array types.
Expression data from Version 1 spotted long oligonucleotide arrays and in situ-synthesized 25-mer arrays are compared in Figure 3. When all 7344 clusters were included in the comparison, there was a clear correlation between M values obtained using the two array types (Fig. 3A, Pearson correlation coefficient r = 0.80). The magnitudes of M values were similar for the two array types. We considered the effect of signal intensity on the M-value comparison. Some long oligonucleotide probes gave signals that were in the same range seen for negative control probes, and the 25-mer probe sets with low signals were usually called "absent" by the MAS 5 algorithm. When we excluded measurements from all probes and probe sets associated with A values less than the median A value for all probes on the array, the correlation between M values for the 2877 remaining genes improved substantially (r = 0.89,Fig. 3B). A very similar correlation was obtained when we reanalyzed the arrays with Affymetrix MAS 5 (for 25-mer arrays) and Axon GenePix 3 (for long oligonucleotide arrays), and then excluded 25-mer data associated with "absent" calls and long oligonucleotide data associated with "not found" spots (data not shown). However, that approach was more difficult to implement because it required manual spot flagging, and special rules were needed to handle missing values from individual arrays. The A values from the two array types were not as highly correlated as the M values (Fig. 3C). To help compare variance within the Version 1 long oligonucleotide array replicates with between-platform variance, we correlated average M values from the first three replicates with average M values from the last three replicates (Fig. 3D). As expected, the within-platform correlation (r = 0.94) was somewhat higher than the correlations obtained for the cross-platform comparison (Fig. 3A, B). We found similar cross-platform correlations when we compared the Version 2 long oligonucleotide arrays to the other two array types (Fig. 4). For all comparisons, there was a clear correlation between differential expression measurements made with different array types, and the correlation improved when measurements from probes with low intensity signals were excluded.
Investigators are often interested in identifying highly differentially expressed genes. To examine how genes with high differential expression measurements compare on the three array types tested, we generated a list of all genes that produced one of the 10 highest or 10 lowest M values on any of the three array types (Table 2). All 7344 gene clusters represented on each of the three array types were considered. By this criterion, a total of 22 genes were found to have extremely high relative expression in K562 RNA (M value in the top 10 on at least one array type). Of these 22 genes,16 were among the top 2% of M values on all three array types, four more genes were among the among the top 2% on 25-mer arrays and one of the spotted long oligonucleotide arrays, and two genes (VCY and COLEC10) were only found to be substantially differentially expressed on the Version 2 long oligonucleotide arrays. The findings were very similar for the 22 total genes found to have one of the 10 lowest M values on at least one array type: 16 were in the bottom 2% on all three array types, five were in the bottom 2% on 25-mer arrays and one of the spotted long oligonucleotide arrays, and one (MT2A) appeared to be highly differentially expressed only on the Version 1 long oligonucleotide array. Of all 44 genes listed in Table 2, there were 12 genes that did not give measurements in the top (or bottom) 2% on at least one array type (rank >2% or <98%). In 11 of these 12 cases, the failure to detect substantial differential expression was associated with low signal intensity or probes predicted to recognize different splice variants (see Table 2).
Graphical comparisons involving larger groups of genes with extreme M values are shown in Figure 5. Genes found to have M values near zero (equal expression in the two RNA samples) using one array type were highly unlikely to have large M values on another array (Fig. 5B). For example, of the 5676 genes with 25-mer array M values between -0.5 and 0.5, the Version 1 long oligonucleotide array results showed that 89% had M values between -0.5 and 0.5 and 99% had M values between -1 and 1 (i.e., less than twofold difference). Most genes associated with the highest and lowest M values on 25-mer arrays gave similar values on long oligonucleotide arrays (Fig. 5B, C). However, long oligonucleotide arrays did tend to give somewhat smaller estimates for the magnitude of M for genes with the most negative M values (Fig. 5C). In summary, genes producing extreme M values on one array type usually also produced large M values on the other two array types, whereas genes that produced small M values on one array type rarely produced large M values on another.
We also examined whether spotted oligonucleotide arrays could be used
successfully with amplified targets. In preliminary experiments involving
unamplified cDNA targets, we found that
We compared estimates of differential gene expression (M) made using cDNA targets and cRNA targets. For consistency, we limited this analysis to the same set of 7344 gene clusters and used the same strategy to filter out low-intensity signals. The cDNA target M values correlated well with one-round cRNA target M values, but the cRNA targets tended to result in smaller estimates of the extent of differential gene expression (Fig. 6C). M values were maintained within a similar range following a second round of amplification (Fig. 6D). Comparisons of A values from unamplified and amplified target hybridizations indicate that a small subset of probes produced higher A values than expected when amplified targets were used (Fig. 6E). These probes may bind nonspecifically to cRNA targets or recognize transcripts that are unusually highly amplified. In contrast, there were very few probes that produced A values dramatically lower than expected. Although the amplification procedure is expected to bias transcript representation to a certain extent, these results suggest that most transcripts that can be measured using unamplified cDNA targets can also be measured using amplified cRNA targets. When used with spotted long oligonucleotide arrays, cRNA targets provided estimates of differential gene expression (M) that correlated well with those obtained using cDNA targets, although the magnitude of M was typically smaller.
Most reported evaluations of DNA microarray platforms have focused on analyzing expression measurements made on a relatively small fraction of the many thousands of genes represented on modern arrays. In those studies, microarray measurements were calibrated using known amounts of input RNAs or were compared to measurements made with another technique, such as quantitative PCR. These approaches have yielded important information about the sensitivity, specificity, and reproducibility of these platforms, at least for the subset of genes studied directly. We took a different approach that allowed us to compare thousands of gene expression measurements made using a total of five different combinations of array types and target preparation methods. By using each method to compare gene expression in the same pair of dissimilar RNA samples, our approach provided a wide range of expression ratios for comparison and made it possible to examine how various sets of measurements made with spotted 70-mer oligonucleotide arrays correlated with one another and with measurements made using in situ-synthesized 25-mer arrays. Our ability to compare results between different data sets, including those based on single-color as well as two-color hybridizations, was facilitated by expressing each measurement as a pair of log-transformed differential expression (M) and total signal (A) values. The general approach described here should prove useful for the ongoing evaluation of new methods for genome-scale gene expression analysis.
Given the serious concerns raised in some previous comparisons of different
microarray platforms (Kothapalli et al.
2002 Spotted arrays are generally used for two-color hybridizations, and many study designs involve comparison of each test sample to a common reference sample. This design has been successfully employed for many spotted cDNA array experiments. To allow for accurate quantification of a particular gene, the reference sample must contain sufficient RNA to produce a clear signal for the corresponding probe. Reference samples are often generated from a pool of different cell lines, as was one of the two RNA samples analyzed here. Using Affymetrix MAS 5 software to analyze our three replicate 25-mer array analyses of the pool sample, we found that only 48%–53% of the probe sets yielded "present" calls (with the remainder being judged as either "marginal" or "absent"). More importantly, we found that a substantial fraction of 70-mer probes were associated with signal intensities that were no brighter than those seen for randomized negative control probes. These results suggest that reference pools such as the one used here may not produce sufficient signal to allow for accurate quantification of some genes on spotted long oligonucleotide arrays. If this is an important issue, the use of different reference samples with gene expression levels similar to those found in the test samples or the use of reference-free designs (where pairs of test samples are compared directly) may be preferable.
Our results indicate that probe selection can have important effects on net
signal intensity and on measurements of differential gene expression. When we
compared two different collections of Selection of a suitable microarray platform for a specific application can be influenced by a number of considerations. Many investigators have used in situ-synthesized 25-mer arrays, but the application of this technology has been limited by cost considerations, especially for projects involving large numbers of samples. Spotted arrays can be produced in quantity by individual laboratories or core facilities at a lower cost, although this can be labor-intensive and considerable expertise is required. Most spotted arrays have been made with probes made from cDNA clones, and much of the effort required to produce spotted cDNA arrays has centered on obtaining, sequence-verifying, and amplifying suitable cDNA libraries. In contrast, collections of long oligonucleotides are available from various sources, and sequence verification and amplification are not required. The overall costs of long oligonucleotide technology will often be lower when labor and other costs associated with obtaining and maintaining cDNA libraries are taken into account, and spotted long oligonucleotide arrays can be printed, hybridized, and scanned using virtually the same methods and equipment used for spotted cDNA arrays. Furthermore, long oligonucleotide probes can be designed to have more uniform hybridization characteristics and to avoid sequences with a high degree of homology to other genes; and probes for novel genes, gene variants, and transgenes can be designed using freely available tools and added easily to existing probe collections. Our large-scale analysis showed a strong correlation between spotted long oligonucleotide array data and in situ-synthesized 25-mer array data, suggesting that long oligonucleotide arrays are a good alternative gene expression analysis platform for many applications.
Spotted Array Fabrication Long oligonucleotides were designed and synthesized by Operon. Operon provided the following information about the oligonucleotides: Human Genome Oligo Set Version 1 included 13,971 oligonucleotides, mostly 70-mers, designed based upon representative sequences in build 119 of the human UniGene database. Human Genome Oligo Set Version 2 included 21,329 oligonucleotides, mostly 69-mers, that were designed based upon UniGene build 147. An amino linker was attached to the 5' end of each oligonucleotide. Oligonucleotides were designed to have melting temperatures of 78°C ± 5°C using the formula Tm = 81.5 + 16.6 x log [Na+] + 41 x %GC - 500/length, where [Na+] = 0.1 M. The GC content (%GC) was 48% ± 6% (mean±sd) for Version 1 and 49% ± 5% for Version 2. For Version 1,>99% of the probes were 70-mers, whereas for Version 2,>99% were 69-mers. In a few cases, probe length was adjusted to keep Tm within the desired range. Probes were 3' biased: 96% of the Version 1 probes and 99% of the Version
2 probes were within 600 bases of the 3' end of the known sequence,
although some sequences were incomplete (lacked a polyA sequence). BLAST
searches were done to exclude probes that cross-hybridized with other
sequences from the UniGene database. Ninety percent of the Version 1 probes
had less than 85% overall identity with any other sequence, and 95% of the
Version 2 probes had less than 54% overall identity with any other sequence.
Both sets also included randomized negative oligonucleotides.
Oligonucleotides were dissolved in 3x SSC at a concentration of 40
µM in preparation for spotting on poly-L-lysine-coated glass slides,
prepared as described at
http://www.microarrays.org
RNA Samples
Preparation of Labeled cDNA Targets for Spotted Oligonucleotide
Arrays
Spotted Oligonucleotide Array Hybridization and Scanning
Spotted Oligonucleotide Array Image Analysis
Measurement of Gene Expression Using In Situ-Synthesized 25-mer
Arrays
Target Amplification for Spotted Oligonucleotide Arrays
Cross-Platform Comparisons of Expression Data
We thank Tanuja Goulet and Michael Salazar for technical assistance and Chris Barker and Dean Sheppard for helpful discussions. This work was funded by the UCSF Sandler Center for Basic Research in Asthma, by NIH grants HL072301, DK54212, and RR00083, and by an NHLBI-funded BayGenomics Program in Genomic Applications (HL66621, HL66600, and HL66590). The publication costs of this article were defrayed in part by payment of page charges. This article must therefore be hereby marked "advertisement" in accordance with 18 USC section 1734 solely to indicate this fact.
Article and publication are at http://www.genome.org/cgi/doi/10.1101/gr.1048803.
6 Corresponding author. Article published online before print in June 2003. [Data from this study are available from GEO (http://www.ncbi.nlm.nih.gov/geo)and are listed under the following accession numbers: GSE344 (for the entire experimental series), GSM4843-GSM4865 (for the expression data from individual arrays), and GPL91, GPL273, and GPL274 (for the three array platforms).]
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Received November 28, 2002;
accepted in revised format April 23, 2003.
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