Analysis of DNA strand-specific differential expression with high density tiling microarrays

Abstract

Background: DNA microarray technology allows the analysis of genome structure and dynamics at genome-wide scale. Expression microarrays (EMA) contain probes for annotated open reading frames (ORF) and are widely used for the analysis of differential gene expression. By contrast, tiling microarrays (TMA) have a much higher probe density and provide unbiased genome-wide coverage. The purpose of this study was to develop a protocol to exploit the high resolution of TMAs for quantitative measurement of DNA strand-specific differential expression of annotated and non-annotated transcripts.

Results: We extensively filtered probes present in Affymetrix Genechip Yeast Genome 2.0 expression and GeneChip S. pombe 1.0FR tiling microarrays to generate custom Chip Description Files (CDF) in order to compare their efficiency. We experimentally tested the potential of our approach by measuring the differential expression of 4904 genes in the yeast Schizosaccharomyces pombe growing under conditions of oxidative stress. The results showed a Pearson correlation coefficient of 0.943 between both platforms, indicating that TMAs are as reliable as EMAs for quantitative expression analysis. A significant advantage of TMAs over EMAs is the possibility of detecting non-annotated transcripts generated only under specific physiological conditions. To take full advantage of this property, we have used a target-labelling protocol that preserves the original polarity of the transcripts and, therefore, allows the strand-specific differential expression of non-annotated transcripts to be determined. By using a segmentation algorithm prior to generating the corresponding custom CDFs, we identified and quantitatively measured the expression of 510 transcripts longer than 180 nucleotides and not overlapping previously annotated ORFs that were differentially expressed at least 2-fold under oxidative stress.

Conclusions: We show that the information derived from TMA hybridization can be processed simultaneously for high-resolution qualitative and quantitative analysis of the differential expression of well-characterized genes and of previously non-annotated and antisense transcripts. The consistency of the performance of TMA, their genome-wide coverage and adaptability to updated genome annotations, and the possibility of measuring strand-specific differential expression makes them a tool of choice for the analysis of gene expression in any organism for which TMA platforms are available.

Figure 1

Flowchart of custom CDF construction. The five steps involved in the filtering and selection of probes present in TMAs or EMAs to integrate them into probesets and generate custom CDFs are indicated. See text and Table 1 for details.

Figure 2

Frequency of probes per gene in custom CDFs. The dashed red line indicates the 11-nucleotide boundary. A) 96.8% of all S. pombe genes are represented by more than 10 probes in Sp_TMA CDF. B) 11.7% and 80.9% of all genes are represented by 10 or 11 probes, respectively, in Sp_EMA CDF. C) 91.5% of all genes are represented by more than 10 probes in Sp_PCR_TMA CDF.

Figure 3

Probe coverage of genes in different microarray platforms. ORFs of the cdc13, taz1, SPAC11D3.01c and SPAC11D3.02c genes are indicated by blue bars with pointed ends towards the direction of transcription. Green and purple rectangles represent probes covering the four ORFs in the Sp_TMA and Sp_EMA CDFs, respectively. Orange bars indicate PCR-amplified fragments in the Sp_PCR_EMA microarray designed by Lyne et al. [10], and Sp_PCR_TMA orange rectangles represent probes in Sp_TMA corresponding to PCR fragments in Sp_PCR_EMA. The size of the three genomic regions shown is 2.2 kb. Genomic coordinates are indicated at the top of each panel. Genome-wide probe coverage in the four different custom CDFs can be accessed from the genome browser on our website http://genomics.usal.es/TMADE/browser.

Figure 4

Comparative analyses of differential gene expression using different platforms. A) Correlation between the level of differential gene expression (log2) of S. pombe genes under conditions of oxidative stress in the Sp_TMA and Sp_EMA platforms. B) Correlation between Sp_TMA and Sp_PCR_EMA platforms. C) Correlation between Sp_TMA and the fraction of probes in the Affymetrix 1.0FR microarray matching sequences in the PCR-amplified fragments that make up the Sp_PCR_EMA microarray. Dashed red lines indicate a two-fold level of positive or negative differential expression as detected by each platform. Pearson correlation coefficients (PCC) are indicated at the top of each panel.

Figure 5

Browser visualization of differential expression. Vertical green lines represent transcription from both DNA strands (indicated by + or -) in control S. pombe cells and in cells under oxidative stress. Darker green indicates a higher level of expression. Red vertical lines indicate a differential over- or under-expression level between both physiological conditions greater than 1.75-fold. Differences below this level are not shown. ORFs are indicated by blue bars pointing towards the direction of transcription. Differentially transcribed regions (dTRs) showing differential expression are represented by red bars. A) Overexpression of dTR310020 and dTR300018 from complementary strands encompassing the SPCC794.03 and SPCC794.04c genes under oxidative stress. B) Overexpression of dTR210364 and antisense dTR200302 from complementary strands encompassing the SPBC21C3.19 gene. C) Overexpression of non-annotated dTR100108 and dTR110092. Antisense transcription is also detected across SPAC10F6.15 under conditions of oxidative stress. D) Strong overexpression of antisense RNA (dTR300178) from a 1.5 kb region at the 5' end of the SPCC1919.05 gene. Quantitative data for all differentially expressed annotated and non-annotated dTRs across the complete genome are shown in Additional File 2. The data can be accessed from the genome browser on our website http://genomics.usal.es/TMADE/browser.