Comparing gene discovery from Affymetrix GeneChip microarrays and Clontech PCR-select cDNA subtraction: a case study

Abstract

Background: Several high throughput technologies have been employed to identify differentially regulated genes that may be molecular targets for drug discovery. Here we compared the sets of differentially regulated genes discovered using two experimental approaches: a subtracted suppressive hybridization (SSH) cDNA library methodology and Affymetrix GeneChip technology. In this "case study" we explored the transcriptional pattern changes during the in vitro differentiation of human monocytes to myeloid dendritic cells (DC), and evaluated the potential for novel gene discovery using the SSH methodology.

Results: The same RNA samples isolated from peripheral blood monocyte precursors and immature DC (iDC) were used for GeneChip microarray probing and SSH cDNA library construction. 10,000 clones from each of the two-way SSH libraries (iDC-monocytes and monocytes-iDC) were picked for sequencing. About 2000 transcripts were identified for each library from 8000 successful sequences. Only 70% to 75% of these transcripts were represented on the U95 series GeneChip microarrays, implying that 25% to 30% of these transcripts might not have been identified in a study based only on GeneChip microarrays. In addition, about 10% of these transcripts appeared to be "novel", although these have not yet been closely examined. Among the transcripts that are also represented on the chips, about a third were concordantly discovered as differentially regulated between iDC and monocytes by GeneChip microarray transcript profiling. The remaining two thirds were either not inferred as differentially regulated from GeneChip microarray data, or were called differentially regulated but in the opposite direction. This underscores the importance both of generating reciprocal pairs of SSH libraries, and of real-time RT-PCR confirmation of the results.

Conclusions: This study suggests that SSH could be used as an alternative and complementary transcript profiling tool to GeneChip microarrays, especially in identifying novel genes and transcripts of low abundance.

Figure 1

Annotation and mapping of SSH sequences to GeneChip^® microarray qualifiers (see Methods).

Figure 2

Genes not represented on Affymetrix GeneChip microarrays identified through SSH: Out of the 1940 transcripts in H56 SSH library, 1409 are represented on the U95 series GeneChip microarrays. 333 of the transcripts have matches in the DNA sequence databases searched, but are not represented on the U95 series GeneChip microarray. 198 of the transcripts have no match in any of the DNA sequence databases searched.

Figure 3

Comparing the SSH data with GeneChip microarray data using subtracted samples as targets. The GeneChip microarrays were screened with cRNA targets made from the same subtracted cDNA used for SSH. (a) Number of transcripts in the H56 SSH library identified as "present" or "absent" on the GeneChip microarrays. (b) The copy number of each transcript in the SSH library plotted against its detectability on the GeneChip microarrays. Each dot represents a distinct transcript identified in the H56 SSH cDNA library. The transcripts that can be detected by the GeneChip microarrays were given "present" calls, while the transcripts that cannot be detected by the GeneChip microarrays were given "absent" calls.

Figure 4

Comparing the SSH data with GeneChip^® data using non-subtracted RNA as targets. The GeneChip microarrays were screened with cRNA targets made from un-modified iDC and monocyte RNA samples, and the concordance of the SSH data with GeneChip data was shown when (a) all the transcripts in the H56 library were considered, or (b) when only the transcripts unique to H56 were considered, after the transcripts appeared in both H56 and the reciprocally subtracted H57 libraries were excluded.